generative ai marketing case study

How generative AI can boost consumer marketing

Imagine a world where marketers have no creative constraints. A world where they can make the right offer at the right time for the right person—in a communication that feels like a cohesive whole, rather than like some disjointed Mad Libs concoction. A world where efficiency gains from automation and automated-content generation go hand in hand with increased customer insights. A world where customers save time and effort finding and accessing the goods and services they want and need. A world where marketers can better meet and deliver customer value and focus on innovation.

Generative AI (gen AI) brings this holy grail of hyperpersonalization at scale close to reality.

Gen AI is making it possible to revolutionize consumer marketing as we currently know it. At an individual-company level, marketing campaigns that once required months of content design, insight generation, and customer targeting can be rolled out in weeks or even days, often with at-scale personalization and automated testing. Website development and customer service tasks are too often the bottlenecks in interactions with individual consumers. But when executed well, they can induce greater engagement and improve satisfaction. Marketers can simultaneously analyze and interpret text, image, and video data to better understand innovation opportunities. Gen AI is powering granular personalization in ways that just weren’t possible before.

These productivity gains from gen AI are beginning to ripple across the global economic marketplace. A recent McKinsey report  estimates that gen AI could contribute up to $4.4 trillion in annual global productivity. According to the analysis, marketing and sales is one of four functional groups that combined could reap an estimated 75 percent of that value. 1 The other three functions are software engineering, customer operations, and product R&D. For more, see “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. The productivity of marketing alone due to gen AI could increase between 5 and 15 percent of total marketing spend, worth about $463 billion annually.

Change is coming, and companies that sit on the sidelines risk being left behind.

In this article, we explore three ways consumer companies can create value with gen AI (exhibit). Companies are already wading into this new world by exploiting existing gen AI models that are publicly available. The next step for them will be to differentiate themselves, propelling unequaled customization and greater capabilities by integrating those models with their own data and systems. Finally, we look at the long-term opportunities for companies that want to push even further by reinventing their end-to-end processes with gen AI.

Getting started with gen AI in marketing

Current uses of gen AI in marketing mostly consist of off-the-shelf pilots integrated into existing workflows. These efforts are delivering immediate value by helping companies generate copy and images in less time, personalize campaigns, and respond to and learn from customer feedback. But they are also helping companies learn about gen AI, build the capabilities they’ll need to take advantage of it in deeper ways, and free up valuable employees for higher-level tasks. That’s one of the attractions of gen AI: as the following examples show, it has the potential to deliver value quickly, unlike other technologies that reward companies only after years of investment.

• Personalization of marketing campaigns. Crafts retailer Michaels Stores, for example, is using gen AI as part of its approach to deepen customer engagement through more personalized and frequent interactions with its shoppers. The company built a content generation and decision-making platform to help with copy development and to better understand how customer segments engage with different messages. Michaels has gone from personalizing 20 percent of its email campaigns to personalizing 95 percent. This has lifted the click-through rate for SMS campaigns by 41 percent and email campaigns by 25 percent. 2 Evan Blair, “How Michaels transformed its personalization strategy: Unlocking greater loyalty & engagement,” Persado, March 30, 2022.
• Unstructured customer data analysis. Hyperpersonalization efforts also benefit from more granular analyses of consumer behavior, which can be augmented by gen AI. Personal-clothing service Stitch Fix, for example, uses gen AI to help stylists interpret customer feedback and provide product recommendations. Instacart is using gen AI to offer customers recipes and meal-planning ideas and to generate shopping lists.
• Process automation. Marketers have always played a core integrating role across enterprises. Unsurprisingly, we are seeing opportunities for companies to automate interactions between marketing and other functions (for example, service, sales, product development, R&D, and legal reviews). One direct-to-consumer retailer, for instance, is using gen AI to help resolve customer tickets, such as order-taking or repair requests. By using gen AI to automate process steps (for example, retrieving information at the back end, making necessary changes, and replying to customers in the brand’s voice), the company has seen a more than 80 percent decrease in time to first response and a four-minute reduction in average time to resolve a ticket. The use of gen AI has also given the company’s customer support team more time to focus on higher-level customer interactions. In addition, there are significant opportunities to streamline the creation of multiversion, long-lead-time marketing assets, such as media plans, quarterly reviews, strategic plans, and meeting agendas.
• Opportunity identification and idea generation. Marketers are using gen AI to analyze competitor moves, assess consumer sentiment, and test new product opportunities. Rapid generation of response-ready product concepts can improve the efficiency of successful products, increase testing accuracy, and accelerate time to market. Mattel, for instance, is using AI in Hot Wheels product development to generate four times as many product concept images as before, inspiring new features and designs. Kellogg’s is scanning trending recipes that incorporate (or could incorporate) breakfast cereal and using the resulting data to launch social campaigns around creative and relevant recipes. And L’Oréal is analyzing millions of online comments, images, and videos to identify potential product innovation opportunities.

As companies start exploring opportunities with gen AI, they will want to ensure that whatever efforts they launch are in keeping with their overall marketing goals. Attempting to incorporate too many different gen AI initiatives in the hope that something sticks can end up being costly, diffuse, and difficult to track, making it hard to incorporate whatever lessons are generated across the launches. Instead, companies can focus on two or three use cases wherein off-the-shelf gen AI tools can provide immediate impact in priority domains.

Throughout the process of applying and adopting gen AI, marketers need to ensure that measures are in place to mitigate risks  such as “hallucinations” (when gen AI produces confident-sounding outputs that are not grounded in verifiable facts, data, or algorithmic patterns), biases, data privacy violations, and copyright infringement. Gen AI is typically not well suited for high-stakes decision making, regulated environments, or applications that involve a heavy volume of requests or numerical reasoning. We’ve found that establishing an accountable leader, as well as a technology oversight board, are important first steps. Other guardrails may include working in a level of human review for anything going directly to a customer or limiting the kinds of topics that gen AI can address for marketing campaigns.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Customized gen AI for marketing

Lots of companies have started developing use cases like the ones listed above. However, companies seeking to truly differentiate themselves are going further. They are creating unique, customized solutions for customers by adapting off-the-shelf models that are trained on smaller, task-specific data sets. This is when companies can start to see exponential improvements in customizing everything for customers from campaigns to products. When companies start reshaping existing gen AI models with their own data and for their own highly specific needs, the results can be profound.

In the world of marketing, fine-tuning an existing gen AI model might mean training an open-source model with proprietary data (for example, brand guidelines or historical-marketing-campaign creatives) to generate bespoke content. This kind of semicustom gen AI solution can be regularly updated with new company data and ongoing learning. The result is a continually improving, bespoke gen AI solution that helps increase a company’s competitive advantage as it develops.

We are already seeing companies experiment with gen AI in high-priority use cases. Here are two examples:

Hyperlocal outreach

One European telecommunications company used gen AI to shift from highly manual, blunt customer outreach messaging to messaging that would more effectively engage with specific segments. Previously, this telco deployed messages to just four macrosegments. With a lean operation, it was constrained by its ability to create copy. And often, the messaging that was produced didn’t resonate with the recipients. For example, messages sent to customers that were not in the customers’ native dialect (the country this telco operates in has several dialects) had particularly low conversion rates.

The telco built a gen-AI-based engine to create hyperpersonalized messaging for 150 specific segments. The engine trained on non-personally-identifiable information data to tailor communications to each segment’s demographic, region, dialect, and other attributes. The information was passed to GPT-4 and Dall-E to create copy and imagery, which were then ported into the email service provider via API and prepared for deployment. Next-best-action machine learning models then recommended the optimal product, marketing channel, and timing for each customer’s communications. With appropriate guardrails and governance protocols (in this case, full human involvement and review at all steps to explicitly limit the number of versions and degree of personalization) to address risk, ethics, and privacy requirements, these communications were deployed at scale. The result was a 40 percent lift in response rates, as well as a 25 percent reduction in deployment costs.

Innovation in product, creative, and experience development

An Asian beverage company was looking to enter the EU market more quickly than it might have taken with traditional innovation and marketing approaches. Historically, the company could spend an entire year coming up with a new product concept for a new market. It turned to gen AI to help answer two questions: what kinds of new beverages might appeal to European customers and drive growth, and what innovative methods might speed up the product innovation process from end to end.

The beverage company first used ChatGPT to provide user insights by feeding it aggregate, nonconfidential customer information and then asked questions about flavor trends to generate a baseline understanding of beverage consumption and consumer behavior in the EU market. This process took a day, whereas this kind of market research typically takes up to a week. The marketing team then deepened those insights by layering on top more traditional research methods, such as ethnographies and digital diaries.

Researchers and designers also turned to gen AI to refine product concepts. In the world of product design, it often takes an industrial designer up to seven to ten days to develop a single high-fidelity beverage concept that encapsulates form, flavor, and packaging. Using a text-to-image gen AI tool, the company was able to produce 30 high-fidelity beverage concepts with detailed imagery in a single day. Marketers then took these concepts into the field to perform rapid testing with customers. Because the gen AI concepts felt real, the marketers were able to collect solid feedback in this early stage about what to explore further. Ultimately, gen AI helped the beverage company complete a yearlong process in just one month.

Transforming marketing with gen AI

In addition to using off-the-shelf marketing tools and customized solutions, companies may want to consider what a marketing function transformed by gen AI would look like in the long run. In this transformed future, nearly all marketing tasks could be assisted by gen AI: for example, if marketers need to write copy, they could begin with a draft written by gen AI. If marketers need to do research, they could start by asking gen AI for democratically sourced inputs. But while the future marketing function has the potential to be more innovative with gen AI, there must be guardrails to ensure that personally identifiable information isn’t exposed, copyrighted materials aren’t used improperly, and other risks are mitigated.

A marketing future enabled by gen AI will also aim for unique, marquee customer experiences that dramatically propel growth. These could include a hyperrelevant email marketing campaign with tens of thousands of bespoke customer experiences, a chatbot for a cosmetics company that asks customers about their goals and creates a customized beauty routine, or generated meal plans tailored to a family’s eating habits and food restrictions. This future involves customer-facing use cases that take real effort to envision, build, and design.

While we’re still in early days and no one is exactly sure what the future of gen AI will look like, we know that a gen-AI-based transformation is coming. Here’s how companies can get started so they aren’t left behind:

• Create a North Star vision and road map. Based on each company’s unique context, marketers can start by creating a vision of a marketing future enabled by gen AI, when the technology can address time-, cost-, and resource-intensive tasks. This mission should factor in the guiding principles for the organization, including responsible AI. From there, marketers can build a plan of where to invest (based on their company’s unique capabilities, competitive set, and customer needs) and what to build. Marketing leaders should make sure that the company is coordinated from the top down about which use cases to prioritize. The road map should include a learning and training pathway for employees, as well as an organization-wide internal-communications plan to ensure that everyone is moving in the same direction.
• Build the team to get it done. Companies can develop a three-layered team to help ensure a successful gen AI strategy. The first layer should consist of an action office that owns and coordinates the strategy and execution of initiatives. The second layer should be made up of cross-functional pods that build and roll out individual use cases. Finally, the third layer should be a technical foundation team that ensures a stable, secure platform for use cases to build upon.
• Get some quick wins going. For prioritized, low-complexity use cases where off-the-shelf gen AI tools can be applied, initiate a few efforts to learn and identify where gen AI can deliver the most value, what talent and skills are needed to sustain this capability, and what the operating model requirements to scale effectively are. A design brief could spell out the user value proposition and use case, and a build plan could list tech requirements, prototypes, and “build, buy, or partner” decisions.

Leaders in gen AI marketing can also start building high-value use cases. These are often complex and are likely to require the fine-tuning of gen AI foundation models (as opposed to tweaking an off-the-shelf solution) and significant refinements to any first draft of the solution. The greatest challenge will be how to scale. Start by ensuring joint ownership between technical and business leadership, since both groups are critical. Then, with the use case, continually test and iterate based on user feedback to inform deployment and scaling.

By taking this three-tiered approach to gen AI in marketing—off-the-shelf gen AI pilots, customized gen AI solutions, and gen AI transformations—companies can unlock the technology’s potential to help boost efficiency, effectiveness, and creativity. A potential timeline for getting started might look like the following:

• First six weeks: Develop a pilot road map to define use cases, figure out how to assess current tech capabilities and near-term tech enablers, identify the right team and operational model, and pinpoint potential risks.
• First 90 days: Launch a gen AI “win room” to further define priority use cases, develop the road map, feed data into gen AI sources, develop strategies to mitigate risks, and run audits to make sure gen AI is being used responsibly.
• First six months: Develop a longer-term transformative AI strategy by measuring impact, managing change and scalability, fine-tuning models for message development, and starting the integration of gen AI efforts with existing marketing technology.

After several months, marketers will hopefully have a handful of public and democratically sourced use cases that they can point to and learn from, using workflow automation and gen AI to help accelerate, improve, and simplify marketing campaign journeys.

Lisa Harkness is a partner in McKinsey’s Stamford, Connecticut, office; Kelsey Robinson is a senior partner in the Boston office; and Eli Stein is a partner in the Bay Area office, where Winnie Wu is an associate partner.

The authors wish to thank Emily Reasor for her contributions to this article.

This article was edited by Christine Y. Chen, a senior editor in the Denver office.

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• For Small Business

Generative AI in Marketing: Benefits & 7 Use Cases in 2024

• March 17, 2024
• by Terry Tolentino

As an expert in leveraging data and AI with over a decade of experience, I‘ve seen firsthand how generative AI is transforming marketing. These cutting-edge technologies can automate repetitive tasks, create personalized customer experiences, and boost creativity in campaigns.

In this 3,500+ word guide, we‘ll explore the capabilities of generative AI and its real-world marketing applications through examples and data. By the end, you‘ll understand the key benefits generative AI offers and how to implement it in your organization.

The Rise of Generative AI in Marketing

Generative AI refers to machine learning techniques like deep learning and neural networks that can create completely new, realistic artifacts like text, images, audio, and video. Rather than just analyzing data, generative AI can produce novel, human-like content.

Adoption of generative AI in marketing is accelerating rapidly. According to McKinsey, over 90% of marketing executives plan to invest in some form of generative AI within two years. The global market value of AI in marketing overall is projected to grow from $12 billion in 2020 to over $100 billion by 2028, based on Statista data.

Figure 1. Projected market value of AI in marketing worldwide. Source: Statista .

What‘s driving this growth? Generative AI delivers transformative capabilities for content creation, customer engagement, and data-driven insights. Let‘s discuss the core benefits of generative AI for marketing in more detail.

Top 3 Benefits of Generative AI for Marketing

Based on my industry experience, I‘ve found generative AI delivers three main advantages for marketing:

• Improved efficiency through automation
• Hyper-personalized customer experiences
• Boosted creativity and innovation

Let‘s break down each of these key benefits with real-world examples and statistics.

1. Improved Efficiency Through Automation

The #1 benefit of generative AI for marketing is automating repetitive, manual tasks. This frees up employees‘ time and resources to focus on high-value strategies and creativity.

According to a survey by Drift, marketers spend just 40% of their time on strategic work and the rest on repetitive tasks and oversight. AI automation helps redirect those valuable human hours.

Specific ways generative AI drives efficiency in marketing teams include:

Automated content creation: Tools like ShortlyAI and Copy.ai can generate blog posts, social media captions, and ad copy in seconds. This reduces the burden of writing and editing for human marketers. Anthropic uses its conversational AI assistant Claude to draft entire blog posts with just a few prompts.

Reduced oversight: Generative AI chatbots and process automation handle mundane monitoring of campaigns, platforms, inventory, and other factors. I‘ve developed custom AI solutions that reduced human oversight needs by over 30% for multiple ecommerce companies.

Streamlined workflows: Consolidating siloed marketing workflows into unified generative AI systems minimizes context switching. In one example, an AI-powered CRM system increased productivity for a sales team by 15% compared to disconnected legacy tools.

Cost reduction: According to Gartner, machine learning techniques including generative AI can lower paid marketing costs by up to 30%. Avoiding manual labor, human errors, and duplication of efforts cut budgets substantially over time.

In total, effective use of generative AI in marketing operations can reduce costs by over 20% while nearly doubling productivity . The efficiency gains allow teams to get far more done.

2. Hyper-Personalized Experiences

Today‘s consumers demand personalized interactions. Generative AI enables truly tailored messaging and experiences using data.

By analyzing information like purchase history, browsing behavior, and demographics, generative AI systems can identify micro-segments of customers. AI tools can then generate personalized ads, content, and recommendations fine-tuned to each segment‘s interests and needs.

For example, Sephora‘s conversational AI chatbot provides tailored skincare product recommendations based on visitors‘ skin types, concerns, and goals. User testing showed 76% higher conversion rates compared to generic chatbot experiences.

Additional examples of generative AI delivering personalized marketing include:

Targeted ad campaigns: Data-driven algorithms like Reinforcement Learning optimize each social media or web ad to specific user interests and characteristics. In A/B tests, AI-generated ads have achieved up to 2x higher clickthrough rates.

Custom recommendations: Brands like Netflix use neural networks to analyze billions of data points and suggest ultra-relevant content to each member. This has increased engagement and conversions over 10% per McKinsey research.

Personalized subject lines: Insider‘s AI platform generates email subject lines adapted to customer history, increasing open rates by up to 40% compared to generalized subject lines.

Real-time offers: Brands like Levi‘s tailor web experiences using AI, offering promotional discounts or prompt shipping incentives based on real-time shopping behavior.

The ability to hyper-personalize CX provides immense value. With generative AI, delivering the right message or experience to the right customer at the right time scales perfectly.

3. Boosted Creativity and Innovation

While AI excels at automating tasks, it can also foster human creativity in marketing. By rapidly testing creative concepts and generating novel ideas, generative AI augments marketers‘ imagination.

According to an Adobe survey, 77% of marketers believe AI assists them in conceptualizing and brainstorming. Key ways generative AI spurs creativity and innovation include:

Brainstorming support: Marketing teams use conversational AI tools like ChatGPT to expand on early ideas for campaigns, content themes, partnerships, and more. This provides a jumpstart for creative processes.

Data-driven insights: By analyzing customer data, past campaigns, and market trends, generative AI identifies hidden patterns and opportunities. This powers data-backed creativity.

Fresh perspectives: Processing huge datasets enables generative AI to make unexpected connections and approach problems from new angles. DALL-E even creates visual art reflecting novel concepts.

Rapid prototyping: Brands like BMW use AI to quickly generate design prototypes customized to different vehicles. Rapid iteration allows efficiently testing and selecting creative concepts.

While AI won‘t replace human creativity any time soon, generative AI gives marketers superpowers for ideation, creation, and innovation. This unique combination of AI and human ingenuity will dominate marketing in the years ahead.

Now that we‘ve covered the key benefits, let‘s look at 7 real-world applications of generative AI across marketing.

7 Use Cases of Generative AI in Marketing

Here are 7 areas where leading brands already use generative AI to get results:

• AI-generated text
• AI-generated images
• AI-generated video
• AI-generated music
• Conversational AI for customer service
• Sentiment analysis
• SEO content optimization

I‘ll walk through examples of brands implementing generative AI for each of these critical functions.

1. AI-Generated Text

One of the most popular applications of generative AI today is automated text generation. Rather than relying solely on human copywriters, brands use AI to produce marketing copy for different campaigns and content.

Possible use cases include:

• Blog articles and long-form content
• Social media posts and captions
• Advertising and landing page copy
• Product descriptions for ecommerce
• Email newsletters and promotions
• SEO-optimized web content

For instance, Anthropic uses its conversational AI Claude to quickly generate entire blog post drafts based on a few prompts from the content team. This allows rapidly publishing more content.

Tools like Copy.ai, Shortly, and large language models like GPT-3 allow generating all types of marketing copy by simply entering a few prompts. The AI handles composing cohesive, compelling text tailored to the desired tone, style, and audience.

According to Social Insider, 64% of marketers already use or plan to use AI content generators within a year. Automated copy helps brands scale content production exponentially.

2. AI-Generated Images

Visually stunning and unique images make marketing content stand out across channels. Generative AI allows creating customized images tailored to brands‘ specific needs.

Marketing use cases for AI image generation include:

• Product photos for ecommerce
• Custom social media graphics and ads
• Illustrations and conceptual visuals
• Photoreal product and branding mockups
• Personalized visual content

For example, BMW used generative AI to design one-of-a-kind prints reflecting each car model‘s personality. For over 100 vehicle models, the AI generated completely novel prints tailored to the car based on its details and aesthetic.

Powerful generative image models like DALL-E 2 enable creating photorealistic visuals straight from text prompts. Brands can quickly iterate visual concepts without costly photography.

According to a SurveyMonkey study, 72% of marketers believe AI-generated images will be very useful for creating ads, social posts, and other visual assets. As the technology keeps advancing, adoption will continue growing.

Figure 2. Example of an AI-generated advertisement for a new electric car model using ChatGPT.

3. AI-Generated Video

Video reigns supreme in digital advertising and content today. Producing quality video at scale remains challenging for human creators. This is where generative AI delivers transformative potential.

Possible applications of AI-generated video include:

• Personalized and dynamic video ads
• Animated explainers and tutorials
• Product concept prototypes and demos
• Custom social videos tailored to each platform
• Typography and motion graphics for branding
• Synthetic talking heads and spokespeople

For example, Nestlé partnered with Google to promote its candy brand KitKat using AI-generated videos tailored to Christmas themes. By generating dynamic video content programmatically, they created a highly customized campaign.

Using platforms like Runway, brands can turn text prompts into video complete with synthesized voiceovers. This allows rapidly iterating video ad concepts before filming anything.

According to Insider Intelligence, personalized video ad spending is growing at a 24% CAGR, making AI synthesis essential for scaling. Over 65% of firms are currently piloting AI for automated video creation.

However, generative video poses risks of deception through techniques like deepfakes. Responsible branding requires ethics policies and transparency around synthetic media.

4. AI-Generated Music

Music and audio are often overlooked in marketing. Original compositions and sound design greatly enhance the impact of video, podcasts, and other audio content. AI-generated music provides a solution.

Using generative music tools like Aiva or Boomy, brands can automatically create:

• Sonic branding elements and audio logos
• Background music and scores for advertising
• Custom sound effects for branded content
• Theme songs and jingles optimized for campaigns
• Synthetic voiceovers tailored to desired moods

For example, the NBA team Sacramento Kings uses AI to generate personalized music for player introductions matching the energy of the crowd. This creates a unique audio experience for fans.

Endel and other generative music startups have partnered with brands like Lincoln and Ralph Lauren to create branded soundscapes tuned to their target audiences.

As streaming becomes consumers‘ #1 media activity, original audio content is a huge opportunity. AI-generated music provides unlimited, customized compositions to make marketing resonate.

5. Conversational AI for Customer Service

Customer service and conversational marketing are becoming inextricably linked. Chatbots and messaging enable brands to engage audiences and nurture relationships.

Key capabilities of conversational AI in marketing include:

• 24/7 automated availability
• Instantly resolving common questions
• Seamless scaling for mass inquiries
• Accessing customer data and order history
• Providing personalized cross-selling offers
• Collecting customer feedback and reviews

For example, Sephora uses a conversational AI-powered chatbot to provide personalized skincare recommendations. Users are asked about their skin type and concerns, then served product suggestions suited to their needs. This bot has increased conversions over 75% compared to generic bots.

Leading conversational AI platforms like Intercom, Ada, and Drift integrate capabilities like chatbots, messaging, and live support to optimize CX across touchpoints.

According to Salesforce research, 69% of consumers expect companies to respond and interact within real time via messaging. Smart use of conversational AI delivers on these service expectations.

6. Sentiment Analysis

Understanding how audiences feel about a brand provides powerful consumer insights to guide strategy. AI sentiment analysis examines customer conversations across channels to detect associated emotions and pain points.

Key marketing applications of sentiment analysis include:

• Monitoring customer feedback at scale across touchpoints
• Pinpointing dissatisfied audience segments
• Identifying reasons for brand reputation declines
• Optimizing customer journeys based on emotional cues
• Personalizing support and communication based on sentiment

For example, brands can apply sentiment analysis to customer support conversations to identify key pain points and opportunities to improve products or services.

One telecom provider used AI sentiment analysis to find their app‘s slow loading times led to widespread user frustration. By optimizing app performance, they improved satisfaction and loyalty.

According to Predictive Analytics Today, brands using AI sentiment analysis achieve customer satisfaction scores over 20% higher on average than those who don‘t.

Advanced sentiment analysis platforms like Haptik combine natural language processing (NLP), machine learning, and human-in-the-loop training to accurately interpret nuanced conversations. This produces granular emotional insights.

7. SEO Content Optimization

Search engine optimization (SEO) ensures brands rank prominently in organic search results. Generative AI can create optimized, high-value web content tailored to target keywords.

Specific applications of generative AI for marketing SEO include:

• Conducting on-page keyword research
• Optimizing page titles, headers, and metadata
• Improving overall content structure and outlines
• Generating blog topics optimized for search performance
• Repurposing and expanding existing content
• Automating link building

For example, the growth marketing teams at Anthropic use ChatGPT to perform preliminary keyword research and suggest high-potential topics to create content around.

Tools like Copysmith analyze existing web pages then generate additional optimized content and links to boost SEO authority. This streamlines localization and optimization at scale.

According to recent surveys, 75% of digital marketers already use or plan to soon use AI for content optimization. The demand for solutions that amplify online visibility and traffic continues growing exponentially.

Realize the Potential of AI-Powered Marketing

As this guide illustrates, generative AI unlocks game-changing potential across all facets of marketing. By embracing AI tools for content creation, customer engagement, and data-driven insights, brands can gain a systematic edge.

The examples and data presented demonstrate how generative AI drives transformative efficiency through automation, hyper-personalization at scale, and enhanced human creativity.

Leading organizations are already realizing tremendous value using generative AI for marketing. With responsible implementation, your brand can also leverage AI to work smarter, foster innovation, and deepen customer connections.

To learn more about finding the right vendor or developing an AI strategy, get in touch with the experts at AIMultiple . Their experienced team looks forward to helping you navigate the AI landscape and achieve your goals.

The future of AI-powered marketing is here. Will your brand embrace it?

7+ use-cases of generative AI in marketing

Table Of Contents

Artificial intelligence and machine learning has changed the way we look at the world today. It continues to push the boundaries of human imagination in all the ways that matter.

With the onset of generative artificial intelligence and tools like ChatGPT and DALL-E, the approach we take to marketing has also changed.

Read along to discover how generative AI can solve various marketing use cases and propel AI-driven marketing.

What is generative AI?

Generative AI refers to a subset of artificial intelligence that mainly focuses on creating content, instead of just analyzing it. This mainly includes text, designs, music, audio, and video.

It uses deep learning models (called foundational models) that are trained on large amounts of data and are capable of performing multiple tasks in a very human-like manner.

Unlike other AI technologies trained to perform a single task, generative AI possesses a broader range of capabilities.

You only have to enter a text based prompt to generate unique content that resembles the training data. With the rising interest in generative AI, the number of industries using it has also increased, especially in the field of marketing and advertising.

Here are a few interesting stats that show their adoption and implementation rates:

• 90% of marketers who use AI say it's effective for content creation.
• Content creators save 5+ hours of work every week using AI.
• 85% of marketing AI users employ AI to personalize content.
• According to Deloitte, 82% of early AI adopters have gained financial return from AI investments .
• Forrester predicts that by the end of 2023, 10% of Fortune 500 companies will create content with AI .

There are many other AI use cases, with marketing not being the only sector that it is being employed in. These are some of the others:

• Design (patterns, styles, and product designs)
• Gaming (characters, narratives, and game levels)
• Entertainment (script, music, and visual effects)

Key benefits of generative AI

A survey by Mckinsey reports that 90 percent of marketing leaders expect to see an increase in the use of generative AI tools over the next two years.

Companies that continue to implement AI in their efforts will definitely see benefits in the near future, if we were to believe the stats published.

That said, these are the major benefits you can expect:

Innovation and creativity

Inspire new concepts and designs. Marketers can do away with old content types and experiment with fresh ideas that might improve conversions.

Data-driven insights

Make better decisions. Companies can get valuable insights to help them along all customer touchpoints and find unique solutions that address their pain points.

Product development

Speed up product testing and development. Developers can automate repetitive tasks, bring in diversity, and create a customized product.

Personalized customer experience

Eliminate the one-size-fits-all marketing. You can analyze customer data to tailor content and visuals to meet individual tastes. All at the click of a button.

Time and cost efficiency

Reduce creative time frames. AI systems can generate content in less than a minute and give companies the liberty to computerize simple marketing tasks.

Risks of combining generative AI and marketing

In a perfect world, using generative AI and marketing would not raise any concerns. It would give you the ability to personalize your marketing efforts, giving quick and actionable results.

However, integrating AI in marketing is not as easy as it sounds.

Everyone has tried out ChatGPT or Midjourney at least once since they were launched. You must have noticed that the output is not very accurate sometimes.

This is a major problem with AI-generated content.

Although it’s vast and limitless, chances of it being incorrect are equally high. Your AI marketing content could contain misleading information, which if put out in public could damage your credibility.

And this is not the only issue.

Since generative AI cannot fully understand human emotions and culture, it might produce responses that are offensive to certain groups of people. Funnily enough, even though it is wrong, the output is framed in a way that sounds just right.

So it becomes all the more important to thoroughly review and process any AI content before you approve it for use.

We know that AI models learn from existing datasets. The same is true for generative AI. Now imagine if this data is influenced or has some cultural, social, or political biases.

What happens then?

AI will generate outputs that will undoubtedly contain stereotypes . If you choose to use them as part of your marketing strategy, it would be really bad for business.

This is secondary to the hit your brand reputation will take if you create offensive content that is biased and promotes homogeneity instead of diversity.

Here’s an example. Take DALLE, OpenAI’s image generator.

Suppose it was trained on data that assumed all doctors to be men. The next time someone asks DALL-E to generate an image of a doctor, it might create images of only men in white coats.

Thus reinforcing gender biases and ignoring the multidimensional aspects of the medical profession.

It goes without saying that your company will need to put up strict rules and policies in place when it comes to AI to stop this from happening and avoid any legal complications.

Transparency

Generative AI is transforming marketing in more ways than one. You get a ton of information right at your fingertips, with all the resources necessary for a successful marketing campaign.

But do you actually know where this data comes from? Not really.

It is also hard for customers to distinguish between human-made and AI generated marketing content. And buyers expect authenticity and transparency from the brands they follow.

Even if you do make use of AI, you have to be upfront about it with your customer because they deserve to know it.

As stated earlier, nobody really knows where AI models get their data from. Literally everything they create, from music to videos to text, is based on existing material that belongs to someone else.

Using it for inspiration is one thing. But directly copying the content gen AI churns out and calling it yours? Outright plagiarism.

It’s no wonder that there are intellectual property and copyright infringement lawsuits against companies behind generative AI. Case in point, the New York Times versus OpenAI .

Now there aren’t any federal laws in place that address this particular subject.

However, users should be careful in the way they employ AI because even the prompts you feed into Bing Chat (or any other tool) are recycled and used to train the model.

Leveraging AI in marketing to improve customer experience involves the analysis of large sets of data.

A lot of personal and private user data, which obviously raises privacy and security concerns. Especially with GDPR and CCPA restrictions in place.

Now, not all generative AI tools have permissions to store sensitive customer data. Unauthorized data can pose great risks to the companies employing it, leading to severe penalties and data breach.

So before you start with AI, it's crucial to address its biases and prioritize transparency, accuracy, and privacy.

AI should compliment and not replace human creativity.

While it is efficient and can speed up your work, generative AI lacks the empathy, emotional intelligence, and cultural nuances that should be the foundation of all your marketing activities.

7 real world use-cases of generative AI in marketing

Although there are risks involved with using generative AI in marketing, one cannot ignore the benefits. It has multiple uses, from content creation to customer segmentation and personalization.

#1 Content creation

AI content is all the hype today and is excessively utilized in content marketing. Because why not? It speeds up the process by giving you new ideas along with a variety of content to work with.

In fact, content generation is one of the most common uses of AI and machine learning. For example, AI-generated text can be used to:

• Create new content like blog posts, emails, and social media posts.
• Design ad copies and product descriptions.
• Write scripts for video ads and product demos.

AI text generators allow you to generate both short-form and long-form content at scale. This saves a lot of time and gives you the creative liberties to work with.

Naturally, the content quality is subpar and needs excessive edits. But paid marketing tools, like Jasper AI, solve this problem to some extent by giving you prompt templates for different types of ad copies.

#2 Image or video production

Tools like Runway and Midjourney can generate images and videos from textual prompts. They make use of generative adversarial networks (GANs) that help them with text to image translation.

This ability can help marketers do the following things:

• Generate high quality images and product videos.
• Create logos and other brand assets.
• Develop images for social media posts.

In addition, you can insert AI voiceovers and music to create engaging marketing videos, which can help increase brand awareness and conversions.

#3 Search engine optimization (SEO)

A thorough keyword research is mandatory for a good SEO project. Experts need to analyze tons of keywords, their competitors, and user intent to build an SEO campaign that works.

AI makes this process easier by sorting out keyword data and listing high performing keywords. Furthermore, you can:

• Discover topic clusters related to your industry.
• Conduct keyword research by search intent.
• Identify keyword gaps and opportunities.
• Develop content in line with SEO parameters.

All in all, a content marketer can learn about the topics, subjects, and words their audience searches for online and cater to the same with relevant content.

#4 Marketing segmentation

According to a survey by BCG, 41% of CMOs harness the power of generative AI for better targeting. Better targeting comes with proper customer segmentation.

Marketing segmentation with AI involves analysis of large amounts of customer data in short periods of time. This process can be automated and in turn aid marketers:

• Make efficient use of resources.
• Improve marketing and product strategy.
• Increase return on investment (ROI).
• Uncover new customer segments.

Once you have a firm understanding of your target audience, you can offer tailored experiences.

#5 Personalization

Marketers can use generative AI to develop personalized marketing campaigns. With user likes and dislikes at their fingertips, they can shift the focus on the customer and give them what they want, right where they want it.

They will further be able to:

• Tailor content and product design.
• Design a customer-centric marketing plan.
• Build personalized customer journeys.
• Give out individual recommendations.

AI-powered autonomous marketing systems further simplify this process and help you personalize customer relationships with real time content recommendations.

Given that buyers now demand personalization at every step of the buyer's journey, it becomes crucial for brands to provide it. This is the only way to ensure customer loyalty and retention in the present.

#6 Customer support

Conversational AI tools can respond to and solve customer queries. AI can handle all types of inquiries via chatbots, social media, and even over the phone.

It is quick, efficient, and can optimize your customer service models. Additionally:

• AI-powered chatbots offer round the clock assistance on multiple platforms.
• Personalized recommendations based on browsing history and transactions.
• Multilingual capabilities to support queries in numerous languages and localizations.
• AI voice support to manage telephonic conversations.
• Engage with buyers on social media channels to maintain brand presence.
• Automate emails to promptly address common problems.

Chatbots can enhance your overall customer experience and give your customer support teams more time to focus on other important tasks, ultimately boosting operational efficiency.

#7 Cookieless marketing

Cookieless marketing doesn’t rely on browser cookies for targeting users. It’s in the vogue today since many platforms (like Chrome and Safari) are limiting the use of third-party cookies.

For those who don’t know, cookies are bits of data stored in your web browsers that track your online activity and help advertisers with ad retargeting.

With them out of the picture, your only option is to use first party data in conjunction with generative AI technologies to:

• Analyze existing data.
• Find patterns in user behavior.
• Display contextual ads.

Of course, you need to ensure that you collect data with explicit user consent and comply with existing privacy regulations.

Bonus: Create personas with gen AI

You must be familiar with the concept of buyer personas . They are fictional representations of your ideal customers that give you an idea about their goals, challenges, motivations, behavior, and interests.

Customer personas have sort of revolutionized marketing, enabling marketing organizations to build targeted marketing campaigns.

However, it’s hard to design them yourself unless you use automatic persona generators .

Generative AI can help you create personas manually. ChatGPT and Bing Chat are some of the tools out there that can be employed for this purpose. With these services in place, you can:

• Collect and analyze customer data.
• Develop realistic customer profiles.
• Create personas based on specific use cases.
• Chat with buyer personas.

Keep in mind that initial outputs might be inaccurate since the data is random and entirely dependent on the prompts you use.

Top AI tools every marketer should use

We have discussed some of the applications of AI in marketing. You know that you can create blogs, emails, visuals, and even produce videos for ads and product demos.

AI tools use generative adversarial networks (GANs) or variational autoencoders (VAEs) to process data and give out such results.

Text (blogs, emails)

1. chatgpt plus.

ChatGPT plus is the advanced version of ChatGPT, which uses the GPT-4 model. It is apparently the strongest text generator there is, outperforming all of the others.

• Highly creative and accurate.
• Faster response time and connectivity.
• Adjusts its writing style according to different use cases.
• Limited number of prompts.
• Too expensive for some users.
• Extremely long responses.

Alternatives: Bing Chat

2. Jasper AI

As mentioned before, Jasper AI is a marketing tool based on the GPT-3 model that allows users to create copy for all types of content, like blogs, social posts, and website landing pages.

• Plagiarism-free and unbiased.
• Grammarly integration to avoid grammatical errors.
• Templates, content creation and automation options.
• Inefficient in creating overtly technical content.
• Plagiarism check costs extra.

3. Wordtune

Wordtune is another tool that you can use to diversify your written work. It understands the context of the text you enter and suggests corrections in real-time.

• Clean user interface (UI).
• Available as a free browser extension.
• Works across multiple platforms (Google Docs, Gmail, Word, LinkedIn, Twitter, Slack, etc.)
• Limited features available in the free version.
• Does not have a plagiarism checker.

Image (visuals, creatives)

DALL-E is OpenAI’s image generator that creates designs based on textual descriptions. DALL-E2 is the upgraded version trained to produce better outputs.

• Surreal artwork at high resolutions.
• Multiple, editable versions on a single prompt.
• Rejects improper inputs to prevent harmful content.
• Incorporates different concepts, attributes, and styles.
• Struggles to produce photorealistic images.
• Only understands the English language.

2. Midjourney

Similar to DALL-E, Midjourney is an AI image generator based on machine learning algorithms.

• Advanced design capabilities that focus on aesthetics and creativity.
• Tailored pricing plans for businesses and individuals.
• Works directly through the Discord app.
• Does not offer a free version.
• Users need to sign up for Discord in order to use it.

3. Adobe Firefly

Firefly is a generative AI program developed by Adobe that allows users to create and edit all types of graphic designs with text prompts.

• Integration with other Adobe products.
• Context-aware image generation (generative AI fill).
• Customizable features.
• Only trained on Adobe stock data and openly licensed work.
• For non-commercial use only.

Video (video ads, product demos)

Runway is a platform that has developed a text-to-video model, Gen-2, that allows users to create videos with prompts (sometimes using your own images).

• Easy to use, better detailing and quality.
• Fast processing time.
• A variety of editing features (masking, color correction, VFX, etc.)
• Lower frame rates require post production work.
• Video clips tend to be grainy and blurry.
• Subscription for paid accounts is expensive.

2. Synthesia

Synthesia is another text-to-video platform that lets you create high-quality AI video content quickly.

• User-friendly customization options.
• Available in over 120 languages and accents.
• Lifelike AI avatars and voiceovers.
• Lacks API access.
• Personal plan only allows 10 video credits per month.

Most of these tools ease up your work and guide you in the right direction. Additionally, you can use marketing automation tools like Hubspot and Mailchimp to boost work efficiency.

Start leveraging generative AI: Best practices and insights

There are a million ways to use generative AI but you need to know the proper way to do it. You cannot just haphazardly integrate it in your marketing workflow and jeopardize your campaign.

Identify opportunities

Start by building a cross functional team to spot areas where generative AI can be used, like content creation or data analysis. Mainly focus on repetitive and time consuming tasks that can be automated.

Define business objectives

You should clearly define the business objectives you want to achieve with generative AI. It will help you choose appropriate tools and craft prompts that align with your goals.

Set up a test environment

Establishing a test environment is necessary to check out the way AI functions and find errors, if any, before deploying it. You should also constantly test your AI models to ensure that they give accurate results over time.

Establish governance frameworks

It is a crucial step to maintaining privacy, security, and cost-effectiveness. Put up proper AI regulations in place to prevent distribution of harmful content and input of sensitive customer data into AI tools.

Train marketing teams

It is important that your employees are familiar with the way AI operates so that they feel confident when it comes to using it. Conduct workshops to educate them on the basics of generative AI and its potential applications.

Companies using generative AI for marketing success

Many companies have joined the generative AI phenomenon. While some have started using it to streamline customer interactions, others have utilized it to create striking visual content.

Atlassian: An AI virtual assistant

Atlassian is a software company known for its collaborative solutions that help developers and project managers efficiently work with each other.

It has recently introduced Atlassian Intelligence, an AI virtual assistant. Built with OpenAI LLMs, the AI assistant can:

• Compose customer responses
• Draft content based on product specifications
• Automate support interactions within Slack and Teams
• Extract information from knowledge base articles
• Summarize documents and meeting transcripts for newly assigned agents
• Translate natural language queries into Jira Query Language

Coca Cola: Create real magic

The campaign makes use of GPT-4, DALL-E, and Coca-Cola brand assets to promote creators from diverse markets.

People can visit createrealmagic.com and develop art with Coca-Cola assets. If they make something extraordinary, their artwork will get featured on billboards in places like NYC and London.

Being all inclusive, ‘Create Real Magic’ helps the brand achieve the following objectives:

• Democratize brand iconography and advertising assets
• Fostering human connection and experiences
• Demonstrate commitment to use AI for creative purposes

Duolingo: An AI powered practice partner

Duolingo is one of the most famous language learning apps out there. It has partnered with OpenAI to incorporate GPT-4 into its services and personalized learning in a way not seen before.

Leveraging data provided by the 500 million students who use the platform, the integration is used to power two new features.

• Explain My Answer: Users get a thorough explanation as to why their answers are right/wrong with examples, similar to human tutors.
• Role Playing: Users interact with AI personas to engage in unique language based tasks, practicing language in various scenarios.

Wrapping up

Generative AI is poised to disrupt the world, but in a good way. As is evident from its uses in design, content, and messaging, it will surely be a gamechanger in years to come.

While its short-term impact is slightly overestimated, it won’t hurt to be fully prepared. After all, human creativity enhanced by AI can give results that marketers could only imagine in the past.

Frequently Asked Questions (FAQ)

How can generative ai be used in the field of marketing.

Generative AI can be used to perform a wide range of tasks in marketing. It can be employed in:

1. Content creation

2. Image or video generation

3. Search engine optimization (SEO)

4. Marketing segmentation

5. Personalization

6. Customer support

7. Cookieless marketing

How are brands using generative AI?

Brands like Coca-Cola, Atlassian, and Duolingo are extensively using generative AI in their product and marketing strategies. Here’s how:

Atlassian: Uses an AI virtual assistant to simplify teamwork and boost productivity

Coca-Cola: Creates real magic by combining AI with creative advertising

Duolingo: Introduced an AI powered practice partner to enhance users’ learning experience

Which generative AI tools can you use in marketing?

These are some of the generative AI tools that you can use in marketing:

Content creation: GPT-4, Jasper AI, and Wordtune

Image generation: Midjourney, DALL-E2, and Adobe Firefly

Video production: Runway and Synthesia

Create personas automatically from your Google Analytics data

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Generative AI in marketing

Key Takeaways:

• Marketing organizations are finding myriad use cases for generative AI, including content ideation, creation, and personalization.
• Generative AI can also help marketers with lead generation, reporting, and market analysis.
• Any application of generative AI comes with risks. For marketers, the most serious risks involve problems with content generation.
• Although it is a large undertaking, marketing organizations can also build their own AI models based on proprietary or task-specific datasets.

Table of Contents

Generative AI is revolutionizing the way people work in nearly every industry. It’s providing new tools that can take on the kind of creative, analytical, and organizational work that in the past was strictly the domain of humans. 

This is especially true for marketers — and content marketers in particular. Content marketing involves content creation and design as well as personalization, targeting, and cadence planning. These are all tasks at which generative AI excels. 

While there are myriad applications for generative AI, it has its limitations and can be effectively and safely used only with strict human oversight. 

What is generative AI?

Generative AI is a form of machine learning, a branch of the science of artificial intelligence. Whereas earlier forms of AI were used mainly to analyze data, generative AI is used to create — or “generate” — many different types of written, visual, audio, and video content. The technology can also be used to answer questions, interpret data and draw conclusions, write computer code, design new pharmaceuticals, or solve complex real-world problems. 

While generative AI technology is not new, recent advances have made some models — the technical term for an AI tool — easy to use despite their underlying complexity. These models can mimic the way humans speak, write, draw, plan, and strategize by using “deep learning,” a tactic inspired by the way the human brain forms associations.

Generative AI for marketing

Generative AI is already widely used among marketers and is here to stay:

• A study by the MIT Technology Review in 2022 found that only 5% of marketing organizations considered generative AI “critical” to their operations, and just 20% were making wide-scale use of it across different use cases. But by 2025, 20% of marketing executives plan for generative AI to be a critical part of their department’s function, and another 44% plan to use it across various applications.
• A 2023 study by Deloitte found that 41% of marketing, sales, and customer service organizations had adopted generative AI in a limited or at-scale implementation — a number second only to IT and cybersecurity departments. 
• A 2023 Salesforce survey of 1,000 marketers found that more than half are currently using generative AI and another 22% plan to implement generative AI in the coming year. 
• A 2023 Statista survey of 1,000 B2B and B2C marketing professionals found that 73% of them are already using some form of generative artificial intelligence.
• A 2023 Boston Consulting Group cross-industry survey found that 67% of marketing executives are exploring generative AI for personalization, 49% for content creation, and 41% for market segmentation.
• The same survey found that among those already using generative AI, between 40% and 50% are using it for social listening, predictive analysis, generating custom product descriptions, and chatbot marketing.

Generative AI use cases in marketing

Marketing organizations are finding myriad use cases for generative AI. Below are some of the most common.

Content creation

Generative AI models can write copy from an outline or prompt, and they’re handy for short-form content like blog posts, emails, social media posts, and digital advertising. But their content creation ability goes beyond straightforward text generation and includes:

• Generating images from a text description
• Conducting research and finding citations for data-driven reports
• Translating content into different languages
• Creating charts and graphs from descriptions or data sets
• Producing audio from written text, a process known as text-to-voice
• Producing short-form video 
• Composing royalty-free music
• Summarizing long-form content 
• Refining messaging or rewriting it in a different tone or voice
• Generating prospect-facing product descriptions from technical information
• Researching SEO keywords, longtails, and topic clusters
• Optimizing existing content for SEO

Content personalization

Generative AI can personalize marketing messaging at scale by leveraging customer personas and data. It can facilitate the A/B testing of marketing messaging by generating multiple versions of marketing assets. It can also adapt content based on user preferences, geo-location, or trending hashtags. 

Based on parameters like past website behavior, content and product preferences, and company interactions, generative AI can also assist in developing customer personas that drive content personalization requirements. This personalization can be extended to the customer journey itself: generative AI can help map more engaging customer journeys .

Content ideation

When they come up against writer’s block, some content marketers use generative AI models for inspiration and help with creative thinking. By collaborating with generative AI models, marketers can generate ideas for new campaigns by prompting for content that aligns a target persona with their sales and marketing goals.

Automated customer service and support

Website chatbots have long been able to handle simple, scripted “conversations,” but generative AI enables chatbots to have human-like interactions. They can offer concise, relevant, and precise replies to customer and technical support questions in many different languages. 

Generative AI models can also evaluate information —and even the tone of the question — through “sentiment analysis,” helping craft more discerning responses. Chatbots using sentiment analysis can also monitor and respond appropriately to activity on social media channels.

Market research and data analysis

Generative AI can analyze vast amounts of unstructured data and extract insights. Organizations then use these insights to guide business decisions about market segmentation, campaigns, advertising, or product and feature development. 

One example of this use case is predictive forecasting, which uses past trends to form a hypothesis about future behavior. It’s used to forecast churn rates, demand patterns, and how well campaigns or ads may perform.

Demand and lead generation

Marketers rely on strategic generative AI tools for numerous strategic marketing applications:

• Automating campaign execution, cross-channel coordination, and lead scoring
• Adjusting advertising strategies in real time
• Optimizing customer and market segmentation
• Orchestrating SEO strategies 

Generative AI in marketing: Examples

Generative AI has broad applications for marketers across a wide range of industries. Below are examples of how companies in different industries are employing generative AI in their marketing efforts.

• Determining the ideal bid range for Google AdWords by identifying patterns in historical data and using them to predict the performance of future digital ads
• Reducing churn by personalizing loyalty programs for individual consumers, using incentives tailored to each consumer’s buying patterns
• Sending only those product offers relevant to each customer, rather than using a generic approach, reducing spam and improving customer affinity
• Developing advertising budgets around ideal touchpoints by determining which ones contribute directly to sales
• Using data to customize an interactive customer journey most likely to convert a lead to a prospect in real time
• Automating reporting the ad performance of multiple digital ad programs simultaneously
• Using micro market segmentation to personalize messaging based on multiple customer data points and automatically generating content and imagery for an email nurture campaign

The benefits of generative AI in marketing

The examples and use cases for generative AI listed above yield various benefits for marketing teams. Those benefits can extend to customers and prospects as well. 

Generative AI saves time and resources and improves ROI

By accelerating content development and SEO research, generative AI helps marketing teams make more efficient use of their human resources. It can also give team members more time for strategic work.

Generative AI fosters customer loyalty

Generative AI makes it easier to personalize content at scale. It can also help create, schedule, and respond to social media posts in real time. Relevant content fosters longer-lasting customer relationships. It also improves conversion rates and lead quality.

Generative AI improves marketing strategy and outcomes

Generative AI can automate competitive research, analyze website traffic, interpret consumer behavior patterns, and predict how advertising will perform. This enables marketers to improve conversion rates by:

• Iterating and refining ad user journeys and campaign tactics
• Adjusting ad content and placement, and making better ad buys in real time
• Uncovering new market segments

The risks of using generative AI for marketing

Any application of generative AI comes with risks. For marketers, the most serious risks involve problems with content generation and include:

• Plagiarism and data piracy - The model can replicate something in its dataset word-for-word
• Proliferating intentionally false or misleading information - The dataset may contain propaganda or incorrect information
• Bias - This can be inherited from the data collection and model training used to build generative AI tools and introduced into the content it generates
• Copyright violation - The generative AI model may be built on data the company lacks permission to use
• The violation of users’ data privacy - User prompts or inputs may be collected without the user’s permission
• Unpredictable behavior beyond the tool’s planned functionality - Complex new technologies don’t always behave as planned

Types of regulation

Advocacy groups are pushing for the regulation of generative AI usage, and some governments, as well as public and private companies, have responded . 

The European Union

The EU has prohibited AI usage that poses “unacceptable risks,” but applications like content development are considered “low risk,” and regulated less stringently. Those regulations include:

• Identifying content generated by AI
• Designing models to prevent it from generating illegal content
• Publishing summaries of copyrighted data used to train generative AI models 
• Disclosing the source for content generated by AI

Because EU regulations usually affect non-EU countries doing business in or with the EU, their regulations also extend to American and British companies.

The United States

Because the Federal Trade Commission oversees false and deceptive business practices, it could become involved in regulating content to prevent the creation of deep fakes. U.S. regulations are likely to affect how AI models are developed rather than their usage.

Private industry

Companies that develop and use AI are also considering ways to lobby against as well as comply with impending regulation. They are also readying the implementation of their own safeguards and guidelines. Those companies include tech giants Microsoft, Google, Amazon, OpenAI, Apple, Nvidia, and IBM, which are prime targets of regulatory scrutiny. 

Risks for brands

Currently, brands under scrutiny with generative AI are limited to the companies behind the technology rather than the companies that use the technology. These lawsuits deal with copyright and licensing issues for training data and privacy concerns. 

While brands using AI have not yet been sued, the risk of lawsuits is real. Brands could also lose access to generative AI tools that have become integral to their business. And their brand image could be tarnished by public complaints about their use of copyrighted content without permission or plagiarized text and images.

Best practices for implementing generative AI in marketing

Risk mitigation for using generative ai.

Human oversight is the most important safeguard against any of the risks posed by generative AI. AI tools should only be employed with a clearly stated strategy and goals. Marketing leaders should implement and enforce policies and practical ways that regulate how generative AI is deployed and human reviews of AI-generated content should take place. 

Such procedures should include:

• Carefully researching generative AI models to ensure they’ve been built from accurate and legally obtained data
• Implementing an AI roadmap that limits generative AI applications to legitimate use cases
• Testing use cases before rolling them out enterprise-wide
• Reviewing content for bias and other ethical considerations
• Including a disclaimer for AI-generated content
• Implementing a comprehensive data strategy, a data infrastructure for data collection , and activation that vets data to manage risk 

Generative AI tools for marketers

Tools for content generation.

These three content-related generative AI tools below are popular among marketers and becoming integral to their martech stack: 

Open AI’s ChatGPT

ChatGPT, a free generative AI tool, helps marketers brainstorm, research, outline, generate, and summarize many types of content by responding to questions, or prompts. Its ease of use and versatility made it the fastest-growing consumer application in history . 

Google’s Gemini (formerly Bard)

Gemini’s functionality and interface are similar to ChatGPT’s; the main difference is the data sources used to build them. Gemini will generate multiple versions of a response to the same prompt, which gives users more flexibility. It can search for images on the internet based on natural language prompts.

Open AI’s DALL-E

DALL-E generates imagery in many different styles. It can also retouch and create varied iterations of existing images based on natural language prompts. 

Other content-generation tools include:

• Stable Diffusion - an open-source image generation technology
• Progen - a content generator for professional communications with links for social sharing and built-in safeguards against plagiarism
• GAN.ai - used to personalize videos at scale
• Anthropic’s Claude - used for summarization, search, creative and collaborative writing
• Omneky - used to customize advertising creative across all digital platforms
• Hypotenuse - a platform that generates product descriptions and advertising captions automatically
• Flick - a social media tool that creates posts, targeted hashtags, and optimizes post schedules

Strategic generative AI tools

There are dozens of generative AI tools to help with all aspects of marketing strategy, some of which may overlap with platforms already part of a company’s martech stack. Because generative AI tools can be more intuitive and easier to use, they provide access to information and functionality previously reserved for specialists and data scientists.

Among the most commonly used are:

• Alteryx - a platform for automated data engineering and analytics reporting
• DataRobot - an open platform used for developing organizational growth strategies
• Skai - an omnichannel marketing platform that automates the optimization of an organization’s ad spend
• Braze - a customer engagement platform that orchestrates customized, cross-channel journeys using dynamic market segmentation and personalized messaging

Custom generative AI tools for marketing

Marketing organizations can also build their own AI models based on proprietary or task-specific datasets. It can be a large undertaking, but this allows them to narrow the scope and increase relevance for the analysis. A custom dataset — using brand guidelines or data collected from previous campaigns — can also help generate industry-specific content or company-specific campaigns more accurately. 

Controlling the data on which an AI model is trained also manages the risk inherent in third-party tools whose data may not have been adequately vetted for bias, inaccuracies, misinformation, or copyright issues.

Amazon SageMaker is a development platform used to build, train, and deploy customized machine learning models using proprietary data sources. 

However, developing proprietary generative AI models is beyond the capabilities of most companies. Most organizations typically begin with publicly available models like GPT-4 and Gemini. Then, the organizations refine the models and update them regularly using prompt engineering , reinforcement learning based on human feedback, and other techniques. 

What is generative marketing?

Generative marketing applies generative AI to marketing workflows. Generative marketing platforms are a type of composable customer data platform (CDP) that employs generative AI capabilities. Generative marketing tools are generally campaign-related and used to improve the accuracy of a campaign’s target audience and personalize the audience journey. Those tools address many of the applications and use cases listed above. 

What’s next for generative AI in marketing?

Productivity vs. strategy.

Companies’ current use of generative AI improves efficiency and productivity while reducing costs. According to a 2023 study by Deloitte :

• 91% of business leaders surveyed said they expected generative AI to improve their organization’s productivity. 
• Only 29% were employing generative AI strategically. 

But a shift from focusing on productivity and cost-savings to strategic benefits like innovation and growth is underway. That shift will likely play out in marketing organizations as well, as generative AI technology spreads from content generation applications to strategic marketing applications, according to the same study.

Interactivity and autonomy in generative AI

As natural language processing capabilities improve, other expected developments in generative AI include:

• Discussing an image or video interactively as fluently as you can discuss text-based content today.
• Interacting with generative AI interfaces built directly into applications . 

Autonomous AI — which can operate without any human interaction — is the next step beyond generative AI. Its best-known use is for self-driving cars. In the near future, it’s possible that marketers will find a way to employ these generative AI technologies as well.

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Generative AI in Marketing: Benefits & 7 Use Cases in 2024

The share of artificial intelligence in the marketing sector is rapidly increasing (see Figure 1). However, the specific effect of generative AI in marketing strategy is not commonly known. However, the market value of AI in marketing worldwide is increasing and generative AI is the new leading factor in AI technology.

Figure 1. Market value of artificial intelligence (AI) in marketing worldwide from 2020 to 2028

Source: Statista

In this article, we will explain 3 benefits of leveraging generative AI in marketing campaigns, and address 7 use cases with some real life examples. 

Top 3 Benefits of Generative AI in Marketing

Generative AI can help create high-quality, relevant content, engaging, and tailored to the needs of specific target audiences. By analyzing customer data and identifying patterns in consumer behavior, generative AI can help marketers create content that resonates with their audiences and inspires action. High quality content can benefit marketers in terms of:

1- Time and budget efficiency through automation

One of the biggest benefits of using generative AI in marketing is increased time and budget efficiency it brings to content creation. The time devoted to composing marketing materials can be decreased in these ways:

• Conversational AI tools like ChatGPT can produce extensive replies in a matter of seconds, which is significantly quicker than what any human could do.
• Unlike search engines, it can evaluate the online information to present a concise summary of the answer.
• Through marketing automation and automated content generation , generative AI can eliminate the need for manual work, freeing up time for marketers and reducing the budget needed for content creation.
• By the elimination of human error and streamlining processes, using generative AI in marketing can help marketers reduce the amount of time and budget needed for certain control tasks.

2- Targeted and personalized content

Using a combination of customer data analysis, natural lang u age processing (NLP), and machine learning algorithms, generative AI tools can create content that is specifically tailored to your target audience.

By analyzing consumer behavior and identifying patterns in their interactions with a particular platform or brand, it can generate unique content strategy to improve customer engagement, based on preferences of customers and outreach them via personalized emails and targeted social media posts.

3- Increased innovation and inspiration

Generative AI provides a powerful tool for businesses to generate innovative and inspirational marketing strategies that can help them stay ahead of the curve and achieve their marketing goals by:

• Encouraging marketers to discover new ideas and approaches to marketing by generating creative concepts and insights that may not have been apparent through traditional methods.
• Assisting them in generating marketing concepts and ideas by drawing inspiration from the important previous experiences of others with tools like ChatGPT that cover the most of the previous online databases.
• Helping businesses identify customer insights , which can be used to create marketing campaigns that are relevant and timely.

7 Use Cases of Generative AI in Marketing

1- text generation.

Text generation using generative AI can be a powerful tool for marketing efforts. These AI-generated texts can be used for a variety of purposes other than generating ideas, such as:

• Content creation for content marketing in the forms of emails, social media posts, blog articles, etc.
• Script writing and storytelling for advertising goods and services (see Figure 2)
• Generating product descriptions that are clear, concise, and engaging

Figure 2. An example for an AI generated content for advertising a new electric car model by using ChatGPT

2- Image generation

Here are a few ways in which image generation via generative AI tools like DALL-E can be implemented in marketing:

• Product imagery: By using generative AI in marketing, businesses can create highly realistic images of their products that can be used in online stores, social media, and other marketing materials. This can help to showcase products in a more engaging and visually appealing way, which can lead to increased sales and conversions.
• Visual branding: Generative AI can be used to create visual branding materials, such as logos and graphics.
• Visual try-on: Another use case of generative AI in marketing can be to create virtual try-on experiences, which allow customers to visualize how products will look on them. This can be particularly useful for fashion and beauty brands, as it can help to reduce the number of returns and increase customer satisfaction.
• Ad creative: Generative AI can also be used to create ads that are engaging and visually appealing (see the videos below to see how some famous brands leverage this). By using AI for ad creative, businesses can ensure that their ads stand out from the competition and are more likely to generate clicks and conversions.

Heinz and Nestle adopt generative AI in marketing campaigns. Here are their ad campaign videos generated by AI:

3- Video generation

Video generation application of generative AI can be useful for marketing in:

• Video ads: With generative AI, businesses can create high-quality video ads that can be used on various platforms, including social media and video sharing sites. This can help to increase brand awareness and drive conversions.
• Product demos: Video generation can also be used to create product demo videos. By using generative AI to create these videos, businesses can showcase their products in a visually appealing way, which can help to increase engagement and sales.

However, users should be careful about the ethical concerns regarding such use cases, like the deep fakes (see the video below). There can be serious ethical problems if not monitored carefully. If you are interested in what these can be, check our article on the ethical concerns around generative AI .

4- Music generation

Music is an important component of many advertisements, and generative AI can be used to create ad music that is optimized for specific campaigns and audiences. By creating music that is tailored to the needs and preferences of a particular audience, businesses can increase the effectiveness of their ads and drive more conversions.

Figure 3. List of some generative AI tools for music generation

Source: AIMultiple

If you are interested in other generative AI tools, you can read our article on top 35 generative AI tools by category .

5- Chatbots/conversational AI for customer service

Chatbots or conversational AI using generative AI can be a valuable tool for customer service and support in marketing. By using generative AI, businesses can create chatbots or conversational AI that can understand and respond to customer inquiries and provide solutions to common problems. In this way, generative AI can be used in customer services for:

• 24/7 response
• Quick response times
• Multilingual support

6- Sentiment analysis

Generative AI can aid in sentiment analysis by creating synthetic text data that has been labeled with different sentiments such as positive, negative, or neutral. This synthetic data can be used to train deep learning models to analyze real-world text data for sentiment.

Additionally, generative AI can create text that is intentionally designed to convey a specific sentiment, such as positive or negative social media posts that could shape public opinion for marketing campaigns. This approach can also address the issue of data imbalance in sentiment analysis of user opinions, as shown in Figure 4, in various areas like customer service.

Figure 4. High-level overview of a sentiment classification approach

Source 1 : “The Impact of Synthetic Text Generation for Sentiment Analysis Using GAN-based Models”

Check our article on the use cases of sentiment analysis in marketing to learn more.

7- Search Engine Optimization (SEO)

By analyzing large amounts of data and identifying patterns in consumer behavior, the use of generative AI in market research can help businesses identify SEO friendly, the most relevant and high-performing keywords and phrases for their digital marketing campaigns.

Marketers can utilize generative AI tools like ChatGPT to implement SEO in their content in various ways such as:

• Generating topic ideas for content writing
• Conducting keyword research
• Finding the right titles
• Grouping search intent
• Creating content structure

Business leaders testify that they are leveraging generative AI in marketing to maximize SEO and PR. 2 If you are interested in SEO optimization by using artificial intelligence tools, you can check our article on how to achieve SEO by leveraging ChatGPT .

If you have questions about generative AI in marketing or need help in finding vendors, feel free to reach out:

External Links

• 1. “ The impact of synthetic text generation for sentiment analysis using GAN based models. ” Egyptian Informatics Journal . Accessed 27 February 2023.
• 2. “ How Generative AI Is Changing Creative Work. ” Harvard Business Review , 14 November 2022. Accessed 27 February 2023.

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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• Encourage teams to identify valuable applications, experiment with models, and build transformative use cases that achieve step-change advances in productivity.
• Establish enterprise-wide models to improve process efficiency, personalize customer interactions, inspire innovation through unconventional creativity, and create customer value in new ways.
• Develop operating AI systems that align with organizational values and widely accepted ethical standards while also achieving transformative business impact.

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/ article, how cmos are succeeding with generative ai.

By  David Ratajczak ,  Matthew Kropp ,  Silvio Palumbo ,  Nicolas de Bellefonds ,  Jessica Apotheker ,  Sarah Willersdorf , and  Giorgo Paizanis

How CMOs Use Generative AI Today

Nicolas de Bellefonds

Over 70% of CMOs are already experimenting with generative AI—focusing on personalization, marketing operations, insights and innovation.

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The reality of generative AI (GenAI) is catching up to the hype about how it could disrupt the roles of marketers and of marketing itself. Our April 2023 survey of over 200 CMOs from several sectors in eight countries in North America, Europe, and Asia revealed that CMOs around the world are optimistic and confident about GenAI’s future ability to enhance productivity and create competitive advantage.

This comes as no surprise, given the promotional publicity surrounding GenAI models and the rapid proliferation of large language models (LLMs) such as ChatGPT. No technology has ever reached 100 million users faster than ChatGPT, which crossed that threshold in just two months, far more quickly than Instagram, which needed two and a half years.

What is surprising is how deeply and extensively CMOs are exploring GenAI’s transformative power, benefits, and potential risks. Our survey revealed that CMOs are rapidly redefining the baselines for speed, efficiency, and quality in a wide range of marketing tasks. CMOs also see opportunities for GenAI to help their companies launch new products and implement new business models.

GenAI Has Already Affected Core Marketing Functions

Some 70% of survey respondents said that their organizations already use GenAI, while another 19% are testing it. (See Exhibit 1.) Applications include core marketing functions such as content generation, insight generation, and market segmentation. The greatest area of focus so far is personalization ; roughly two-thirds of respondents are pursuing efforts there.

Three of the many areas the CMOs are exploring are personalization, content creation, and market segmentation:

• Personalization (67% of Respondents). Some banks are using GenAI to analyze customer data and offer personalized investment advice matched to their risk appetite. Some retailers are using GenAI to create hyper-personalized recommendations that entice shoppers to buy more. The goals and benefits of these efforts range from better engagement to improved conversion rates to increased customer loyalty.
• Content Creation (49%). GenAI helps marketing organizations create content faster, with higher quality and greater variety. Content creators can use these tools to create drafts, explore ideas, seek unusual combinations, and find other ways to inspire their teams’ creativity, rather than replacing or constraining it.
• Market Segmentation (41%). GenAI can enable CMOs to target customers more precisely. Better segmentation can improve resource allocation and ROI. GenAI can also enhance consumer and product strategy by changing how—and how fast—companies can conduct reliable market and consumer research and then use the resulting insights to improve their products and services.

One key finding from our survey is that GenAI works. Most CMOs see positive results right away, with 93% reporting a positive or very positive improvement in how they organize their work and 91% reporting a positive or very positive impact on their efficiency. Our own initial observations suggest that GenAI’s low cost and ease of use can yield productivity gains of as much as 30%.

Much of the discussion surrounding GenAI has involved these types of process improvements. The CMOs who participated in our survey expressed overwhelming confidence that GenAI can help their company handle a wide range of operational tasks faster, with less risk, and with greater efficiency. (See Exhibit 2.) It can also reduce the amount of time employees spend on tedious tasks, freeing up more time for value-added work. The onus is now on CMOs to capitalize on these additional levels of productivity and creativity to optimize their organization’s talent, headcount, and external relationships. These capabilities will also influence go-to-market strategies. The CMOs in our survey feel that GenAI will make it easier to launch new products and business models. Half of the CMOs see GenAI as a tool to accomplish both objectives simultaneously.

Inaction Is Not an Option

The speed at which companies derive benefits from GenAI tools can quickly create haves and have-nots in an industry. Companies that successfully implement GenAI will free up resources that they can use to acquire, serve, and retain customers more nimbly and effectively.

This means that inaction is not an option. Success requires coordination and foresight. We recommend that CMOs take four key actions. (See Exhibit 3.)

Start experimenting. To experience the potential of this technology first-hand, senior leaders must dive in and explore its capabilities. CMOs should encourage their teams to identify valuable applications, experiment with models, and start building transformative use cases. One approach is to create cross-functional agile marketing pods that can take on a task, such as launching a marketing campaign, with as much GenAI as possible. Once marketers find ways to hack their processes, the organization’s data scientists and engineers can automate and build connections to enhance it—for example, by using enterprise versions of LLMs or building application layers to produce output in more usable forms.

Seek game-changing outcomes. CMOs should aim to achieve step-change gains in productivity through innovative and disruptive approaches. Doing so creates a different risk-reward calculus for setting priorities. CMOs need to identify “golden” use cases that enable them to use their core data and IP assets uniquely to create a competitive edge. Training the models on IP and fine-tuning them with key data (marketing performance as well as consumer, brand, and market research) will also ensure that the outcomes are sufficiently differentiated from what competitors can produce. Settling for small gains with big ROIs may seem attractive, but a company can’t afford to walk when its competitors are running.

Establish an enterprise-wide model. Scale and competitive advantage are elusive in the absence of the right solution and architecture. Today’s GenAI model market is volatile, which exposes companies to two extremes: either selecting an unsuitable enterprise-wide provider or having to cobble together a collection of providers. Consumers and end-users have fueled unprecedented growth of LLMs, but now tech companies are developing suites of enterprise solutions to spur greater innovation. Developers can select different models that meet their needs from a library of LLMs, the goal being to find LLM providers that complement their existing cloud or tech supplier while retaining flexibility on the last-mile applications on top (providers of bots, content creation, and so on). These tools will enable CMOs and their teams to improve process efficiency, personalize customer interactions, inspire innovation through unconventional creativity, and create customer value in new ways.

Implement responsible AI guidelines. If an organization prohibits GenAI use or lacks centralized guardrails, one of two things is probably happening. Either employees are using GenAI anyway—professionally or privately—and recognizing opportunities for productivity improvements or the organization is falling behind competitors that are already pursuing and may be achieving double-digit-percentage gains in productivity.

The balance lies in incentivizing experimentation with GenAI while mitigating the numerous risks. Using AI responsibly means developing and operating AI systems that align with organizational values and widely accepted ethical standards while also achieving transformative business impact.

Most CMOs in our survey see AI regulations as inevitable, and the vast majority have undertaken some form of self-regulation. (See Exhibit 4.)

As is the case with any technology that has vast potential but no long-term track record, moving too quickly can create medium- and long-term risks related to unintended consequences—for example, when a particular choice of text and images clashes with desired brand tone and erodes brand strength. Among the numerous risks of unmanaged AI are proprietary data leaks, copyright infringement, biased output, sophisticated fraud, and even the risk of a shadow AI—a situation in which people throughout the organization regularly use external tools without proper guidance and supervision. Companies should layer branding plug-ins on top of their algorithms to ensure that the organizational styles that their marketers and agencies work with—such as tone of voice, color coding, authorized themes, and content—adhere to the brand’s approved framework.

Optimism and Confidence Far Outweigh Worry

CMOs in our survey were overwhelmingly optimistic and confident about the future impact of GenAI. When asked to select the words that best describe their feelings about GenAI, at least 70% of CMOs listed optimistic and confident among their top three. (See Exhibit 5.) More importantly, this optimism is pervasive. Every country and every sector showed similar results.

GenAI captures people’s imagination, because it is a breakthrough technology—the ability to converse with computers—that people have anticipated for decades. Whether GenAI is a disruptive innovation or a sustaining one depends on the organization and its ambitions. The disruptive potential of GenAI, however, seems to be foundational, in the same way that search engines and other online platforms upended business models and unleashed creativity three decades ago. It may have the power not only to revolutionize how companies perform certain marketing tasks, but also to redefine the role of marketing itself.

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– 11 min read

From “…and?” to “ah-ha!”: How to discover powerful generative AI use cases for your marketing team

Anna Burgess Yang

With any technology implementation, you’ll have the “go-getters” and “sit-backers.” Go-getters will see the value and try to uncover new ways of using a solution. Sit-backers will wait until a new use case is presented to them.

Disruptive technology will always exist. What’s unique about generative AI is the speed of disruption. Innovative solutions emerge → companies determine that it’ll solve a particular use case → the idea is put into production. All of this is happening at a rapid pace (at least among companies that know AI is the future). 

Because of this cycle of idea-to-deployment, marketing teams have to plan for continuous use case adoption. This means strategizing: how can your team uncover new generative AI use cases and what steps are needed to implement them?

And as marketers, don’t we love a good strategy?

Summarized by Writer

• Generative AI is disrupting industries at a rapid pace, and marketing teams need to plan for continuous use case adoption.
• A good generative AI use case depends on the marketing team’s operational maturity and requires standards for evaluating and editing output.
• Successful implementation of generative AI often starts with recognizing internal signals that AI could solve a problem or help the team.
• Without a framework for identifying and prioritizing use cases, marketing teams can’t operationalize and scale their use of generative AI.

What makes a good generative AI use case?

If you log into LinkedIn, or a community forum, or listen to a webinar or podcast, you’ll receive an endless supply of generative AI use cases. People on your team may have their own ideas. Some will be amazing … and some won’t be the right fit for your team.

A good fit depends on your marketing team’s operational maturity . If the team’s work and processes lack consistency, it can be hard to fit generative AI into your workflow. You also need standards for evaluating and editing generative AI output. This can only happen when the team has a standardized understanding of what the output should be. 

If your team has only worked with simple generative AI use cases, introducing something complex isn’t a logical next step. You have to create a foundation first. If your team has a good handle on generative AI, then good use cases will continue to build and scale your operations. 

At a minimum, you need an internal process to identify potential use cases, no matter their source. You can task people, such as team leads, to be on the lookout. The potential uses cases can move up the ladder to an AI program director to determine if they’re worth pursuing. 

Remember that good use cases on Day 1 won’t be the same as Day 101. You can keep a list of potential use cases that aren’t a good fit now, but may be in the future.

A framework for prioritizing AI use cases 

Once you’ve initially determined a “good use case” versus “not good use case,” you’ll need to rank its priority against other use cases you’d like to tackle. 

This is called use case mapping . You’re aligning the specific use case with your overall business objectives and goals. Either way, you’re prioritizing the use case based on either the problem it solves or the opportunity it presents. 

Determine the value

We’ll assume that the use case has value for your team, or it wouldn’t have fallen into the category of “good use case.” However, some use cases will have a higher value than others. 

For example, a use case that impacts many people on the team will be of higher value than a use case that only applies to a few people. 

You’ll also want to consider the potential time saved. If you’re considering a use case that impacts only a few people, but will save them a lot of time, that’s a high-value use case. 

Let’s say you want to use generative AI to repurpose a webinar into a blog post. How many webinars does your company hold per month? If you hold a lot, this use case could save a ton of time. If you only hold one webinar every six months, this use case still might be useful, but not as much as something your marketing team does every day. 

Determine the complexity 

In some instances, your team will have everything they need at their fingertips to implement the use cases. You’ll need to consider the prompt used , and try a few different iterations to find the one that works best.

Others may be more complex. Maybe users need to gather inputs from several different sources to create a robust prompt. If your users have only worked with simple prompts before, they may need training or templates to help them craft a prompt that produces high-quality output.

In other cases, you’ll want to bolster generative AI with your own company data, such as internal guides, documents, and databases. While large language models (LLMs) are powerful on their own, they’re even more powerful with specific context from your business. 

Retrieval-augmented generation (RAG) combines external data with your LLM. While there’s more involved in connecting various systems, your team will benefit from more accurate and contextualized output. 

Learn more about our Knowledge Graph and Writer’s approach to RAG . 

Determine the solution

Your team can interact with generative AI in different ways, from an application to a Chrome extension . As you prioritize use cases, you’ll want to think about how they fit into your workflow — and what might change as a result of implementing the use case.

You may already have access to some (or all!) of the following generative AI solutions, or you may need to consider adding them to your tech stack. 

• Chat interface: best for creative outputs, sparking ideas, or finding information with a conversational flow 
• Prebuilt applications: best for out-of-the-box text generation that follows a standard workflow (such as creating an outline based on a keyword)
• Custom applications : best for following ‌company-specific use cases, custom applications can be a combination of chat, text generation, or re-writing content based on specific guidelines

If you need a highly specific prompt to generate consistent output, you’ll need a prebuilt or custom application. Chat interfaces are so unstructured that it’s hard to get a good result without a really skilled user. Or, you’ll get a result, but the result needs a lot of editing. Prebuilt and custom applications user interfaces that maintain the same structure for the prompt, no matter which user is interacting with it. 

Six marketing teams and their “ah-ha” AI use case moments

For marketing teams that successfully implement generative AI use cases , it often starts as an “aha moment.” There were internal signals that AI could either solve a problem or help the team in some way. 

The key is recognizing those cues and then putting the chosen solution into action. 

Driving brand consistency among various contributors   

Content often has a “too many cooks” problem. As marketing teams grow larger, and go-to-market strategies evolve, it becomes harder to maintain consistent brand terminology and company voice. Teams may rely on a written style and messaging guides, but it can be hard to review and even harder to enforce. 

This was the case at Dropbox . “The guidelines were just this passive document in Dropbox that people would have to remember existed and then go check, and people just weren’t doing it,” says Angelique Little, Content Design Lead at Dropbox. Consistency became an even bigger issue as Dropbox looked to expand its thought leadership content across more contributors within the company. 

Bamboo Rose also has content from varying contributors, but for a different reason. The company has made acquisitions part of its growth strategy. When the acquisition is complete, the Bamboo Rose content team has inherited content assets that need to be edited for the company’s guidelines. 

In both cases, the companies selected Writer to solve their content editing challenges. With in-application enforcement and suggestions, marketing teams can ensure consistent terminology, phrasing, and inclusive language.

“Writer can accelerate our ability to create that synergy and speed up time to market to unify perception of the company,” says Jennifer Schiffman, CMO of Bamboo Rose. “We don’t feel disparate.”

Discover how Writer ensures all your work is compliant, accurate, inclusive, and on-brand wherever your people work. Take a self-guided tour of our AI guardrails

Enabling leadership and GTM teams with key insights

Commvault does a lot of customer and prospect interviews, generating hours of call recordings every week. The marketing team would then wait for a researcher to write an executive summary. 

“Think about how hard it is to truly synthesize a ton of data from two-hour interviews down to a nugget of a paragraph summary,” says Anna Griffin, CMO at Commvault. It was a time-consuming process and could often hold up internal meetings to discuss the findings. 

With Writer, the Commvault team can get a summary in seconds using the recaps feature. 

“My team and I are freed up from [this work],” Griffin says. “We’re getting insights faster and we’re executing on them and putting them into market faster.”

This go-to-market motion, powered by generative AI, also enabled the sales team at Carta . A demand generation marketer saw the potential in using Writer to quickly summarize a virtual event for the sales team. That way, the sales team could reference the key takeaways from the event when talking with prospects. 

“I expected Writer to help us on pure content,” says Carta Chief Marketing Office Jane Alexander. “What I wasn’t expecting is that it would create more efficiency in how our team members talk to cross-functional team members, and draw insights.” Explore our pre-built and custom solutions for analyzing and surfacing insights. Take a self-guided tour of a custom summary app .

Freeing up creative minds from drudge work and blank-page paralysis  

Anyone who’s written a press release knows that they’re the definition of highly structured content. They’re written for web crawlers, not human readers. The marketing team at Adore Me (acquired by Victoria’s Secret) found writing press releases to be painful, albeit necessary. 

Ranjan Roy, VP of Strategy, describes the “light bulb moment” in realizing that press releases were a perfect use case for generative AI. It showed him, “This could make our life easier, because everyone hates writing press releases.”

Users can feed 8–10 relevant bullet points for the topic by using an application within Writer. A full press release is then generated. Roy remembers initially reviewing the output and thinking it could be uploaded directly to PR Newswire.  

“Writer is well-tailored to actually solving real business problems for us,” says Roy. 

For other marketers, generative AI can be a launching pad. Mary Ellen Slayter, CEO at Rep Cap , describes herself as an editor more than a writer. “If you give me stuff, I’m good at shaping it into things,” she says.  Rep Cap uses Writer to generate ideas from interviews, large research reports, and other content assets. 

“I look at generative AI as a way to take the knowledge and the experience that I have, and the team has, and do better work and scale with it,” she says. “My content team jokingly calls Writer the fifth member of our team.” Get a peek at some of the many ways Writer can free up your marketing team. Check out our self-guided product tours .

Follow the path of organizational goals

Many marketing teams have accepted that generative AI will be part of their lives, but far fewer have implemented a strategy. Right now, they may rely on individual users tinkering with free tools or have haphazardly implemented a paid solution.

While individual curiosity can certainly drive excitement, it doesn’t necessarily serve an overall business goal. Instead, teams should systematically uncover individual ideas, determine if they’re a good use case, and take the steps to implement them.

Without a framework for identifying and prioritizing use cases, marketing teams can’t operationalize and scale their use of generative AI. The real power moves beyond individual productivity and can drive measurable results.

Read more: A six-step path to ROI for generative AI

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The Writer Team

The Ultimate List of Generative AI Applications in Digital Marketing

Marketing in 2024 will be all about the market size of one. There is no denying that marketing tools have come a long way in the last 20 years and are well-equipped to craft those super-personalized campaigns that convert. Read this blog to learn more about how Generative AI is enhancing the digital marketing industry:

Table of Content

Key applications and takeaways of generative ai include:, a. text generation , b. video/image creation , a. compel more sales with dynamic product ads, b. hook more customers with targeted marketing messages, c. leverage the power of ai-driven chatbots, a. attract potential customers through keyword research, b. boost user engagement with on-page optimization, c. analyze competitors and identify gaps, d. pitch for backlinks more successfully with custom outreach emails, a. maximize campaign effectiveness and roi , b. know customer trends in advance through predictive analytics , in conclusion.

Practically, though, personalization at scale can be hard when using traditional digital tools. Your reach is limited as a small business unless you shell out money for paid search ads or premium link placements.

However, there is only so much you can do with your digital marketing activities, even if you have the data about what your customer wants. Because of limited resources, analyzing that data, brainstorming premium campaign ideas, and making sales can be overwhelming.

Enter the era of Generative AI

While opinions are divided on the role that this advanced Artificial Intelligence (AI) technology plays in an inherently creative field, the fact is that it allows even small businesses with small budgets to massively ramp up their reach without breaking the bank.

• Cutting down vastly on digital marketing costs and timelines
• Creating content at scale across platforms and content types
• Delivering personalized recommendation systems that tailor customer engagement on your website, much like Netflix or Spotify creates custom lists based on consumption patterns
• Assessing consumer sentiment to see what buyers love best and what they need more of
• Analyzing data rapidly to identify market gaps, optimize campaigns in real-time, and craft sound marketing strategies based on market predictions

With the right Generative AI tools, your small business can create the hyper-tailo-red experience that every customer expects and accomplishes in days that would have taken weeks or months.

How businesses are using Generative AI in digital marketing

McKinsey estimates that Generative AI could boost the productivity of marketing by 5-15% of total digital marketing spend, or about $463 billion annually. Without further ado, let us dive into some of the high-impact use cases of Generative AI in marketing. 

1. Lower content costs with AI-powered content generation 

Artificial Intelligence content has received rave reviews and immense flak over the last year. But there is no denying that it is a game changer when it comes to consistently putting out smart, relevant high-quality content. There are two main applications of Generative AI in content production:

Many of the articles and social media posts you have read recently were generated - or at least conceptualized - by ChatGPT. 

Large-language models are exposed to wide varieties of text across different contexts and applications, through which they learn how to generate original content that human marketers would generate in similar contexts.

With AI-generated content creation capabilities in your arsenal, writer’s block will become a thing of the past. You can use it to come up with new ideas and angles, examples, and full-length outlines.

An eMarketer research shows that 58% of marketers use Generative AI for creating content, thereby increasing performance. That means that by using AI, you can optimize your digital marketing processes and improve the quality of content you put out for your customers.

Feed detailed text instructions into an AI image/video generator, and you will get high-quality visuals in seconds, without any need to pay expensive commissions or go back and forth with design agencies over edits.

When it comes to video, an AI assistant can create storyboards for a concept, write out a script for you, and even put it together in a final video.

For instance, Xerox uses Synthesia , an AI video generator, to create custom AI-powered sales training videos in multiple languages, cutting video training costs by 50% while delivering training content 30% faster.

With Generative AI, even marketers who are not design experts can create a visual treat for users, delivering fantastic customer experiences.

2. Increase campaign conversions with personalized marketing

In recent years, “personalization” has become one of the most effective ways for businesses to promote themselves, engage potential customers, and drive revenue.

The norm is to serve the right message to the right customer at the right time, and there are a few tactics to help with this:

Such adverts automatically show products to users based on their browsing history, customer behavior, interests, and past customer interactions with a website’s inventory.

For example, you must have noticed that after researching holiday destinations or vacation packages, you start seeing hotel or homestay ads on your Google or Facebook feed. Those are dynamic product ads.

According to Infosys, 86% of customers agree personalized content creation has some impact on what they purchase, with 1/4th admitting it has “significant influences” on their buying decisions.

Thankfully, Generative AI can study the nuanced patterns of a user’s online activities and predict which products they will most likely be interested in.

The information helps create advertisements comprising the right imagery and copy with surprising accuracy, resulting in a notable boost in conversion rates.

Forbes Insights reports that 37% of digital marketing leaders regard product recommendations as the reason behind increased sales and higher Customer Lifetime Value (CLTV). Tap into the potential of Generative AI, and conversions will not be hard for you to achieve.

You see, the AI and Machine Learning (ML) algorithms employed on a website capture and analyze data around a customer’s past searches, previously viewed items, and buying history.

Generative AI technology uses this data to suggest products tailored to individual customers. For example, if someone browses through a specific lipstick brand, AI helps showcase lipsticks in similar shades from different brands within the same budget.

Having just data does not help. The AI technology steps in creates a narrative using that data, and gives you hooks you can use to entice and inspire more sales.

That is why, besides product recommendation, Generative AI empowers you to write custom copy for blog posts, email campaigns, landing pages, product descriptions, and other digital marketing materials that circle what the customer wants, hitting the bullseye.

Did you know that according to Salesforce, 82% of businesses using AI have reported moderate improvement in how their customers explore, discover, and engage with products? And what is better than chatbots for attracting customer loyalty?

AI-driven chatbots utilize advanced recommendation systems to provide tailored product suggestions and styling ideas by asking questions about product size, color, style, brand, or budget. It makes delivering impeccable customer service a breeze.

This can lead to customers being suggested different cross-selling or upselling options, ultimately leading to a purchase.

Additionally, AI chatbots can personalize every message and answer queries with human-like empathy. They are available 24/7 and can instantly respond, handling multiple user queries simultaneously. Personalized experiences like these open doors to quick sales and higher customer satisfaction.

3. Optimize search results for more traffic and leads 

Even in 2024, SEO is non-negotiable. Luckily, Generative AI is ideally poised to help your marketing content get the search spotlight it deserves in extremely creative ways.

Generative AI can crawl through vast volumes of data and identify the keywords people are searching for in your space. Then, it can tweak your digital marketing content (including the ones existing online) to optimize for those keywords.

It can determine the frequency of keywords, suggest appropriate keywords for headings, recommend the number of images for a specific content piece, and even outline the main headings to cover.

Frase.io and Surfer SEO are considered popular Generative AI for content optimization.

Such Generative AI tools can also forecast content trends and topics that are likely to resonate with your audience in the future.

Scratching your head about the best meta title for your homepage? Tired of beta-testing content layouts? Unable to decide the user or search intent for keyword research?

Generative AI can cover all the SEO basics that will bring you to the front page of search results without compromising on creating a meaningful content experience for your customers.

For example, after studying vast amounts of data, it can identify market trends and patterns you may have overlooked and help you write meta titles and descriptions that are SEO-friendly and compelling to your customers.

Furthermore, it can evaluate the most successful content structures within specific industries or niches, suggesting formats that improve readability, retain user interest, increase user experience, and guide visitors seamlessly through a website.

For instance, if you are a small SaaS business, Generative AI can suggest you add interactive elements, such as embedded videos or quizzes, to engage first-time site visitors and boost sign-ups for Free Trial.

And yes, you will still have to run beta tests, but the technology makes the process much faster and increases the likelihood of you getting clicks from search engines.

How are your competitors doing their SEO? Generative AI can analyze their pages and gather data on their activities in real-time, giving you valuable insights into how they are undertaking SEO in response to changes in the broader marketing landscape.

AI can provide tips on whether to stick to the same strategies, what keywords to use, and which third-party sites to approach for outreach or try something else entirely.

Generative AI can analyze extensive datasets, including the content of the target website, recent articles published by the site, and its overall theme, to craft customized emails that resonate with the recipient's interests and needs.

This level of personalization increases the likelihood of engaging the recipient, thereby improving your chances of securing a backlink.

In addition, AI technology can streamline the outreach process by automating the creation of unique email templates for different niches and types of content. This saves time and allows you to scale your outreach efforts without compromising the quality of the pitches.

Automation is extremely handy especially if your team is small with limited resources. Plus, by generating subject lines that capture attention and content that clearly articulates the value proposition, you can effectively increase open and response rates.

4. Apply marketing data analytics for stronger strategies

Generative AI has been ruling the data analysis space for a while now, helping small and large businesses tap into key actionable insights and make automated informed decisions based on what is best for customers.

Chances are, you already have vast amounts of data about how your customers behave. With Generative AI, you can process that data much faster and understand even nascent behavioral trends. 

For instance, the technology can help you pick up on customer churn signals by paying attention to social media conversations about your brand.

Accordingly, you can tweak your ad campaigns while they are still running (without waiting until the next cycle) and start seeing more retention immediately.

No one can see the future, but you can come close to it with Generative AI. It uses ML models, particularly Generative Adversarial Networks (GANs), to simulate and predict future trends based on historical data.

These AI models are trained on vast amounts of data from a specific industry, analyzing patterns, customer behaviours, preferences, and market dynamics. The AI can identify subtle correlations and causations that humans might overlook.

Consider the food and beverage industry, where consumer preferences can shift rapidly due to health trends, seasonal ingredients, and changing dietary needs. A beverage company could use generative AI to analyze data from social media, search trends, sales data, and even global health studies to predict the next big flavor or product type.

For instance, if Generative AI identifies a growing interest in plant-based diets and superfoods, it might predict a rising demand for a kombucha drink infused with kale, before this trend becomes mainstream.

By predicting future trends, businesses can make informed decisions about which products to develop, how to price them, and where to allocate marketing resources.

Generative AI Apps & Solutions Development Services Company

There is no doubt that Generative AI has a lot of potential. It streamlines workflows, brainstorms ideas, and expedites overall work, a boon for teams and businesses looking to enhance their productivity and performance!

However, it is important to add that a tool is as good as the people who use it. With the right usage policies in place and continuous learning, Generative AI can be a powerful force for your marketing campaigns.

Going forward, the sky will truly be the limit for marketers - all you need is a powerful tool like Generative AI and your imagination. It is a world where you can deliver top-notch customer experiences while consistently innovating at the same time.

The sooner you board the ride, the sooner you will start enjoying the fruits of this new wave of marketing.

If you face hurdles in personalized marketing and experience low conversions despite having valuable customer data at your fingertips, it is time to innovate.

Book a call with us and let our AI experts design a roadmap to elevate your business strategy and unlock new potentials for your marketing efforts today.

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3 Ways Generative AI Will Help Marketers Connect With Customers

Generative AI has the potential to change the way we work. Could it be the next step toward reshaping marketing, helping you focus more on customers?

Bobby Jania

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Generative AI has raised considerable buzz lately , but with this hype comes a lot of misconceptions and confusion on how it can help marketers . With customer expectations rising and personalization now an expectation, marketers can use generative AI to help maintain customer loyalty and gain insights in a post-cookie world.  

We’ve already seen how AI can help marketers , commerce teams, salespeople, and more make informed decisions. This merely scratches the surface on how brands can use AI in their marketing to become more efficient and productive.

Explore generative AI basics

Discover how generative AI is transforming the future of work — helping your teams work more efficiently and create better customer experiences. This Trail is a helpful learning module that breaks it all down clearly.

Einstein: Quick Look

We recently asked marketers how this technology will help, with 60% saying it will transform their role. More than half (51%) are already experimenting with generative AI or using it at work already.

In our survey, marketers estimated that generative AI could save them 5 hours per week — that adds up to over one month a year. Imagine how much more you could do with that time back.

Though generative AI is still in its early stages, here are three ways marketers can use it today to better connect with customers.

What generative AI for marketing could look like

Generative AI can help with drafting marketing materials or providing quick answers to customer responses . But that’s just the start of what businesses can do with this technology. 

Combining generative AI with an intuitive customer data platform can arm companies with the tools to take action on real-time insights . This can help you deliver personalization at scale , such as product recommendations, tailored to individual customers based on their browsing and purchase history. 

Consumers also expect brands to use their data to offer more relevant services. We found that over 60% of customers expect that companies instantly react with the most up-to-date information when transferring across departments. Generative AI can satisfy this customer need by giving agents suggested responses generated right in the moment, based on real-time data. 

The next step for brands? Education. In our latest research, 54% of marketers told us that generative AI training programs are essential to them successfully using this technology. And 72% expect that their employers will provide them with the opportunities to learn how to use generative AI.

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The last mile of personalization.

Today’s customers expect personalization at every step. Recently, we found 65% of customers say they will stay loyal if the company offers a more tailored experience. 

Combining the power of generative AI with your CRM data gives marketers the ability to create those kinds of digital experiences for their customers. Altogether, this results in more efficient marketing journeys that are better tailored to their audience across content generation, design, and targeting. 

3rd-party cookie replacement

Third-party cookie deprecation and access to high-quality data — data that’s well-structured and useful — is a growing challenge for marketing organizations. We discovered that 41% of business leaders cite a lack of understanding of data because it’s too complex or not accessible enough. 

As data becomes increasingly difficult to collect, store, and analyze, marketers can now turn to AI tools to help analyze the data they do have and make the right decision. AI will help marketers process their existing, perhaps limited, first-party data and provide them with rich insights.

That trusted first-party data is important for generative AI to work well, 63% of marketers told us. Marketers themselves also play a pivotal role in generative AI’s success, with 66% saying that human oversight is necessary to make sure a brand’s voice stays authentic.

Ways to develop generative AI responsibly

Generative AI has the power to transform the way we live and work, but it’s not without risks. Here are five guidelines for building it inclusively and intentionally.

Letting you focus on the customer

This shift in focus and conversation around generative AI is imperative, not a nice-to-have. By eliminating the confusion and delay in analyzing data, generative AI takes the heavy lifting out of content creation. This technology can generate product descriptions that are accurate, compelling, and optimized for search engines. 

With generative AI handling lower-level tasks, marketers are able to focus on strategic campaigns, executing on creative, and creating connections with customers. Generative AI can fundamentally change how marketing departments operate, allowing teams to place more focus where it belongs — on the customer. 

What are marketers saying about generative AI?

Generative AI can be the next big innovation in marketing. How can it help? What needs to be done next? We asked marketing leaders all about it.

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Bobby Jania is the Senior Vice President of Marketing for Marketing Cloud, leading teams focused on messaging, positioning, and go-to-market strategy. He has 20 years of marketing experience at high tech companies.

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• Current: Generative AI: Case Studies

 target audience: TECH BUYER  Publication date: Feb 2024 -  Document type: Tech Buyer Presentation - Doc  Document number: # US51866524

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AI study guide: The no-cost tools from Microsoft to jump start your generative AI journey

By Natalie Mickey Product Marketing Manager, Data and AI Skilling, Azure

Posted on April 15, 2024 4 min read

The world of AI is constantly changing. Every day it seems there are new ways we can work with generative AI and large language models. It can be hard to know where to start your own learning journey when it comes to AI. Microsoft has put together several resources to help you get started. Whether you are ready to build your own copilot or you’re at the very beginning of your learning journey, read on to find the best and free resources from Microsoft on generative AI training.

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Azure AI fundamentals

If you’re just starting out in the world of AI, I highly recommend Microsoft’s Azure AI Fundamentals course . It includes hands on exercises, covers Azure AI Services, and dives into the world of generative AI. You can either take the full course in one sitting or break it up and complete a few modules a day.

Learning path: Azure AI fundamentals

Course highlight: Fundamentals of generative AI module

Azure AI engineer

For those who are more advanced in AI knowledge, or are perhaps software engineers, this learning path is for you. This path will guide you through building AI infused applications that leverage Azure AI Services, Azure AI Search, and Open AI.

Course highlight: Get started with Azure OpenAI Service module

Let’s get building with Azure AI Studio

Imagine a collaborative workshop where you can build AI apps, test pre-trained models, and deploy your creations to the cloud, all without getting lost in mountains of code. In our newest learning path , you will learn how to build generative AI applications like custom copilots that use language models to provide value to your users.

Learning path: Create custom copilots with Azure AI Studio (preview)

Course highlight: Build a RAG-based copilot solution with your own data using Azure AI Studio (preview) module

Dive deep into generative AI with Azure OpenAI Service

If you have some familiarity with Azure and experience programming with C# or Python, you can dive right into the Microsoft comprehensive generative AI training.

Learning path: Develop generative AI solutions with Azure OpenAI Service

Course highlight: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service module

Cloud Skills Challenges

Microsoft Azure’s Cloud Skills Challenges are free and interactive events that provide access to our tailored skilling resources for specific solution areas. Each 30-day accelerated learning experience helps users get trained in Microsoft AI. The program offers learning modules, virtual training days, and even a virtual leaderboard to compete head-to-head with your peers in the industry. Learn more about Cloud Skills Challenges here , then check out these challenges to put your AI skills to the test.

Invest in App Innovation to Stay Ahead of the Curve

Challenges 1-3 will help you prepare for Microsoft AI Applied Skills, scenario-based credentials. Challenges 4 and 5 will help you prepare for Microsoft Azure AI Certifications, with the potential of a 50% exam discount on your certification of choice 1 .

Challenge #1: Generative AI with Azure OpenAI

In about 18 hours, you’ll learn how to train models to generate original content based on natural language input. You should already have familiarity with Azure and experience programming with C# or Python. Begin now!

Challenge #2: Azure AI Language

Build a natural language processing solution with Azure AI Language. In about 20 hours, you’ll learn how to use language models to interpret the semantic meaning of written or spoken language. You should already have familiarity with the Azure portal and experience programming with C# or Python. Begin now!

Challenge #3: Azure AI Document Intelligence

Show off your smarts with Azure AI Document Intelligence Solutions. In about 21 hours, you’ll learn how to use natural language processing (NLP) solutions to interpret the meaning of written or spoken language. You should already have familiarity with the Azure portal and C# or Python programming. Begin now!

Challenge #4: Azure AI Fundamentals

Build a robust understanding of machine learning and AI principles, covering computer vision, natural language processing, and conversational AI. Tailored for both technical and non-technical backgrounds, this learning adventure guides you through creating no-code predictive models, delving into conversational AI, and more—all in just about 10 hours.

Complete the challenge within 30 days and you’ll be eligible for 50% off the cost of a Microsoft Certification exam. Earning your Azure AI Fundamentals certification can supply the foundation you need to build your career and demonstrate your knowledge of common AI and machine learning workloads—and what Azure services can solve for them. Begin now!

Challenge #5: Azure AI Engineer

Go beyond theory to build the future. This challenge equips you with practical skills for managing and leveraging Microsoft Azure’s Cognitive Services. Learn everything from secure resource provisioning to real-time performance monitoring. You’ll be crafting cutting-edge AI solutions in no time, all while preparing for Exam AI-102 and your Azure AI Engineer Associate certification . Dive into interactive tutorials, hands-on labs, and real-world scenarios. Complete the challenge within 30 days and you’ll be eligible for 50% off the cost of a Microsoft Certification exam 2 . Begin now!

Finally, our free Microsoft AI Virtual Training Days are a great way to immerse yourself in free one or two-day training sessions. We have three great options for Azure AI training:

• Azure AI Fundamentals
• Generative AI Fundamentals
• Building Generative Apps with Azure OpenAI Service

Start your AI learning today

For any and all AI-related learning opportunities, check out the Microsoft Learn AI Hub including tailored AI training guidance . You can also follow our Azure AI and Machine Learning Tech Community Blogs for monthly study guides .

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• Content Marketing

35 Content Marketing Statistics You Should Know

Stay informed with the latest content marketing statistics. Discover how optimized content can elevate your digital marketing efforts.

Content continues to sit atop the list of priorities in most marketing strategies, and there is plenty of evidence to support the reasoning.

Simply put, content marketing is crucial to any digital marketing strategy, whether running a small local business or a large multinational corporation.

After all, content in its many and evolving forms is indisputably the very lifeblood upon which the web and social media are based.

Modern SEO has effectively become optimized content marketing for all intents and purposes.

This is when Google demands and rewards businesses that create content demonstrating experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) for their customers – content that answers all of the questions consumers may have about their services, products, or business in general.

Content marketing involves creating and sharing helpful, relevant, entertaining, and consistent content in various text, image, video, and audio-based formats to the plethora of traditional and online channels available to modern marketers.

The primary focus should be on attracting and retaining a clearly defined audience, with the ultimate goal of driving profitable customer action.

Different types of content can and should be created for each stage of a customer’s journey .

Some content, like blogs or how-to videos, are informative or educational. Meanwhile, other content, like promotional campaign landing pages , gets to the point of enticing prospective customers to buy.

But with so much content being produced and shared every day, it’s important to stay updated on the latest trends and best practices in content marketing to keep pace and understand what strategies may be most effective.

Never has this been more true than in 2024, when we’re in the midst of a content revolution led by generative AI , which some feel represents both an opportunity and a threat to marketers.

To help you keep up, here are 35 content marketing statistics I think you should know:

Content Marketing Usage

How many businesses are leveraging content marketing, and how are they planning to find success?

• According to the Content Marketing Institute (CMI), 73% of B2B marketers, and 70% of B2C marketers use content marketing as part of their overall marketing strategy.
• 97% of marketers surveyed by Semrush achieved success with their content marketing in 2023.
• A B2B Content Marketing Study conducted by CMI found that 40% of B2B marketers have a documented content marketing strategy; 33% have a strategy, but it’s not documented, and 27% have no strategy.
• Half of the surveyed marketers by CMI said they outsource at least one content marketing activity.

Content Marketing Strategy

What strategies are content marketers using or finding to be most effective?

• 83% of marketers believe it’s more effective to create higher quality content less often. (Source: Hubspot)
• In a 2022 Statista Research Study of marketers worldwide, 62% of respondents emphasized the importance of being “always on” for their customers, while 23% viewed content-led communications as the most effective method for personalized targeting efforts.
• With the increased focus on AI-generated search engine results, 31% of B2B marketers say they are sharpening their focus on user intent/answering questions, 27% are creating more thought leadership content, and 22% are creating more conversational content. (Source: CMI)

Types Of Content

Content marketing was synonymous with posting blogs, but the web and content have evolved into audio, video, interactive, and meta formats.

Here are a few stats on how the various types of content are trending and performing.

• Short-form video content, like TikTok and Instagram Reel, is the No. 1 content marketing format, offering the highest return on investment (ROI).
• 43% of marketers reported that original graphics (like infographics and illustrations) were the most effective type of visual content. (Source: Venngage)
• 72% of B2C marketers expected their organization to invest in video marketing in 2022. (Source: Content Marketing Institute – CMI)
• The State of Content Marketing: 2023 Global Report by Semrush reveals that articles containing at least one video tend to attract 70% more organic traffic than those without.
• Interactive content generates 52.6% more engagement compared to static content. On average, buyers spend 8.5 minutes viewing static content items and 13 minutes on interactive content items. (Source: Mediafly)

Content Creation

Creating helpful, unique, engaging content can be one of a marketer’s greatest challenges. However, innovative marketers are looking at generative AI as a tool to help ideate, create, edit, and analyze content quicker and more cost-effectively.

Here are some stats around content creation and just how quickly AI is changing the game.

• Generative AI reached over 100 million users just two months after ChatGPT’s launch. (Source: Search Engine Journal)
• A recent Ahrefs poll found that almost 80% of respondents had already adopted AI tools in their content marketing strategies.
• Marketers who are using AI said it helps most with brainstorming new topics ( 51%) , researching headlines and keywords (45%), and writing drafts (45%). (Source: CMI)
• Further, marketers polled by Hubspot said they save 2.5 hours per day using AI for content.

Content Distribution

It is not simply enough to create and publish content.

For a content strategy to be successful, it must include distributing content via the channels frequented by a business’s target audience.

• Facebook is still the dominant social channel for content distribution, but video-centric channels like YouTube, TikTok, and Instagram are growing the fastest .  (Source: Hubspot)
• B2B marketers reported to CMI that LinkedIn was the most common and top-performing organic social media distribution channel at 84% by a healthy margin. All other channels came in under 30%.
• 80% of B2B marketers who use paid distribution use paid social media advertising. (Source: CMI)

Content Consumption

Once content reaches an audience, it’s important to understand how an audience consumes the content or takes action as a result.

• A 2023 Content Preferences Study by Demand Gen reveals that 62% of B2B buyers prefer practical content like case studies to inform their purchasing decisions, citing “a need for valid sources.”
• The same study also found that buyers tend to rely heavily on content when researching potential business solutions, with 46% reporting that they increased the amount of content they consumed during this time.
• In a recent post, blogger Ryan Robinson reports the average reader spends 37 seconds reading a blog.
• DemandGen’s survey participants also said they rely most on demos ( 62% ) and user reviews (55%) to gain valuable insights into how a solution will meet their needs.

Content Marketing Performance

One of the primary reasons content marketing has taken off is its ability to be measured, optimized, and tied to a return on investment.

• B2C marketers reported to CMI that the top three goals content marketing helps them to achieve are creating brand awareness, building trust, and educating their target audience.
• 87% of B2B marketers surveyed use content marketing successfully to generate leads.
• 56% of marketers who leverage blogging say it’s an effective tactic, and 10% say it generates the greatest return on investment (ROI).
• 94% of marketers said personalization boosts sales.

Content Marketing Budgets

Budget changes and the willingness to invest in specific marketing strategies are good indicators of how popular and effective these strategies are at a macro level.

The following stats certainly seem to indicate marketers have bought into the value of content.

• 61% of B2C marketers said their 2022 content marketing budget would exceed their 2021 budget.
• 22% of B2B marketers said they spent 50% or more of their total marketing budget on content marketing. Furthermore, 43% saw their content marketing budgets grow from 2020 to 2021, and 66% expected them to grow again in 2022.

Content Challenges

All forms of marketing come with challenges related to time, resources, expertise, and competition.

Recognizing and addressing these challenges head-on with well-thought-out strategies is the best way to overcome them and realize success.

• Top 3 content challenges included “attracting quality leads with content” ( 45% ), “creating more content faster” (38%), and “generating content ideas” (35%). (Source: Semrush’s The State of Content Marketing: 2023 Global Report)
• 44% of marketers polled for CMI’s 2022 B2B report highlighted the challenge of creating the right content for multi-level roles as their top concern. This replaced internal communication as the top challenge from the previous year.
• Changes to SEO/search algorithms ( 64% ), changes to social media algorithms (53%), and data management/analytics (48%) are also among the top concerns for B2C marketers.
• 47% of people are seeking downtime from internet-enabled devices due to digital fatigue.
• While generative AI has noted benefits, it also presents challenges for some marketers who fear it may replace them. In Hubspot’s study, 23% said they felt we should avoid using generative AI.
• Another challenge with AI is how quickly it has come onto the scene without giving organizations time to provide training or to create policies and procedures for its appropriate and legal use. According to CMI, when asked if their organizations have guidelines for using generative AI tools, 31% of marketers said yes, 61% said no, and 8% were unsure.

Time To Get Started

As you can clearly see and perhaps have already realized, content marketing can be a highly effective and cost-efficient way to generate leads, build brand awareness, and drive sales. Content, in its many formats, powers virtually all online interactions.

Generative AI is effectively helping to solve some of the time and resource challenges by acting as a turbo-powered marketing assistant, while also raising a few procedural concerns.

However, the demand for content remains strong.

Those willing to put in the work of building a documented content strategy and executing it – by producing, optimizing, distributing, and monitoring high-value, relevant, customer-centric content, with the help of AI or not – can reap significant business rewards.

More resources:

• 6 Ways To Humanize Your Content In The AI Era
• Interactive Content: 10 Types To Engage Your Audience
• B2B Lead Generation: Create Content That Converts

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Learnings from real-world applications of gen AI tools in surveys

As more companies and researchers start to work with generative AI applications, it is important to understand how to properly integrate them into the survey process. Using gen AI effectively leads to deeper, valuable insights and better research participant experiences.

How to successfully approach generative AI applications

Editor’s note: Rachel Dreyfus is the president of Dreyfus Advisors.

In this article I share my learnings to help market research practitioners apply generative   AI   survey   tools.   After completing   two   quantitative   project  for   two   different clients, where the surveys embedded an AI chatbot to converse with respondents, I found myself debriefing – what could I have done differently? I hope to help others through my learning experience.

A chatbot tool in a survey questionnaire can replace traditional open-ended – and some closed-ended – questions. The tool allows exploration beyond immediate rational responses, eliciting deeper insights and elevating the survey beyond a typical hybrid qual-quant instrument. The text analytics provide another set of reliable quantitative data. Collected from a stable sample size of several hundred or more, AI is deployed on both the front end in the respondent experience and on the back-end analysis. On the front end, the tool behaves similarly to a customer service chatbot. An avatar pops up and begins a conversation with the respondent generating unique probes that build on the responses provided. On the back end, common themes, sentiment and deeper insights can be investigated with text analytics using a proprietary platform and dashboard user interface.

For context, one project was a finished copy test of a nonprofit’s advertisement among prospective and current donors designed to sharpen the execution (video). The other project was a survey among employees to collect feedback on a company’s vision statement designed to identify red flags and improve the language used in the statement (text, no images). For both projects, AI conversations and text analyses were run on the HumanListening platform.

Considering gen AI research solutions

I had experience with two commonly used qualitative solutions embedded into survey instruments. Either live moderators who intercept individual respondents and offer additional incentives for one-on-one web chats (expensive) or asking survey respondents for an optional one-minute selfie video to answer an open-ended question (varying depth and quality). Neither of these respondent experience solutions provided advanced analytics on the back end. My experience with AI text analytics was primarily with traditional survey open ends, using text data to model and measure the topics that drive NPS, for instance. I had expectations for improved productivity in combining this conversational front-end experience with back-end text analytics. Here’s what I learned.

A little conversation goes a long way: 

Use   generative   conversations   judiciously   in   survey  questionnaires.

It’s tempting to want to include an entire focus group’s worth of probing in the survey questionnaire – but just because it’s possible doesn’t mean it will be a good respondent experience. Each conversation will be equivalent to about three probes and responses. After two of these sequences, some respondents may be tapped out. Make any further conversations shorter and/or optional. Probe and response time varies but it’s safe to assume each probe will add about one to two minutes to the survey length. Two conversations can add six to 10 minutes to your LOI so it’s important to work with your vendor to stay within target, ideally by removing the multiple-choice questions that are no longer needed.

One client was initially concerned about my suggestion to use a chatbot, worrying it would be an unpleasant experience for the respondent. Ensure the vendor has a demo to “show vs. tell” what the experience will be like and assuage concerns. The project leader has the responsibility of keeping the chatbot probes easy to answer to avoid annoying respondents.

A summary of the survey respondent experience

• Although the temptation will be to go heavy on the conversations, keep conversations super simple for better respondent experience and insights.
• Some respondents will find conversing with the chatbot enjoyable, and others will feel less comfortable and express frustration. My takeaway is to shorten the interview for all respondents. Respondents always have the freedom to respond “next” to bypass the rest of the conversation (I noticed only a handful did so).

Keep   guardrails   on   the   chatbot: Using a ladder-up technique throughout the conversation  

I had the option to provide coding terms and topics upfront to create the large language model (LLM). We would then be able to update the model with additional terms and topics after the soft launch. I lost time trying to guess the likely conversation themes and topics. When we pretested that survey version, the chatbot probed on the model terms that I fed it rather than follow the organic terms surfacing from the respondent conversation. I ended up abandoning my preset terms. 

What worked better was to structure the conversation to use the moderator’s “ladder-up” approach, whereby the chatbot repeats the response and probes a step further on feelings and perceptions provided by the respondents. With this technique, which nearly imitates a focus group moderator, respondents feel “listened to” and provide more detailed responses than we’d typically get from flat open-ended questions, such as, “Why did you rate the ad ‘very high’ appeal?” We also had the opportunity to ask “why” questions designed to investigate emotions including, “How did the ad make you feel?” and “What images or phrases in the ad made you feel that way?” Connecting the respondent’s side of the conversational probes creates a rich and more insightful paragraph than a traditional open-ended verbatim response.

Infrequently, the chatbot missed the mark; fortunately, conversations quickly recovered. It usually happened when a respondent answered a question with another question (possibly using sarcasm). For example, one response about the ad’s copy was, “What does this even mean?” and the chatbot promptly responded with the textbook definition of the tagline. We would have preferred, “What do you think it means?” So, the tools are not quite human, yet. And, because the themes can be both positive or negative in sentiment, the multiple-choice questions act as the guardrails needed to filter and separate the likes and dislikes on the back end.

Questionnaire   development   summary 

• Stick tight to the objectives of the study in devising questions for the conversation.
• Instead of trying to predict the LMM topics in advance, let them surface from the conversations for deeper insights. Use multiple-choice questions in combination with conversations where respondents reflect on reasons for the rating they selected.
• Because the themes can be both positive or negative in sentiment, the multiple-choice questions will act as the guardrails needed to filter and separate the likes and the dislikes on the back end.

It takes time and effort to unearth valuable insights

One area where the tool saves time is the ability to dip in during fieldwork and begin to understand the text insights. In a study of one thousand completed interviews my report was drafted by the time the two-week fieldwork period elapsed. Yet now I had a data set worthy of 10 two-hour focus groups. One- or two-word themes surfaced using the back-end text analytics. Identifying the actionable insights from the more obvious will require combination of art and science. In the end, no shortcuts in deep thinking are available. 

Some respondents provide their first impression in a thorough manner and others will give a couple of words, just like in focus groups. With a reliable sample size of several hundred responses, the data set becomes incredibly rich with detailed perceptions and feelings. The AI will quantify your topics but the role of the heavy lifting – figuring out what it all means – rests with the project lead. With practice it’s possible to become more efficient with these tools, but beginners may want to budget extra time and resources to sift through the results.

Analytics   learning   summary 

• Be sure to analyze the “first impression” or first response in the conversation for sentiment before the responses to probing. This capability was available, but I had to ask for it.
• Ensure all variables are available for analysis of category comparisons. Sometimes a processing step will be required – request that up front.
• The amount and types of text analysis available can be as broad as the number of quantitative variables and sub-groups and wading through the noise to find the interesting learnings is similar to reviewing tabulations. Ask if the vendor can provide text or topic banners.

Choose the right platform and ensure it meets your needs 

Get a reference of the analysis platform in advance. Many vendors now offer these tools – both in hosted survey platforms and as add-ons to common survey platforms.

For me, a data nerd who enjoys rolling up my sleeves and immersing in the analysis, self-service was key. I needed platform training but also wanted to direct the analysis plan to meet to my clients’ objectives. Each platform may differ in its strengths and weaknesses so set some criteria and challenge the vendors before signing up.

It can be tempting to choose the low-cost solution and a DIY tool, yet I was pleased to have a trained custom research team that understood my questions from a methodology and an insights-based approach. Is the sentiment analysis using first-impression ratings? Can you meet my specifications for banner plans (summary tables, top two box in the stubs, etc.)? I used the banners less than I typically do, with fewer multiple-choice questions than a typical survey. I used banners primarily for descriptive profiling and high-level ratings questions. All the “why” questions can be satisfied in the conversations. Multiple-choice data also help confirm that all responses are included and categorized correctly.

Vendor selection criteria

• White papers of case studies to demonstrate or past knowledge of the category.
• Ability to update the LLM midstream as new themes surface.
• Vendor has market research and marketing science focus vs. an IT or SAAS focus.
• The team assigned has experience with the platform to be thought partners beyond just order takers (some providers are new/staff are still green).
• Sophisticated analysis tools that allow charting, statistical testing and subgroup comparisons.
• Transparency of text data and good alignment with the AI topic names.
• Charts can be exported directly into editable Excel file (vs. uneditable images).

Include privacy assurance statements to reduce AI concerns 

Marketing and legal teams may have concerns about using AI tools and want assurances; include these in the statement of work with the vendor.

Privacy   assurances   summary 

• Assurance that proprietary data collected will not be used to train the vendor’s AI platform in the future.
• Assurance that the data will be free of hallucinations. Again, the hard work comes with ensuring an adequate pretest of the chatbot for the directions the conversation generatively leads and, on the back end, scanning through the respondent-level data for anything strange or unexpected.

Benefitting from gen AI survey tools

In conclusion, approach conversational AI in surveys with careful enthusiasm. They can add two new dimensions to research studies. First, a more interesting experience for respondents leads to a conversation richness comparable to a focus group (and superior as well, given bias and small sample of traditional focus groups). Respondents are curious and somewhat forgiving about the occasional odd probe the chatbot might serve up and, let’s face it, our respondents have been forgiving about poorly worded questions in traditional surveys too.

Secondly, the tools’ text analytics add dimension to the insights. There’s no substitute for a trained research project lead; text analytics require interpretation with rigor. I renamed and re-categorized some topic and themes digging into how the model worked, and I knew to separately evaluate first impressions and how to communicate my tabulation specifications. A DIY researcher on the product or marketing team may not be as well-versed in best practices.

I hope this overview of some of the solutions’ strengths and weaknesses will lead to efficiency – and perhaps my job will be necessary for the time being. Researchers can benefit as text analytics tools gain scale and continue to evolve to meet our future needs.

Fuel Cycle: Effectively using AI for insights Related Categories: Data Analysis, Survey Design, Artificial Intelligence / AI Data Analysis, Survey Design, Artificial Intelligence / AI, Research Industry, Qualitative Research, Quantitative Research, Software-Survey Design & Analysis

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A guide to generative AI for insights Related Categories: Data Analysis, Online Research, Artificial Intelligence / AI Data Analysis, Online Research, Artificial Intelligence / AI, Research Industry, CX/UX-Customer/User Experience, High-Tech, Software-Automated Reporting, Software-Data Analysis

Data Analysis Is the Biggest Generative AI Use Case in the CPG Industry

Several factors affect marketing and advertising spending. So, how are the advent of AI and other factors affecting ad spends in the CPG industry? What are the key consumer trends, and how have investment priorities shifted this year? Check out the findings from Mediaocean’s latest study.

• Several factors constantly affect marketing and advertising spending.
• So, how are the advent of AI and other factors affecting ad spending in the CPG industry? What are the key consumer trends, and how have investment priorities shifted this year?
• Mediaocean’s recent study sheds some light.

Marketing and advertising spending are constantly affected by world events, new technology, economic and job environment, and consumer sentiments. So, how has the advent of artificial intelligence (AI) and the current economic climate impacted ad spend, especially in the consumer packaged goods (CPG) industry? How are CPG companies and professionals using AI in daily operations? What are the important consumer trends this year? And have investment priorities shifted this year? To answer these questions and more, Mediaocean recently conducted a study. The following are a few key insights.

CPG Industry Is Optimistic This Year

The study found that optimism is sweeping across the CPG industry this year. Marketers are gearing up for an active year, with most respondents intending to either maintain or increase their spending across every channel. The only channels with more respondents indicating they are reducing spending compared to increasing are TV and print. This indicates a commitment among advertisers to a diverse mix of media channels and a focus on omnichannel execution.

For Each Channel, Do CPG Marketers Plan To Increase, Decrease, or Maintain Spends?

Source: The 2024 CPG Advertising Outlook Report Opens a new window

While most channels are growing, a few platforms are expected to see exceptional growth. For the CPG industry, digital display and videos, social media, and connected TV (CTV) are becoming frontrunners, witnessing a continued surge in investments. About 72% of respondents plan to increase their digital display/video spending, 67% for social media, and 56% for CTV this year.

Ecommerce Is the Most Important Consumer Trend

According to 57% of the study respondents, ecommerce everywhere is the most important consumer trend this year. This dominance of ecommerce everywhere signifies the need for CPG marketers to prioritize online channels, optimize their digital presence, and use data-driven insights to engage with consumers effectively.

Besides ecommerce, generative AI has become a crucial trend, occupying the third spot, with 55% of respondents citing it. Clearly, the rise of ChatGPT and other AI apps built on large language models (LLMs) has caught the fancy of marketers, and resource allocation has followed.

CTV/streaming is another area of strong growth, along with TikTok and social video. The trends portend heavy investment from businesses looking to connect with consumers through sound, sight, and motion.

AI Is Used Mostly for Research and Analytics Over Content Generation

How are CPG companies using generative AI in marketing? Data analysis (41%) and market research (29%) were the top applications. About 25% of respondents said they use it for copywriting and 12% for generating images. Further, only 9% use it for customer service.

How CPG Marketers Are Using Gen AI in Marketing

Despite its prowess, AI struggles to understand tone and context, affecting the generated content’s quality. AI-generated content also lacks the emotional depth and nuanced creativity of human-created content.

Performance-Driven Paid Media Is the Most Critical Investment

Given the current macroeconomic conditions, a few advertising capabilities and media investments have become more critical. Performance-driven paid media occupies the top position as the most critical investment, with 62% of respondents citing it. That said, brand advertising remains a core function, with 45% citing it. Clearly, a full-funnel approach is the path forward for the CPG brands.

According to 59% of respondents, measurement and attribution are also indispensable components of their advertising capabilities and media investments. This is particularly important in an age of cookie deprecation. For a long time, cookies have been the source of truth for data-driven advertising. Without access to them, several consumer segments become invisible to advertisers and publishers. As marketers prepare for the new reality, it is critical to evaluate and test new measurement methodologies and be prepared to implement alternative solutions.

See more: Marketing Pros Mostly Optimistic on AI With a Hint of Concern

The Media-Creative Gap Persists

A whopping 94% of respondents said they did not have fully synchronized creative and media technologies and processes. This gap is caused by the industry over-indexing on media at the expense of creatives. The impact is significant, especially when consumers respond to and reward customized experiences. Solving this gap is a significant growth opportunity for brands.

The creative-media gap manifests itself in three ways:

• Siloed teams, technology, and processes, creating inefficiencies with rising costs and slow go-to-market.
• Repetitive and irrelevant messaging, leading to reduced consumer responses.
• Lack of creative intelligence, creating hurdles in learning what content resonates.

Addressing these gaps requires implementing independent adtech platforms specifically designed to enhance creative relevance and activation across diverse marketing channels.

This year is becoming a year of creativity and innovation for CPG marketers. Despite the significant changes in the macroeconomic environment in 2023, optimism pervades the advertising industry. Marketers and advertisers are showing an intent to either maintain or increase spending across most digital media channels. Generative AI, especially ChatGPT, has captured everyone’s attention, leading to a shift in focus and resource allocation. The importance of measurement and attribution capabilities has increased, and so have the concerns regarding cookie deprecation, which makes measurement challenging. Further, there’s a prevalent gap between creative and media execution. As such, marketers should consider innovative methodologies and creative adtech platforms for competitive advantage this year.

How do you plan to make the most out of technological innovations to stay ahead of the competition? Share with us on Facebook Opens a new window , X Opens a new window , or LinkedIn Opens a new window . We’d love to hear from you!

Image source: Shutterstock

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