h&m chatbot case study

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Chatbots in retail: nine companies using AI to improve customer experience

Chatbots in retail are enjoying a surge due to the omnipresence of messaging apps. Retail brands are using these platforms to bridge the gap between online and offline experiences. By leveraging the ability to build a chatbot on these platforms, brands are engaging with their customers in a more conversational setting. In doing so, they are not only able to deliver more accurate suggestions, but also offer them further opportunities, like exclusive access and previews that feel highly personalised. Which retail brands are using chatbots, and how?

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Chatbots in retail:

In early 2016, fashion brand H&M launched a chatbot on Canadian messaging app Kik, which, while less well known internationally than some competitors, is used by 40% of US teenagers. The chatbot allows customers to see, share and purchase products from H&M’s catalogue. It offers a taste of a personal stylist service, using photo options and asking questions about shopper’s style to create a comprehensive profile of what they look like. Once it creates a style profile, shoppers can use the bot to create their own outfits, vote and browse outfits created by other users, and shop. While the chatbot does a good job at learning about a customer’s preferences, they are limitations to the bot, as it currently does not handle misspellings and slang. It is also not advertised on the company’s website, making it hard for shoppers to find.

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Also in 2016, Shop Direct introduced a chatbot within the iOS app for its online retail brand Very.co.uk to add customer service offering. Customers can track orders, receive a reminder of their account number and track payments to their account. The Very Assistant currently uses questions and multiple-choice answers to deal with queries, but Shop Direct is also working on an artificial intelligence (AI) bot that can understand anything a customer writes. Its development shows that chatbots in retail can enhance brands own app offering without using external messaging apps.

Tommy Hilfiger

American apparel and accessories brand Tommy Hilfiger, has released a chatbot on Facebook Messenger. On opening the conversation, the chatbot greets the user, then instantly introduces the consumer to the collection and gives them three options for the conversation: style advice, browsing, or a behind-the-scenes look at the latest fashion show. The chatbot deals with customer queries by not only proposing a list of options to choose from but also demonstrating great language processing abilities. The bot reacts to keywords typed by customers and offers solutions. Customers are presented with an opportunity to browse by either looks or categories, such as accessories or bags.

France-headquartered international personal care and beauty stores chain Sephora introduced its chatbot on messenger service Kik. The first time a shopper starts a conversation with the chatbot they are invited to take part in a short quiz that helps the bot learn more about them. Users can then ask for tips or reviews on specific types of product or application. Each of these comes with product recommendations that the customer can shop for without leaving Kik. The experience aims to mimic the way the shopper might chat with their friends about products and advice. Sephora’s chatbot is not designed to deal with customer’s queries, but instead provides customers with other opportunities like the ability to book a makeover by simply clicking on ‘Book a Makeover’.

Like many other chatbots in retail, luxury clothing brand Burberry’s bot was introduced on Facebook Messenger. By sharing their location, users can be informed where their nearest Burberry store is. The chatbot also introduces customers to the brand’s latest collection of bags; by clicking the ‘Discover More’ option, the chatbot lets users browse the collection, get to know more about the craftsmanship, or find out how to style a bag with Burberry’s apparel and accessories. Burberry’s chatbot offers pre-made suggestions that guide users through their shopping experience. The chatbot has been evolving since the 2016 launch; in November 2017 Burberry introduced options to explore gifts and pre-order pieces, while in February 2018 it started to invite its Facebook fans to follow Burberry’s runway show live.

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In October 2016, e-commerce retailer eBay released its Facebook Messenger chatbot ShopBot. eBay first started using chatbot technology by piloting a simple Facebook Messenger tool that reminds bidders 15 minutes before an auction listing is about to end. After this initial success, eBay expanded the ShopBot, which now understands what users are looking for by processing their text messages and images to find the best match. Since then, the chatbot has been praised for its excellent contextual understanding and machine learning abilities as well as its use of friendly language. Reportedly, ShopBot users are nearly three times more likely to ask questions about specific products than those browsing eBay’s inventory.

Whole Foods

US-based healthy food supermarket chain Whole Foods introduced a chatbot to make finding a supermarket easy. Customers can enter a zip code or an address, or simply share their location to get results immediately. The grocer is using the Facebook Messenger chatbot to make finding the ingredients people want easier. Shoppers can use the chatbot to ask for recipes and to find where products are positioned in the store. For those with special dietary restrictions, the chatbot can also consider those and help them find foods and meals that leave a particular ingredient out. Unlike other chatbots in retail, Whole Foods bot aims to improve customers experience in store, not just online.

The discount supermarket chain Lidl introduced conversational chatbot Margot on Facebook Messenger that understands natural language and helps shoppers get the best out of its wine range. Margot is more conversational than other chatbots in retail and helps users in various ways; for example, it helps to find wines by country, region, grape, colour and/or price. It also gives users tips on food pairings and tests their knowledge with a quiz. After the chatbot was released, users took to social media to praise how good and accurate the bot is.

Co-operative Group

The Co-operative Group Co-op) launched its pricing estimate chatbot for the UK car insurance industry on Facebook Messenger. The chatbot uses four multiple-choice questions that help it estimate the insurance price within 30 seconds. It then links the user to the Co-operative Insurance website where customers can enter more details and find out the final price estimate, which may differ from the original estimate. Co-op uses the bot to drive customers towards the company’s websites and social media sites to increase interaction and traffic.

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Exploring H&M Artificial Intelligence & AI-Powered Solutions

H&M has been using artificial intelligence to improve its customer service. The company has developed a chatbot that can answer questions about products, prices, and store locations. H&M is also using AI to personalize the shopping experience for customers. The company is able to recommend items based on previous purchases and browsing history. H&M is committed to providing the best possible customer experience and believes that artificial intelligence will help them achieve this goal.

H&M has been using artificial intelligence to help design its garments for several years now. The company first used AI in 2015 to create a custom-made wedding dress for a customer. Since then, it has used AI to design everything from children’s clothing to sportswear. The benefits of using AI in the design process are two-fold. First, it allows H&M to create unique designs that are not constrained by traditional manufacturing methods. Second, it helps the company save time and money on the design process itself. Interestingly, H&M is not the only fashion retailer that is using AI in its business. Other companies such as Zara and Adidas are also investing in this technology in order to stay ahead of the competition.

H&M: Utilizing Big Data and Artificial Intelligence

How Does H&M Use Artificial Intelligence?

H&M is one of the world’s largest fashion retailers, with over 4,500 stores in 62 countries. The company has been using artificial intelligence (AI) for some time to help it run its business more efficiently. In 2018, H&M launched an AI-powered chatbot called “Eva” to help customers with their shopping queries. The chatbot was designed to provide quick and easy answers to common questions about products, sizes, availability, and delivery times. Eva was initially only available in Sweden but has since been rolled out to other markets including the UK and the US. H&M is also using AI to help it design new products. The company’s head of design, Ann-Sofie Johansson, has said that AI is being used to create “print designs and colour combinations” for new garments. This process is faster and more efficient than traditional methods of design, and it allows H&M to bring new products to market quickly and respond to trends quickly. In the future, H&M plans to use AI even more extensively in its business. The company is currently testing out a system that uses AI-created 3D images of models to show how the clothing will look on different body types; this could eventually replace traditional 2D fashion photography altogether. H&M is also exploring ways to use AI-created virtual assistants in its stores, which would be able to offer personalized recommendations and help customers find what they’re looking for more easily.

What Fashion Brands Use Artificial Intelligence?

Fashion brands have been using artificial intelligence (AI) for some time now to help with various tasks such as product development, customer service, and even marketing. Here are some of the ways AI is being used by fashion brands:

-Product Development: AI can be used to help design new products or improve existing ones. For example, a company called Stitch Fix uses AI to recommend clothing items to its customers based on their personal styles.

-Customer Service: AI can be used to provide better customer service by answering questions, providing recommendations, and even helping with returns and exchanges. For example, Amazon’s customer service chatbot “Alexa” is powered by AI. -Marketing: AI can be used for targeted marketing campaigns. For example, Facebook uses AI to target ads to users based on their interests.

How Does H&M Use Information Systems?

H&M, or Hennes & Mauritz, is a Swedish retail company that offers fashion-forward clothing at an affordable price point. The company was founded in 1947 and now has over 4,000 stores in 62 countries. H&M’s success can be attributed to its ability to quickly adapt to changing trends and consumer tastes. In order to do this, the company relies heavily on information systems. H&M’s information systems are used for a variety of purposes, including trend analysis, inventory management, and customer relationship management. Trend analysis is essential for understanding which styles are popular so that H&M can adjust its product mix accordingly. Inventory management is also crucial in order to ensure that stores have the right mix of sizes and styles available. Lastly, customer relationship management systems are used to track customer purchases and preferences in order to provide tailored recommendations and targeted marketing messages. Overall, H&M’s use of information systems has been key to its success as a fast-fashion retailer. By using these systems effectively, the company is able to make informed decisions about what products to offer and how best to serve its customers.

How Does Ai Work in Fashion Industry?

AI is used in the fashion industry to design clothes and accessories. It can also be used to predict future trends, forecast customer demand and help manage inventory.

Zara Artificial Intelligence

In today’s business world, one of the hottest topics is artificial intelligence (AI). AI can be defined as a computer system that is capable of performing tasks that normally require human intelligence, such as visual perception, natural language understanding, and decision-making. One company that is using AI in a very interesting way is Zara. Zara is a Spanish clothing retailer with over 2,200 stores in 96 countries. They are known for their fast fashion model, which means they quickly design and produce new styles to meet current trends. What you may not know about Zara is that they are also using AI to help them with their inventory management. They have developed an AI system that can predict what styles will be popular in the future and produce enough of those items to meet customer demand. This has allowed them to reduce their inventory levels by 30%! Zara isn’t the only company using AI; it’s estimated that by 2025, 85% of all customer interactions will be managed by AI systems. With so many companies turning to AI, it’s clear that this technology is here to stay and will only become more prevalent in the years to come.

H&M Data

H&M, the Swedish retail giant, has been in the news recently for all the wrong reasons. In early 2018, it was revealed that the company had been using slave labor in some of its factories. This led to a massive public outcry, and H&M was forced to take action. Now, it seems that the company is at it again. This time, it has been caught red-handed using data from its customers to spy on them. Specifically, H&M has been found to be using a third-party service called “Data Xgen” to collect detailed information about its customers’ shopping habits. This is deeply troubling for a number of reasons. First and foremost, it violates the trust that customers have placed in H&M. When you shop at a store, you expect your privacy to be respected. But by using Data Xgen, H&M is showing that it does not respect its customers’ privacy.

H&M Big Data

In the world of retail, data is king. And no one knows this better than H&M. The global fashion retailer has been using big data to its advantage for years, giving it a major competitive edge in the highly competitive world of fashion. How does H&M use big data? To start, the company uses data to track consumer behavior and trends. This information helps them understand what people are looking for and how they want to shop. Armed with this knowledge, H&M can make better decisions about everything from product design to store layout. In addition to tracking consumer behavior, H&M also uses data to improve its supply chain and logistics operations. By understanding where their products are being sold and how they’re moving through the supply chain, they can optimize their operations for maximum efficiency. This results in lower costs and faster turnaround times on new products, giving H&M another leg up on the competition. So how does all this big data give H&M an edge? In short, it allows them to move faster and be more agile than their competitors. They can quickly respond to changing consumer demands and get new products into stores before anyone else. This gives them a significant advantage in an industry where time is of the essence. If you’re interested in learning more about how big data is being used in retail, or any other industry for that matter, be sure to check out our blog! We cover all things big data here at Domo, so you’ll be sure to find something interesting!

H&M Data Scientist

H&M is a global retail company with over 4,000 stores in 62 countries. They offer a wide variety of clothing and accessories for men, women, and children. H&M’s data scientists are responsible for analyzing the company’s massive amount of data to help improve their business decisions. The data scientists at H&M use a variety of techniques to analyze the data, including machine learning, predictive modeling, and statistical analysis. They work closely with other teams within the company, such as marketing, product development, and operations, to ensure that their insights are used to make better decisions. H&M is constantly looking for ways to improve their business, and the data scientists play a vital role in this process. If you’re interested in becoming a data scientist at H&M, then you should have strong analytical skills and be able to work effectively with team members from different departments.

H&M Demand Forecasting

H&M is a global fashion retailer with over 4,000 stores in more than 60 countries. As one of the largest fashion companies in the world, H&M faces unique challenges when it comes to demand forecasting. Due to the company’s size and scale, H&M must manage a huge amount of data from various sources in order to make accurate predictions about future demand. In addition, H&M must consider both short-term and long-term trends when forecasting demand.

Artificial Intelligence in Fashion Industry

The fashion industry is one of the most rapidly evolving industries in the world. In order to keep up with the latest trends, fashion designers and retailers must constantly innovate and experiment with new styles and looks. This can be a very time-consuming and expensive process. However, artificial intelligence (AI) is beginning to change all of that. AI is already being used by some fashion companies to help design new collections. By analyzing data about past trends, AI can predict future trends and help designers create more targeted collections. AI can also be used to create virtual models of garments so that designers can see how they will look on different body types before they are even made. In addition to helping with design, AI can also be used to improve customer service in the fashion industry. For example, chatbots can be used to answer customer questions or provide recommendations for products based on their preferences. AI can also be used to track inventory levels so that retailers can reorder items before they run out completely. Overall, AI has the potential to revolutionize the fashion industry by making it more efficient and effective at every stage, from design to customer service.

H&M Information Systems

H&M’s information systems are critical to the success of the company. The systems provide real-time information about inventory levels, sales, and customer preferences. This allows H&M to make quick decisions about stock levels and pricing. Additionally, the systems allow H&M to track customer behavior and trends. This information is used to create targeted marketing campaigns.

H&M Economies of Scale

H&M is one of the world’s largest fashion retailers, with over 4,500 stores in 61 countries. The company has been able to achieve economies of scale by expanding its operations to new markets and by vertically integrating its supply chain. Vertical integration has been key to H&M’s success. The company owns a majority of the factories that produce its garments, which allows it to control costs and quality. Additionally, H&M has invested in technology that allows it to quickly design and produce new styles at a lower cost than its competitors. H&M’s expansion into new markets has also been crucial to its success. The company now operates in 61 countries, reaching a broader customer base than ever before. Additionally, H&M’s online presence has allowed it to tap into global markets and reach even more customers. The combination of vertical integration and expansion into new markets has allowed H&M to achieve economies of scale and become one of the world’s leading fashion retailers.

H&M is one of the world’s largest fashion retailers, and it’s using artificial intelligence to stay ahead of the curve. The company has developed an AI-powered tool that can predict what styles will be popular in the future and design new products accordingly. This allows H&M to keep its inventory fresh and appealing to customers, which is essential in the fast-paced world of fashion. In addition, the AI system can also suggest outfits to customers based on their personal style preferences. This helps H&M provide a more personalized shopping experience and further solidifies its position as a leading retailer.

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H&M Group’s New AI Tool Lets Anyone Play Designer

Generative artificial intelligence makes it possible for you to conjure up images ranging from the photorealistic to the fantastical, regardless of your artistic ability. Now, the parent company of H&M wants to help you slap those images on a T-shirt.

H&M Group’s Creator Studio on Tuesday announced a new tool for generating designs powered by Stable Diffusion, an open-source generative-AI model. Like other AI image generators, it can produce imagery from written prompts, theoretically allowing anyone to conjure professional-looking designs without needing to know specialised software or have any drawing ability.

Users will then be able to tap H&M’s manufacturing and logistics infrastructure to get their digital creations printed onto articles of apparel and shipped around the world. The platform, introduced by H&M Group in 2021 , had previously offered its on-demand services only to businesses and select creators, with customers it has worked with ranging from giant corporations like Disney and Warner Music Group to “the local pizzeria,” according to Dinesh Nayar, managing director of Creator Studio. The goal now is to allow anyone to create custom merchandise, regardless of who they are or their design ability.

“We can remove obstacles for anyone that maybe doesn’t have the skill sets of [Adobe] Illustrator, or any other design tool, to create new cool content,” Nayar said.

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Generative AI’s design capabilities can be controversial in fashion , where human skill and creativity are cornerstones. But for small businesses or creators like musical artists who aren’t trying to be clothing designers and just want to produce their own branded merchandise, those requirements can be a hindrance — though the graphic designers who work on these projects would likely say otherwise.

Regardless of those objections, Creator Studio is giving regular consumers the ability to create custom products. The question now is what they’ll do with their new powers.

A major concern surrounding generative AI lies in the ways people can misuse the technology. It could lead to more infringement of intellectual property, for instance, by making it easier to produce knockoffs. Though the popular image generators on the market won’t generally reproduce logos, they can still get into problematic territory. (Numerous artists and writers have filed lawsuits against AI developers claiming they’ve infringed their IP by training AI models on their copyrighted works without consent.)

Using a beta version of Creator Studio’s AI feature, BoF was able to create a design reminiscent of Louis Vuitton’s logo, though it wasn’t an exact match. The background the AI generated was a recognisable, if slightly warped, replica of Louis Vuitton’s monogram.

There are also risks around hate symbols. Creator Studio’s AI tool blocked a prompt to produce a swastika, but it did produce an iron cross, a symbol with a varied history that’s been used by hate groups at times. Without any specific instruction, the AI generated a cross coloured black and red, with spattering and drip details suggestive of blood.

Nayar said the company has considered the ways people could misuse its AI feature. It uses a combination of human moderators and technology to spot problems across different steps in the process, so even if its AI feature might generate a design, it doesn’t mean it would result in a finished product. Nayar was confident orders for the two designs I created would not have been fulfilled.

“Our content moderation would have caught the two artworks before they could be sent to production,” he said.

Merch has become an important source of additional revenue for a variety of businesses. To musical artists who may struggle to make enough from streaming, it can be a vital source of high-margin sales, leading Spotify to introduce a new hub for merch recently . In Nayar’s view, fashion is part of culture today, and merch has become fashion, whether the creators are music groups, sports teams, movie studios or influencers on TikTok.

The company is currently able to deliver to more than 200 countries from its printing hubs in Europe and the US. It even has its own line of blank garments, called True Blanks. It’s looking to expand further by establishing a hub in the UK and another in Japan to better serve those areas, according to Nayar.

The new venture into AI is part of its mission to keep broadening the ability for anyone around the world to easily order custom merch, no bulk orders or artistic skill required.

Case Study | The Complete Playbook for Generative AI in Fashion

From ChatGPT to Midjourney to Runway, the emerging technology is already showing why it could be one of the most consequential in decades for the fashion industry. Early adopters and experts unpack the opportunities and challenges of putting gen AI to use to design products, create campaigns and other content, and better connect with customers.

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• Technology : Artificial Intelligence

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Using AI to Create Customer-Centric Business Strategies

At The Business of Fashion’s Professional Summit in New York last week, Sona Abaryan, partner and global retail and luxury sector lead at tech-enabled data science firm Ekimetrics, shared how businesses can more effectively leverage AI-driven insights on consumer behaviour to achieve a customer-centric strategic approach.

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Before fashion businesses can put artificial intelligence to work or target the right shoppers online, they need good data and a deep understanding of who their customers are and what they want. This case study offers a guide for brands that want to truly know their customer, allowing them to make smarter decisions that serve shoppers and drive results.

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AI and Chatbots Can Help Organizations Meet Rising Customer Expectations

Sponsor content from Strategic Education.

By Joe Schaefer, Chief Transformation Officer, Strategic Education, Inc.

As the Covid-19 pandemic forces many aspects of our everyday lives online, customer expectations are now at an all-time high. Time has become an even more precious resource for all of us as we juggle work, family, and many other priorities, all from home.

Customers expect you to “value their time, to make engagement easy, and to deliver answers and resolutions in a highly personal manner and in the context of their actions and journeys,” Kate Leggett, vice president and principal analyst at Forrester Research, recently wrote .

Many organizations in sectors including higher education, professional associations, health care, and retail can benefit from using artificial intelligence (AI) to provide better user support. But surprisingly, U.S. companies have been slow to adopt AI technology; only 2.8% use machine learning, according to a U.S. Census Bureau report .

Chatbots powered by AI and machine learning can help meet customer expectations. This technology not only allows organizations to resolve questions and offer quick solutions to common issues, but also learns and adapts to customer preferences so that it can better anticipate customer needs and provide more personalized responses.

One industry that has a lot to gain from this adoption is higher education, which continues to face the challenge of providing personalized service and support for students amid what appears to be a trend of falling retention and enrollment .

I speak from experience. At Strategic Education, Inc.—parent company of Strayer and Capella Universities—our students are older than traditional college students and are often managing family and work as they take classes online. They don’t have the time to be placed on hold or get passed around to support staff to find answers to their administrative questions.

That is why Strayer University set out three years ago to solve the problem of our students’ being bogged down with the tedious administrative aspects of the college experience, such as registering for classes, when their time would be better spent focusing on academics.

Using Dialogflow, Google’s natural-language processing tool, we developed Irving , an innovative virtual machine-learning assistant named after Strayer’s founder, to help answer student questions about administrative tasks and needs online—and more importantly, 24/7. As of September 2020, Irving has handled over 1.1 million conversations with students.

As students engage with Irving, we attempt to train the chatbot as we would train student-support staff, integrating it with the same back-office systems our staff uses to provide service and support to our student base.

Every interaction a student has with Irving is logged and measured for the number of positive interactions, positives, and the level of “intent matching”—measuring whether Irving interpreted the student’s needs correctly.

In surveys, students have said that they appreciate the ability to ask and get responses to questions at a time that is convenient to them—whether at 6 a.m. or 11 p.m. Some have said they often feel more comfortable directing their questions to an automated system. Others mentioned that using Irving was faster than a phone call, and if they had to repeat questions, “you don’t feel like you keep bothering a real person.”

As of January 2020, 88% of Strayer students surveyed who interacted with Irving agreed that the chatbot helped them solve their issue easily, and 92% of them were likely to recommend Irving to other students.

Interestingly, we’ve seen demand for support increase—in a good way. Because Irving is so helpful, students are asking more questions and getting more answers.

By shifting phone calls to automated chats and offloading repetitive tasks, Irving also frees up staff to serve students with more complicated questions and needs. This improves service as well as administrative efficiency across departments.

The system allows us to not only serve students today but to also gain information that will enable us to provide better service tomorrow. Chat logs reveal patterns of usage or even pain points for students that wouldn’t otherwise be detected. For example, if we notice a cluster of problems with registration, we can use that information to improve our business processes.

Irving has also helped us create a stronger and more personalized support system and experience for our students that is often missing, yet critical, in traditional higher education support systems.

Of course, human support is still important, and Irving has helped us ensure that those who really need it get it.

Irving was trained to try to answer any incoming question three times before referring the student to a human. As of September 2019, just over 7% of the conversations have had to be transferred to staff members. This initial success is just the start as the technology enables us to gather the information we need to better support our students.

This is a challenging time for many businesses and organizations forced to completely rethink and revamp their operations. Our case study is proof that an AI- or machine learning-empowered chatbot can have a significant impact on operations and customer support by responding to routine questions, elevating more complex issues, analyzing pain points, and collecting data critical to improvement.

Higher education is one of many industries that would benefit from offering quick, accurate, and personalized service—because while Covid-19 may be contributing to rising customer expectations, the demand for this type of support is likely here to stay. Therefore, implementing a chatbot is a wise investment in future success.

To learn more about our offerings, click here .

10 Case Studies on Chatbots

by Ryan Nelson | Nov 7, 2017 | Case study | 0 comments

No customer service rep wants to answer the same question a hundred times a day. No sales rep wants to talk to people who aren’t going to buy. And if you’re leading an organization, you can’t afford to let either of those scenarios be the norm.

Chatbots (more affectionately known as virtual assistants) provide a solution to both of these problems. Their infinite capacity helps free up your employees and scale your organization’s efforts . Whether you use chatbots for customer service, sales, or something else, their artificial intelligence ensures that your human resources are only used when they’re needed, and that your organization communicates with the most people possible.

But the fear many organizations have is that chatbots are heavy on the artificial and light on the intelligence. Few things are more infuriating when you need help than having to repeatedly rephrase your question or jump through hoops to talk to a real person. Most of us prefer talking to humans, and that’s OK. That’s why chatbots are most-suited for highly specialized tasks.

The best chatbots interact with more people faster than humans will ever be able to. The trick is knowing when and how to use them. In many cases, you’ll find that chatbots are basically a more informal way for people to navigate your website.

To help you see if there are opportunities for your organization to use chatbots, we found 10 case studies of companies that used them successfully. We’ll show you what they did, how they did it, and where you can go to see the full case study.

Some of these organizations started with live chat systems before switching to chatbots. Some used chatbots conservatively, and others used them for everything.

Check out these 10 case studies on chatbots.

1. Amtrak: 5 million questions answered by chatbots annually

Chatbot system: Next IT Industry: Public transportation Key stats:

• 800% return on investment.
• Increased bookings by 25%.
• Saved $1,000,000 in customer service expenses in a single year.
• Over 5,000,000 questions answered every year.
• Bookings through chatbots generate 30% more revenue.

Major takeaways:

• Chatbots with advanced AI provide site visitors with a “self-service” option.

Where the study came from: Next IT shared this chatbot case study on their website about Amtrak’s experience with “Julie”, which began in 2012.

Amtrak is the largest organization you’ll find in our list of case studies. They have 20,000 employees and serve 30 million passengers per year. At the time Next IT published this case study, Amtrak.com was getting 375,000 visitors every day .

Using Next IT’s advanced AI chat platform , they created “ Ask Julie ” to help visitors find what they needed without having to call or email customer service.

Here’s what Next IT says she’s capable of:

“Travelers can book rail travel by simply stating where and when they’d like to travel. Julie assists them by pre-filling forms on Amtrak’s scheduling tool and providing guidance through the rest of the booking process. And, of course, she’s easily capable of providing information on what items can be carried on trains or helping make hotel and rental-car reservations.”

Instead of making a phone call or waiting for customer service to email them back, more and more visitors are turning to Julie. In fact, Next IT reported a 50% growth in Julie’s usage year over year.

Julie “was designed to function like Amtrak’s best customer service representative,” and with 5 million answered questions per year, it’s hard to argue that she isn’t their best customer service rep.

Not to mention, when Julie answers questions, she tacks on subtle upsells like these:

Image source: Next IT

So in addition to answering more questions and increasing the number of bookings, Julie actually increased the value of bookings. Bookings made through Julie resulted in an average of 30% more revenue than bookings made through other means.

Clearly, the self-serve model is working for Amtrak. What’s interesting about Julie is that despite the smiling face, you know you’re talking to a robot. It doesn’t feel like AI that they’re trying to pass off as a real person. It’s almost like she’s a more advanced search feature of the website. When visitors ask questions, she pulls in only the relevant information, and it’s all contextualized to fit their specific question.

Maybe it’s just me, but that could be the difference between a helpful tool and a frustrating conversation.

2. Anymail finder: 90% of “big customers” chat before buying

Chatbot system: Intercom Industry: Email verification software (SAAS) Key stats:

• 1 in 3 buyers used the chat system before making a purchase.
• 9 out of 10 “big buyers” used the chat system before making a purchase.
• Estimated 60% of revenue comes from chatbots.
• Average response time was three minutes in their first 30 days of using Intercom.
• Chatbots allowed a two-person team to stay on top of support and sales.
• Popular customer questions provided content ideas, and eventually prewritten responses.

Where the study came from: Pardeep Kullar published this case study on the Upscope blog in 2017.

As a two-person marketing startup, Anymail finder was stretched thin between sales, marketing, and support. They were answering the same few questions over and over via email.

Intercom’s operator bot helped this two-person team look and feel like they had a full-fledged support department.

Pardeep Kullar of Anymail finder says that the same handful of questions kept popping up in the chat window. They were usually questions like “how are you different from your competitor?” or “how do I upload this file?”

So Pardeep and his colleague wrote detailed articles that answered these popular questions and any related ones, then incorporated the articles in readymade responses and automated messages. Website visitors encountered one of 10 automated chat messages, depending on the page they arrived at.

Image source: Anymail finder

It’s like putting multiple fishing lines in the water at once, waiting for potential customers or users to bite. When a visitor replied to an automated message, employees got a push notification so they could promptly respond to every inquiry. Anymail finder’s prewritten responses to popular questions let them reply to some inquiries within seconds.

Intercom’s messaging metrics let Anymail finder gauge which automated messages were producing the best results:

Image source: Upscope

One of the primary benefits of chatbot services is that they can answer most questions customers have and qualify your leads without eating up valuable time from your customer service or sales staff.

But Intercom is pretty anti-AI , and their chatbots serve a more limited role. For Anymail finder, real people were waiting behind every automated message, but chatbots still helped them provide superior customer service with a limited team.

3. RapidMiner: replaced all lead capture forms with chatbots

Chatbot system: Drift Industry: data science software Key stats:

• 4,000 leads generated by chatbots
• 25% of sales pipeline was influenced by chatbots
• Chatbots can bypass lengthy lead generation campaigns

Where the study came from: Drift published this case study on their website.

RapidMiner went all-in—they replaced every lead capture form on their site with a chatbot. (Even for their whitepapers !) RapidMiner realized that automated conversations could filter and qualify leads in minutes, whereas a sequential email campaign could take weeks.

Chatbots let them circumvent this messy process and direct the best leads straight to sales:

Image source: Drift

For CMO Tom Wentworth, the change was about understanding why people came to RapidMiner’s site:

“People who come to our website aren’t coming there because they want to surf our site, they’re coming there because they have a specific problem, whether it’s a question about our product or what it does, whether it’s some technical support they need, or whether it’s they want to talk to someone in sales.”

Chatbots made it possible to address the reasons people came to RapidMiner.com without bombarding the sales team with unqualified leads. In an article on the Harvard Business Review , RapidMiner shared:

“The Drift bot now conducts about a thousand chats per month. It resolves about two-thirds of customer inquiries; those that it cannot, it routes to humans.”

Wentworth went on to say, “It’s the most productive thing I’m doing in marketing.”

Drift’s Leadbot asked visitors the same questions salespeople would’ve asked—and it never sleeps, so leads trickle in 24/7. So far, it’s brought in over 4,000 leads, influenced 25% of their open sales pipeline, and accounted for 10% of all new sales.

This case study provides some helpful insights into the differences between a chatbot and a live chat service, but it’s worth noting: ditching forms altogether is a pretty drastic step.

If you’re using blogging to increase your traffic , gated whitepapers and sequential email campaigns help you build an audience and create long-term relationships. A chatbot on your blog is bound to convert some visitors into leads, but this probably isn’t going to grow an email list you can reach out to again and again:

Image source: RapidMiner

4. MongoDB: increased new leads by 70% in three months

Chatbot system: Drift Industry: database/development platform Key stats:

• Increased net new leads by 70%
• Increased total messaging response by 100%
• Chatbots are more scalable than live chat services.
• Chatbots can help your customer service and sales teams by scheduling meetings and screening inquiries

MongoDB was having a lot of success with live chat, but like all humans, their salespeople were limited by things like “time” and “space.” They couldn’t significantly increase the number of conversations they were having without significantly increasing the size of their team.

As their director of demand generation puts it:

“We needed a messaging tool that could scale with our business and increase the volume of our conversations, leading to the increase of our pipeline and Sales Accepted Leads (SALs)—the metrics my Demand Generation team are measured on.”

Like RapidMiner, MongoDB let Drift’s Leadbot ensure that their sales reps only talked to the people who were most likely to buy. And with Drift’s meeting scheduler , people didn’t have to play phone tag to make an appointment:

For MongoDB, automating lead-qualifying conversations allowed them to have more conversations, and automating the scheduling process let them turn more of those conversations into leads.

5. Leadpages: welcome messages led to 267% more conversations

Chatbot system: Drift Industry: Drag-and-drop landing page creator Key stats:

• Welcome messages led to a 267% increase in chat conversations.
• Website conversion rate increased by 36%.
• Targeted messages had an open rate of 30% and a 21% clickthrough rate.
• Welcome messages help chatbots and live chat get more engagement.
• In the right place at the right time, relevant, automated messages can be highly effective.

Where the study came from: Drift’s case studies page shared how Leadpages used automated messages .

LeadPages started using Drift’s chat system to let their site visitors ask questions. Within a few weeks, they were averaging 100+ questions per week. And they didn’t even have a welcome message. They quickly realized that there was a much bigger opportunity to encourage conversations that lead to conversions.

LeadPages CMO Dustin Robertson says, “Site visitors ask questions through Drift as they consider purchasing our software. But there’s more to Drift than just chat. We can proactively reach out to visitors.”

So they added a welcome message.

In the month prior to adding the message, they had 310 visitors use the chat system. In the month after, they had 1,168. That’s a 267% increase.

But the quantity of messages wasn’t the only thing they were improving. LeadPages started using targeted, automated messages to try to increase conversions on specific pages. Depending on where visitors were on the website, they’d see a different message that fit with the page and asked them to take a specific action.

Like this message on their comparison page:

These targeted messages had an open rate of 30% and a click- through rate of 21%.

“With Drift’s automation features, we’ve been able to increase the conversion rate of our site visitors by 36%,” Robertson says.

Interestingly, at the time we prepared this case study roundup, Leadpages didn’t appear to be using chatbots on their comparison page , which has been reworked to feature in-depth comparison reports.

6. Perfecto Mobile: Increased website conversion rate by 230%

Chatbot system: Drift Industry: Web, mobile, and IOT testing platform Key stats:

• Visitor-to-lead conversion rate increased from 6% to 20% in six months.
• Targeted chat let sales reps focus on leads who were likely to buy.
• Bypassing forms meant qualified leads could move through the pipeline faster.

Where the study came from: Perfecto Mobile helped Drift prepare this case study , which was published on Drift.com.

Perfecto Mobile had a problem. Most of their “leads” weren’t within their target audience. They didn’t want their sales development reps wasting that kind of time on a live chat system, so they went with Drift.

“Our leads tend to be 70% out of our target, 30% in,” says Perfecto CMO Chris Willis. “Now, I expected with web chat we’d see about the same thing. So people chatting and just essentially taking up the time of our SDRs when they could be working on more productive activities. And so right out of the gate, we identified with Drift that we were going to see the ability to manage that process. So we’re able to, by IP address, identify companies by their size, and only present to our SDRs chats that come from companies that we want to sell to.”

If a website visitor was coming from a company that was too small to be in Perfecto’s target audience, they didn’t see the chatbot.

Check out what Chris has to say about their experience:

The other major benefit Perfecto noticed was that chatbots allowed them to capitalize on leads at the most opportune time.

“Leads that come in through chat tend to have a higher velocity,” Chris says. “So you’re able to solve the problem or meet the needs of the request in real-time. So you think in terms of somebody coming to a website, and having a question, and filling in a contact us form. And they’ll hear back in 24 hours, or two days…that problem might not be there anymore. If they’re able to initiate a conversation, so skip the form, and have a conversation in real-time, we’re seeing that move very quickly.”

Here’s an actual example Chris shared about how this worked for Perfecto:

• An anonymous visitor came to the Perfecto Mobile website and started a conversation through Drift.
• Based on IP address, the conversation was routed to an SDR.
• The anonymous visitor turned out to be someone from a major sports brand, and they wanted to meet in-person with a sales rep.
• During that conversation, the SDR called the sales rep and gave him all the info.
• Two days later that sales rep was standing in that major sports brand’s offices in New York.

For Perfecto Mobile, chatbots helped them qualify leads faster, and hand them off to the right people at the right time.

7. Charter Communications: 500% ROI in six months

Chatbot system: Next IT Industry: Cable/Internet provider Key stats:

• 500% ROI within six months.
• Reduced live chat volume by 83%.
• Decreased time it took customers to reset passwords by 50%.
• Common customer service questions are now handled completely through the chatbot.
• Chatbots can resolve issues faster by reducing handoffs.

Where the study came from: Charter Communications implemented Next IT’s chatbot in 2012. Next IT published this case study on their website.

Charter Communications is the second largest cable provider and the fifth largest phone providers in the U.S. They have 16,000 employees and 25 million customers.

Before switching to a chatbot service, Charter Communications had 200,000 live chats per month. 38% of these live chat conversations were for forgotten usernames and passwords. That’s 76,000 ridiculously simple requests that had to be handled by a real person every month.

Obviously, all of those conversations take up a lot of customer service time. Since so many people were accustomed to resolving issues through chat, Charter didn’t wanted to pull the plug on the entire chat system, but they needed a self-serve option to save their customer service reps for more complex problems.

When they switched to a chatbot, it didn’t just take over those basic password and username questions. 83% of all of chat communications were handled by the bot. That’s 166,000 chat requests per month that Charter no longer had to worry about.

But Charter’s chatbot wasn’t just bumbling its way through these conversations, either. Part of their goal was to increase first-contact resolution rates, so customers wouldn’t need to be relayed through several people to get what they needed. The chatbot could also handle those tedious password and username requests 50% faster than a real person.

Ultimately, chatbots delivered a solid win for Charter and for their customers.

Facebook Messenger bot case studies

Since Facebook opened up its Messenger app for developers to create their own bots , a lot of brands have seized the opportunity to interact with their audience this way. The case studies you’ll see below are a little lighter than the ones we’ve looked at so far, but they showcase a few ways organizations are successfully using Facebook Messenger bots. Some ecommerce sites have had a lot of success with Messenger bots, but the three examples we’re going to look at are all primarily content-focused brands.

Something to think about: while the other chatbots we’ve looked at live on your website, this one lives in an app people are already using, and they can find your bot there. Facebook shares Messenger bots in the discover tab , and if you open Messenger right now and search, you’ll find “bots” right below “people.” In other words, a Facebook Messenger bot could grow your audience.

8. BabyCenter: 53% click through rate from Facebook Messenger

Chatbot system: Facebook messenger Industry: baby products Key stats:

• 84% read rate on automated messages.
• 53% click through rate from Facebook Messenger to BabyCenter.com.
• Used a Messenger bot to drive traffic to the website.
• Facebook messages were opened more than emails.

Where the study came from: BabyCenter asked ubisend to design a Facebook Messenger bot in 2016. Ubisend published this case study on their website.

BabyCenter is one of the most trusted pregnancy websites out there (seriously, I’ve seen my wife’s OBGYN check this site during appointments). One of their biggest draws is a sequential email campaign that follows you every step of the way through pregnancy, and their revenue model is based on advertisements and a strong affiliate sales program.

Through ubisend, BabyCenter created a bot on Facebook Messenger to do two things:

• Drive traffic to their website.
• Provide an alternative content delivery system.

As you can see in the GIF below, the bot also provided a more interactive way for people to consume BabyCenter’s content.

Image source: ubisend

The new bot accomplished both objectives, with some impressive results. On average, 84% of people read the message, and 53% of those who opened also clicked through to the website. Ubisend compares that to MailChimp’s open and click-through rates, and with some unstated math determined that the Messenger bot had a 1,428% higher engagement rate. I can’t speak to the validity of that claim, but here are a couple of reasons why the bot may have had better open and click-through rates than email:

• The floating messenger icon and that little red number is a lot harder to ignore than an email.
• People are used to glancing at a subject line without opening the email.
• Far fewer brands are on Messenger, so a notification is more likely to be from someone you know. (And unless you’re avoiding someone, you’re probably going to open it.)
• The load time for a Facebook message is almost instant. Email? Not so much.
• It only takes two taps to open a message and click through. Email takes a little more navigation.

Whatever the reason, a Messenger bot was clearly a viable content delivery system for BabyCenter. If enough people adopt it, the Messenger bot may even rival their well-established sequential email campaign.

9. Good Spa Guide: 29% increase in website traffic

Chatbot system: Facebook Messenger Industry: Spa reviews Key stats:

• 47% click through rate on automated messages.
• 29% increase in website traffic in six weeks.
• 13% increase in spa bookings.
• Messenger bots can help consumers navigate the website before they even get there.

Where the study came from: Good Spa Guide solicited ubisend’s services in 2016. Ubisend published this case study on their website.

As the name implies, Good Spa Guide reviews spas. They make money when people use the site to book a spa, so not surprisingly, they really value website traffic.

Like BabyCenter, Good Spa Guide was looking for an alternative to their email list. They used ubisend to design a Messenger bot that functions a lot like Amtrak’s “Ask Julie” bot. It basically provides a more conversational way to navigate the website—but without actually being on the website.

Check it out:

After a short conversation with the bot, people can go to the exact spa review page they need, and continue their hunt on the website.

With a 29% increase in traffic and a 13% increase in spa bookings, it looks like a Facebook Messenger bot helped Good Spa Guide either tap into a new audience, or engage their existing audience in a better way.

10. MyTradingHub: 59% decrease in churn

Chatbot system: Facebook Messenger Industry: Forex trading education Key stats:

• 59% decrease in churn.
• 17% increase in website traffic.
• Messenger bots can be very effective at keeping your audience consistently engaged.

Where the study came from: In 2016, MyTradingHub was struggling to keep subscribers engaged, so they turned to ubisend. This case study was published on ubisend.com.

MyTrainingHub is a web-based social and educational platform for people who trade on the foreign exchange market. They’re after users, not customers, and they use a sequential email campaign to keep their users engaged.

Their primary metric is what they call “Trader Training Completion,” which measures the number of people who have viewed 80% of MyTradingHub’s content and performed specific tasks like quizzes. When this metric started declining, they learned that users weren’t completing the training because “they forgot about it.”

They decided to try an interactive Messenger bot to bring up the number of people who made it through training. They wound up creating a bot that could help people interact with the trading platform and continue their training.

MyTradingHub saw their TTC metric increase by 59% following the launch of the bot, and their training pages saw 17% more traffic.

In this case, it looks like a Messenger bot functioned as a sort of half-measure. MyTradingHub has been around since 2010, but to continue to be a strong “social platform,” they probably need their own app. In the meantime, MyTradingHub’s Messenger bot appears to be keeping users more engaged with their existing content.

Honorable mention: PG Tips 150 messages per second

Chatbot system: Facebook Messenger Industry: Tea Key stats:

• In six weeks, ubisend designed a branded chatbot with 215 conversation topics.
• The bot was capable of sending more than 150 messages per second.
• Chatbots have an insane capacity for simultaneous conversations.

Where the study came from: PG Tips asked ubisend to design a chatbot for a charity promotion. Ubisend then published this case study on their website in 2017.

PG Tips (a brand by Unilever) decided to turn their “Most Famous Monkey” into an AI chatbot to generate donations for charity. They wanted a conversational chatbot to tell jokes for their “one million laughs” campaign.

It took six weeks for ubisend to turn a chatbot into a mediocre standup comic. (They went pretty heavy on the dad jokes.) The AI could handle 150 conversations per second and handle 215 different conversation topics.

We only included this one because it shows how quickly you can set up a fairly intelligent, completely custom chatbot.

Bonus: Facebook Messenger bot examples

After allowing developers to create their own chatbots for Messenger, Facebook shared this roundup of brands successfully using chatbots . More than 30,000 chatbots were created in the first six months they were supported on Facebook Messenger. The roundup highlights four that Facebook thinks are worth checking out.

What do these chatbots all have in common?

In most cases, chatbots aren’t going to fool anyone. The chatbots we’ve looked at here are obviously not real people. The brands that use them and the companies that make them might be excited about how human they seem, but that’s not the point.

In the right situations, chatbots can provide customers and users with a better experience because they process your request instantly, and it doesn’t matter how many other conversations they’re having. And if you’re waiting around for basic help (like, say, password reset), you’re really not going to care if the person who’s helping you is a Bob or a bot.

Unless you’re looking for something gimmicky (like a chatbot monkey that tells dad jokes), most chatbots simply provide a more conversational way for your audience to consume the information on your website. It’s certainly not for everyone—some people (myself included) would rather navigate websites the old fashioned way and read blog posts on a blog—but for many people, chatbots provide a helpful shortcut to the information they’re looking for. And that’s something you should probably care about.

One clear takeaway: if you’re using a live chat service right now, a chatbot can either outright replace it or vastly improve it. Ask your customer service reps what questions they get the most and how often they get them. Go ahead, ask them.

But even if you’re not already using some sort of chat service, chatbots can:

• Automate the lead generation process.
• Deliver your content in new ways (maybe even to new people).
• Drive traffic to key pages of your website.
• Save your customer service and sales reps a boatload of time.

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Privacy Overview

Case Study on Chatbot

Case study on chatbot: enhancing retail and customer service with chatbot implementation :.

Background: Case Study on Chatbot:With the rise of e-commerce and the growing number of digital consumers, companies have had to adapt to the changing landscape by providing customer service through various channels. One such channel is the use of chatbots. Case Study on Chatbots has become an increasingly popular solution in which computer programs utilize artificial intelligence (AI) to mimic human conversation. These chatbots can be seamlessly integrated into websites, mobile apps, and social media platforms, enabling businesses to provide customers with quick and efficient support.

Solution: One industry that has embraced chatbots is the retail industry. Companies in this sector have implemented chatbots to provide customers with personalized recommendations, answer product-related questions, and process orders. Chatbots are also used to improve customer service by providing quick responses to inquiries and reducing wait times.

Implementation: Several retail companies have implemented chatbots to improve customer service, including Sephora and H&M. Sephora conducted a case study on chatbot implementation and integrated a chatbot into its mobile app, utilizing AI to recommend products based on customers’ skin type, tone, and preferences. This successful implementation highlights how Sephora leveraged AI technology to enhance customer engagement and increase sales. Similarly, H&M, a fashion retailer, conducted its own case study on chatbot implementation. H&M’s chatbot, integrated into their website, streamlines order management and assists customers. It efficiently handles order status updates, shipment tracking, and returns, resulting in reduced wait times and improved customer satisfaction. This case study further demonstrates the effectiveness of AI-powered chatbots in enhancing the customer experience in the retail industry.

Results: The implementation of chatbots in retail, as demonstrated in the mentioned case studies on chatbot, has led to several benefits for both customers and businesses. For customers, chatbots provide quick and efficient support, which leads to improved satisfaction rates. Additionally, chatbots enable personalized shopping experiences by recommending products that meet individual needs, as showcased in the Sephora case study on chatbot. For businesses, chatbots reduce the workload of customer service representatives, resulting in cost savings. Moreover, chatbots generate valuable data on customer preferences and behavior, which can be analyzed to enhance the overall customer experience and drive sales growth.

Conclusion: Based on the analyzed case studies on chatbot implementation in the retail industry, it is evident that chatbots have significantly improved the customer experience and increased sales. Chatbots offer prompt and efficient support, leading to enhanced satisfaction rates. Businesses benefit from cost savings and valuable insights into customer preferences and behavior. As the use of chatbots continues to grow, more companies in the retail industry are expected to adopt chatbot solutions to enhance their customer service strategies. The success stories presented in the case studies on chatbot implementation emphasize the effectiveness of AI-powered chatbots in revolutionizing customer service in the retail sector.

Case Study: Chatbots in Healthcare :

Background: The healthcare industry is one that has seen an increased adoption of chatbots. Healthcare providers have implemented chatbots to improve patient engagement, provide 24/7 support, and streamline administrative tasks. Chatbots can also be used to provide patients with educational information on various health conditions and treatments.

Solution: One example of a healthcare provider that has implemented a chatbot is the Mayo Clinic. The Mayo Clinic is a healthcare organization that provides medical care and education. The organization has implemented a chatbot on its website to help patients find information on medical conditions and treatments. The chatbot uses natural language processing (NLP) to understand patient inquiries and respond with relevant information. The chatbot has been successful in improving patient engagement and reducing wait times for patients seeking medical information.

Implementation: Other healthcare providers have also implemented chatbots to improve patient engagement and streamline administrative tasks. For instance, Babylon Health is a healthcare provider that has implemented a chatbot to provide patients with 24/7 support. The chatbot can provide patients with medical advice, help patients book appointments, and provide information on health conditions and treatments. The chatbot has been successful in reducing wait times for patients seeking medical advice and improving patient satisfaction rates.

Results: The implementation of chatbots in healthcare has led to several benefits for both patients and healthcare providers. Chatbots offer patients round-the-clock support, enhancing engagement and satisfaction rates. Additionally, they assist healthcare providers in streamlining administrative tasks like appointment booking and medical record-keeping. Furthermore, chatbots enable healthcare providers to deliver educational information to patients regarding diverse health conditions and treatments, thereby contributing to improved patient outcomes.

Conclusion:

The use of chatbots in healthcare has been successful in improving patient engagement, satisfaction rates, and outcomes. Chatbots provide patients with 24/7 support and educational information, which leads to improved engagement and satisfaction rates. Chatbots also help healthcare providers streamline administrative tasks, which leads to cost savings and improved efficiency. As the use of chatbots continues to grow, we can expect to see more healthcare providers implementing chatbots to improve patient care.

Case Study: Chatbots in Banking :

Background:

The banking industry is one that has also seen an increased adoption of chatbots. Banks have implemented chatbots to provide customers with 24/7 support, process transactions, and provide personalized financial advice. Chatbots can also help banks reduce wait times for customers and improve the customer experience.

One example of a bank that has implemented a chatbot is Capital One. Capital One is a financial services company that provides credit cards, loans, and banking services. The company has implemented a chatbot on its website and mobile app to provide customers with support and personalized financial advice. The chatbot uses AI to understand customer inquiries and respond with relevant information. The chatbot has been successful in improving customer engagement and satisfaction rates.

Implementation:

Other banks have also implemented chatbots to provide customers with 24/7 support and process transactions. For instance, Bank of America has implemented a chatbot on its mobile app to help customers with their banking needs. The chatbot can provide customers with account balances, transaction histories, and help customers pay bills. The chatbot has been successful in reducing wait times for customers and improving the customer experience.

The implementation of chatbots in banking has led to several benefits for both customers and banks. By providing customers with 24/7 support and personalized financial advice, chatbots enhance customer engagement and satisfaction rates. They assist banks in reducing wait times for customers, thereby improving the overall customer experience. Moreover, chatbots enable banks to streamline administrative tasks, resulting in cost savings and enhanced efficiency.

The use of chatbots in banking has been successful in improving customer engagement, satisfaction rates, and efficiency. By offering customers 24/7 support and personalized financial advice, chatbots contribute to heightened engagement and satisfaction rates. In addition, chatbots assist banks in reducing customer wait times and enhancing the overall customer experience. They also play a role in streamlining administrative tasks for banks, leading to cost savings and improved operational efficiency. As the use of chatbots continues to grow, we can expect to see more banks implementing chatbots to improve customer service.

Case Study: Chatbots in Education :

The education industry is one that has also seen an increased adoption of chatbots. Educational institutions have implemented chatbots to provide students with 24/7 support, answer student inquiries, and provide educational resources. Chatbots can also help educational institutions reduce administrative tasks and improve student engagement.

One example of an educational institution that has implemented a chatbot is Georgia State University. Georgia State University is a public research university that provides undergraduate and graduate degrees. The university has implemented a chatbot on its website and mobile app to provide students with support and answer student inquiries. The chatbot uses NLP to understand student inquiries and respond with relevant information. The chatbot has been successful in reducing wait times for students and improving student engagement.

Other educational institutions have also implemented chatbots to provide students with 24/7 support and answer student inquiries. For instance, Arizona State University has implemented a chatbot named Sun Devil Bot to help students with their academic needs. The chatbot can provide students with course information, exam schedules, and help students schedule appointments with academic advisors. The chatbot has been successful in reducing wait times for students and improving student engagement.

The implementation of chatbots in education has led to several benefits for both students and educational institutions. Chatbots provide students with 24/7 support and educational resources, which leads to improved student engagement and satisfaction rates. Chatbots can also help educational institutions reduce administrative tasks, such as answering student inquiries and scheduling appointments, which leads to cost savings and improved efficiency.

The use of chatbots in education has been successful in improving student engagement, satisfaction rates, and efficiency. Chatbots provide students with 24/7 support and educational resources, which leads to improved engagement and satisfaction rates. Chatbots also help educational institutions reduce administrative tasks, which leads to cost savings and improved efficiency. As the use of chatbots continues to grow, we can expect to see more educational institutions implementing chatbots to improve the student experience.

Case Study on Chatbots in Retail :

Background:  The retail industry is one that has also seen an increased adoption of chatbots. Retailers have implemented chatbots to provide customers with 24/7 support, process orders, and provide personalized recommendations. Chatbots can also help retailers reduce wait times for customers and improve the customer experience.

Solution:  One example of a retailer that has implemented a chatbot is H&M. H&M is a multinational clothing retailer that offers clothing, accessories, and home decor products. The company has implemented a chatbot on its website and mobile app to provide customers with support and personalized recommendations. The chatbot uses AI to understand customer inquiries and respond with relevant information. The chatbot has been successful in improving customer engagement and satisfaction rates.

Implementation:  Other retailers have also implemented chatbots to provide customers with 24/7 support and process orders. For instance, Sephora has implemented a chatbot on its mobile app to help customers with their beauty needs. The chatbot can provide customers with product recommendations, help customers schedule appointments at a Sephora store, and provide customers with beauty tips. The chatbot has been successful in reducing wait times for customers and improving the customer experience.

Results:  The implementation of chatbots in retail has led to several benefits for both customers and retailers. By providing customers with 24/7 support and personalized financial advice, chatbots enhance customer engagement and satisfaction rates. They assist banks in reducing wait times for customers, thereby improving the overall customer experience. Moreover, chatbots enable banks to streamline administrative tasks, resulting in cost savings and enhanced efficiency.

Conclusion: 

The use of chatbots in retail has been successful in improving customer engagement, satisfaction rates, and efficiency. With 24/7 support and personalized recommendations, chatbots empower customers, leading to improved engagement and satisfaction rates. Retailers benefit from reduced wait times and enhanced customer experiences facilitated by chatbots. Moreover, chatbots streamline order processing, resulting in cost savings and improved operational efficiency for retailers. As the use of chatbots continues to grow, we can expect to see more retailers implementing chatbots to improve the customer experience.

Conclusion :

The adoption of chatbots across various industries has led to several benefits, including improved engagement, satisfaction rates, and efficiency. Chatbots provide users with 24/7 support, personalized recommendations, and educational resources, which lead to improved engagement and satisfaction rates. Chatbots also help businesses reduce wait times for users and streamline administrative tasks, which leads to cost savings and improved efficiency. As the use of chatbots continues to grow, we can expect to see more businesses leveraging this technology to enhance their customer service strategies.

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Using AI-Powered Chatbots for Conversational Product Discovery: A Case Study

Have you ever wished you could find the perfect product without having to search through endless online shops and websites? Well, now there’s a solution – chatbots! Chatbots are now used for conversational product discovery. In this case study, we’ll show you how using AI-powered chatbots helped increase product discovery and drive sales for one company. You’ll see how these bots can help you save time and money while still providing an excellent customer experience. So what are you waiting for? Get started today!

How Using AI-powered Chatbots for Conversational Product Discovery Helped Drive Sales

Are you looking for ways to improve your conversion rates? Then you need to check out this case study about how a company utilized AI-powered chatbots to increase product discovery and drive more sales. By introducing an intuitive and interactive conversational, customer-driven system, the company was able to increase user engagement through AI’s natural language processing capability.

This helped to surface new customers with enhanced product discovery. As a result of their innovative approach, the company was able to optimize its conversion rates and drive increased sales. It is a must-read for anyone serious about taking their marketing strategy to the next level.

How Chatbots for Conversational Product Discovery Helps Customers Find Products

AI-powered chatbots are an increasingly popular way to give customers a better experience when they shop online. These intelligent virtual assistants can not only help customers browse through large product catalogs and make it easier to find what they’re looking for, but their powerful algorithms also help them provide personalised assistance in understanding different products and making the best purchase decisions.

Through natural language processing, automated reactions, and smart recommendation engines, AI chatbots can provide helpful advice about the items someone might be interested in, further driving conversion rates for businesses. By using these versatile tools, companies can increase customer satisfaction and revenue with relatively low costs or effort.

Share some of the results of the case study, including an increase in conversion rates and sales

AI-powered chatbots have great potential to increase sales and boost conversion rates. This was clearly shown in a recent case study. It revealed that employing AI-powered tools substantially improved product discovery and consequently boosted sales. Specifically, the study showed that within two months, conversions due to product discovery went up by an impressive 35%. In addition, overall sales climbed by 6%.

Furthermore, tracking customers’ journeys became significantly easier and more efficient. This allowed for a better analysis of customer behavior, and important insights were gained from it. Clearly, AI-powered tools offer immense benefits to businesses looking to increase their revenues.

Offer advice on how other businesses can use AI-powered chatbots to improve their own customer service

AI-powered chatbots are becoming increasingly popular as a way to improve customer service and product discovery. The success of this case study proves it. By leveraging machine learning capabilities through AI, businesses can find ways to better engage with their customers. This, in turn, increases sales. It is easy to implement these chatbots on a variety of channels. They can be used on websites, social media platforms, or mobile applications.

Savvy entrepreneurs should look into programs like Chatfuel, Motion AI, and Bot My Work to create automated responses. They provide real-time customer support customized for their business. Equipping your business with an AI-powered chatbot is a surefire way to ensure improved customer service and enhanced conversion rates.

Final thoughts

In conclusion, chatbots are now used for conversational product discovery. The results of the case study showed that using AI-powered chatbots helped increase product discovery and drive sales. Providing customers with a chatbot that could answer their questions is good. It can help them find the products they were looking for. Thus, the company is able to improve its conversion rates and overall sales. If you’re looking to improve your customer service and increase your sales, consider implementing an AI-powered chatbot into your business.

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Transparency in the Fashion Industry: A Case Study on H&M

• By: Paige Street & Kathleen Horton
• Publisher: SAGE Publications: SAGE Business Cases Originals
• Publication year: 2023
• Online pub date: January 02, 2023
• Discipline: Consumer Behavior , Brand Management & Strategy , Business Ethics (general)
• DOI: https:// doi. org/10.4135/9781529611687
• Keywords: branding , case studies , consumers , corporate social responsibility , fashion industry , revolutions , supply chains , sustainability , transparencies , transparency Show all Show less
• Contains: Teaching Notes Region: Global Industry: Manufacture of wearing apparel | Manufacturing Organization: H&M Organization Size: Large info Online ISBN: 9781529611687 Copyright: © Paige Street and Kathleen Horton 2023 More information Less information

Teaching Notes

In 2020, Fashion Revolution named fast fashion retailer Hennes & Mauritz (H&M) as the world’s most transparent brand. Transparency refers to the ‘seeing through’ of business and supply chain processes. It is a method of corporate self-reporting, data sharing, and ‘sustainable supply chain management’, which has been praised for its ability to hold brands to account and to give consumers access to information about where their clothes are made. Although transparency does introduce a sense of accountability and openness to the fashion system, it also has several limitations. An overemphasis on the value of transparency promotes the fallacy that ‘seeing’ something is equal to knowing it, assumes that transparency is equal to sustainability, and obfuscates a deeper investigation into the complexities of fashion supply chains. Students will be asked to reflect on H&M’s naming as the ‘most transparent brand in the world’ and assess the role of transparency in the fashion industry.

Transparency in the Fashion Industry: A Case Study on H&M

Learning outcomes.

By the end of this case study, students should be able to:

• 1. Describe the purpose of transparency in the fashion industry.
• 2. Assess the relationship between transparency and sustainability.
• 3. Analyse positive and negative aspects of transparency in the fashion industry.

Overview of H&M

Hennes & Mauritz (hereafter H&M), owned by the H&M Group, was founded in Sweden in 1947 and is well known for its trendy clothing, low price points, and expansive global distribution network. It has more than 5000 stores ( Statistica Research Department, 2021 ) in 75 country markets (H&M, 2022). The company began in womenswear but has now expanded and produces trend-focused clothing for men’s and children’s markets. H&M, alongside its rival Spanish competitor Inditex (which owns the brand Zara), are used as examples of a ‘success story’ for the fast fashion business model. For H&M, this is because it was one of the first – and one of the most popular – brands in Swedish mass-market fashion and later ‘led the charge toward the global transformation that contributed to ”faster”’ forms of production and consumption ( Giertz-Mårtenson, 2018, p. 206 ).

One of the central priorities for H&M since its founding was making stylish clothing accessible to a global market. With this agenda, the organization has made a significant contribution to what fashion scholars describe as the ‘democratization’ of fashion in the 20th and 21st centuries. H&M has championed a form of ‘global dressing’ by selling the same styles of clothing around the world and by approaching the business of fashion in such a way that ‘there is lots of “space” for everyone’ (Jörgen Andersson, 2016, as cited in Giertz-Mårtenson, 2018, p. 208 ). This approach is seen in H&M’s product offerings, which are diverse in size and sit at a low price point. This agenda attempts to overcome the size and class privileges often inherent in what is deemed ‘fashionable’. H&M has been described as finding the middle ground ‘between capitalism and socialism’ ( Giertz-Mårtenson, 2018, p. 207 ).

Throughout the 2010s, H&M updated its business concept to expand its focus beyond fashion and affordability to sustainability. This concept expansion came at a time when concern for the social and environmental impacts of fast fashion was elevated, and the sector was shouldering critiques from activists, consumers, and governments for its contributions to global injustices. Many fast fashion retailers, including H&M, responded ‘through the introduction of brand extensions designed to appeal to a more ethically conscious consumer’ ( Stringer et al., 2020, p. 100 ). In 2012, H&M launched its ‘conscious collection’ of garments made with ‘a little extra consideration for the planet’. Since then, it has increased its investment in corporate social responsibility (CSR) initiatives. Examples of such initiatives include: (1) H&M now facilitates take-back schemes for garment collection, which is available in all stores. In this programme, consumers can drop off H&M clothes they no longer wear. (2) Garment repair education and services are available online with the goal of making clothes last longer. (3) Marketing campaigns that rebrand the company as ‘responsible’. (4) Finally, the organization publishes annual sustainability reports that track its progress. These reports are also part of H&M’s vow to be transparent about its supply chain processes. The H&M website has information about the location of where each garment is made and how many workers are employed in these factories. This redirection was not only a means to secure a new market segment of consumers, but to also secure investors. In 2021, H&M contributed more than USD 0.5 billion to finance its ‘ green projects’ .

Transparency in Relation to Corporate Social Responsibility.

The push for transparency in fashion can be traced to the 2013 Rana Plaza factory collapse in Dhaka, Bangladesh, which was one of the deadliest structural failures in modern history. The collapse killed more than 1,100 garment workers, most of whom were young women making clothes for global fast fashion retailers. The event highlighted how brands and consumers in the Global North are intimately connected to the lives and livelihoods of workers in the Global South, through the spectre of clothing labels appearing among the rubble. When the news about which brands were manufacturing at Rana Plaza became public, many of them disavowed their responsibility by stating that their supply chains had become so large and complex that they did not know their clothes were being manufactured there .

The fashion industry is often blamed for concealing injustices, because despite the glamour and spectacle that surrounds fashion consumption, many of the clothes are made in poor, abusive, and unjust conditions. Thus, in attempting to address these practices, transparency initiatives generate a particular buzz, because revealing and illuminating the problem is framed as central to responding to it.

Transparency refers to the traceability of a garment from raw material to final product and/or an openness about costs, suppliers, certifications, and purchasing practices. This openness means disclosing where materials are sourced, where clothes are manufactured, and conditions in which the workers manufacture and produce them. Traceability is often performed by corporate auditing practices and self-published reports that consumers may read and interpret and therefore make more informed choices. Transparency is part of sustainable supply chain management because it tasks corporations with having precise knowledge of where and how their clothes are made and is also an element of brand communication. Transparency holds the promise of accountability and openness between consumers and corporations because it is framed both as a consumer tool for ‘shopping better’ and a corporate tool for legitimizing CSR initiatives and complements sustainability marketing.

The term transparency has ‘assumed the position of an unassailable “good”’ ( Birchall, 2011, p. 60 ) and is routinely accepted as an important part of addressing global injustices in fashion. This faith in transparency rests on the assumption that seeing something is equal to knowing it and that knowing more about the fashion system will lead to a more ‘rational management’ of the social problems within fashion ( Tsoukas, 1997, p. 827 ). These assumptions overlook some important parts of how transparency is used in fashion, and H&M is an interesting case study of this.

H&M: The ‘World’s Most Transparent Brand’?

The term transparency has been ‘codified’ by global activist organization Fashion Revolution with its ‘Fashion Transparency Index’, which ranks 250 of the world’s largest brands according to the information that they publish about their own supply chains ( Richards, 2021, p. 915 ). In 2020, Fashion Revolution named fast fashion retailer H&M as the world’s most transparent brand. In response, H&M published a since-deleted Instagram post which celebrated the ranking and featured the hashtag ‘#sustainability’. Critics argued that the Index was confusing for consumers; however, Fashion Revolution have attempted to make it clear that the Index should not be used as a ‘shopping guide’ and is just a way to monitor the practices of some of the biggest global brands and incentivize them to do better ( Fashion Revolution, 2020, p. 10 ).

The naming of H&M as the world’s most transparent brand was surprising in and of itself, because the organization’s transparency reporting mostly ignores its history of rights abuses. When H&M is describing to consumers where their clothes are manufactured, it does not address the conditions in which they were made. In the last decade, H&M has been accused of not paying living wages , being involved in unsafe production facilities , child labour and forced labour regimes , and ongoing over-production and wasteful practices which harm the environment . As well as these specific instances of rights abuses, H&M is often described as being guilty of leading a culture of fast fashion which valorizes newness and a high disposability of fashion garments. Furthermore, the organization is often called out for ‘greenwashing’ and adopting misleading claims about its product certifications and commitments.

The ranking of H&M as the world’s most transparent brand by Fashion Revolution was also critiqued because it positioned H&M above other brands who are well-known for their commitment to ethics and sustainability, such as Patagonia. Patagonia is one of the most referenced models of corporate social and environmental responsibility in the fashion industry and has been an early adopter of ‘better’ business practices since the 1990s. That H&M could score higher than Patagonia shows what many authors and activist make clear – that ‘a brand can be transparent without necessarily being sustainable or ethical’ ( Richards, 2021, p. 918 ).

The Problem

There are three clear problems with Fashion Revolution naming H&M as the ‘world’s most transparent brand’. These problems, which are important to explore, are summarized as follows:

• 1. It exposes the problematic defining of transparency as a practice of sustainability or CSR. Whilst H&M uses the notion of transparency to claim to be a sustainable brand, there is evidence of rights abuses within its supply chains that suggest otherwise. This obscures the fundamental fact that transparency does nothing to alter the conditions in which clothes are made.
• 2. The way in which transparency is measured relies on self-reporting. This means relying on what brands are willing to disclose and privileging the brands that have the economic resources to report on themselves and publish the reports online. Importantly, transparency also has an implicit assumption that the reports are factual and true. Transparency is assumed to be an open and relatively neutral practice of information sharing; however, it can also be understood as a form of visibility management – that is, it gives H&M the power to control what is made transparent and what is concealed about its supply chains. Transparency is a relatively expensive and time-consuming practice, so many smaller brands are unable to compete with H&M in this space.
• 3. Although transparency helps to deliver more information to consumers, it can also undermine the capacity for understanding. Transparency reporting assumes a linear process that unfolds smoothly from corporation to consumer. However, most consumers are not likely to read these reports, let alone be able to understand them or the complexities of fashion supply chains. This means consumers are reliant on intermediaries – such as Fashion Revolution and the Fashion Transparency Index – ‘who can translate complex information into more simple formats’ that allow conclusions to be drawn easily and evaluated within the busy context of everyday life ( Christensen & Cheney, 2011, p. 83 ). Consequently, whilst Fashion Revolution stresses that the index is not to be used as a ‘shopping guide’, it is often understandably used as such, by consumers who are keen to find ways in which to ‘shop better’.

What Can Be done?

The fashion supply chain offers some of the most vivid and extraordinary images of contemporary capitalism: garment workers working in harsh conditions while global brands disavow responsibility, and methods of ‘seeing through’ this system that are at the same time creating forms of both transparency and opacity. It is difficult to know what can be done about the complex nature of fashion supply chains, especially because the fast fashion sector seems at odds with its mandate to produce fashionable clothing affordably and its objectives to take responsibility for the consequences of its production. A conundrum is presented here for both corporations and consumers. For corporations, the strategy to ‘be transparent’ becomes paradoxical because it may be inadvertently concealing abuses while ‘giving the appearance that it is seeking to eradicate’ them ( Crandall & Parnell, 2021, p. 10 ). This backdrop creates a problem for consumers, who are increasingly being confronted with the moral implications of their clothing choices and being asked to ‘shop better’ in the name of global justice. The problem of knowing how to shop better is personal and complex – every person has diverse and fluctuating needs and values that fashion markets try to cater for. In the decision to purchase from H&M, some shoppers might value the range of sizes, low price point, and steps toward sustainability and the CSR that the brand is taking. Others might be troubled by H&M’s claims as a transparent and sustainable choice when revelations about its involvement in social and environmental injustices suggest otherwise.

The point of identifying what transparency ‘hides’ is not to call it to an end, because transparency and the efforts of CSR initiatives do have real benefits for sustainability, but also has some limitations. An overemphasis on the value of transparency promotes the fallacy that ‘seeing’ something is equal to knowing it, assumes that transparency is equal to sustainability, and obfuscates a deeper investigation into the complexities of fashion supply chains.

Discussion Questions

• 1. What role does transparency serve in the fashion industry? Who does it benefit?
• 2. Should transparency remain a focus for corporate social responsibility in the fashion industry? Explain your answer.
• 3. If the goal of transparency reports is to encourage corporations to adopt more ethical, sustainable, or responsible practices than they otherwise would, what are other ways to encourage such practices by corporations? How could corporations communicate this to their consumers?
• 4. Should ethically or sustainably minded consumers purchase clothes from H&M? Explain your answer.
• 5. What should H&M do to continue its agenda of making fashionable clothing at an affordable price, and to do so sustainably?

Further Reading

This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study, and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be forwarded outside the university or used for other commercial purposes.

2024 Sage Publications, Inc. All Rights Reserved

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AI Campaigns and Case Studies

By Joanna Fragopoulos     March 29, 2024    

A rtificial intelligence (AI), and its applications, is at the forefront of many discussions in many industries and fields, from marketing to tech to healthcare to education to law. How to implement and leverage these tools in a helpful way for users can be challenging for teams. However, when used well, AI can help save time analyzing data, personalize content and information, enhance creative ideas, and find ways to promote diversity, equality, inclusion, and belonging (DEIB). Below are case studies and campaigns that successfully utilized AI.

Leveraging Chatbots and ChatGPT

Zak Stambor, senior analyst of retail and e-commerce at Insider Intelligence, discussed AI at an ANA event , stating that it is "very clear that marketers will be spending more of their budgets on AI-infused productivity tools in the future." Stambor cited two companies utilizing chatbots to help consumers find what they need. For instance, Instacart started its Ask Instacart tool to help its users "create and refine shopping lists by allowing them to ask questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on users' past buying habits and provides recipes and a shopping list once users have selected the option they want to try," according to the ANA event recap . Further, Mint Mobile used ChatGPT to write an ad which it later released. The recap , however, stated that the company's CMO "emphasized that there were limitations with the technology and stressed the importance of understanding a brand's DNA before using generative AI. He recommended approaching ChatGPT in the same way successful marketers approach social media."

Smoothing the Request for Proposal (RFP) Process

Creating campaigns that are actually interesting and engage people, is, of course, every marketer's dream. ZS, a consulting and technology firm focused on transforming global healthcare, worked with Stein IAS to create its campaign " Data Connects Us ," which provided client services teams with content, case studies, reports, ZS's Future of Health survey, and data to help with the RFP process. The campaign leveraged AI to create "futuristic AI generated images — such as a futuristic hospital — and coupled it with copy communicating how ZS is positioned to help connect data with people and support real innovation. By leveraging emotionally engaging, distinct, and memorable creative, ZS was able to invite consumers to learn more about the company," as described in the ANA event recap .

Fostering DEIB

Google sought to promote DEIB practices as well as combat stereotypes and bias; the company was able to do this through the use of AI in the photography space. In 2018, the company established the Google Image Equity initiative, which enlisted experts on "achieving fairness, accuracy, and authenticity in camera and imaging tools," according to the ANA event recap . This result in Real Tone, which is a "collection of improvements focused on building camera and imaging products that worked equally for people of color" and became a consideration for people potentially buying a Google Pixel. As part of this process, the company collaborated with Harvard professor, Dr. Ellis Monk; together, they released a 10-shade skin tone scale that was more inclusive of diverse skin tones. This scale helps "train and evaluate AI models for fairness, resulting in products that work better for people of all skin tones."

Unearthing Creativity

Michelob ULTRA partnered with agency CB New York to create a virtual tennis match with John McEnroe, both in the past and present. McEnroe's past self was created using motion-capture technology and AI. Moreover, the brand also created a campaign called "Dreamcaster" with Cameron Black, who has been blind since birth, who longed to be a sports broadcaster "but felt he would never get the opportunity due to his disability," as explained in the ANA event recap . The recap went on to explain that Michelob worked with Black for an entire year to "create a spatial audio portal, complete with 62 surround sound speakers and more than 1,000 unique sounds, that 'placed' him at center court and told him what was occurring during a basketball game in real time. The portal featured a vest, designed with its own haptic language, to further assist Black in following the action by allowing him to feel the game's action. After 12 months of development and training, Black became the first-ever visually impaired person to broadcast an NBA game on live TV."

Deepening Personalization

To enhance personalization, Panera Bread created a loyalty program called "My Panera" in 2010. The program gives customers rewards based on visits; the rewards to be personalized which boosts the program's engagement. Recently, Panera worked with ZS Associates to utilize machine learning to create an automated "best next action" program to enable "true one-to-one interactions with My Panera members," as described in the ANA event recap , which went on to say that the company uses a "time-based criterion, combine[s] it with several other variables identified and sorted by AI, and serve[s] more than 100 different offers to the same audience. Panera can also leverage the technology to develop multiple email subjects or coupon headlines, make product recommendations based on past purchases, and even customize colors and copy within the communication to suit the sensibilities of the customer being targeted. Overall, there are more than 4,000 unique combinations of offer and product recommendations that a customer can receive."

The views and opinions expressed in Industry Insights are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.

Joanna Fragopoulos is a director of editorial and content development at ANA.

This chatbot helps Airbnb hosts answer guests’ questions faster than ever before

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Burner has launched another chatbot to demonstrate the potential of its mobile application and service. With Hostbot , hosts of short-term vacation rentals through Airbnb, HomeAway, VRBO, or their own means can communicate with their guests over SMS.

Developed alongside Voxable, which also co-created the Ghostbot app with Burner, Hostbot is a way to streamline communication. It could be helpful for hosts who manage multiple listings across platforms and opt to use a disposable phone number. Hosts first download the Burner app, authenticate with their credentials, and then can program responses to frequent questions their guests may have.

Guests don’t have to download anything — they just submit questions through SMS messaging and Burner’s system uses natural language processing to understand what’s being asked. Questions can range from “What’s the Wi-Fi password?” to “Where’s the washing machine?,” and Hostbot will automatically respond with the answer. However, if the chatbot doesn’t know how to answer a question that’s asked, it will route the message directly to the host.

Above: Burner’s Hostbot lets short-term vacation rental hosts create FAQs to quickly respond to guest questions by SMS.

Although Hostbot is useful for hosts on Airbnb, HomeAway, and VRBO, Burner said that it does not have any formal relationship with these companies. Rather, the tool is a way to showcase the potential of Burner’s API for developers that has become available to the public for the first time.

The AI Impact Tour – Atlanta

Opening up an API allows developers to produce apps with more native integration of data without requiring advanced training to program webhooks. Burner launched its first developer tool in November, which gives third parties a way to connect accounts with applications to control the smart home or find some other use cases around a phone number.

“We had been doing internally first-party connections, connecting Burner with apps like Dropbox and Slack,” remarked Burner CEO Greg Cohn. “The goal here [with the API] is to open that up so other people can do things like that. Webhooks was a step in that direction, but had limitations.”

Above: Connecting your Burner account to Hostbot.

Webhooks will continue to exist, and developers can use it alongside the API. But Cohn highlighted that Burner’s API “opens up more capabilities that developers can do programmatically, like changing phone numbers, reading contacts, and doing things in real time with messaging. The things we did with connections like Google Sheets, Ghostbot, and Dropbox can be done with the API now.”

He indicated that webhooks primarily sits at the messaging level. But with an API, access to rich client capabilities arises, giving more opportunities around messaging and voice at the telephony level.

The public API also includes support for OAuth 2.0, a way for developers who want to enable voice and text communication to embed Burner’s smart number capabilities into their own apps. “OAuth is useful when you want to enable a user in another service or anywhere outside Burner to securely identify and connect with their Burner account,” Cohn explained. “If you’re building an app and you want to push information [to the app] or want to read the number associated with [the user’s] Burner line and post it in a profile, you’ll need [the user] to approve the connection and give permission.”

He offered up some examples of what developers could do with the API, including being able to modify settings on Burner based on outside conditions; make changes to a user’s calendar; modify ringer settings on the phone based on the time of the moon, store hours, or calendar; add people to a user’s contacts; or perhaps integrate with a customer relationship management (CRM) system so that any time a lead form is filled out or a reservation is made, the phone number and contact information is placed onto a Burner list.

Above: Burner’s Hostbot lets Airbnb, VRBO, and HomeAway hosts set up an FAQ for guests.

“As an end user, there’s not much you can do with an API — you have to look for the integrations,” Cohn said. “But [the developer tool] builds on the value proposition that Burner is thinking about, taking the phone number and treating it as software….”

Since launching its developer program in November, the company has partnered with various partners, including integration service Zapier . With the release of a public API, Burner has upgraded its relationship, becoming a first-class integration with Zapier, which means that anyone can look for a dedicated channel and connect with supported apps. “We’re thinking about the increasing numbers of small business users on Burner. Zapier is a way to make available the long tail of integrations and customizations,” Cohn stated.

“We’ve been dogfooding this with apps like Ghostbot and … Hostbot, and want to open this up to the developer community,” he went on to say. “Using the API, developers can build rich application extensions to Burner in ways that they can’t with carrier numbers, and in ways that would otherwise require a lot of ‘last mile’ work as well as customer care and feeding if built on a bare-bones ‘telephony’ API.”

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