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Advertising Effectiveness

By Peter J. Danaher

The internet has enabled many business developments, but it has turned media allocation and planning on its head. In traditional mass media like television, advertisers can purchase a commercial slot and expect large audiences.

However, many of those reached are not interested in the advertised product or service, so a large percentage of those exposed to advertising do not respond to the message. In digital advertising, websites containing specialized content (e.g., model airplanes) allow advertisers to display their products to loyal and attentive audiences. In the social media space, Facebook delivers ad content to ideal target audiences by examining the web activity of users and their networks. Paid search advertising sends firms customers who are already “in the market” for their products, as indicated by their keyword use.

Over the past 15 years, television channels have grown in number. But the more significant change has been the exponential growth in websites supporting themselves with advertising, not to mention the rapid uptake of paid search advertising.

Advertisers have moved to new digital media outlets not only because of their ability to target customers, but also their lower cost compared to traditional media. Furthermore, digital media allows firms to connect ad exposures and search clicks to downstream sales, a feature Danaher and Dagger (2013) suggest eludes traditional media. Sethuraman, Tellis, and Briesch (2011) show the most convincing way for firms to demonstrate advertising’s effectiveness is by linking the effort to sales. In turn, researchers can use two methods to assess advertising effectiveness: field experiments and econometric models.

Field Experiments

Targeting and retargeting customers who are more likely to respond to offers, an increasingly common practice, makes advertising appear more effective than it is. Lambrecht and Tucker (2013) , in an award-winning Journal of Marketing Research paper, reported a comparison of advertising response between customers exposed to standard banner ads and retargeted banner ads showed the ads displaying products previously viewed were six times more effective at generating sales. However, the consumers receiving retargeted ads had already demonstrated product predilection. The researchers therefore randomly assigned consumers to a treatment group seeing retargeted, product-specific ads and a control seeing generic product category ads. They found the retargeted ads were less effective than the generic ads, as the customers were in different stages of the purchase funnel, and while retargeted ads work well near purchase, they are not effective for the larger group of customers embarking on their search.

The use of field experiments to determine ad effectiveness has subsequently blossomed, with studies using “ghost ads” on Google ( Johnson, Lewis, and Nubbemeyer 2017 ) and Facebook ( Gordon et al 2019) to create randomized control groups. For example, Sahni (2016) used a field experiment to show digital ads for one restaurant increased sales at competing restaurants offering similar cuisine.

In every case, these field experiments have shown that advertising effects are often difficult to detect. For example, the study of Facebook ads by Gordon and colleagues (2019) examined 15 campaigns and found that only eight produced a statistically significant lift in sales.

Econometric Models

The studies by Johnson, Lewis, and Nubbemeyer and Gordon and colleagues also highlight the challenges of designing an experiment to assess digital ad effectiveness. Individual customers use the internet in different ways, and providers deliver digital ads via unique online auction processes. Econometric models therefore provide a versatile approach to gauging advertising effectiveness. And while field experiment studies have been limited to examining one medium at a time, econometric models allow researchers to compare effectiveness across several media.

Researchers can use econometric models to examine time series data, such as weekly or monthly advertising and sales records. Dinner, van Heerde, and Neslin (2014) studied traditional and digital advertising’s effects on in-store and online sales for an upscale clothing retailer across 103 weeks. The retailer made about 85% of its sales in-store, and the researchers examined three media: traditional (i.e., total spend on newspapers, magazines, radio, television, and billboards), online banner advertising, and paid search. They found online display and paid search were more effective than traditional advertising. Although firms might expect digital advertising to influence only online sales, the researchers found it also influenced in-store sales.

Researchers can also use econometric models to examine single-source data linking individual-level ad exposure to sales, the strategy employed by Danaher and Dagger in 2013. They examined 10 media types employed by a large retailer: television, radio, newspaper, magazines, online display ads, paid search, social media, catalogs, direct mail, and email. The researchers found traditional media and paid search effectively generated sales, while online display and social media advertising did not.

Multimedia, Multichannel, and Multibrand Advertising

Danaher and colleagues (2020) also used single-source data but extended it to multiple retailer-brands, two purchase channels, and three media (email, catalogs, and paid search). They collected the data from a North American specialty retailer selling mostly apparel, where 80% of sales were in-store. The parent retailer owned three relatively distinct brands operating independently. They collected customer data in a combined database, giving them information on sales for each retailer-brand over a two-year period.

The researchers found emails and catalogs from one retailer-brand negatively influenced competing retailer-brands in the category. Paid search influenced only the focal retailer-brand. However, competitor catalogs often positively influenced focal retailer-brand sales among omni-channel customers. The researchers also segmented customers by retailer-brand and channel usage, revealing customers shopping across multiple retailer-brands and both purchase channels were the most responsive group to multimedia advertising.

In the contemporary business environment of ever-increasing media channels but static advertising budgets, firms must be able to measure advertising effectiveness. Many businesses have shifted their advertising expenditure toward digital media, but multiple studies show traditional media remain effective.

How do marketing managers decide what is best for their companies? Digital media firms like Google and Facebook offer in-house field experiment methods of examining advertising effectiveness. For multimedia studies, analysts can apply econometric models in any setting where time series or single-source data are available.

Peter Danaher is Professor of Marketing and Econometrics and Department Chair at Monash Business School in Melbourne, Australia. He was recently appointed a co-editor of the Journal of Marketing Research .

Danaher, Peter J. (2021), “Advertising Effectiveness,” Impact at JMR , (January), Available at: https://www.ama.org/2021/01/26/advertising-effectiveness/

Danaher, Peter J., and Tracey S. Dagger (2013), “Comparing the Relative Effectiveness of Advertising Channels: A Case Study of a Multimedia Blitz Campaign,” Journal of Marketing Research , 50(4): 517-534. https://doi.org/10.1509/jmr.12.0241

Danaher, Peter J., Tracey S. Danaher, Michael S. Smith, and Ruben Laoizo-Maya (2020), “Advertising Effectiveness for Multiple Retailer-Brands in a Multimedia and Multichannel Environment,” Journal of Marketing Research , 57(3): 445-467. https://doi.org/10.1177/0022243720910104

Dinner, Isaac, Harald J. van Heerde, and Scott A. Neslin (2014), “Driving Online and Offline Sales: The Cross-channel Effects of Traditional, Online Display, and Paid Search Advertising,” Journal of Marketing Research , 51(5): 527-545. https://doi.org/10.1509/jmr.11.0466

Gordon, Brett R., Florian Zettelmeyer, Neha Bhargava, and Dan Chapsky (2019), “A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook,” Marketing Science , 38(2): 193-225. https://doi.org/10.1287/mksc.2018.1135

Johnson, Garrett A., Randall A. Lewis, and Elmar I. Nubbemeyer (2017), “Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness,”  Journal of Marketing Research , 54(6): 867-84. https://doi.org/10.1509/jmr.15.0297

Lambrecht, Anja, and Catherine Tucker (2013), “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research , 50 (October): 561-576. https://doi.org/10.1509/jmr.11.0503

Sahni, Navdeep S. (2016), “Advertising Spillovers: Evidence from Online Field Experiments and Implications for Returns on Advertising,” Journal of Marketing Research , 53(4): 459-78. https://doi.org/10.1509/jmr.14.0274

Sethuraman, Raj, Gerard J Tellis, and Richard A. Briesch (2011), “How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities,” Journal of Marketing Research , 48 (June): 457-471. https://doi.org/10.1509/jmkr.48.3.457

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Published December 14 th 2016

How to Conduct Advertising Research

Advertising research will help you understand your customers and prospects so you can design the perfect campaign and then measure its success.

Strawberries and cream. Rock and roll. Research and analysis. Some things just work well together.

Advertising research brings together two strategies together to help improve your marketing from two different approaches. It takes a 360-degree view to maximize the lessons you can take from each marketing campaign.

The first is about laying the foundations for good marketing: understanding your audience. The second is a retrospective look at how the campaign performed, allowing you to retain elements that worked and remove ones that didn’t.

What is advertising research?

Advertising research is a specialized form of market research which aims to discover which ads will be most effective with the existing and potential customer base. It does this both through detailed research before a campaign and by analyzing the success of the campaign.

The aim of advertising research is to understand your customers and their motivations better so that you can produce better ads that demonstrate why your product meets their needs. Once you have an understanding of the people you are targeting, an analysis of the campaign will tell you how successful the campaign was, and help you to iterate your campaigns to continually improve results.

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Conducting social media research: how to find real consumer insights.

Social is a rich source of data but only if you know how to conduct good social media research. This post explains the methods for finding consumer insights

Conducting pre-campaign advertising research

The advertising research carried out before the campaign is about understanding your audience. There will be different groups of prospects and customers. The research should uncover the different market segments so you can target your campaign at specific groups.

Online surveys can be easily set up with sites like Survey Monkey, and are one of the best ways to understand your customers. This allows you to ask specific questions, although you need to take response bias into account and carefully consider the questions you ask. It might be fun to find out their favorite Madonna songs, but too many questions may mean a smaller response rate.

Google Analytics

Analytics can tell you more than the amount of traffic you are receiving. The Audience tab shows geography, interests, and a range of demographics.

Keyword research

Conducting online keyword research is the foundation of SEO, but it also tells you what consumers are interested in and the relative level of interest. It also helps to reveal the language being used to talk about these topics.

Customer reviews

Reading customer reviews can highlight common problems or wishes for a product and common frustrations.

Q&A sites

Sites like Quora contain questions and crowdsourced answers on a wide range of topics, including discussions about brands. These sites can give you an idea of the questions and concerns that people have in relation to your service or product.

Competitor analysis

Looking at your competitors’ websites and social media accounts can provide useful information about consumers that are shopping in your vertical but have chosen not to buy from your brand. Don’t hate on them; find a way to make them come around to your side.

Blog comments

Does your blog have comments enabled? If so, reading through any comments is a good way of discovering questions your audience might have.

Google Trends and Consumer Barometer

Google Trends can help you to understand if a topic is becoming more or less popular. The Consumer Barometer  allows you to build interactive charts with various filters applied, although the questions are limited to consumer online behavior.

Syndicated data

Government data is available which is free and can help you understand a group, and  several other sources  can also be accessed for free.

Twitter Insiders

Twitter Insiders is a 12,000 strong focus group of US and UK Twitter users who can be asked to perform a range of activities over a four to six week period. It’s an interesting concept of a focus group at scale.

Social media

Comments on your social media profile or posts can be a good source of information. Likes, shares, and other social media metrics can be useful to understand how popular your campaign is.

Social intelligence

To really make the most of social media, and turn millions of organic conversations into a giant focus group, you need a good social listening platform. A tool like Brandwatch will allow you to gain an in-depth research into your audience and the segments within it, as this guide to social media research demonstrates .

The simple audience research starts by searching for mentions of your brand and products. You can then look at the inbuilt details about these people. All mentions are marked up with gender, profession, location and interests when they are crawled, so there is no extra work for you to do.

With a little manual work, more detailed insights can be surfaced. One method is to build a panel of users who have mentioned your brand or industry (perhaps more than once in a set period, so the group more accurately represents repeat customers rather than one-offs).

Depending on the size of your search results, you can take a sample or use the whole data set, and read through each mention. Brandwatch allows you to assign unlimited tags to each mention, meaning you can tag emotional responses, mentions that include you and a competitor, author types (say for example if the buyer and user are different for your product).

Once you have tagged the mentions you can start to analyze further and cross the different categories and tags to unearth more detail about your audience.

Conducting post-campaign advertising research

Campaign analysis is a simple task for a social intelligence platform and by combining it with other data you can build up an accurate picture of the response to your campaign.

If you have set up UTM codes your web analytics will be able to tell you how traffic came to your site, and if it was as a result of your campaign.

Email automation software will tell you open rates and click-thru rates. You can benchmark this against previous efforts or look at an industry study.

Social intelligence can help to understand some solid campaign metrics and provide a deeper understanding of the effect the campaign had. You can take some of your advertising research from the earlier stage and look at what has changed in response to the campaign. You can also use it to write a social media report , describing the response to the campaign on social.

Volume of mentions

A simple metric that will give you an indication of whether your campaign has increased brand awareness and conversation around the brand.

Our analysis shows that up to 96% of brand conversations happened outside owned channels, or with mentions that don’t tag the brand, meaning a social intelligence is the only way to pick up all the conversation around your campaign.

Share of voice

While you might see an increase in your number of mentions across the web, you want to benchmark this against your competitors to see how you have increased your share of voice in comparison to them. You can also break share of voice down further, to see who is winning a particular segment.

Reach is the potential number of people that those mentions will be seen by. It takes into account the number of followers of each author who mentions you. If your campaign included a celebrity or influencer, they are likely to generate much higher reach.

How many people actually took an action when seeing your campaign? This can give you an indication of the number of people that actively engaged with the campaign. These people would be more likely to recall it even if they didn’t go on to click through to your site.

News coverage

Part of your campaign analysis will be to understand how many media mentions you earned and categorize them into different tiered publications.

Social intelligence will make sure you don’t miss any mentions, and also make it easy to categorize the publications. Mentions are automatically categorized by site type. This allows you to read through the news mentions and report on the top publications that have covered your campaign.

Purchase intent

You can create complex Boolean queries in a social intelligence tool like Brandwatch, meaning you can measure if purchase intent language has increased .

Monitoring for increases in this type of language can again give you an indication of the number of people who have seen the campaign and intend to take an action but have not done so yet.

Sentiment and emotional response

You can easily monitor for positive or negative responses to your campaign as a good social intelligence tool will have sentiment analysis built in.

This can give you an overview of public perception, and you can categorize mentions to understand how sentiment changes in relation to the brand, products, or campaign itself.

As mentioned earlier, mentions can be manually tagged to understand the emotional response to the campaign.

Brand associations

You can discover the qualities people associate with your brand or product by creating rules that segment mentions of your brand that feature adjectives to discover brand associations . Monitoring these over time can reveal changing attitudes and associations that your campaign has influenced.

Conducting research before your campaign and measuring the impact of your advertising is the best method for ensuring success. Understanding who you are marketing to will help design a campaign that is likely to connect with those people and their needs. Measuring and refining your marketing is the fine tuning that will make your efforts really shine.

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Open Access

Peer-reviewed

Research Article

The impact of interactive advertising on consumer engagement, recall, and understanding: A scoping systematic review for informing regulatory science

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Affiliation RTI International, Research Triangle Park, Durham, NC, United States of America

Roles Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Roles Data curation, Writing – review & editing

Roles Conceptualization, Funding acquisition, Writing – review & editing

Affiliation Office of Prescription Drug Promotion, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, United States of America

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Kristen Giombi, 
• Catherine Viator, 
• Juliana Hoover, 
• Janice Tzeng, 
• Helen W. Sullivan, 
• Amie C. O’Donoghue, 
• Brian G. Southwell, 
• Leila C. Kahwati

• Published: February 3, 2022
• https://doi.org/10.1371/journal.pone.0263339
• Reader Comments

We conducted a scoping systematic review with respect to how consumer engagement with interactive advertising is evaluated and if interactive features influence consumer recall, awareness, or comprehension of product claims and risk disclosures for informing regulatory science. MEDLINE, PsycINFO, Business Source Corporate, and SCOPUS were searched for original research published from 1997 through February 2021. Two reviewers independently screened titles/abstracts and full-text articles for inclusion. Outcomes were abstracted into a structured abstraction form. We included 32 studies overall. The types of interactive ads evaluated included website banner and pop up ads, search engine ads, interactive TV ads, advergames, product websites, digital magazine ads, and ads on social network sites. Twenty-three studies reported objective measures of engagement using observational analyses or laboratory-based experiments. In nine studies evaluating the association between different interactivity features and outcomes, the evidence was mixed on whether more interactivity improves or worsens recall and comprehension. Studies vary with respect to populations, designs, ads evaluated, and outcomes assessed.

Citation: Giombi K, Viator C, Hoover J, Tzeng J, Sullivan HW, O’Donoghue AC, et al. (2022) The impact of interactive advertising on consumer engagement, recall, and understanding: A scoping systematic review for informing regulatory science. PLoS ONE 17(2): e0263339. https://doi.org/10.1371/journal.pone.0263339

Editor: Qihong Liu, University of Oklahama Norman Campus: The University of Oklahoma, UNITED STATES

Received: September 15, 2021; Accepted: January 15, 2022; Published: February 3, 2022

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Funded through a contract from the U.S. Food and Drug Administration to RTI International (Contract 75F40120A00017, Order Number 75F40120F19003). KG, CV, JH, JT, BS, LK are employees of RTI International. HS and AO are employees of the U.S. Food and Drug Administration. HS and AO (employees of the sponsor) participated in the study design, decision to publish, and critically reviewed the manuscript prior to submission.

Competing interests: HS and AO are employees of the study sponsor. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

1. Introduction

In 2020, it is estimated that nearly $356 billion was spent on digital advertising in the United States [ 1 ]. Much of this advertising consists of display ads, social media ads, search engine marketing, and email marketing often with interactive components to target the 85% of US adults who go online daily [ 2 ]. An interactive ad encourages consumers to interact with the ad (and thus the brand), rather than just passively view the ad. Although interactivity is often considered a vital element of successful online advertising [ 3 , 4 ], its impact on consumer engagement and decision-making is not entirely clear.

The academic definition of interactive advertising has evolved and varied at least in part as possibilities for ad design and placement have shifted, meaning interactive advertising can be defined differently depending on the context. Experts have defined interactive ads in terms of processes, features, and/or user perceptions, and no consensus about the definition has been reached to date [ 5 – 14 ]. Conceptual frameworks considered by researchers in approaching interactive advertising have tended to include descriptions of how users behave in response to ads [ 13 , 15 – 17 ]. Metrics employed by the advertising industry also have shifted over time. The operationalization of interactive advertising often has been determined by the conceptual framework used and the outcome of interest to the researcher.

With an increased presence of interactive advertising in digital and social media [ 18 ], it is critical to understand how consumers engage with these types of advertisements and whether interactive features influence consumer recall, awareness, or comprehension of product claims and risk disclosures. This is of particular importance for products or services for which advertising content is regulated, such as prescription drugs, alcohol, tobacco, and financial products or services, to ensure that such advertising does not introduce barriers or challenges to consumer understanding of risks associated with such products. Especially within the past decade, regulatory science researchers have embraced the tools of social science to assess consumer perceptions of risk as well as potential impediments to consumer understanding [ 19 , 20 ]. Social science research can offer evidence of advertising effects on consumer perceptions, and agencies such as the U.S. Food and Drug Administration have used such approaches to assess consumer engagement with different types of advertisements, such as direct-to-consumer prescription drug television ads [ 21 ]. In order to assess whether interactive advertising poses new theoretical challenges or opportunities, we conducted a scoping systematic review to summarize the research related to consumer engagement with interactive advertisements and impact on recall and understanding of product claims and risk disclosures.

The protocol for this scoping review was registered at Open Science Framework on October 26, 2020 [ 22 ]. The goal of this scoping systematic review was to describe the extant literature on interactive advertising and consumer engagement, particularly as it concerned regulated product advertising and its influence on comprehension of product claims and risk disclosures. We designed the four research questions (RQs) that guided this scoping review to identify gaps in the evidence base and summarize important considerations needed to inform the design and conduct of future primary research studies in this area. The four RQs were:

• RQ 1: What methods and measures are used to evaluate consumer engagement with interactive advertisements in empirical studies?
• RQ 2: In empirical studies of interactive advertising in naturalistic or real-world contexts, to what extent do consumers engage with interactive advertisements?
• RQ 3: What is the association between features of interactive advertisements for goods or services and consumer engagement, recall, awareness, or comprehension of product claims and risk disclosures?
• RQ 4: How do interactive advertisements for goods and services compare to non-interactive advertisements (e.g., traditional print or broadcast advertisements) with respect to consumer engagement, recall, awareness, and comprehension of product claims and risk disclosures?

2.1 Search and data sources

We searched MEDLINE via PubMed, PsycINFO, Business Source Corporate, and SCOPUS for original research published in English from January 1, 1997, through February 17, 2021, using search terms related to advertising and marketing, internet, and the outcomes of interest (e.g., engagement, knowledge, click-through rate). Little research on digital advertising was conducted prior to the mid-1990s, and our preliminary evidence scan showed very few papers published prior to 1997. The detailed search strategy is in S1 Appendix . We also searched reference lists of systematic and narrative reviews and editorials where relevant.

2.2 Study selection

Two reviewers independently screened titles/abstracts and full-text articles for inclusion based on study selection criteria for each research question. Disagreements at the full-text review stage were resolved by a third reviewer. Detailed study selection criteria are described in S2 Appendix . In brief, we included all studies among persons of any age in the general public who were characterized as being a potential consumer target for interactive advertising. For all RQs, we included studies that examined exposure to interactive advertisements, which we defined as the promotion of a product, service, or idea using various features or tools that provide the opportunity for persons to interact directly with the ad and potentially influence/inform the remaining sequence, appearance, or content to be presented about the product, service, or idea. For RQ 2, we included only studies with exposure to interactive advertising in naturalistic or real-world contexts. For RQ 3, studies that compared alternative versions of advertisements with interactive elements that varied with respect to the type or level of interactivity were selected. For RQ 4, studies that compared interactive advertisements with traditional advertising (i.e., print ads, broadcast ads, or online/internet ads without interactive elements) were included.

Eligible outcomes varied by RQ. For RQ 1, we included studies with any measure of consumer engagement. For RQ 2, we required objective measures of engagement such as time spent viewing, content navigation, click-through rates, page views, shares, likes, or leaving comments. For RQs 3 and 4, we required studies to report outcomes including consumer recall, awareness, and comprehension of product claims, risk disclosures, or both. Lastly, we included only studies conducted in countries designated as very highly developed per the United Nations Human Development Index to maximize applicability to decision-makers in such settings [ 23 ].

2.3 Data abstraction and synthesis

For each article included, one reviewer abstracted relevant study characteristics and outcomes into a structured abstraction form, and a second senior reviewer checked the form for completeness and accuracy. We narratively synthesized findings for each RQ by summarizing the characteristics and results of the included studies in narrative and tabular formats. Because this was designed as a scoping review, we did not conduct risk of bias assessments on included studies, quantitatively synthesize findings, or conduct strength of evidence assessments.

We screened 3,765 titles and abstracts and 136 full-text articles. We included 32 studies published in 33 articles ( Fig 1 ) [ 7 , 24 – 55 ]. Twenty-three studies addressed RQ 1, eight studies addressed RQ 2, nine studies addressed RQ 3, and four studies addressed RQ 4. An overview of included studies is provided in Table S4-1 in S4 Appendix . A list of full-text studies that we reviewed and excluded is provided in the S3 Appendix .

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https://doi.org/10.1371/journal.pone.0263339.g001

3.1 Research question 1: What methods and measures are used to evaluate consumer engagement with interactive advertisements in empirical studies?

3.1.1 study characteristics..

We identified 23 studies eligible for RQ 1 that were published between the years 1997 and 2019 and conducted across multiple countries [ 24 – 30 , 33 , 35 – 42 , 46 , 47 , 50 – 53 ]. An overview of the studies is in Table S4-1 and S4-2 in S4 Appendix . Six were observational studies evaluating consumer response to real-world advertisements or campaigns [ 24 , 25 , 28 – 30 , 37 ]. The rest of the studies were experiments conducted in laboratory or controlled environments. The sample sizes across the included studies ranged from 20 to 116,168 participants; however, two studies [ 29 , 30 ] did not report the number of persons participating in the study.

The types of interactive advertisements evaluated varied across the included studies. Six studies [ 26 , 33 , 35 , 40 , 47 , 50 ] evaluated banner ads, three studies [ 7 , 36 , 46 ] evaluated product websites, three studies [ 29 , 30 , 41 ] evaluated paid search engine ads, three studies [ 38 , 51 , 52 ] evaluated interactive television ads, two studies [ 24 , 27 ] evaluated social network site ads, one study [ 39 ] evaluated a pop-up ad and the rest of the studies evaluated other types of digital ads. This included short-message-service TV marketing [ 37 ], an ad with a video clip embedded in a digital magazine [ 42 ], ads within a simulated online store [ 53 ], and combinations of different types of digital and online ads [ 25 , 30 ]. The type of products advertised across the included studies included unregulated consumer products (e.g., digital cameras) and services (e.g., travel planning); regulated products and services (car insurance, financial); and health/health behavior campaigns.

3.1.2 Findings.

An overview of findings is in Fig 2 . Authors of the six observational studies reported engagement outcomes associated with real-world advertising or marketing campaigns [ 24 , 25 , 28 – 30 , 37 ]. Authors of four studies reported objective measures of the proportion of users exposed to an ad that clicked on the ad (i.e., “click-through rates”) by using platform-specific (e.g., Facebook, Google AdWords) analytic tools for advertisers [ 24 , 29 ], specialized web tracking software that members of a market research panel consented to have installed on their computers to monitor web behavior [ 28 ], or a unique event identifier created on the advertiser’s server whenever an online ad was clicked [ 30 ]. Authors of the other two observational studies reported subjective measures of engagement. In one study, authors used audio, computer-assisted self-interviews that asked respondents about their engagement with online marketing of a specific class of product [ 25 ]. In the other study, authors used post-campaign surveys (mode not specified) to evaluate engagement outcomes [ 37 ].

https://doi.org/10.1371/journal.pone.0263339.g002

Authors of the 17 experimental studies reported engagement outcomes from experiments using actual real-world ads or from experiments using fictitious ads designed specifically for the experiment. Authors of the experimental studies controlled participant exposure to the ads, and depending on the measure, outcome measurement occurred either concurrently with the ad exposure or through completion of post-exposure surveys or interviews.

Two of the experimental studies used objective measures of ad engagement employing eye-tracking technologies during exposure to evaluate user engagement with digital ads placed on online platforms (Facebook page, blog, and industry-specific search engine) [ 26 , 50 ]. In Muñoz-Leiva, Hernández-Méndez, and Gómez-Carmona [ 26 ] the ads used were fictitious, and the sites they were placed on were mocked up to resemble existing platforms (e.g., Facebook). In Barreto [ 50 ], each participant’s own Facebook page and the authentic Facebook page for a specific brand of athletic shoe was used. In both studies, authors first calibrated the eye-tracking equipment for each participant, then assigned one or more tasks for the participants to complete (e.g., navigate to find a specific item). The eye-tracking technology measured fixation counts and duration of fixation on the ad portion of the screens as participants navigated through the task.

Seven of the experimental studies were designed using a within- or between-subjects randomized factorial design or both [ 27 , 33 , 36 , 38 , 41 , 51 , 52 ]. In these studies, authors manipulated two or more ad features, including message/information content, tone, amount, or presentation order; images; screen placement; and level of interactivity. Eight of the experimental studies were parallel-group randomized experiments with one group assigned to a manipulated ad exposure in one or more ways and the other group assigned to a control ad exposure [ 7 , 35 , 39 , 40 , 42 , 46 , 47 , 53 ]. In both types of experimental studies, measures of ad engagement varied and included both subjective (e.g., user intentions as to whether they would click the ad or like or share the ad post) and objective measures (e.g., actual click-through rates on ads encountered, view duration tracked by computer). Nearly all studies also measured additional outcomes such as attitudes toward ads, ad or brand recall, or purchase intentions through post-exposure surveys.

3.2 Research question 2: In empirical studies of interactive advertising in naturalistic or real-world contexts, to what extent do consumers engage with interactive advertisements?

3.2.1 study characteristics..

Eight studies addressed RQ 2; these were published between 2006 and 2019 (Table S4-3 in S4 Appendix ) [ 24 , 28 – 31 , 39 , 47 , 54 ]. Six were observational studies [ 24 , 28 – 31 , 54 ], and two studies were experimental but conducted in real-world (i.e., not laboratory) settings [ 39 , 47 ]. The sample sizes across the included studies ranged from 30,638 to 2,000,000 participants. The types of interactive advertisements evaluated varied and could include more than one type of ad. Three studies evaluated banner ads [ 28 , 30 , 47 ], two studies evaluated social network site ads [ 24 , 31 ], and one study evaluated a pop-up ad [ 39 ]. Three studies evaluated other types of digital ads including paid search engine ads and video ads [ 28 , 29 , 54 ]. The type of products advertised across the included studies included unregulated consumer products and services and health/health behavior campaigns.

Authors measured consumer engagement with click-through rates; page views; and/or number of “likes,” comments, or shares on social media. The two experimental studies analyzed click-through rates for banner and pop-up ads [ 39 , 47 ], while the six observational studies analyzed click-through rates for banner ads [ 28 , 30 ], search ads [ 28 – 30 , 54 ], and social media interaction [ 24 , 31 ].

3.2.2 Findings.

An overview of findings is in Fig 3 . The level of engagement by consumers varied across studies. Six studies reported click-through rates ranging from 0.02% to 2.30% [ 24 , 29 , 31 , 39 , 47 , 54 ]. Two of these studies also reported differences in click-through rates when selected characteristics of the ad were varied, such as differences on which page the ad was placed, a variable delay before the ad was displayed [ 39 ], or whether the ads were static or morphing and whether they were context matched to the website on which they were placed [ 47 ]. In contrast to other studies reporting click-through rates, Graham et al. [ 30 ] reported a much higher click-through rate (81.6%); this study used ads to recruit individuals to a website to register for smoking cessation treatment.

https://doi.org/10.1371/journal.pone.0263339.g003

Other measures of consumer engagement beyond click-through rates included number of page views (after clicking through an ad) and interactions such as liking, sharing, or posting comments to ads on social networking platforms. Two studies measured page views, which is the number of pages the viewer visited after going to the landing site [ 29 , 39 ]. In Birnbaum et al. [ 29 ] the median number of pages visited on the website (not including other relevant websites that were linked on the study site) was 1.29. Moe [ 39 ] measured the difference in number of page views when users were exposed to the ad on a gateway page of an informational website compared with exposure to the ad from a content page of the website. The mean number of page views after an ad on a content page (6.31) was higher than page views after an ad on a gateway page (4.86, P < .001), suggesting greater engagement from consumers when involved in the content.

Two studies measured interactive engagement with social media ads through “likes” and shares [ 24 , 31 ]. Horrell et al. [ 24 ] defined levels of consumer engagement as “low” if a consumer liked a page or reacted to a post and “medium” if a consumer shared or commented on a post. Over a 5-week advertising campaign targeted to 91,385 users of a specific Facebook page site targeting lung cancer awareness, the page had 2,602 reactions to posts, 149 page likes, 452 shares, and 157 comments [ 24 ]. Similarly, Platt et al. [ 31 ] reported findings from a 1-month time period in which a Michigan biobank advertising campaign was targeted to an estimated 2 million state residents aged 18 to 28. The campaign’s social media presence garnered 516 page likes, 477 ad likes, 25 page post shares, and 30 entries into an advertised photo contest. This study also reported that a greater percentage of viewers clicked an ad or post they saw when it was associated with the name of a friend who had already liked the Facebook page [ 31 ].

3.3 Research question 3: What is the association between features of interactive advertisements for goods or services and consumer engagement, recall, awareness, or comprehension of product claims and risk disclosures?

3.3.1 study characteristics..

We identified nine studies eligible for RQ 3 that were published between the years 1997 and 2019 (Tables S4-4 and S4-5 in S4 Appendix ) [ 26 , 32 , 34 , 36 , 43 – 45 , 51 , 53 ]. Eight studies were conducted as experiments [ 26 , 32 , 34 , 36 , 43 , 44 , 51 , 53 ], and the remaining study was a meta-analysis [ 45 ]. The sample sizes across the included primary research studies ranged from 60 to 1,600 participants. The type of advertisements evaluated varied. Four studies [ 32 , 34 , 36 , 44 ] evaluated product websites, one study [ 26 ] evaluated banner ads, one study [ 43 ] evaluated both banner ads and advergames, and two studies [ 51 , 53 ] evaluated other types of digital ads (e.g., interactive TV ads and interactive ads in a simulated online store). The included studies manipulated the ad stimuli to vary the level of interactivity or the type of interactive features included in the ad. Interactive features used in these studies included clickable hyperlinks, navigation bars, navigation buttons, rollover and clickable animation, responsive chat features, comment forms, and interactive game elements. The type of products advertised across the included studies included unregulated consumer products and services as well as regulated products or services.

The meta-analysis reported on 63 experimental studies (total N = 13,484) that evaluated how web interactivity affects various psychological outcomes and how those effects are moderated [ 45 ]. Of the included studies, half focused on interactivity within an advertising context, and 25% reported cognition outcomes, the only outcomes of relevance to this review.

3.3.2 Findings.

An overview of findings is in Fig 4 . In the meta-analysis, Yang and Shen [ 45 ] defined cognition measures such as comprehension, elaboration, knowledge acquisition, and recall. The authors reported no significant association between interactivity and cognition (correlation coefficient 0.05, P = .25). Across the eight primary research studies for this RQ, outcomes varied widely by level or type of interactivity. Five of the studies measured consumer recall of the brand, product, or service advertised [ 32 , 34 , 36 , 43 , 44 ]. Four of these involved websites or web pages with varying levels of interactivity [ 32 , 34 , 36 , 44 ].

https://doi.org/10.1371/journal.pone.0263339.g004

In Chung and Zhao [ 36 ], undergraduate university students viewed websites advertising cameras, which were classified as either low, medium, or high interactivity based on the number of hyperlinks included. They found a significant association between a higher number of clicks available and higher memory scores [ 36 ].

In Chung and Ahn [ 32 ], authors asked participants to view either a website with a linear structure (scroll to bottom of page and click link to move to next page), an interactive structure (multiple links available on the page), or a mixed linear and interactive structure and asked them to write down all the product information they could recall after exposure [ 32 ]. The authors found that participants who viewed the linear web page exhibited the highest memory score [ 32 ].

In Macias [ 44 ], participants viewed either a low or high interactivity website that advertised one of two consumer products. The high interactivity websites included rollover animation, hyperlinks, comment forms, and chat features. The authors found that participants who viewed the high interactivity website exhibited greater comprehension [ 44 ].

Polster et al. [ 34 ] reported the results of a study comparing interactive and noninteractive versions of a website with important safety information (ISI) about a fictitious medication viewed either on a desktop computer or smartphone. Authors found that a higher percentage of participants allocated to noninteractive websites saw any ISI as measured through objective clicking and scrolling behavior compared with participants who were allocated to the interactive websites ( P < .001). Further, a higher proportion of desktop-using participants allocated to noninteractive websites recalled at least one relevant side effect compared with participants allocated to the interactive websites ( P < .001) [ 34 ]. A higher proportion of participants using a smartphone allocated to noninteractive websites also had higher recall of at least one relevant side effect compared with participants who were allocated to interactive sites, but this finding was only statistically significant for one of the two noninteractive layouts [ 34 ]. Authors also reported the mean percentage correct recognition of medication side effects and conducted additional analyses of recognition limited to those participants who saw any ISI (Table S4-4 in S4 Appendix ).

Finally, in Daems et al. [ 43 ], Belgian secondary students viewed ads for a fictitious smartphone that were either interactive advergames, static in-game ads, interactive banner ads, or noninteractive banner ads. Authors found that interactive banner ads led to the highest percentage of participants exhibiting brand recognition (60.4%), followed by static in-game ads (22.4%), noninteractive banner ads (21.3%), and finally advergames (14.3%) [ 43 ]. They also found that interactive banner ads led to the highest memory of product characteristics (8.22 out of a 12-point scale), while noninteractive banner ads led to the lowest memory (3.87) [ 43 ].

Three studies measured time spent viewing ads and results were mixed [ 26 , 51 , 53 ]. In Cauberghe and De Pelsmacker [ 51 ], participants from a Belgian market research firm watched a Dutch travel agency interactive TV ad with low, medium, or high interactivity. The interactivity level varied based on the presence of clickable links, navigation bars, and two-way communication. The authors reported significantly more time spent viewing the high interactivity ad (6.1 minutes) than the low interactivity ad (4.4 minutes) [ 51 ]. In Yang [ 53 ], each participant viewed one interactive ad and one noninteractive ad for one of two consumer products. The high interactivity ads offered more user control over order of information, duration of each page, and ability to skip information. Authors found that interactive ads were viewed for less time than noninteractive ads ( P < .01) [ 53 ]. In Muñoz-Leiva et al. [ 26 ], the authors compared “Travel 2.0 websites” with embedded vertical banner ads on 3 different platforms: a Facebook page, a blog, and a Tripadvisor page that varied by level of interactivity [ 26 ]. While the banner ads on all three platforms included a call to action and a clickable link to an airline website, the Facebook ad was the most interactive with the ability to like, comment, and share the ad followed by the blog with the ability to comment on the blog post and finally the Tripadvisor page. The authors used eye-tracking technology to measure the number of visual fixations on the ad, number of seconds until the first fixation on the ad, and total duration of fixations on the ad. They found a significant difference in the number of ad fixations (Facebook, 19.1; blog, 11.7; Tripadvisor (6.1), P < .001). Significant differences were also observed across platforms for other measures (Table S4-5 in S4 Appendix ) [ 26 ].

3.4 Research question 4: How do interactive advertisements for goods and services compare with noninteractive advertisements (e.g., traditional print or broadcast advertisements) with respect to consumer engagement, recall, awareness, and comprehension of product claims and risk disclosures?

3.4.1 study characteristics..

We identified three studies eligible for RQ 4 that were published between the years 2008 and 2018 (Table S4-6 in S4 Appendix ) [ 30 , 48 , 49 ]. One was an observational study [ 30 ], and the other two studies were conducted as experiments. The sample sizes for the two experiments were 233 [ 49 ] and 9,902 [ 48 ] participants; the observational study [ 30 ] did not report the number of persons evaluated. The types of interactive advertisements varied. The observational study [ 30 ] compared banner ads and paid search ads (interactive advertising) with billboards, TV ads, radio ads, outdoor signage, direct mail, and physician detailing (noninteractive advertising). One experimental study [ 48 ] had print flyer, online flyer, and no flyer groups, while the other experimental study [ 49 ] compared a standard TV commercial, a PC advergame, and an interactive TV commercial offering an advergame.

Eligible outcomes for this review reported across the three included studies also varied. The observational study [ 30 ] evaluated outcomes associated with real-world advertising including the number of log-ins and pages viewed, session length, and long-term cookies. Authors of the two experimental studies [ 48 , 49 ] randomized participants to different ad types and evaluated recall in addition to other outcomes such as attitudes, which were not within the scope of this review.

3.4.2 Findings.

An overview of findings is in Fig 5 . Across the three included studies, outcomes varied widely. Graham et al. [ 30 ] examined how online advertising increases consumer demand for smoking cessation treatments in Minnesota and New Jersey (N = NR) by comparing the impact of interactive advertisements (banner ads, paid search ads) versus traditional advertisements (billboards, TV ads, radio ads, outdoor signage, direct mail, physician detailing). Outcomes related to engagement are reported in the RQ 2 section of this review. Ultimately, 9.1% of those who clicked the interactive ad registered for treatment compared with 18.6% of those who were directed to the website from traditional media [ 30 ]. The authors found that compared with traditional ads, online ads engaged a higher percentage of males, young adults, racial/ethnic minorities, individuals with a high school education or less, and dependent smokers. While the authors found significant differences in website engagement metrics (e.g., average session length, pages viewed, percentage posting in public forums) between online and traditional ad responders, they noted that the differences in utilization are too small in magnitude to be meaningful.

https://doi.org/10.1371/journal.pone.0263339.g005

Ieva et al. [ 48 ] estimated the effect of an online versus print promotional advertising flyer on customer response with an experimental design recruiting from a random sample of customers from a supermarket chain (N = 9,902; however, only the 303 that reported viewing the flyer were included in the analysis). The online flyer was a replication of the print flyer with no banners, videos, or embedded links; however, users could click to zoom or move to another page. The authors found no statistically significant differences in recall, recognition, or advertisement memory measures between the online and print flyers.

Bellman et al. [ 49 ] compared the effectiveness of PC advergames, TV commercials, and interactive commercials enhanced with advergames on recall for four test brands of food or personal hygiene products in an experimental study. Members of an Australian audience panel (N = 233) were randomized to one of three ad types. The authors reported significantly higher unaided recall of at least three unique points about the ad content for participants who viewed the PC advergame compared with those who viewed the traditional TV commercial and as compared with the interactive TV ad. Authors observed no significant difference between participants who viewed the interactive TV commercial and the traditional TV commercial.

4. Discussion

4.1 summary of evidence.

Study design and outcomes varied widely within the evidence base for each RQ. That variation itself is noteworthy, as it affects comparability of results and suggests strengths and weaknesses of different approaches for future research in this arena. Through this review, we also can see ways in which existing literature may not yet be optimal for answering questions about consumer risk perception and decision making in response to interactive advertising; much available evidence focuses on indicators of short-term consumer attention in engaging with advertising more than on consumer information processing beyond eyeball movement or click behavior.

Within the 23 studies eligible for RQ 1 (which summarized methods and measures used to evaluate consumer engagement), six were observational studies and 17 were experimental studies. In the experimental studies, methods included within- and between-subjects randomized factorial design and parallel-group randomized experiments. In both types of studies, objective (e.g., click-through rates, eye-tracking metrics) and subjective (e.g., post-campaign surveys, interviews) measures were used to report engagement outcomes. This variability in methods is understandable. Some measures of engagement are most optimally assessed with experimental designs that allow control over content and resource-intensive measurement of respondents (e.g., eye-tracking metrics). Observational studies nonetheless also can offer objective measures of engagement on a larger scale and without the generalizability concerns stemming from volunteer bias inherent to small sample-sized experimental designs. We also did find examples of large-scale experiments [ 39 , 42 ] involving manipulation of advertising stimuli conducted with various kinds of media (digital magazine, websites publishing reviews, news, or information).

Based on this review, consumer engagement is an umbrella concept covering a range of operationalization efforts. The ways in which studies measured engagement reflect 1) varying levels of technologic sophistication of the advertising platform or ad itself, 2) the salience of click-through rates as a metric in commercial advertising (regardless of the theoretical value of that metric to understanding consumer decision making), and 3) varying levels of integration into a broader social media campaign. We did not identify any differences in the way engagement was measured for regulated versus non-regulated products in this scoping review, per se, but the number of studies focused on regulated products or services also was quite limited. Future research on consumer engagement with interactive ads for regulated products should be able to use both observational or experimental designs, depending on the specific outcomes in question.

For RQ 2, eight identified studies reported on the extent to which consumers engage with interactive advertisements in naturalistic or real-world contexts. Consumer engagement was measured with click-through rates; page views; and/or number of “likes,” comments, or shares on social media. Click-through rate was the most common engagement measure used for this RQ; however, the way in which click-through-rates were calculated varied, limiting direct head-to-head comparisons across studies. A click-through rate may offer a conceptually simple way of measuring consumer engagement because it is closely aligned to the evaluation of cost-per-thousand advertisement impressions (i.e., cost-per-mille) and cost-per-click advertising campaigns. In practice, however, variability in click-through rate calculation limits the ability of current literature to offer definitive conclusions related to the concept. Moreover, in the context of evaluating regulated advertising, crude click-through-rates of a single hyperlink in a digital ad may not be enough to provide a nuanced understanding of whether users engage with specific parts of an ad, specifically, claims of benefits, risk disclosures, or both.

For RQ 3, we identified nine studies, eight of which were experiments, that focused on the association between features of interactive ads and consumer engagement, recall, awareness, or comprehension of product claims and risk disclosures. The studies varied the type or level of interactivity in the ad. Some studies found significant associations between higher levels of interactivity and higher memory scores, comprehension, and brand recognition. Other studies found the opposite: better recall and higher memory scores with fewer interactive features. Studies that measured time spent viewing the ads also had mixed results: one study found higher levels of interactivity led to more time spent viewing the ad, whereas one study found the opposite. Further, a meta-analysis reported no correlation between interactivity and measures of cognition.

The evidence for clear relationships between interactive features and outcomes of interest for this scoping review was mixed, precluding any definitive conclusions. Further, some studies addressing this RQ were published during an early era of online advertising that has faded in relevance to present circumstances. Importantly, we also found instances of confounding. In addition to manipulating interactivity, advertisers often manipulated other aspects of the ad not related to interactivity (e.g., tone, text or graphic content). Previous studies have demonstrated that for regulated products, such as prescription drugs, these features moderate consumer understanding of product claims and risk disclosures [ 21 , 56 ]. Thus, future studies evaluating variations in interactive ads of regulated products and services should ensure that study designs and ad manipulations are robust for evaluating independent effects and potential interactions.

For RQ 4, three identified studies compared interactive with noninteractive advertisements with respect to consumer engagement, recall, awareness, and comprehension of product claims and risk disclosures. One observational study found that compared with traditional ads, online ads engaged certain segments of the population better. The two experimental studies found no significant differences between the interactive and traditional ads, but one study found significantly higher unaided recall for participants who viewed a PC advergame compared with those who viewed the traditional or the interactive ads. With the mixed results from this limited number of heterogeneous studies, there is no conclusive evidence on how interactive advertisements compare with noninteractive advertisements with respect to consumer engagement, recall, awareness, and comprehension of product claims and risk disclosures. The limited number of studies may reflect the challenge in conducting direct comparisons of traditional and interactive advertising in the same study. Digital and online advertising offer new and, in some cases, more objective ways of measuring advertising effectiveness that have no counterpart in the evaluation of traditional advertising. Given shifts away from traditional advertising to digital and online marketing because of better returns on investment and ability to target audiences, comparing traditional to interactive ads may not be a relevant comparison for future studies.

4.2 Limitations of evidence

Studies were quite heterogenous with respect to study design, populations evaluated, types of ads used, and measures reported; this limited our ability to conduct a robust synthesis of outcomes. Many studies were conducted among university students; whether findings from such studies would generalize to broader populations is not known. The measures used by some studies to evaluate product or service information recall or knowledge did not appear to be validated. The era over which studies were conducted was broad; some of the interactivity features or platforms used in included studies are likely obsolete or have been replaced by more sophisticated approaches to interactive advertising.

4.3 Limitations of this review

We limited this scoping review to studies published in English from very highly developed countries to increase applicability of findings to policy makers concerned with regulation of interactive advertising in such countries. Study indexing in bibliographic databases was variable and inconsistent; thus, it is possible we missed some relevant studies. Our RQs were focused on outcomes related to consumer engagement with interactive ads, and information recall and comprehension, as it related to product information or risk disclosures. We did not consider consumer attitudes or purchase behavior. We limited measures of engagement to studies conducted in naturalistic or real-world contexts because experimental studies typically manipulated ad exposure or instructed participants what to view and may have put limits on duration of exposure that would not reflect engagement outside of a controlled environment. We did not assess the risk of bias of included studies consistent with a scoping review approach.

4.4 Research gaps

Although the research on interactive advertising is extensive in terms of the volume of available publications, as judged by the size of our initial search yield, the amount of research specifically focused on the influence of interactive advertising on product information recall and specifically risk perception is sparse. Several studies that we screened but excluded as not eligible for this scoping review focused on evaluating tone, content, graphics, placement, or variable deployment of an interactive ad and impact on consumer attitudes about the product or brand or subsequent purchase intention or behavior (see S3 Appendix for a list of excluded studies). Whether such outcomes correlate to an accurate understanding of product features or services and risk disclosures is not known but could be relevant when considering interactive advertising for regulated products, such as prescription drugs, alcohol, tobacco, and financial products or services. Regardless, it is clear that available research on interactive advertising does not provide much of the evidence most useful to regulatory science focused on whether regulated advertising encourages informed decision making about products.

We need rigorously designed studies of consumer experiences with interactive advertising that use objective and validated measures to assess recall and understanding of product or service information and risk disclosures. We note a disjuncture between our selected studies and recent work on social media activities. A type of study we commonly encountered during screening but excluded as not eligible were studies evaluating the impact of influencer marketing through social media. Though not a focus of this scoping review, we noted many of these studies in the latter part of the time period we searched, suggesting an increasing use of this type of digital, interactive advertising for the future and a possible area for future inquiry.

5. Conclusion

This scoping systematic review summarized the research related to consumer engagement with interactive advertisements and impact on recall and understanding of product claims. The evidence shows that consumers do engage with interactive advertisements, but the evidence is mixed as to whether features of interactive advertising increase consumer engagement, recall, awareness, or comprehension of product claims and risk disclosures. Only a few studies compared traditional advertisements with interactive advertisements on these outcomes and these results also were mixed. Some of the limitations of existing interactive advertising literature as a source for informing regulatory science appears to reflect inconsistent labeling of concepts as well as adherence to industry metrics rather than regulatory science needs.

Supporting information

S1 checklist. prisma checklist..

https://doi.org/10.1371/journal.pone.0263339.s001

S1 Appendix. Detailed search strategy.

https://doi.org/10.1371/journal.pone.0263339.s002

S2 Appendix. Study selection criteria.

https://doi.org/10.1371/journal.pone.0263339.s003

S3 Appendix. Excluded studies.

https://doi.org/10.1371/journal.pone.0263339.s004

S4 Appendix. Results tables.

https://doi.org/10.1371/journal.pone.0263339.s005

Acknowledgments

The authors acknowledge Sharon Barrell and Loraine Monroe for editing and document preparation.

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Consumer Behaviour to Be Considered in Advertising: A Systematic Analysis and Future Agenda

Ahmed h. alsharif.

1 Azman Hashim International Business School, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia

Nor Zafir Md Salleh

Shaymah ahmed al-zahrani.

2 Department of Economic & Finance, College of Business Administration, Taif University, Taif 21944, Saudi Arabia

Ahmad Khraiwish

3 Department of Marketing, Faculty of Business, Applied Science Private University (ASU), Amman 11931, Jordan

Associated Data

Not applicable.

In the past decade, neurophysiological and physiological tools have been used to explore consumer behaviour toward advertising. The studies into brain processes (e.g., emotions, motivation, reward, attention, perception, and memory) toward advertising are scant, and remain unclear in the academic literature. To fill the gap in the literature, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to extract relevant articles. It extracted and analysed 76 empirical articles from the Web of Science (WoS) database from 2009–2020. The findings revealed that the inferior frontal gyrus was associated with pleasure, while the middle temporal gyrus correlated with displeasure of advertising. Meanwhile, the right superior-temporal is related to high arousal and the right middle-frontal-gyrus is linked to low arousal toward advertisement campaigns. The right prefrontal-cortex (PFC) is correlated with withdrawal behaviour, and the left PFC is linked to approach behaviour. For the reward system, the ventral striatum has a main role in the reward system. It has also been found that perception is connected to the orbitofrontal cortex (OFC) and ventromedial (Vm) PFC. The study’s findings provide a profound overview of the importance of brain processes such as emotional processes, reward, motivation, cognitive processes, and perception in advertising campaigns such as commercial, social initiative, and public health.

1. Introduction

Self-report has been adopted in marketing activities to evaluate and identify consumer responses toward stimuli in the marketing sector, such as advertising practices. According to Carrington, et al. [ 1 ], the self-report methods reflect/measure the conscious responses (e.g., perception, approach/withdrawal attitudes, and thoughts) toward advertising. In fact, self-report cannot measure the unconscious or subconscious responses, which represent the majority of consumer responses, such as decision-making. Thus, researchers and marketers have adopted neuroscience tools such as electroencephalography (EEG) in the marketing field, to better understand the unconscious responses of consumers [ 2 , 3 , 4 ], which has led to an emerging new field, the so-called “Neuromarketing”. Smidts [ 5 ] was the first business researcher who coined the term “neuromarketing”, in 2002. Neuromarketing is a hybrid field containing numerous areas/fields such as psychology, marketing, and neuroscience [ 6 ]. According to Fortunato, et al. [ 7 ], the thanks for spreading this term was given to the Bright House Company when it created the first fMRI laboratory for marketing research.

In the hyper-competitive environment, neuromarketing is a mainstay in advertising because it has an opportunity to gauge consumers’ neural responses as emotional responses toward advertising; thereby, it is a revolutionary field for a better understanding of the subconscious and unconscious consumer responses. According to the literature, the rapid technological progress in neuroscience technology led to a better understanding of consumers’ behaviour in several contexts, such as, but not limited to, advertising [ 8 , 9 , 10 , 11 ]. Therefore, marketing and advertising leaders have used this technology to boost innovation and success in marketing and advertising, by controlling and minimizing task conflict [ 12 ]. According to Ramsoy [ 13 ], neuromarketing tools have been divided into four clusters, as follows: (1) neuroimaging tools such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and transcranial magnetic stimulation (TMS); (2) physiological tools such as galvanic skin responses (GSR), eye-tracking (ET), electrocardiogram (ECG), and electromyography (EMG); (3) self-report methods such as surveys, observations, focus groups, and interviews; (4) behavioural measurements, such as the implicit association test (IAT). For instance, neuroimaging tools have been used to gauge emotions, attention, and memory regarding advertising [ 14 , 15 ]. At the same time, physiological tools have been used to gauge the physiological responses of consumers, such as, but not limited to, visual fixation in-store at the purchasing point, thereby providing valuable and fruitful insights into the attitudes of consumers (i.e., approach, withdrawal) [ 16 , 17 , 18 ]. Behavioural measurements are used to measure the reaction time of consumers toward stimuli, and self-reporting is used to measure the conscious behaviour of consumers toward stimuli such as approach/withdrawal attitudes [ 18 ].

According to the literature, the first fMRI investigation in neuromarketing was carried out by McClure, et al. [ 19 ], and largely contributed to the practical studies of neuromarketing [ 18 ]. Therefore, neuromarketing research has received attention from both academia and the industrial world as a means of filling the gap in traditional marketing methods and overcoming the limitations by reducing consumer social-bias (e.g., consumer choices can be affected by others) [ 7 ]. However, understanding the global trends in advertising research within the neuromarketing field is still unclear in academic studies. In addition, to date, no investigation has determined the current neurophysiological and physiological techniques that have been used in studying the unconscious/subconscious responses of consumers toward advertising such as YouTube video scenes, TV ads, public health ads (antismoking), initiative ads (encouraging the use of seat belts in cars). To sum that, this study tries to analyse the extracted articles in depth, to provide a precise and concise conclusion. The contributions of this work are summarized as follows:

• Provides a profound evaluation of the current advertising research that has been used to investigate unconscious and subconscious consumer behaviour, such as emotional dimensions, perceptions, reward processes, and approach/withdrawal motivation toward advertising.
• Provides an overview of the current neurophysiological and physiological tools that were used in advertising within the neuromarketing context between 2009 and 2020.

In this vein, the current paper provides an inclusive overview of neuromarketing research and the current research objectives. Section two presents the methodology and data-collection process. Section three presents the growth of the publication, topics of interest and a thematic analysis. A discussion of the study’s findings is presented in Section four. Section five presents a concise conclusion and the implications of our work. Finally, Section six presents the limitations and future directions.

2. Materials and Methods

This review study is designed to select empirical articles from the Web of Science (WoS) database in advertising within the neuromarketing context, to fill the gap in the literature. The reason behind choosing the WoS database over Scopus is the availability of cleaner data, which means reducing the duplications as compared to the Scopus database; additionally, WoS includes publications in top-tier journals [ 20 ]. The first step was to follow the instructions of the PRISMA protocol to select the empirical articles which used neuroimaging and physiological tools to investigate consumer responses to advertising research within neuromarketing [ 21 ]. The reason behind the use of the PRISMA protocol to select the relevant articles for this study were that it has been widely used in social science and business to extract and select articles related to the study, for example, online learning [ 22 ], neuromarketing [ 23 ], and service and healthcare [ 24 , 25 ].

For the second step, we used the content analysis of selected empirical articles for this study. The above processes will provide us with a profound insight into the advancement in advertising research by identifying and analysing the general and specific areas. Additionally, providing us with a better understanding of advertising research that used neuroimaging and physiological tools/methods and which can be considered when we are conducting further research into advertising research. Therefore, the findings of this study provide a guide for new scholars who are interested in advertising research within a neuromarketing field.

Relevant empirical articles have been selected by using the following query applied to the title, abstract, and keywords: (((“neuromarketing” OR “consumer neuroscience”) AND (“advertising” OR “advertisement”) AND (neuroimaging OR physiological))). This study extracted 76 empirical articles relevant to this review paper from January 2009 to December 2020. This study focused on empirical journal-articles, in comparison to conferences and book chapters, which generally undergo a much more rigorous review process and therefore improve the credibility of published research in journals [ 26 ]. Figure 1 demonstrates the selection of articles processed for this study, as follows:

• Methods: neuroimaging and physiological tools;
• Publication year: January 2009 to December 2020;
• Language: English;
• Document type: original articles (chapters of books, articles from conferences, reviews, and proceedings books were excluded).

PRISMA protocol to extract empirical articles for this systematic study.

3.1. Growth of the Publication

Seventy-six articles were extracted from the WOS which used neuromarketing tools. According to the literature, McClure, et al. [ 19 ] published the first neuromarketing study in 2004, when they investigated the neural correlates of consumer behaviour (e.g., choice, decision-making) toward two brands (Coca-Cola vs. Pepsi Cola). However, the first investigation into advertising was carried out by Morris, et al. [ 27 ]. They found the gyri regions of the brain were highly related to pleasure/displeasure and high/low arousal. From January 2009 to December 2020, there was a fluctuation in the number of published articles in advertising. In 2020, it was the highest number of annual publications with thirteen articles, as depicted in Figure 2 .

By exploring the relevant articles to develop this review article, it was possible to classify the global academic-research trends and advancement in advertising and neuromarketing, as follows: (i) neuroimaging and physiological tools used in advertising; and (ii) consumers’ brain processes to be considered in advertising, such as emotions, motivation, reward process, attentions, perception, and memory. By reviewing the selected articles, we can enrich our understanding, to achieve the objectives of this study. Table 1 shows the summary of the neuroimaging and physiological tools that were used to investigate the brain processes of consumers toward advertising research.

Classifications of neuroimaging and physiological tools used in advertising research.

3.2. Topics of Interest and Thematic Analysis

3.2.1. neuroimaging and physiological tools used in advertising.

We found that the neural-response recording-tool most used in advertising was EEG. EEG is an electrical and also a non-invasive technique to gauge the unconscious/subconscious responses of consumers toward ads by recording the voltage changes of frequencies at scalp regions [ 28 , 29 , 30 , 31 ]. According to [ 32 ], there are five frequency bands (e.g., delta, theta, alpha, beta, and gamma). The EEG tool uses a 10–20 global system, which is used to describe the electrode locations on the volunteer’s scalp, for example, (Fp), (F), (P), (O), (C), and (T) describe the prefrontal, frontal, parietal, occipital, central, and temporal, respectively. Moreover, it uses the same number of electrodes on both side of the volunteer’s head (i.e., right and left side) [ 33 , 34 ]. In addition, it has an excellent temporal accuracy (estimated in milliseconds (ms)) and a poor spatial accuracy (estimated at 1 cm 3 at the scalp regions) [ 35 , 36 , 37 ]. In addition, it is not expensive or noisy [ 38 ]. fMRI and fNIRS are non-invasive, metabolic tools. Both are used to record oxygenated and deoxygenated haemoglobin in the brain [ 39 , 40 ]. fMRI has a superior spatial accuracy (estimated at 1–10 mm 3 of the deep structure of the brain) compared with fNIRS, which has poor spatial accuracy (estimated at 4 cm of cortical-activity regions) [ 41 ]. Meanwhile, both have acceptable temporal accuracy (estimated in seconds) [ 42 , 43 ]. fMRI and fNIRS have been used in marketing research to record the subconscious/unconscious responses of consumers (e.g., preference, perceptions, purchase decisions, choices) toward marketing stimuli [ 41 , 43 , 44 ]. fNIRS is a portable, novel, promising, and silent neuroimaging technique, which is also cheaper than fMRI [ 39 , 45 , 46 ].

ET is a portable technique that is used to gauge physiological reactions such as eye movements, pupil dilation, saccade, and fixation toward the stimuli of marketing [ 18 ]. According to Hoffman [ 47 ], it is used for measuring eye movements and the attention of consumers, which is beneficial for psychology and neurological research. Eye fixations last between 200 and 350 ms while reading a text and watching video scenes, respectively, while 200 ms refers to the duration of saccadic eye-movements [ 48 ]. The set of fixations and saccades is named the scan route, and analyses visual perception and cognitive purpose [ 49 ]. Pupil dilation with a longer blink-period gives us better information about processing [ 18 ]. The GSR and ECG tools are used to gauge the emotional reactions of consumers toward ads [ 50 ]. For example, sweating level is recorded by the GSR tool and the heart rate/heartbeat is measured by the ECG tool [ 50 , 51 , 52 ]. In addition, both of them can measure the autonomic nervous system and evaluate the internal emotional status of consumers [ 53 ]. Therefore, GSR and ECG are convenient and reliable techniques for measuring the emotional status of consumers and changes in skin conductance, respectively [ 54 ]. IAT is able to identify the customers’ attitudes toward marketing stimuli such as brands or ads (e.g., like/dislike) by recording the reaction time of customers [ 18 ]. EMG is used to measure the reactions shown on individuals’ faces (e.g., pleasure/displeasure, …, etc.) toward advertising [ 55 ], because facial-expression analysis is significant for marketers and advertisers, because faces can provide beneficial information about perceptions of customers toward ads in terms of measuring visible and invisible facial-muscle reactions [ 29 ].

3.2.2. Brain Processes to Be Considered in Advertising

Emotion and feelings.

The feeling is a relatively conscious aspect of emotional status [ 56 ], which derives from individuals’ judgments such as level of pleasure or unpleasure toward advertising [ 31 ]; it is likely the best way to understand and explain the physiological responses of the consumer toward ads [ 31 , 57 ]. Many research studies have affirmed that ad-elicited feelings are strong indexes of consumers’ response toward advertising [ 58 ]. On the opposite side of the spectrum, emotion is an unconscious aspect of emotional status which correlates to an automatic somatic response such as increased heart beat in some conditions (fright, anger) [ 56 , 59 ], which is crucial for making decisions, learning, and solving problems [ 60 ]. Advertisers and marketers can use both in advertising to captivate consumers’ attention, thereby enhancing purchase intention. Emotions are accompanied with changes in the autonomic nervous system (i.e., zygomatic facial muscles, corrugator facial muscles, and heart-rate), which can provide rich information about the emotional status of consumers. Therefore, the study of emotions has attracted many researchers and advertisers [ 61 ].

Emotion is constructed from a neural network in the brain, which performs basic psychological activities/functions (e.g., memory, perception, salience detection) [ 62 ]. Therefore, emotion is defined as the set of changes in the individual’s physiological and subconscious and unconscious responses, based on the individual’s experiences [ 63 , 64 ]. In addition, emotion is a relationship between humans and the environment, including multiple subcomponents (e.g., physiological, behavioural, appraisal, and expression [ 65 ]. The cognitive and neurological frameworks of the role of emotion in decision-making has been investigated more through Damasio’s theory, which is also known as the somatic marker hypothesis [ 66 , 67 ]. Consequently, researchers have agreed on two dimensions for measuring emotional responses toward stimuli: (i) valence/balance (i.e., pleasure or displeasure, depression or excitement), (ii) arousal (e.g., high or low) ( Figure 3 ) [ 28 , 68 , 69 ]. Figure 3 shows that the valence indicates either positive or negative emotional-status which is evoked by external stimuli such as advertising. In addition, valence is measured from the positive to the negative side. On the other side of the spectrum, arousal indicates the level of excitement; whether high or low, it is measured from high to low levels [ 68 , 70 , 71 ].

Dimensions model of emotions [ 68 ].

Table 2 shows several methods to measure emotions and feelings toward marketing stimuli. Previous studies have used, for example, self-report and physiological methods to analyse the emotional responses of consumers toward advertising [ 72 ]. For example, the EMG and self-report investigation of Lajante, et al. [ 73 ] gauge the consumer’s pleasure/displeasure toward advertisements. The findings of the experiment revealed that pleasure/displeasure had positively impacted the attitudes of customers toward advertisements. The ECG, EDA, and questionnaire study of Baraybar-Fernández, et al. [ 50 ] found that audio and visual ads with sad messages have more influence on participants. Barquero-Pérez, et al. [ 53 ] used the ECG, EDA and questionnaire in their experiment. They found that each type of ad generated a different emotion, such as surprise, anger, and so forth. In addition, physiological tools have been used to investigate the effectiveness of online advertising (e.g., YouTube) [ 51 ]. For example, Guixeres, et al. [ 51 ] used brain response, ECG, and ET to investigate the relationship between ad effectiveness and the number of views on YouTube. They found that there is a solid relationship between ad effectiveness and the number of views on YouTube. Herrador, et al. [ 74 ] conducted an EDA experiment and the findings revealed that both groups of participants (male and female) indicated a strong initial activation; moreover, they noticed a reduced activation during the most significant part of video material in the male group. Venkatraman, et al. [ 28 ] used several neuromarketing tools to evaluate participant’ responses to a 30-second TV advertisement. The findings revealed that the activity in the ventral striatum could be the predictor of response to advertising. Grigaliunaite and Pileliene [ 75 ] conducted an experiment by using ET and they found that emotional and rational advertising appeals influence consumers’ responses in various ways. The IAT and ET investigation of Pileliene and Grigaliunaite [ 76 ] found that warm-colour temperature attracts more visual attention to the advertisement; thereby generating a positive implicit attitude and inducing the buying intentions toward the advertised product, compared with cool-colour temperature advertisements, whether the spokesperson is a female or male celebrity. Similarly, Grigaliunaite and Pileliene [ 77 ] found that negative smoking images reflected a negative implicit attitude/behaviour of individuals toward those images and smoking behaviour, increasing the influence on individuals’ intention to whether to quit or not to start smoking. The ERP, ET, and questionnaire investigation of Pileliene and Grigaliunaite [ 78 ] found that a well-known female spokesperson has a significant impact on the effectiveness of fast-moving consumer-goods advertising.

Neuromarketing tools to measure emotions and feelings.

There has been an interesting growth in understanding the non-verbal responses of emotional status toward advertising by using neuroscience methods such as fMRI, EEG, fNIRS [ 31 , 51 , 79 , 80 , 81 , 82 ]. For example, Plichta, et al. [ 45 ] conducted an fNIRS experiment to investigate the detection of sensory activity by measuring emotional signals in the auditory field. The findings revealed that pleasurable/unpleasurable sounds increased the activity in the auditory cortex, compared to neutral sounds. Gier, et al. [ 41 ] conducted the fNIRS experiment to explore whether the fNIRS tool had the ability to predict the success of point-of-sale elements by measuring the neural signals of brain regions such as the dlPFC. The findings revealed that the fNIRS has the ability to predict the success elements at the point of sale, relying on the cortical-activity effect.

The EEG investigations of Vecchiato, et al. [ 83 ], and Vecchiato, et al. [ 84 ] found that activity in the right frontal alpha is associated with pleasure/liking ads, while the left frontal alpha correlated to displeasure/disliking ads. Additionally, Vecchiato, et al. [ 85 ] found that there were gender (i.e., male and female) differences in interest toward commercial categories and scenes in two ads. The EEG experiment of Harris, et al. [ 86 ] found that emotional advertisements are more effective than rational advertisements, which leads to a positive change in decision-making, inducing donation, and liking. The findings of Chen, et al. [ 87 ] revealed that e-cigarette ads increased the smoking desire; additionally, e-cigarette ads increased activity in the left middle-frontal-gyrus, the right medial-frontal-gyrus, the right parahippocampus, the left insula, the left lingual gyrus/fusiform gyrus, the right inferior-parietal-lobule, the left posterior-cingulate, and the left angular-gyrus. Wang, et al. [ 88 ] and Royo González, et al. [ 89 ] found that the narrative approach of ads and exposure to branding products have a favourable influence on the consumers’ preferences and excitement. The fMRI investigations of Morris, et al. [ 27 ] and Shen and Morris [ 90 ] found that pleasure and displeasure are correlated with more activity in the inferior frontal- and middle temporal-gyrus, respectively, while low and high arousal is associated with the right superior-temporal- and right middle-frontal-gyrus, consecutively. Leanza [ 91 ] used EEG and found that some of the emotive features of the virtual reality (VR) experience significantly influenced consumers’ preferences. Ramsoy, et al. [ 92 ] found that arousal and cognitive load were highly connected to subsequently stated travel-preferences; moreover, consumers’ subconscious emotional and cognitive responses are not identical to subjective travel-preference. Shestyuk, et al. [ 93 ] found that the EEG is a convenient tool to predict the success of TV programs and determine cognitive processes. Wang, et al. [ 94 ] conducted an experiment to propose a generative-design method using EEG measurements. The findings revealed that the product image that was generated with preference EEG-signals had more preference than the product image generated without preference EEG-signals. Kim, et al. [ 95 ] conducted an experiment to identify the effect of visual art (e.g., Mondrian’s and Kandinsky’s artworks) on consumers’ preferences, by using EEG. The findings showed that the visual effects induced high emotional arousal, which might promote heuristic decision-making. Mengual-Recuerda, et al. [ 96 ] found that food served by a chef positively influences emotions, while dishes with special presentations attract more attention than traditional dishes. The EEG study of Eijlers, et al. [ 31 ] found that arousal is positively connected to prominent ads in the wider population and negatively to consumer attitudes toward these ads.

According to Lang and Bradley [ 97 ], emotions and motivation processes are highly intersected and correlated. Chiew and Braver [ 98 ] and Pessoa [ 99 ] found that cognition and consumers’ behaviours are highly affected by motivational processes. For example, positive motivational stimuli will urge individuals toward achieving goals (e.g., obtain or predict a reward by performing a task correctly) [ 100 ], while negative motivational stimuli can lead to distraction [ 101 ].

Pessoa [ 102 ] and Raymond [ 103 ] suggested that motivational processes are a compass of consumers’ attitudes toward external stimuli to engage with the environment and achieve goals. Higgins [ 104 ] suggested two dimensions for measuring motivational processes such as withdrawal and approach attitudes. Researchers and practitioners attempted to investigate the neural responses of motivational processes to better understand consumers’ behaviours toward advertisements and products [ 83 ]. For example, Cherubino, et al. [ 105 ] carried out an experiment using EEG to investigate the relationship between the PFC and motivational dimensions. The findings revealed that the PFC is related to motivational dimensions, wherein the right PFC correlated to withdrawal attitudes and the left PFC related to approach attitudes. The EEG investigations of Pozharliev, et al. [ 106 ] and Zhang, et al. [ 107 ] recorded the brain responses of subjects toward luxury products (motivations). The findings showed that social motivations have a vital role in influencing the purchase of luxury products in order to satisfy social goals (at least one goal). The EEG investigation of Bosshard, et al. [ 108 ] found that liked brands reflect more motivational aspects and activity signals in the right parietal-cortices than disliked brands.

Therefore, there is a strong relationship between the activation of the PFC and motivational dimensions toward marketing stimuli such as advertisements [ 109 ]. Therefore, marketing researchers and practitioners have to focus on the motivational processes of consumers to orient the marketing mix (e.g., target-appropriate audiences and markets, increasing the effectiveness of ads and products) [ 110 ]. According to previous studies, NM research has used the approach/withdrawal attitude to evaluate TV ads [ 111 ]. Therefore, approach/withdrawal motivational attitudes are highly significant for marketing and advertising research.

Reward Processing

According to the literature, it is highly significant for researchers and practitioners to study and know the neural responses that are responsible for reward processing, such as money, food, and social activities [ 112 , 113 , 114 , 115 ]. This is because the positive rewards such as gaining money, foods, or other types of rewards, enhance the accuracy and cognitive task [ 116 , 117 , 118 ] through modifying the early attentional process. Anderson, et al. [ 101 ] demonstrated that visual features (e.g., product design) that are correlated to reward, will make the consumer prioritize, therefore attracting the consumer’s attention automatically. For example, the design preference of a product or brand can increase the activity in the regions which are responsible for reward processing, thereby, causing more activation in regions of motivations that might impact consumers’ purchase decisions [ 29 ]. Many studies concentrated on the individual’s response toward a monetary reward by studying the approach/avoidance attitude [ 112 , 113 ]. For example, Bechara, et al. [ 119 ] carried out an experiment named the “Iowa Gambling Task” by using GSR to investigate the influence of reward on decision-making. They divided participants into two groups, the healthy group and the group with lesions in the vmPFC. The findings revealed that healthy participants sweated more, which led them to infer that participants had a negative emotional experience toward picking up cards from a monetary losing deck; meanwhile, the lesion group picked up cards regardless of whether they were cards with monetary wins or losses. Consequently, reward highly influenced decision-making [ 112 , 120 , 121 ].

Many researchers have confirmed that the importance of the striatum activity in reward processing, wherein the components of the striatum such as the caudate nucleus, nucleus accumbens (NAcc), and putamen play a central role in expectation and evaluation of reward [ 115 , 122 , 123 ]. For example, Galvan [ 124 ] and Geier, et al. [ 125 ] carried out an experiment to investigate the relationship between reward processing and the striatum. Their findings revealed that the ventral striatum (VS) has a key role in the prediction of reward. Jung, et al. [ 126 ] found that the rewards, memory, semantics, and attention regions in the brain were lit up when viewing a combination of a celebrity face and a car, compared with viewing a combination of a non-celebrity face and a car. In addition, car favourableness correlated positively with activation in the left anterior-insula, left OFC, and left higher-order visual cortex in the OL. Padmanabhan, et al. [ 127 ] investigated the influence of the reward system on attention processes. Their findings showed that incentives facilitate cognitive control. Previous neuroimaging studies demonstrated that rewards activate the ventral medial prefrontal cortex (vmPFC) and ventral striatum [ 128 , 129 , 130 ]. The ventral striatum has been discussed before as a part of the reward system [ 131 ]. Therefore, findings suggest that neurodevelopmental changes in the striatum systems may contribute to changes in how reward influences attentional processes [ 56 ].

Attention is defined as the way “people tend to seek, accept and consume the messages that meet their interests, beliefs, values, expectations and ideas, and overlook the messages that are incompatible with this system” [ 132 ]. It has also been defined as selective perception [ 133 ]. Selective perception is associated with filtering out information and concentrating on significant information (e.g., different aspects of stimulus or different stimuli) [ 134 ]. For instance, consumers are exposed to nearly 10 million bits of visual information (e.g., ads, images, sound, video, and colour) per second through their senses (e.g., eyes, ears, skin) daily. Most input information goes by unnoticed, with consumers able to process almost 40 bits of input information per second [ 29 , 135 ]. This lead us to infer that attention has a strong influence on consumer behaviour in how consumers represent, perceive and process information and thus select and prioritize information. Attentional and emotional processes are relatively intersected/connected, and emotion is considered a reliable and effective source for attracting consumers ’ attention [ 136 , 137 ]. For example, the activation in the amygdala (AMY) and cingulate cortex (CC) in the brain is related to emotional stimuli.

Attention is a significant brain process, which has a central role in measuring the effectiveness of advertising campaigns; thereby, it is an indicator of consumer’s behaviour and the effectiveness of advertising [ 138 ]. According to the literature, the majority of researchers have agreed on two systems to measure attention toward advertising: (i) bottom-up, and (ii) top-down, systems [ 28 , 139 , 140 ]. Bottom-up (visual saliency/exogenous/ involuntary) attention is the type of attentional system which is initiated by external stimuli such as colour, voice, promotion, faces, text, novelty, brightness, and so forth, which lead to a process in which information in external stimuli is received automatically. Top-down (goal-driven/endogenous/voluntary) attention is the other type of attentional system, which is initiated by internal and external goals and expectation; thereby, it is necessary to focus all your mental power toward the goal that you are looking to achieve, thereby filtering goals to achieve your goals ( Figure 4 ) [ 2 , 140 , 141 ].

The bottom-up and top-down attention processes [ 59 ].

For this reason, the underlying brain reactions of attention and visual processing are highly interesting for advertising. Moreover, the anterior cingulate cortex (ACC) is highly related to the function of top-down and bottom-up attentional systems [ 142 , 143 ]. For example, Smith and Gevins [ 144 ] found that the occipital lobe (OL) is associated with attentional processes toward TV advertisements. The EEG investigation of Kong, et al. [ 145 ] found that variation in activity in the cerebral hemisphere related to the cognitive task can help to determine the success or lack of success of the advertisement. A recent fMRI investigation by Casado-Aranda, et al. [ 146 ] found that the correspondence between advertising and gender voice (male, female) induces attention regions in the brain. Ananos [ 147 ] carried out experiment using ET to investigate the attention level and processing of information in advertising (content recognition) among elderly and young people groups. Their findings revealed that the attention level among both groups was the same, but recognition by the young-people group was higher than that of the elderly-people group for untraditional advertising. Guixeres, et al. [ 51 ] conducted an experiment to investigate the effectiveness of an ad (e.g., a recall ad) and the number of views on YouTube channels, using neural networks and neuroscience-based metrics (e.g., brain response, ECG, and ET). Their findings suggest an important relationship between neuroscience metrics and self-report of ad effectiveness (e.g., recall ad) and the number of views on YouTube. Cuesta-Cambra, et al. [ 148 ] investigated how information is processed and learned and how visual attention takes place. Their findings indicated that the visual activity of men has different patterns from women, and does not influence subsequent recall, wherein recall relies on the emotional value of ads and simplicity, while complex ads need more visual fixation and are therefore hard to remember. In addition, the importance of the playful component of memory and low-involvement processing were confirmed by EEG. Treleaven-Hassard, et al. [ 149 ] examined the engagement of the consumer with interactive TV ads with a particular brand’s logo compared with non-interactive TV ads. The findings revealed that brands linked with interactive ads attract more automatic attention. Boscolo, et al. [ 81 ] conducted an experiment using ET and questionnaires to investigate differences in the visual attention between genders (male and female), toward print ads. Their findings revealed that there is difference in visual attention in the case of male, while no differences were noticed in the case of females.

According to Simson [ 150 ], studies into the perception of value and how it is formed reflect what is known in marketing theory, wherein the marketing-mix elements can be changed to influence the perceived value of a product. However, studies on how attention systems impact consumers ’ perception and actions have been limited to consumer report and behavioural studies, which depend on a rational report; this is not enough to explain attention processes, wherein there are two attentional systems influencing consumers’ perceptions (e.g., top-down and bottom-up attention system) ( Figure 5 ) [ 59 ]. Consumer perception is the first step in engagement with marketing stimuli or any other stimuli in the environment [ 151 ]. Hogg, et al. [ 152 ] defined perception as “the process by which marketing stimuli are selected, organised, and interpreted”. Therefore, individuals add meaning and interpret it in a certain way, which leads to the perceptions of the individual’s findings for each one. As stated by Belch and Belch [ 153 ] perception processing is extremely reliant on internal processes such as prior knowledge (experiences), current goals, beliefs, expectations, needs, and moods, and also external stimuli such as colour, orientation, intensity, and movement [ 59 ]. Although this explains the process of how consumer perceptions are formed, the exact the part concerning the explanation of sensations and the internal and unique assignation of meaning to sensations is what lies concealed, and remains unexplained in detail in the current consumer-behaviour literature. However, it is widely believed that this process is driven by the unconscious.

Two attentional systems impact the consumers’ perceptions.

Cartocci, et al. [ 154 ] and Modica, et al. [ 155 ] conducted experiments to estimate the accuracy measurement of the cerebral and emotional perception of social advertising campaigns (i.e., antismoking) using several methods such as EEG, GSR, and ECG. The findings showed that the antismoking campaign which was characterized by a symbolic communication style gained the highest approach-values, as evaluated by the approach/withdrawal index. Meanwhile, an image based on “fear-arousing appeal” and with a narrative style reported the highest and lowest effort-values index, respectively. The fMRI investigation of Falk, et al. [ 156 ] predicted the out-of-sample (population) effectiveness of quit-smoking ads. The findings revealed that activity in the prior mPFC was largely predictive of the success of various advertising campaigns in the real world. Plassmann, et al. [ 157 ] carried out an experiment to study the perception of pleasantness in the taste of wines, using the fMRI tool. Their findings showed a stronger activation in the medial OFC (mOFC) regions in the brain when subjects believed they are drinking expensive wine, showing that the mOFC is responsible for experiencing pleasantness. This led to infer that the pleasantness report was correlated with perceived value and price of product more than taste itself. Neuroscientists have found that the OFC and ventromedial prefrontal cortex (vmPFC) are involved in decision-making, through the perceived value of products [ 158 ]. Nuñez-Gomez, et al. [ 159 ] carried out an experiment using EEG to examine how advertising material is perceived by two groups (e.g., healthy group and group with Asperger syndrome). The findings revealed that there are large difference between these groups in their perception of emotion and their attention variables. Gong, et al. [ 160 ] carried out an experiment to identify the influence of sales promotion (e.g., gift-giving, discount) on the perception of consumers and purchase decisions by using EEG/ERP. The findings revealed that discount promotions have more impact on purchase decisions than gift-giving sales promotions.

Memory is defined as an ongoing learning-process, which has input and output functions in the brain [ 161 , 162 ]. The input function encodes information, while the output function retrieves information, and this is very important for advertising research [ 137 , 163 ]. For example, recall and recognition advertising-information is a retrieving function [ 28 ]. Myers and DeWall [ 162 ] and Atkinson and Shiffrin [ 163 ] presented the multistore model of memory, which includes three steps, as follows: (i) a sensory memory, (ii) short-term memory (STM), and (iii) long-term memory (LTM) ( Figure 6 ) [ 164 ]. Brain processes related to memory have revealed certain valuable outcomes, as to the factors which influence the consumers’ behaviour ,such as recall- and recognition-advertising [ 165 ]. Input and output functions in the memory are highly important for marketers and advertisers, due to each function having an awareness and unawareness aspect in the brain [ 137 , 166 ]. Memory and emotion are highly connected to each other. For example, previous studies confirmed that the emotional events are usually remembered more than neutral events, and especially if emotions correspond to events at that moment [ 167 ].

The information phases in the memory’s stages [ 164 ].

The memory process has been widely studied, and it has concluded that the hippocampus (HC), located in the temporal lobe (TL), plays a major role in generating and processing memories [ 165 ]. Additionally, activation of the HC has a strong relationship with LTM and STM, which highly impacts consumers’ purchase decisions [ 168 , 169 ]. In addition, the AMY is located next and close to the HC, which is significant for the memory system [ 165 ]. For example, the EEG investigation by Rossiter, et al. [ 170 ] found that the left hemisphere is correlated with encoding in the LTM. Similarly, the EEG investigations by Astolfi, et al. [ 171 ] used EEG to determine the brain regions that were triggered by the successful memory-encoding of TV ads. They found stronger activity in the cortical regions. Morey [ 172 ] investigated the impact of advertising message on recognition memory. The findings revealed stronger activity in the gamma band, which directly affected memory. The fMRI investigation by Bakalash and Riemer [ 173 ] and Seelig, et al. [ 174 ] measured the brain regions of memory ads. The findings revealed that stronger activity in the amygdala (AMY) and frontotemporal regions is associated with memorable and unmemorable ads. Similarly, [ 175 ] carried out experiments to investigate the content of ads and the activity of frontal regions and memory. The findings showed that the content of ads increased the activity in the frontal regions and the input function (encoding) of memory.

The study of these mental processes such as emotion and feelings, attention, memory, reward processing, motivation, and perception are highly important considerations for advertising research.

4. Discussion

A total of 76 articles were extracted and analysed, wherein the content analysis of the relevant articles revealed that the annual and the accumulative number of publications has been growing since 2009, reaching its peak in 2020 with twelve empirical articles that used neuroimaging, physiological, and self-report techniques to study the consumers’ brain processes such as, but not limited to, emotions toward the stimuli of marketing such as advertising. We followed the PRISMA protocol to select the relevant empirical articles for this study as brain processes such as emotions, feelings, motivation, reward, attention, and memory need to be considered in advertising research. The findings of the study revealed that the neuroimaging tool that is used most in studying the brain processes of consumers is the EEG, with 38 empirical articles, followed by the fMRI with 20 articles; it was also noticed that only four articles used the fNIRS tool in neuromarketing research. In addition, for physiological tools, it was observed that five techniques were used in neuromarketing studies to investigate consumer responses toward the stimuli of marketing such as advertising. The ET was used in 14 articles alongside neuroimaging tools such as EEG, wherein ET is the most used tool, followed by GSR with 12 articles; it was also used alongside other physiological tools such as ECG and EMG. Finally, self-report (i.e., surveys, interviews, focus groups, and observation) was used in seven articles.

This study found that the brain processes to be considered most in advertising research are emotions, feelings, attention, memory, perception, approach/withdrawal motivation, and reward processing. The findings demonstrated that the strongest activity in the inferior-frontal- and middle-temporal-gyri are associated with pleasure and displeasure, while the activity in the right superior-temporal and the right middle-frontal-gyrus correlated with high or low arousal [ 90 ]. As well as this, we found the OL connected with the attention system [ 144 ], and the HC, located in the temporal lobe (TL), plays a major role in generating and processing memories [ 165 ]. In addition, the VS, which is located in the basal ganglia plays a central role in the reward system; for example, the key functions of VS (i.e., control movement and planning) have a vital role in the reward system, while the components of VS such as the putamen, caudate nucleus, and nucleus accumbens (NAcc) have a central role in the assessment of consumer expectations, compared to the actual reward received [ 123 ]. In addition, the ventral tegmental region is considered a part of the reward system, which passes the neurotransmitter dopamine to other brain areas, thereby affecting goal-seeking behaviour [ 123 ]. For motivation, it was found that the anterior cerebral hemispheres play a central role in withdrawal and approach motivation; for example, the increase in activation in the right PFC is linked to withdrawal behaviour, while the increase in activation in the left PFC is associated with approach behaviour [ 105 , 176 ]. Finally, in accordance with the literature, it was found that the OFC and vmPFC regions play vital roles in perception (i.e., perceived value) [ 158 ].

5. Conclusions and Implications

Implication of the research findings for theory and practice: Theoretically, the current findings can be divided into three areas, as follows: firstly, neuroscientific techniques and methods enable the capture/measurement of the activity signals of the brain and body relating to consumers’ responses (e.g., emotions and feelings, attention, memory, perception, reward processing, and motivation) toward advertising campaigns. For example, neuroimaging tools (e.g., fMRI, EEG/ERP, fNIRS) enable the recording of the neural signals of the mental responses (e.g., pleasure/displeasure, low/high arousal, advertising recall and recognition) toward advertising, which can be beneficial for advertisers and marketers in creating more effective advertising campaigns to attract, captivate, and impact consumers. Meanwhile, physiological tools (e.g., ET, GSR, EMG, and ECG) enable researchers to gauge the physiological reactions of the consumer, such as pupil dilation, fixation, eye movements, saccade, heart rate, blood pressure, sweating level, and reaction time toward advertising. Secondly, neuroimaging and physiological tools will help advertisers and scholars to identify the weak elements in advertising which lead to withdrawal behaviour and to address these, besides identifying the strengths which lead to approach behaviour, and to enhance them. Thirdly, many articles have concentrated on detecting the neural and physiological responses of emotions, feelings, attention, memory, reward processing, motivation and perception toward advertising such as the presenter’s features (i.e., celebrity), because these processes play a key role in the decision-making of consumers (i.e., purchasing decisions). Additionally, some advertising research concentrated on gender voice (i.e., male, female), ads appeal, faces of celebrity, social campaigns (i.e., using seat belts in the car), and public health (i.e., anti-smoking campaigns). These areas can provide a reasonable explanation of the neural and physiological correlates of emotions and feelings (e.g., pleasure/displeasure, high/low arousal), attention (e.g., top-down, bottom-up), memory (e.g., encoding, retrieving), motivation (e.g., approach/withdrawal), reward processing, and perception (e.g., perceived value of ads) to be considered in advertising research. Thus, an application of this research perhaps offers reasonable explanations of how advertising works in consumers’ minds, and the relationship between the neural correlates of consumers’ brain and physiological responses toward advertising, thereby better understanding consumers’ behaviour, which leads to the creation of more attractive advertising for political, social and business sectors.

General Conclusion : Neuromarketing is a promising field, not only to study and solve the commercial issues such as the weaknesses of advertising campaigns and to reduce the wastage of advertising budgets, but also to create more effective advertising campaigns in social, political, and public-health sectors, in order to increase human awareness. In today’s hyper-competitive environments among advertising agencies, each agency seeks to find the most beneficial methods to beat competitors and be the first priorities in the consumer’s mind. Thus, advertisers have adopted neuroscientific methods in their research to study, analyse, and predict the neural and physiological responses of consumers toward the stimuli of marketing (i.e., advertising), thereby identifying the most important mental and physiological responses to be considered in advertising research to raise advertising effectiveness. Most studies in advertising research have determined the main mental processes of interest for advertising research, such as emotions and feelings, attention, memory, reward processing, motivation, and perception.

The findings of the study suggest that neuroscientific methods and techniques are significant to gauge the brain and physiological reactions of consumers toward the stimuli of marketing, such as advertising research. For example, neuroimaging tools are able to gauge the neural-activity signals of the consumer’s brain. At the same time, physiological tools can gauge physiological reactions such as eye movements, sweating level, and fixation.

6. Limitations and Future Directions

This paper tried to minimize the limitations in methodology; however, some limitations occurred and provided several directions for further research. This research concentrated on the English articles that were published in open-access journals from 2009 to 2020 and were listed in the WOS database. Therefore, this paper overlooked non-English articles, non-open-access articles, and other documents such as books, review papers, conference proceedings, and so forth. Thus, this paper is not free of bias. For future directions, we hope to overcome the obstacles in the future, which include the cost of research and techniques, lack of labs and facilities, use of time (e.g., data interpretation, recruiting participants, and so forth), and increased investment and funding in neuromarketing research and technique [ 177 ]. We encourage researchers and marketers from emerging countries to enter this embryonic field and leave their footprint by publishing articles for future works. In addition, we suggest that researchers and scholars identify the influence of advertising on consumers persuasion, engagement, and excitement, as well as the contributions of neuromarketing research to various domains (e.g., social sciences, public health, politics, and stock exchanges). We believe that this review study provides a profound overview of the global academic-trends in neuromarketing research, using the neuroimaging and physiological studies in advertising to study the brain processes of consumers. Thus, it provides valuable and reliable insights into the appropriate brain processes to be considered in future research.

Acknowledgments

The authors would like to thank Universiti Teknologi Malaysia (UTM), Azman Hashim International Business School (AHIBS); Taif University, Department of Economic & Finance, College of Business Administration; and Applied Science Private University (ASU), Department of Marketing for supporting this study.

Funding Statement

This research received no external funding.

Author Contributions

A.H.A., conceptualization, methodology, writing—original draft preparation, result discussion, and data curation; N.Z.M.S., supervision, review and editing; S.A.A.-Z., review, editing, and methodology; A.K., review and results discussion. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

9 Advertising Trends to Watch in 2024 [New Data + Expert Insights]

Published: April 09, 2024

Advertising is an ever-changing beast — with those on ad and marketing teams working hard to stay ahead of trends.

In 2023, we saw the emergence of AI in all forms of content, the rise of personalization, augmented realities (including the metaverse), and the explosion of short-form video.

As a seasoned content marketer working in tech, I’ve noticed that advertising trends in 2024 seem to respond to the trends we saw in 2023.

For example, if my ad team was firmly against using artificial intelligence last year, I’d need to catch up with everyone who initially embraced it.

You might even be using AI without knowing it, with Google using AI in paid search to create relevant ads.

In 2024, advertisers will need to stay on top of trends, or their ad money won’t go as far. In this article, I’ll discuss upcoming advertising trends and how you can leverage these stats to increase engagement, value, and sales.

Digital Advertising Trends in 2024

Social media advertising trends in 2024, other emerging advertising trends in 2024, understanding ad trends in 2024.

While ad spending is predicted to spike in 2024 , teams will likely be more responsible for how ads perform as companies tighten their budgets in anticipation of a recession.

Knowing and using the latest trends to your advantage can help you get more value out of the money you spend on advertising this year.

Artificial intelligence has existed, to some degree, for the last 70 years. Yet, it’s only in the last couple of years that it’s become a mainstream idea in the advertising space.

Many business leaders believe we underestimate AI’s impact on companies, with 65% reporting that AI will rival the Industrial Revolution in its impact on productivity.

What does this mean for you?

In 2024, AI will be one of the best ways businesses can scale their growth and increase the quality of their advertisements.

If you want to leverage AI more than you currently do, consider hiring an AI expert or consultant to help you integrate AI into your work processes and advertising efforts.

I may not have an AI expert on staff, but I definitely experiment with these new tools to see where they work best. I’m not alone. HubSpot Program Manager Kaitlin Milliken also takes the time to test AI solutions like ChatGPT.

"AI may not be the best at coming up with creative concepts or writing the copy itself, but I use AI to eliminate manual tasks and supplement my skills," Milliken says. "I had to work with large sets of images for a project. I used ChatGPT to create a Python script that managed the files for me. That’s saved me hours."

If you don’t have the budget to hire an AI expert right now, there’s no reason why you can’t start consulting AI programs like Gemini or ChatGPT during every stage of the advertising process.

I like to use AI during brainstorming, ideation, content writing, planning, and more. Check out this blog on AI in Digital Marketing for more information.

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The State of Marketing in 2024

HubSpot's Annual Inbound Marketing Trends Report

• Top Marketing Channels
• AI in Marketing
• Managing Privacy
• The Future of Marketing

You're all set!

Click this link to access this resource at any time.

2. Integrating Email Marketing With Other Channels

" While 87% report email is critical to their success, only 24% of email marketing programs are integrated into other marketing channels ." ( Litmus )

It’s no secret that email marketing has one of the best ROIs of any digital marketing channel. Unfortunately, most marketing teams aren’t integrating email marketing programs into other marketing channels, limiting email’s impact.

How can you integrate email into your other marketing channels?

The first step to successfully integrating email programs into other channels is segmenting your lists (of emails) by audience type and making sure advertisements and content fit their needs.

For example, in my email lists, I will segment by industry, size of their business, products purchased in the past, and areas of interest. If you don’t know the answers to these questions, adjust your forms to require these questions.

Once you’ve segmented, you will then want to optimize your email delivery and automate your distribution. If you’re running an advertisement that’s costing you a fortune in PPC, be sure those on your email lists know about it first.

For more ideas on integrating email campaigns, check out this blog here.

3. Social Media Advertising

" Facebook, Instagram, YouTube, and TikTok have the strongest ROI — and these align with where marketers are investing in 2024 ."  ( HubSpot State of Marketing )

I’ve found that where I choose to display my ads is just as important as the actual ads. If you’re not currently investing in one of these platforms, there’s a good chance you’re missing out on potential leads.

In fact, 27% of marketers who don’t use YouTube plan to start in the year ahead.

There’s also the looming possibility of the U.S. government banning TikTok, which could, in turn, skyrocket the cost of advertising on remaining platforms and oversaturate those markets if you aren’t ahead of the curve.

How can you get a strong ROI from social media platforms?

In 2024, a deep understanding of social media buying is vital for getting a good ad ROI. Careful planning and organization will ensure you’re investing in the right platforms. Check out this free media buying template to get started.

Be sure to monitor your ads’ performance to identify which platform provides the best bang for your buck. If a certain platform is lagging, consider investing in organic content marketing on that website instead of paid ads.

Creating a video-centric social media strategy might be the most crucial trend you will follow in 2024.

While certain social media platforms have always favored video (Youtube, TikTok, etc.), platforms that haven’t always done so now do favor video (Instagram, Facebook, X). Here are some social media ad trends you need to know.

4. Consistently Post on X

" 66% of marketers will keep their brand on Twitter/ X."  ( HubSpot )

Despite Twitter’s rebranding to X and extensive layoffs last year by owner Elon Musk, the majority of marketers intend to keep their brand on X.

This is likely due to how simple the app is to use for quick updates, PR, and conversion, even with minimal time and money.

There are two different types of video ads that marketers can use on Instagram: traditional advertisements, which can be static images or videos, and sponsored video content.

Sponsored video content is when your brand pays an influencer with either product, money, or both to talk about your brand. Most social media platforms require influencers to use hashtags that identify the video as a paid advertisement so viewers will know that the influencer was compensated to say what they’re saying.

Why Sponsored Video Content Works on Instagram

Many have speculated that Instagram changed its algorithm to favor video content as a response to the growing popularity of TikTok.

With the introduction of the “Instagram Reels” feature, brands have begun publishing short-form video content on the app.

2024 is likely to bring a lot of competition to the Instagram Reels scene, particularly if a TikTok ban does occur. So get started on posting on Instagram today with these helpful tips.

6. Influencer Marketing

"30% of brands already work with influencers/creators, and 42% plan to begin this year."  ( HubSpot Instagram Marketing Report )

Influencer content is predicted to have a high ROI in 2024 , so it makes sense that more brands plan to work closely with creators. There are many ways to work with influencers.

While most influencer-involved content has to be labeled as an advertisement, you move into the gray area of advertising by sending influencers Public Relationship (PR) boxes.

PR boxes are a way for influencers to try new products and give their honest review. However, there’s no guarantee that they will review your PR box, so be sure to do your homework and pay careful attention to personalization.

Check out this blog on influencer marketing in 2024 for more ideas.

Because so many famous TikTok influencers were on the Tarte trip, everyone’s "for you" feed was flooded with Tarte-sponsored content.

While the Tarte trip must have cost a pretty penny, in my opinion, they likely made this back tenfold with the brand and product awareness they got.

Other ad trends in 2024 include marketers’ preferred audiences, experiential marketing, and the importance of personalization. Keep reading to learn about other emerging trends in advertising.

7. Experiential Marketing

"16% of marketers plan to try experiential marketing (engaging audiences in real life with pop-ups and events) for the first time." ( HubSpot )

Experiential marketing is the actual experience your audience has at events, trade shows, or during campaigns.

The 2020 pandemic canceled most in-person events or forced marketers to make them virtual events, putting experiential marketing in the back seat.

However, now that restrictions have been lifted, experiential marketing is having a hay day, with 77% of marketers using it as a key part of their plan.

How can you use experiential marketing in 2024?

If you’re planning on attending any shows this year, make sure your booth is both eye-catching and memorable . If you’re marketing a new product this year, think outside the box and really consider your user experience.

Some past experiential campaigns have included branded filters on social media, fun pop-up shops, interactive content , giveaways, and more. You might also consider how virtual reality can improve your marketing.

8. Personalization

"73% of marketers say personalization is important, but only 35% believe their customers get a very personalized experience from their brand."  ( HubSpot State of Marketing)

Personalization is an important aspect of marketing in 2024 because most industries are supersaturated with similar products, and it’s getting harder to stand out .

While creating a personalized experience can be time-consuming, there are now several tools that can assist in the process.

For example, I was once contacted by a marketer from Reachdesk (a company that specializes in personal gifting), and the marketer sent me a watercolor set because my profile described my love of painting.

This attention to detail and personalization got my attention and my interest in their product. If you’re new to personalization and want to consult an expert, consider trying Hubspot’s Technical Consulting .

Check out this blog to explore more brands that take personalization seriously.

What does personalization look like in 2024?

In 2024, personalization looks like full names in email subject lines, abandoned shopping cart emails/texts with discount codes, product recommendations based on search history, chatbots to customize web experiences, and more.

To ensure your customers are getting the best-personalized experience, consider sending out surveys that ask customers how easily they can navigate your website and what features they’d like to see added.

9. Targeting Generational Audiences

"74% of marketers want to reach Millenials, 67% want to reach Gen X, and only 27% are interested in Baby Boomer audiences."  ( HubSpot State of Marketing)

Generations that grew up using the internet are increasingly the target of marketers. Seen as “digital natives,” millennials and Gen X are often key decision-makers with purchasing power at organizations.

In my opinion, this trend is likely due to marketing largely taking place online versus traditional advertising means, which had more in-person meetings and physical advertisements.

How can you reach digital natives in 2024?

The best way to reach each generation is to understand what kind of advertising works for them. Millennials primarily engage with social media, apps, and brands that care about social issues.

Gen X prefers to discover products through search, TV ads, and specific social media platforms (Facebook, YouTube, and Instagram). These trends make social media an important investment for companies hoping to reach digital natives.

Only 4% of boomers have purchased a product through social media, while TV ads, internet searches, and retail stores work better for their generation. Check out this blog on how each generation shops for more ideas.

Staying on top of trends is an important part of any marketer or advertiser’s job description. I’ve found it super helpful to subscribe to or follow the HubSpot Blog so I get the latest stats on which trends are working and which ones to avoid.

Some aspects of marketing will never change, such as solving customer problems and "making sure that the customer is representative of a large market" so you can "have a pretty good formula," as Melanie Perkins advises.

As you solve customer problems and try the latest trends this next year, be sure to be consistent in your efforts, track your results, and stay open to new ideas.

Don't forget to share this post!

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Effect of AI Generated Content Advertising on Consumer Engagement

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With a series of major breakthroughs in AI technology, the use of AI generated content (AIGC) has become one of the most popular research topics. Social media marketing has become also a mainstream and irreplaceable way for marketing subject. Many companies are starting to use AI technology to optimize existing marketing activities to reduce costs and improve efficiency. However, how companies use the latest AIGC to generate ads that affects consumers engagement needed to be further discussed. Thus, the purpose of this paper is to investigate how firm used AIGC advertising influence customer psychological engagement and behavioral engagement with the consideration of ad emotion level and whether the firm used AIGC is explicitly labeled. Online experiments are conducted and data is analyzed through MLR models. Results show that AIGC could positively influence customer both psychological and behavior engagements, with psychological engagement being mediating factor between AIGC and behavioral engagement. Emotion level plays a negative moderating role. In addition, we find that labeling AI-generated advertisements does significantly affect customer behavior engagement.

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The research was financially supported by the National Natural Science Foundation of China under grant [number 71831005], Natural Science Foundation of Shenzhen under grant [number JCYJ20220531095216037], and Natural Science Foundation of Guangdong Province [number 2023A1515012520].

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Du, D., Zhang, Y., Ge, J. (2023). Effect of AI Generated Content Advertising on Consumer Engagement. In: Nah, F., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2023. Lecture Notes in Computer Science, vol 14039. Springer, Cham. https://doi.org/10.1007/978-3-031-36049-7_9

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Violent crime is a key midterm voting issue, but what does the data say?

Political candidates around the United States have released thousands of ads focusing on violent crime this year, and most registered voters see the issue as very important in the Nov. 8 midterm elections. But official statistics from the federal government paint a complicated picture when it comes to recent changes in the U.S. violent crime rate.

With Election Day approaching, here’s a closer look at voter attitudes about violent crime, as well as an analysis of the nation’s violent crime rate itself. All findings are drawn from Center surveys and the federal government’s two primary measures of crime : a large annual survey from the Bureau of Justice Statistics (BJS) and an annual study of local police data from the Federal Bureau of Investigation (FBI).

This Pew Research Center analysis examines the importance of violent crime as a voting issue in this year’s congressional elections and provides the latest available government data on the nation’s violent crime rate in recent years.

The public opinion data in this analysis is based on a Center survey of 5,098 U.S. adults, including 3,993 registered voters, conducted Oct. 10-16, 2022. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology . Here are the questions used in the survey , along with responses, and its methodology .

The government crime statistics cited here come from the National Crime Victimization Survey , published by the Bureau of Justice Statistics, and the National Incident-Based Reporting System , published by the Federal Bureau of Investigation. For both studies, 2021 is the most recent year with available data.

Around six-in-ten registered voters (61%) say violent crime is very important when making their decision about who to vote for in this year’s congressional elections. Violent crime ranks alongside energy policy and health care in perceived importance as a midterm issue, but far below the economy , according to the Center’s October survey.

Republican voters are much more likely than Democratic voters to see violent crime as a key voting issue this year. Roughly three-quarters of Republican and GOP-leaning registered voters (73%) say violent crime is very important to their vote, compared with around half of Democratic or Democratic-leaning registered voters (49%).

Conservative Republican voters are especially focused on the issue: About eight-in-ten (77%) see violent crime as very important to their vote, compared with 63% of moderate or liberal Republican voters, 65% of moderate or conservative Democratic voters and only about a third of liberal Democratic voters (34%).

Older voters are far more likely than younger ones to see violent crime as a key election issue. Three-quarters of registered voters ages 65 and older say violent crime is a very important voting issue for them this year, compared with fewer than half of voters under 30 (44%).

There are other demographic differences, too. When it comes to education, for example, voters without a college degree are substantially more likely than voters who have graduated from college to say violent crime is very important to their midterm vote.

Black voters are particularly likely to say violent crime is a very important midterm issue. Black Americans have consistently been more likely than other racial and ethnic groups to express concern about violent crime, and that remains the case this year.

Some 81% of Black registered voters say violent crime is very important to their midterm vote, compared with 65% of Hispanic and 56% of White voters. (There were not enough Asian American voters in the Center’s survey to analyze independently.)

Differences by race are especially pronounced among Democratic registered voters. While 82% of Black Democratic voters say violent crime is very important to their vote this year, only a third of White Democratic voters say the same.

Annual government surveys from the Bureau of Justice Statistics show no recent increase in the U.S. violent crime rate. In 2021, the most recent year with available data , there were 16.5 violent crimes for every 1,000 Americans ages 12 and older. That was statistically unchanged from the year before, below pre-pandemic levels and far below the rates recorded in the 1990s, according to the National Crime Victimization Survey .

For each of the four violent crime types tracked in the survey – simple assault, aggravated assault, robbery and rape/sexual assault – there was no statistically significant increase either in 2020 or 2021.

The National Crime Victimization Survey is fielded each year among approximately 240,000 Americans ages 12 and older and asks them to describe any recent experiences they have had with crime. The survey counts threatened, attempted and completed crimes, whether or not they were reported to police. Notably, it does not track the most serious form of violent crime, murder, because it is based on interviews with surviving crime victims.

The FBI also estimates that there was no increase in the violent crime rate in 2021. The other major government study of crime in the U.S., the National Incident-Based Reporting System from the Federal Bureau of Investigation, uses a different methodology from the BJS survey and only tracks crimes that are reported to police.

The most recent version of the FBI study shows no rise in the national violent crime rate between 2020 and 2021. That said, there is considerable uncertainty around the FBI’s figures for 2021 because of a transition to a new data collection system . The FBI reported an increase in the violent crime rate between 2019 and 2020, when the previous data collection system was still in place.

The FBI estimates the violent crime rate by tracking four offenses that only partly overlap with those tracked by the National Crime Victimization Survey: murder and non-negligent manslaughter, rape, aggravated assault and robbery. It relies on data voluntarily submitted by thousands of local police departments, but many law enforcement agencies do not participate.

In the latest FBI study, around four-in-ten police departments – including large ones such as the New York Police Department – did not submit data, so the FBI estimated data for those areas. The high nonparticipation rate is at least partly due to the new reporting system, which asks local police departments to submit far more information about each crime than in the past. The new reporting system also makes it difficult to compare recent data with data from past years.

While the total U.S. violent crime rate does not appear to have increased recently, the most serious form of violent crime – murder – has risen significantly during the pandemic. Both the FBI and the Centers for Disease Control and Prevention (CDC) reported a roughly 30% increase in the U.S. murder rate between 2019 and 2020, marking one of the largest year-over-year increases ever recorded. The FBI’s latest data , as well as provisional data from the CDC , suggest that murders continued to rise in 2021.

Despite the increase in the nation’s murder rate in 2020, the rate remained well below past highs, and murder remains the least common type of violent crime overall.

There are many reasons why voters might be concerned about violent crime, even if official statistics do not show an increase in the nation’s total violent crime rate. One important consideration is that official statistics for 2022 are not yet available. Voters might be reacting to an increase in violent crime that has yet to surface in annual government reports. Some estimates from nongovernmental organizations do point to an increase in certain kinds of violent crime in 2022: For example, the Major Cities Chiefs Association, an organization of police executives representing large cities, estimates that robberies and aggravated assaults increased in the first six months of this year compared with the same period the year before.

Voters also might be thinking of specific kinds of violent crime – such as murder, which has risen substantially – rather than the total violent crime rate, which is an aggregate measure that includes several different crime types, such as assault and robbery.

Some voters could be reacting to conditions in their own communities rather than at the national level. Violent crime is a heavily localized phenomenon , and the national violent crime rate may not reflect conditions in Americans’ own neighborhoods.

Media coverage could affect voters’ perceptions about violent crime , too, as could public statements from political candidates and elected officials. Republican candidates, in particular, have emphasized crime on the campaign trail this year.

More broadly, the public often tends to believe that crime is up, even when the data shows it is down. In 22 of 26 Gallup surveys conducted since 1993, at least six-in-ten U.S. adults said there was more crime nationally than there was the year before, despite the general downward trend in the national violent crime rate during most of that period.

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8 facts about Black Lives Matter

#blacklivesmatter turns 10, support for the black lives matter movement has dropped considerably from its peak in 2020, fewer than 1% of federal criminal defendants were acquitted in 2022, before release of video showing tyre nichols’ beating, public views of police conduct had improved modestly, most popular.

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Google Ads To Retire Customizers For Text Ads

Google to stop serving ad customizers for text ads and dynamic search ads, pushing marketers to responsive search ads.

• Google is ending ad customizers for text ads and Dynamic Search Ads.
• Advertisers must transition ad customizers to responsive search ads by that deadline.
• This continues Google's push for responsive search ads as the primary ad format.

In an email sent to Google Ads advertisers this week, the company announced a change coming to search ads.

Effective May 31, 2024, ad customizers will stop serving for expanded text ads (ETAs) and Dynamic Search Ads (DSAs).

The notification reads in part:

“On May 31, 2024, existing ad customizers for text ads, expanded text ads and Dynamic Search Ads will stop serving (after this date they will only be able to serve with their default value).”

For advertisers leveraging customizers with their text ads or DSAs, Google recommends “transitioning to responsive search ads and creating ad customizers for responsive search ads by May 31, 2024.”

Reactions Within The Search Marketing Community

News of the impending change sparked discussion among paid search professionals.

“I’ve never been a huge fan of customizers (both anecdotally and when looking at large data sets), but I respect that they do work for some advertisers. If you’re currently running ad customizers on your DSA or ETAs – now is the time to build them out as RSAs so you can gradually move to the new format.”

Hopkins clarified that the customizers will cease serving text ads and DSAs, not the ad types.

“If you didn’t bother with [customizers]…keep calm and carry on with your amazing human augmented creative segmentation!”

The Writing On The Wall For Text Ads

Google’s push for responsive search ads (RSAs) as the go-to search ad format has been apparent for some time.

In 2021, Google shared that RSAs would become the only search ad type advertisers could create or edit in standard search campaigns.

The following year, in June 2022, Google stopped allowing advertisers to create or edit expanded text ads within any of its surfaces—a clear sign that RSAs were taking over as the primary ad unit.

Many marketers have invested in responsive search ads over the past couple of years, and this latest move seems to be another step in that continued shift.

For those still leveraging custom ad text with their text ads and DSAs, the clock is ticking to rebuild those customized experiences with responsive search ads instead.

How does Google Ads’ change regarding ad customizers impact advertisers?

To maintain personalized advertising experiences, Google Ads advertisers now need to:

• Transition their existing ad customizers to responsive search ads (RSAs) by the May 31 deadline.
• Rebuild customized ad experiences within the RSA format, which offers dynamic customization capabilities.
• Adapt to a new ad landscape where RSAs are becoming the primary format for search ads.

What are the steps for advertisers to transition from ETAs to RSAs?

Advertisers must take proactive measures to ensure a smooth transition from expanded text ads to responsive search ads:

• Review current ad campaigns using ETAs and identify which utilize ad customizers.
• Create new responsive search ads that implement customizers before the deadline.
• Test and optimize these RSAs for performance against current ETAs to ensure minimal disruption.
• Gradually phase out ETAs in favor of RSAs to become accustomed to the new format.

Why is Google pushing for a transition to responsive search ads?

Google’s push towards responsive search ads is rooted in adaptability and efficiency. The transition reflects an effort to:

• Simplify ad creation while maximizing reach and relevance across different search queries.
• Employ a more automated approach to ad optimization using machine learning algorithms.
• Streamline the ad platform by focusing on a single, more effective ad type that can adjust to user queries and device types.

Featured Image: Vladimka production/Shutterstock

Matt G. Southern, Senior News Writer, has been with Search Engine Journal since 2013. With a bachelor’s degree in communications, ...

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Warner Bros. Discovery Hires Canoe Ventures CEO David Porter as Head of Ad Sales Research

By Todd Spangler

Todd Spangler

NY Digital Editor

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David Porter , an ad industry veteran and former Turner exec, is joining Warner Bros. Discovery as head of ad sales research, data and insights.

Porter reports to Jon Steinlauf, Warner Bros. Discovery’s chief of U.S. advertising sales, and will split his time between WBD’s New York City and Atlanta offices. The announcement of Porter’s hiring comes a month ahead of the sale org’s May 15 Upfront presentation in New York City . Porter replaces Andrea Zapata, who exited WBD at the end of 2023 and is now T-Mobile’s VP of advertising data, measurement and partnerships.

Popular on Variety

Prior to joining Canoe in 2020, Porter was VP of ad innovation and programmatic solutions at Turner, which is now part of WBD. Additionally, Porter worked at AOL as global video monetization lead and at Microsoft as global sales strategy lead for video. Before that he spent 10 years at Cox Communications where he served as VP of new media, developing new ad capabilities within the cable footprint.

At Warner Bros. Discovery, Porter will oversee ad sales research and data functions across the company’s portfolio of streaming, digital and linear platforms. He will lead overall strategy and execution for alternative currencies, advanced analytics, cross-platform measurement and insights, streaming and digital engagement, and data clean rooms. Additionally, Porter is tasked with the development of advanced tools and technology to let advertisers maximize their first-party data, optimize campaigns and measure impact across the WBD converged video platform.

“We take a data-driven approach to develop impactful partnership opportunities across the portfolio, and David’s work is essential to that offering,” Steinlauf said in announcing Porter’s hire. “David has invaluable experience that will play a key role in continuing to deliver best-in-class client services and consumer experiences.”

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