Although firms invest much in targeted advertising, little is known about when it is most effective. A new Marketing Science Institute (MSI) working paper now shows that such advertising helps facilitate the purchase journey along different stages. While targeting on social media is found effective for consumers during the early stages of that journey, retargeting is best served for consumers who are closer along the line to making that purchase. Another important finding was that cross-platform targeting does not motivate consumers who are unfamiliar with the product or brand.
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At this Insights Studio, authors from three different continents showcase their recently published work—including the JAR Best Paper 2022 on how advertising expenditures drive consumers’ perceptions of ad and brand quality. Also featured are studies on deepfakes and AI reshaping the advertising industry, the success of using product endorsers who are actors known for roles portraying despicable characters, and KPI patterns of social media influencers across several platforms. Talking points in the concluding Q&A span the future of AI in advertising and influencer marketing, machine-driven decisions for choosing endorsers, and factors (product- and economic-related) affecting consumer perceptions of quality in TV ads and engagement in user-generated content.
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Retail media networks, generative AI across creative, market research and trust, ad effectiveness and attention: These are among the topics highlighted on the Journal of Advertising Research’s list of 2024 research priorities. The list is a result of one-on-one interviews with advertising professionals by Editor-in-Chief Colin Campbell, who asked: "What are your biggest needs and challenges?"
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Chinese researchers have found that social media advertising can make a significant impact on people who are socially connected to the viewers of ads—even more so than on the targeted viewers. This effect is strongest among consumers with weaker rather than strong social ties, and it challenges targeting methods rooted in customer centricity models.
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