purchase journey

The State of Retail Media Networks & Consumer Behavior

  • Shopper 2025

On May 21, the industry’s top minds gathered in Chicago for a look at the future of retail, media, and consumer behavior and dove into the rapidly evolving role of Retail Media Networks (RMNs). Attendees gained actionable insights on the opportunities and challenges that RMNs present. Leading experts led discussions on optimizing RMN investments, navigating sales attribution complexities, adopting an "omni-normal" approach to connect with shoppers across all touchpoints, harnessing AI for brands and consumers, and more.

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Enhancing Household Marketing: The Power of Dyad-Exposure Advertising

  • JOURNAL OF ADVERTISING RESEARCH

The household is a crucial unit of consumption that involves joint decision-making. While many studies have focused on individual-level advertising impacts, the interactions among household members have been largely overlooked. This study investigates a dyad-exposure advertising method that targets both spouses as decision-makers in purchasing household products. The findings reveal that dyad exposure significantly increases conversion rates by stimulating intra-couple interaction.

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Shopper Insights Council: Diving into Key Issues in Shopper Marketing

  • Shopper Council

As the shopper marketing landscape continues to evolve rapidly, the ARF’s Shopper Insights Council held a half-day event that offered two unique opportunities: 1) To hear from knowledgeable speakers about Retail Media Networks and omnichannel shopper marketing; 2) To participate in focused workshops and share perspectives, concerns and best practices with others in the industry having similar roles.

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Using AI Assistants to Predict Purchase Intent

  • MSI

This study examines user interactions with AI assistants to infer purchase intent. By analyzing the text of user-initiated interactions, researchers build a bipartite network of nouns and verbs and measure the distance of specific words to "golden" purchasing words like "purchase," "buy" or "order." The study uses large language models, specifically Chat-GPT4, to annotate data with a measure of purchase intent and validates this method by comparing the results with cost-per-click (CPC) for keywords in Google Ads. The findings suggest that words used in an exchange with an AI assistant can predict purchase intent without customer tracking across interactions.

These findings have implications for using customized small versus large language models and can potentially inform advertising decisions. The study highlights the importance of understanding consumer behaviors in interactions with AI assistants. It provides a method to predict purchase intent based solely on the textual content of these interactions.

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