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OTT 2024: Today's Dynamic Media Landscape -- What's Next?
Attendees joined us on October 23 for our annual OTT conference, offering the latest research on shifts in the TV and video landscape, viewer behavior, and cross-platform measurement. Industry experts discussed trends in viewing habits, advertising innovations, and predictions for 2025. Attendees also had the opportunity to participate in discussions and network with industry peers over breakfast, lunch, and the cocktail reception.
Retail Media Networks: Growth, Opportunities and Challenges
Retail media networks (RMNs) are rapidly becoming one of the largest and fastest-growing ad verticals in the U.S., offering personalized ads across retailers’ ecosystems. These networks also provide brands with targeted ads and higher conversion rates, while retailers gain new revenue streams and enhanced shopping experiences.
Despite staggering growth, RMNs face many challenges as they continue to grow globally, including a lack of standardization across networks, measurement difficulties, rising costs and complexity. Agencies face additional issues, such as teams lacking the necessary expertise to help clients navigate RMNs.
Improve Marketing Mix Model (MMM) Accuracy by Identifying these Effects
This study explores the identification of nonlinear and time-varying effects in marketing mix models (MMM). It highlights the challenges of conflation in model selection and proposes a framework for simulating and estimating these effects using Gaussian processes. The study emphasizes the importance of accurately identifying the underlying response to optimize marketing spending.
The research provides insights into the complexities of marketing effectiveness and offers practical solutions for improving model accuracy. By addressing the issue of conflation, the study aims to enhance the decision-making process in marketing strategies.
Creative Effectiveness 2024
On September 26, the ARF held our third annual Creative Effectiveness conference where we discussed and debated questions around reclaiming creativity in the age of AI. Brand, agency, media and research sages showcased examples of how they are stimulating and measuring creative with various approaches and tools. Following the conference, attendees joined us for an evening of celebrating at the ARF David Ogilvy Awards — honoring research- and insights-driven advertising.
Using “Containing Language” Effectively Boosts Price Communications Effectiveness
This study explores the impact of using "containing language" in advertisements on perceived offer fairness and consumer behavior. Identifying useful phrases like "That's it!" and "Period!" can reduce perceived price complexity and enhance perceived offer fairness, leading to higher purchase intentions, the researchers conclude. These findings suggest that marketers can use such language to communicate prices more effectively and responsibly.
The study involved multiple experiments and a large-scale field study, demonstrating that containing language can positively influence consumer perceptions and responses. The research provides valuable insights for marketing practitioners on how to design advertisements that improve consumer trust and engagement.
Using AI Assistants to Predict Purchase Intent
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.