Research
The NRF Business of Retail Initiative at Georgetown University’s McDonough School of Business supports faculty-driven research that addresses critical challenges in the retail industry. Through close collaboration with retail partners, the initiative generates actionable insights, informs business practice, and contributes to the ongoing transformation of the retail sector.
Retail Traffic, Technology, and the Future of Promotion Measurement
Professor Ronald Goodstein
Key Finding: Tracking shopper arrival traffic gives retailers a more accurate measure of promotional success and helps optimize staffing, inventory, and operations.
The latest research from Ronald Goodstein, Associate Professor of Marketing at Georgetown McDonough, explores how retailers can use sensor technology and shopper traffic data to better evaluate the true impact of promotions. Rather than focusing only on sales outcomes, the study emphasizes the importance of store arrival traffic as an early indicator of retail performance. Using shopper-specific arrival data captured through in-store sensors, the research develops a model that isolates how promotions influence customer traffic patterns beyond normal fluctuations.
The study demonstrates that understanding traffic flow provides retailers with a more complete view of the customer journey, from store entry to conversion and purchase behavior. In a real-world case study analyzing a two-week price promotion, the model identified approximately 2,880 additional store arrivals generated by the campaign. The findings also show how retailers can use traffic insights to improve staffing decisions, optimize inventory planning, and identify operational gaps when promotions drive visits but fail to convert shoppers into buyers.
Beyond measuring promotional success, the research highlights the growing role of accessible retail technologies in improving operational agility and customer experience. By leveraging real-time traffic patterns and predictive modeling, retailers can make faster decisions, adjust resources dynamically, and better understand consumer behavior throughout the shopping journey. The study reinforces how data-driven retail strategies are becoming increasingly critical as retailers balance operational efficiency with customer engagement in today’s evolving retail landscape.”
Read the Full PaperKey Finding: Tracking shopper arrival traffic gives retailers a more accurate measure of promotional success and helps optimize staffing, inventory, and operations.
The latest research from Ronald Goodstein, Associate Professor of Marketing at Georgetown McDonough, explores how retailers can use sensor technology and shopper traffic data to better evaluate the true impact of promotions. Rather than focusing only on sales outcomes, the study emphasizes the importance of store arrival traffic as an early indicator of retail performance. Using shopper-specific arrival data captured through in-store sensors, the research develops a model that isolates how promotions influence customer traffic patterns beyond normal fluctuations.
The study demonstrates that understanding traffic flow provides retailers with a more complete view of the customer journey, from store entry to conversion and purchase behavior. In a real-world case study analyzing a two-week price promotion, the model identified approximately 2,880 additional store arrivals generated by the campaign. The findings also show how retailers can use traffic insights to improve staffing decisions, optimize inventory planning, and identify operational gaps when promotions drive visits but fail to convert shoppers into buyers.
Beyond measuring promotional success, the research highlights the growing role of accessible retail technologies in improving operational agility and customer experience. By leveraging real-time traffic patterns and predictive modeling, retailers can make faster decisions, adjust resources dynamically, and better understand consumer behavior throughout the shopping journey. The study reinforces how data-driven retail strategies are becoming increasingly critical as retailers balance operational efficiency with customer engagement in today’s evolving retail landscape.”
Brand Visibility and Marketing in the Age of AI
Professor Luc Wathieu
Key Finding: Brands with clear and consistent, digital information are more likely to be recommended by generative AI, making AI visibility a critical part of modern marketing strategy.
The latest insights from John Gale and Luc Wathieu, Adjunct Faculty Member and NRF Business of Retail Research Director at Georgetown McDonough, respectively, explores how generative AI is reshaping brand discovery and what companies must do to remain visible as consumers increasingly rely on AI assistants for recommendations. In their Harvard Business Review article, “How to Get AI to Surface Your Brand,” rather than focusing solely on traditional brand awareness or search engine optimization, they argue that companies must ensure AI systems can accurately recognize, interpret, and recommend their brands. By presenting clear and consistent information across digital channels, organizations can improve the likelihood that AI-generated responses include their products and services.
The article demonstrates that AI recommendation systems prioritize brands with well-defined positioning and strong digital authority over those with broad name recognition alone. Because AI models synthesize information from company websites, third-party publications, and reviews, brands that consistently communicate what they offer, who they serve, and what differentiates them are more likely to appear in AI-generated recommendations. Gale and Watheiu illustrate that even highly recognizable brands may be overlooked if their digital presence lacks the clarity and consistency AI systems require to retrieve information and recommend them to users.
Beyond brand visibility, the article highlights the broader implications of AI for marketing strategy and customer engagement. As generative AI becomes an increasingly important gateway for consumer decision-making, organizations will need to adapt their content, messaging, and digital strategies to optimize for both human audiences and AI systems. The research emphasizes that success in the AI era will depend not only on building memorable brands but also on creating structured, credible, and accessible information that enables AI to accurately represent and recommend brands throughout the customer decision journey.
Read the Full PaperKey Finding: Brands with clear and consistent, digital information are more likely to be recommended by generative AI, making AI visibility a critical part of modern marketing strategy.
The latest insights from John Gale and Luc Wathieu, Adjunct Faculty Member and NRF Business of Retail Research Director at Georgetown McDonough, respectively, explores how generative AI is reshaping brand discovery and what companies must do to remain visible as consumers increasingly rely on AI assistants for recommendations. In their Harvard Business Review article, “How to Get AI to Surface Your Brand,” rather than focusing solely on traditional brand awareness or search engine optimization, they argue that companies must ensure AI systems can accurately recognize, interpret, and recommend their brands. By presenting clear and consistent information across digital channels, organizations can improve the likelihood that AI-generated responses include their products and services.
The article demonstrates that AI recommendation systems prioritize brands with well-defined positioning and strong digital authority over those with broad name recognition alone. Because AI models synthesize information from company websites, third-party publications, and reviews, brands that consistently communicate what they offer, who they serve, and what differentiates them are more likely to appear in AI-generated recommendations. Gale and Watheiu illustrate that even highly recognizable brands may be overlooked if their digital presence lacks the clarity and consistency AI systems require to retrieve information and recommend them to users.
Beyond brand visibility, the article highlights the broader implications of AI for marketing strategy and customer engagement. As generative AI becomes an increasingly important gateway for consumer decision-making, organizations will need to adapt their content, messaging, and digital strategies to optimize for both human audiences and AI systems. The research emphasizes that success in the AI era will depend not only on building memorable brands but also on creating structured, credible, and accessible information that enables AI to accurately represent and recommend brands throughout the customer decision journey.
Consumer Behavior using Product Customization
Professor Suh Yoeh Kim and Rebecca Hamilton
Key Finding: Product customization experiences encourage repeat customization and builds long-term customer value through increased spending and engagement.
New research from Georgetown McDonough Professors of Marketing Suh Yeoh Kim and Rebecca Hamilton and their collegagues explore how product customization influences customer behavior throughout the customer journey and why an initial customization experience can create lasting value for firms. Their research examines whether encouraging customers to customize products affects future purchasing behavior beyond the initial transaction. Drawing on nine years of retailer transaction data and a series of longitudinal experiments, they demonstrate that customers who customize a product once are significantly more likely to customize again, spend more, purchase more items, and return to the retailer more frequently over time.
The article demonstrates that the benefits of customization extend far beyond offering customers personalized products. Rather than viewing customization as a one-time marketing tactic, the research shows that an initial customization experience can fundamentally reshape future customer behavior by increasing engagement and strengthening the customer–brand relationship. Customers who actively participate in designing or personalizing products become more invested in the purchasing process, making them more likely to continue interacting with the brand and generating greater long-term customer lifetime value. The findings also suggest that firms can strategically encourage repeat customization by designing positive and accessible first-time customization experiences.
Furthermore, the article highlights the broader implications of customization for marketing strategy and customer relationship management. As companies increasingly compete on customer experience rather than price alone, providing meaningful opportunities for personalization can serve as a powerful mechanism for building loyalty and increasing long-term profitability. The research emphasizes that customization should be viewed not simply as a product feature but as a strategic investment that encourages deeper customer engagement, strengthens ongoing relationships, and creates sustainable value for both customers and organizations.
Read the Full PaperKey Finding: Product customization experiences encourage repeat customization and builds long-term customer value through increased spending and engagement.
New research from Georgetown McDonough Professors of Marketing Suh Yeoh Kim and Rebecca Hamilton and their collegagues explore how product customization influences customer behavior throughout the customer journey and why an initial customization experience can create lasting value for firms. Their research examines whether encouraging customers to customize products affects future purchasing behavior beyond the initial transaction. Drawing on nine years of retailer transaction data and a series of longitudinal experiments, they demonstrate that customers who customize a product once are significantly more likely to customize again, spend more, purchase more items, and return to the retailer more frequently over time.
The article demonstrates that the benefits of customization extend far beyond offering customers personalized products. Rather than viewing customization as a one-time marketing tactic, the research shows that an initial customization experience can fundamentally reshape future customer behavior by increasing engagement and strengthening the customer–brand relationship. Customers who actively participate in designing or personalizing products become more invested in the purchasing process, making them more likely to continue interacting with the brand and generating greater long-term customer lifetime value. The findings also suggest that firms can strategically encourage repeat customization by designing positive and accessible first-time customization experiences.
Furthermore, the article highlights the broader implications of customization for marketing strategy and customer relationship management. As companies increasingly compete on customer experience rather than price alone, providing meaningful opportunities for personalization can serve as a powerful mechanism for building loyalty and increasing long-term profitability. The research emphasizes that customization should be viewed not simply as a product feature but as a strategic investment that encourages deeper customer engagement, strengthens ongoing relationships, and creates sustainable value for both customers and organizations.
Published Papers
Consumer Behavior
Consumer Psychology (March 10, 2022) Zhongbin Wang, Luyi Yang, Shiliang Cui, Sezer Ülkü And Yong-pin Zhou. “Pooling Agents For Customer-intensive Services.” Operations Research (March 9, 2022).
- Key Finding: Pooling service agents improves response efficiency and customer satisfaction in high-demand services.
Shawn Mankad, Masha Shunko And Qiuping Yu. “Too Close For Comfort? Understanding Peer Effects In Large Franchised Networks.” SSRN Electronic Journal (August 26, 2021).
- Key Finding: Customers’ comfort and behavior are strongly shaped by those around them in large retail networks.
Retail Technology & AI
Yin, Mingzhang, Ziwei Cong, and Jia Liu. “Unraveling Multifaceted User Preferences on Digital Platforms: A Bayesian Deep-Learning Approach.” Forthcoming at Marketing Science (2026).
- Key Finding: Retail and digital platforms can improve personalization and customer experience by using Bayesian deep learning to identify consumers evolving preferences.
Cinthia B. Satornino, Dhruv Grewal, Abhijit Guha, Elisa B. Schweiger And Ronald C. Goodstein. “The Perks And Perils Of Artificial Intelligence Use In Lateral Exchange Markets.” Journal Of Business Research, 158 (March 1, 2023): 113580. DOI: https://doi.org/10.1016/j.jbusres.2022.113580.
- Key Finding: Artificial Intelligence can enhance retail-like marketplaces by improving search recommendations.
Pricing & Promotions
Chun, SoYeon and Rebecca W. Hamilton. “Should I Pay with Money or Redeem Points for This Purchase? Effects of Exchange Rate Stability on Loyalty Point Redemption.” Journal of Marketing Research, 61, 5 (October 2024): 858-871.
- Key Finding: Consumers are more likely to redeem loyalty points when point-to-currency exchange rates are stable and predictable.
Chris Hydock and Luc Wathieu. “Not Just About Price: How Benefit Focus Determines Consumers’ Retailer Pricing Strategy Preference.” Journal of Consumer Research (August 1, 2023)
- Key Finding: Consumers’ preferred pricing strategies depend on whether they focus more on monetary savings or other benefits, not just the price itself.
Service and Customer Experience
Consumer Psychology (March 10, 2022) Zhongbin Wang, Luyi Yang, Shiliang Cui, Sezer Ülkü And Yong-pin Zhou. “Pooling Agents For Customer-intensive Services.” Operations Research (March 9, 2022).
- Key Finding: Pooling service agents improves response efficiency and customer satisfaction in high-demand services.
Shawn Mankad, Masha Shunko And Qiuping Yu. “Too Close For Comfort? Understanding Peer Effects In Large Franchised Networks.” SSRN Electronic Journal (August 26, 2021).
- Key Finding: Performance and operational decisions within franchise networks are influenced by nearby peer locations, creating measurable spillover effects.
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