The value of online interactions for store execution
Problem definition: Omnichannel retailers interact with customers both online and offline. So far, they have used the richer information available—gathered from customer interactions across digital and physical channels—to optimize the sales process by designing the right channel and supply chain st...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | Dadun. Depósito Académico Digital de la Universidad de Navarra |
| Idioma: | inglés |
| OAI Identifier: | oai:dadun.unav.edu:10171/118340 |
| Acceso en línea: | https://hdl.handle.net/10171/118340 |
| Access Level: | acceso abierto |
| Palabra clave: | Shopping lists Shopping baskets Clickstream Webrooming Demand estimation Layout |
| Sumario: | Problem definition: Omnichannel retailers interact with customers both online and offline. So far, they have used the richer information available—gathered from customer interactions across digital and physical channels—to optimize the sales process by designing the right channel and supply chain structures and by personalizing offers, pricing, and promotions. We advance an additional dimension of omnichannel value: retailers can use online clickstreams to better understand customer needs and optimize store layouts to maximize webrooming conversion, which we define as the ratio of sales to webrooming activity. Methodology/results: We develop a model in which in-store purchases depend on the customer’s shopping list and the effort required to locate and reach the products within the store. Category location in the store thus drives the likelihood of a sale. We then apply our model to a large home improvement retailer and find that shoppers’ preferences are revealed by nearby online traffic and hard-to-reach locations lead to lower webrooming conversion. Finally, we optimize category location assignments using our demand model and find that putting higher-interest and higher-price items in the most effective locations can increase revenues by about 2%–5% in comparison with models that ignore online clicks. Managerial implications: We show how using online clickstream information for optimizing offline operations can create significant value. More fundamentally, our results provide a word of caution that in some retailing segments such as home improvement, longer in-store paths might not necessarily be better. |
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