Borja Apaolaza

Problem definition: Omnichannel retailers interact with customers both online and offline. So far, they have used the richer information available to optimize the sales process by designing the right channel and supply chain structures, and by personalizing offer, 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 to 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 like home improvement, longer in-store paths might not necessarily be better.

Online rental platforms that allow customers to purchase rented goods present a complex pricing challenge: setting buyout prices that balance immediate sales revenue with future rental income, while accounting for item-specific factors such as condition, popularity, and customer preferences. In this paper, we develop, estimate, and validate a data-driven framework to inform buyout prices in this setting. Leveraging a Markov Decision Process (MDP), our framework assesses individual item value based on rental demand, product attrition, and customer purchase likelihood. We use real-world data from a leading fashion rental company to demonstrate that our methodology significantly improves profitability compared to existing practices and alternative benchmarks. We estimate that the proposed pricing policy increases earnings by 3.1% over the company’s current practice. Our analysis also shows that operating a rental-only business model leaves revenue opportunities untapped, underscoring the strategic value of buyout options in managing inventory and generating additional income.

Free shipping threshold policies – where shipping fees are waived for orders exceeding a minimum dollar threshold – are widely used in online retail, yet choosing the optimal threshold remains a complex challenge. This paper presents a practical, data-driven framework to (1) assess the profitability of a retailer’s current free shipping threshold policy and (2) identify the optimal threshold. Our framework incorporates several consumer reactions to the chosen threshold: for some consumers a threshold could deter purchases altogether, while for others it could entice them to increase their order (order padding), all of which influences the likelihood and volume of returns. We estimate our framework using transaction-level sales and return data from a leading U.S. apparel retailer. By linking estimated behavioral effects to financial outcomes, our framework allows retailers to optimize their threshold policies. We find that either the retailer should always offer free shipping (i.e., a zero threshold) or, more generally (as with our partner retailer), an intermediate threshold is best – low (but not zero) thresholds are consistently sub-optimal. Our results offer actionable guidance for retailers to design more effective and financially sound free shipping threshold policies.

Problem Definition: What defines a “responsible” work schedule — and how does this definition vary across different operational and geographical contexts? Major retailers implement reforms based on the assumption that the same dimensions of schedule quality matter everywhere. At the same time, policymakers increasingly mandate scheduling practices (e.g., Fair Workweek laws). This paper examines which scheduling characteristics predict employee turnover across firms, regions, and worker groups. Methodology/Results: We use administrative data covering 280 million shifts, 1.3 million employees, and 17,456 stores across 20 major U.S. retail chains. We construct over 100 schedule characteristics based on prior literature and apply LASSO variable selection to identify which metrics predict turnover. We estimate models separately across companies, U.S. states, zip-code socioeconomic groups, and employee subgroups (e.g., part-time, low-tenure, female). This analysis reveals that the set of predictive scheduling variables is highly context-dependent. For example, while advance notice is a significant predictor of retention at some firms, it has no predictive power at others. Quantifying this variation, we find over 80% of selected predictors are present in fewer than half of the models. Ultimately, no single scheduling characteristic consistently predicts turnover across all analyzed contexts. Managerial Implications: These findings call into question the effectiveness of uniform scheduling mandates and highlight the limitations of “one-size-fits-all” strategies. For managers aiming to reduce turnover, the results underscore the importance of tailoring scheduling practices to the specific needs of their workforce and operational environment. For policymakers, our analysis reveals that rigid standards may misalign with local realities, inadvertently benefiting some workers while disadvantaging others. In a landscape where the very definition of a “responsible” schedule remains elusive, identifying which scheduling features matter most to employees is not a secondary concern — it is a foundational empirical task, and a necessary first step toward both sound policy design and effective workforce management.