In digital marketplaces, product ratings play a critical role in shaping consumer demand and seller success, yet their relationship with logistical factors remains unexplored. This study investigates how deviations from promised delivery dates, whether early or delayed, influence product ratings. Using transaction-level data from a major e-commerce platform encompassing over 11M product purchases and 500K customers, we estimate the causal effect of deviating from the promised delivery time on both rating incidence and valence. To overcome the challenges of matching imposed by an unbalanced treatment and control distribution, we used a machine learning based estimator, R-learner, to estimate the effects of interest. We find that both delayed and early deliveries increase customers’ likelihood of leaving a rating but reduce rating valence. Delayed deliveries lower rating valence by an average of 0.4, with some categories seeing a drop of up to 0.6. Early deliveries also reduce rating valence, with an average decrease of 0.2 and reductions of up to 0.5 depending on the product category. The negative effect of early delivery is consistent across all categories, except for food and beverages, where experienced customers show a small positive response. This is due to reduced uncertainty from reordering previously purchased products, a pattern that does not occur as often in other categories. We contribute to the service and logistics performance literature by establishing the causal link between delivery performance and product ratings by quantifying the direction and magnitude of these effects across various product categories and customer segments. We propose managerial implications for mitigating customer dissatisfaction arising from both early and delayed deliveries.
Nozari et al. (Fri,) studied this question.