Combining the insights from our survey of academic research and interviews with practitioners, we provide directions for future academic research that take advantage of the availability of big data. Future research on retail analytics can contribute to existing work by: (i) studying new decisions, (ii) using more advanced analytics, (iii) leveraging new data sources, or (iv) applying more sophisticated methods.

Apparel retailers have generally followed one of two supply chain approaches: the traditional approach (lacks quick response capabilities and produces more durable products) or the fast-fashion approach (has quick response capabilities and produces less durable products). Using an infinite horizon game theoretic model, we compare the profitability and environmental impact of the two approaches. We characterize win-win scenarios (higher profit and lower environmental impact) for both approaches.

We conduct a field experiment to examine whether algorithms should be automated or be used to augment human decision-makers. Unlike the common practice of allowing managers to override the output of algorithms, we allow retail merchants to override the inputs in order to capture the private information they possess. Our results show that the input augmentation model increases profitability by nearly 4% compared to the automation model where merchants were not involved.