Believing in Analytics : Managers' Adherence to Price Recommendations from a DSS
Information Systems and Operations Management
Par : Anna SAEZ DE TEJADA CUENCA, 5th year PhD
UCLA Anderson School of Management
HEC Campus - Jouy-En-Josas - Bât. V - Salle Bernard Ramanantsoa
Fast fashion retailer Zara holds a clearance sales period at the end of every season during which managers set weekly markdowns. In 2008 the firm implemented a decision support system (DSS) that suggests revenue-maximizing prices. Managers' initial adherence to the DSS's recommendations was low. Zara performed two interventions, in the form of changes to the DSS's interface, to entice managers to increase their adherence: 1. showing a metric of revenue (in 2011); and 2. showing a reference point for that metric (in 2012).
In this paper we use data collected by Zara during seven clearance sales campaigns to analyze the effect of the two interventions and the behavioral drivers of managers' adherence decisions and the size of their deviations. Our results show that that Intervention 1 did not alter managers' adherence significantly, but Intervention 2 increased it, and also decreased their likelihood to mark a product down when the optimal decision was to keep its price unchanged.
Managers were more likely to adhere to the DSS's recommendations when the suggested price was aligned with the heuristic they followed before the DSS was implemented. Managers' decisions were consistent with inventory minimization, as opposed to revenue maximization. For countries in which unsold inventory can be salvaged, higher salvage values were related to higher adherence but also to larger deviations when managers did not adhere. Finally, we find that managers were minimizing the number of different prices to set and basing their pricing decisions on metrics that were aggregated at the group level, instead of at the individual product level. These findings can be explained by some cognitive biases: preference for the status quo, salience of the inventory (compared to a revenue forecast), loss aversion, and inattention. Some of these biases were mitigated after the interventions.
Our findings provide managerial insights on how to increase voluntary adherence that can be used in any context in which a company wants to implement a DSS or any other analytical tool to be adopted organically by its users.
Anna is a fifth year doctoral candidate in Decisions, Operations and Technology Management at the UCLA Anderson School of Management, advised by Felipe Caro. Her research interests include empirical operations management, social responsibility, behavioral operations management, supply chain management, and the apparel industry.
Before starting her doctoral studies, Anna was a research assistant at IESE Business School, supervised by Victor Martinez-de-Albeniz, and a junior R&D engineer at Sabirmedical. During her PhD, she interned at Amazon, in the Supply Chain Optimization Technologies team. Anna has a BSc in Mathematics and a MSc in Mathematical Engineering from the School of Mathematics and Statistics at UPC-BarcelonaTech.