# Anna Sáez de Tejada Cuenca

## Loss Aversion in Managers' Pricing Decision Making at a Fast Fashion Retailer

### Abstract

When country managers at Zara set weekly prices during the clearance sales season, they consistently deviate from the revenue-maximizing prices (recommended by a DSS), usually by setting prices that are lower than the optimal ones. This pattern is consistent with a number of well-known behavioral biases: loss aversion, salience of the inventory, status quo bias, and inattention. In this paper, we aim to disentangle managers' degree of loss aversion from other behavioral biases. To do so, we build a structural model to replicate managers' price decision making process, and we fit it using data collected by Zara prior to the DSS's implementation.

In our model, managers choose prices to maximize their utility over the whole season, subject to a number of constraints given by the firm's pricing rules. The utility function consists of a revenue component and a loss aversion component that depends on a loss aversion parameter. Both components include demand uncertainty and contain all products of the same type (shirts, pants, etc.). This model is, therefore, a dynamic program over a finite horizon with a large state space and uncertainty set. We use a certainty equivalent for the demand function, and discretize the problem, given that the set of available prices is discrete. Our model thus becomes a mixed integer linear program.

We find, for each value of the loss aversion parameter, its corresponding set of utility-maximizing prices, and then pick the value of the parameter that best fits the prices that managers implemented. We then compare their degree of loss aversion across product groups (e.g. fashion or basic products) and across country managers. We also compare their degree of loss aversion to that of non-expert subjects reported the behavioral literature.

### Additional information

This is work in progress. It is joint work with Felipe Caro and Keith Chen, from UCLA. I am presenting it in the **TA11 session at INFORMS, at 7.30 AM on Tuesday, November 6, at 125B, North Bldg**. I am the second speaker of the session (out of 4).