The impact of practitioner business rules on the optimality of a static retail revenue management system

Abstract

Retailers engaging in revenue management rarely implement theoretically optimal prices from an optimisation system directly. Rather, they adjust these with business rules – simple empirical guidelines derived from best practices – such as using discrete price points ending in ‘9’. Similarly, competing objectives of maximizing sales or store visits are regularly considered, which may contrast the profit optimal solution. Although these rules obviously constrain the solution space for the price optimization, little is known about their consequences on overall profits. This study provides an empirical analysis on the impact of commonly used business rules of using (i) discrete price points, (ii) maximum price moves, (iii) corridor pricing and (iv) passive pricing on the size and the quality of the problem’s solution space and their monetary impact. As expected, we find that each additional business rule further constrains the solution space, offering fewer valid price vectors. However, while the combinations of multiple rules substantially reduce the solution space and yields suboptimal solutions that deviate up to 20 per cent from the profit maximum, the application of only individual rules will still provide some optimal solutions. At the same time, business rules enable the estimation of larger assortment subcategories, which allow results more representative for retail practices. This suggests that price vectors which reflect business rules allow not only an increased adherence to business reality, but may lead to little or no deviation from the optimal solution for larger assortments than in unconstrained optimization systems.

Appendix

Data parameters and model results

We determine initial price and cost c _i for 20 products i within empirical relevant bounds as summarized in section (a) of Table A1. All prices end in ‘9’ so that the initial price vector is valid under conditions (D1) and (D2). We assign price tiers as defined in condition (C1) so that 20 per cent of the products are priced in , 40 per cent within , and 40 per cent in . Cost is determined randomly so that the initial product specific gross margin h _i is between 16.7 per cent and 30.1 per cent. Section (b) of the table presents the model results for the 5, 10, and 20 product case. For each of the objectives stated in 3, the model results of the price vector that maximizes the respective objectives are presented: We show the percentage difference between current and optimal price (Δ per cent(p^opt−p⁽⁰⁾)), the difference between current and optimal gross margin (Δ(h^opt−h⁽⁰⁾)) as well as the absolute and percentage change of the four objectives. Table section (c) presents the model parameters for the category and the market share model.

Table A1 Data parameters and model results