Academic journal article Academy of Marketing Studies Journal

Improving Price Performance When Demand Elasticity Is Difficult to Establish

Academic journal article Academy of Marketing Studies Journal

Improving Price Performance When Demand Elasticity Is Difficult to Establish

Article excerpt

INTRODUCTION

Companies are always seeking opportunities to improve their bottom-line. The current volatile economy has only increased the intensity with which such opportunities are sought, evaluated, and implemented. Among several decisions that a company may be faced with, optimizing supply chains is becoming ever more critical to improving operations. Very frequently decision-support tools grounded in advanced statistics and mathematical modeling are employed to help with the decision-making process.

Among several levers that a company can operate to optimize its supply chain, identifying the optimal retail price of the products is crucial. The price of a product is a function of several costs, such as procurement, inventory, transportation, and marketing, and the expected profit margin. One would think that if all factors remaining unchanged, raising price will raise profit margin. However, academic literature and industry practice suggests that price increase is almost always accompanied with demand decrease. The price elasticity of demand is a frequently used metric to quantify the reduction in demand for a unit increase in price.

Models exists that can help determine the optimal price of a product if the demand is elastic to price. The assumption in most of these models is that enough transactional data exists to help determine the elasticity value based on statistical analysis. Though the advancement in information technology has helped in collecting ample data, there are limitations to statistical analysis when SKU counts are very high and transactions per SKU per time period are limited. There are several commercial sectors where transactional data is too limited to derive statistically significant elasticity values. One such segment is wholesale distribution where we observed both of these phenomena. Through a case study we share insights gained by analyzing their data and deriving elasticity values for several products. We indicate that traditional models for price optimization may not be appropriate in such instances and sound heuristic approaches to price improvement may be required.

The case background is a U.S. national retail network of non-perishable products, with hundreds of retail outlets. Local management has significant autonomy with respect to items carried, prices, and inventory policies. At the national level, management is responsible for sourcing, distribution, back office support, advisory, and monitoring services to improve performance of the overall network. A central information systems (IS) service provides back office support through the company's commercial ERP system. Prices are set at the local level by applying a gross margin percentage (product markup) to product families in the ERP's pricing matrix. Price levels have been established informally at the local level with little guidance from analytic approaches. The ERP system captures detailed point-of-sales (POS) data with several years of history available. Currently the overall organization is profitable, yet central management believes there is potential to improve profitability even further with a more systematic approach to setting prices at the local level, particularly given substantial disparity in performance levels between retail locations in markets deemed comparable in potential.

This paper presents a systematic approach for price improvement recommendations in this context. Our contributions are two-fold:

1. Using a systematic approach towards analyzing actually sales data, we attempt to determine the price elasticity values. We identified several product family and customer type combinations where the demand seems to behave independently of price.. Using a theoretical break-even analysis, we suggest an approach to share with the management insights on the effect of the direction and degree of price change on overall company profitability.

2. To help such managers actually realize benefits, we propose an intuitive and easy-to-implement heuristic to improve their price performance, and in turn their bottom-line. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.