Analytical Engine

One of the most common problems of retail sector is pricing a product. There are numerous similar products lying in the shelves that are at different life stages and have complex interrelationship with other products & customers. Providing an optimal price of an SKU in order to extract maximum customer surplus is a challenge in itself.

In our recent stint with a Europe based retail chain for electronics goods, we helped our client use analytics to better price cell-phone accessories. A lot of advanced sophisticated pricing models can be built and deployed; however, when efforts and incremental value of such models are weighed against the approach of using elasticity and bundling, the latter option seems to be an optimal choice to get some quick hits for the business. For the uninitiated, here is a quick review of elasticity and bundling:

— Price elasticity of demand: The measure of responsivenesses in the quantity demanded for a commodity as a result of change in price of the same commodity.
— Product bundling: A marketing strategy that involves offering several products for sale as one combined product

Price elasticity approach: We calculated the price elasticity, and accessories that are having extremely high or extremely low elasticity along with reasonable business impact (sales volume or margins or life-stage) are picked for analysis. With the help of gross margins, we came up with the best price change for such accessories. In order to evaluate the financial impact, we built a business case to find out the potential revenue impact of such price changes.

Product bundling approach: A basic market basket analysis can help find products customers like to buy together. However, bundle needs to be evaluated for elasticity as well as likelihood of success before taking it to the customer. In order to increase the sale of some of the underperforming accessories, such accessories can be strategically bundled with highly elastic products. Gross margin of the bundle can be used for driving the price of the bundle.

Both these methods fetch actionable recommendations which can be tested in a very short time-frame in a test environment. One of our recommendations led to an additional £100K revenue impact per week for selected SKUs. A similar approach can be used for other items to better understand the nature and the monetary potential of the products lying in a store.