Retail - Method for Pricing

Effectively determining the correct price point for a retailer’s assortment is a challenging task. Retailers must balance consumer price perception (the general image a customer has of the retailer’s prices) and profitability. Setting prices too low will harm profitability, while setting them too high affects demand.

 

In practice, retailers will tend to price their key value items (KVIs) competitively while making up margins with the lower demand items in the assortment (so-called “long tail” items). Keeping abreast of competitors' prices and reacting accordingly is difficult to do manually, especially in large assortments. Determining prices for the long tail with limited historical data also presents a challenge. 

Utilizing Machine Learning-based dynamic pricing to increase profitability for online retailers.

Dynamic Pricing software utilizes near real time monitoring of competitor prices coupled with Machine Learning algorithms on large historical transaction data sets to optimize prices and react to changes in the market.

 

The most well known example of utilizing this approach is Amazon. The online retailer is reported to adjust its prices as frequently as every 10 minutes, increasing profitability on average by 25%2. Typical inputs to the pricing decisions include inventory availability, competitor pricing, order history and expected margin.

 

Such pricing strategies are not only limited to large retailers. An increasing number of software as a service (SaaS) platforms have become available, serving not only smaller online retailers but bricks and mortar stores too.