Top 10 Ways AI Drives Price Optimization in Retail

There are several techniques in use in various stages if maturity in retail and e-commerce. Many different tools and techniques feed into AI powered price optimization for retailers. When used together these can drive very significant top line and bottom-line results for retailers, and allow them to be much more agile in their response to changes in market conditions like competition, costs, inventory levels, and more.

Here are the top 10 “need to know” concepts in the use of AI in price optimization for retailers.

The first key in optimizing pricing is understanding how to group together like products, stores, and potentially customers. This involves what is referred to as classification algorithms which are part of most machine learning libraries these days. You will hear names like K means and X mean clustering, CART, Random Forest, etc. here. The best technique depends on the data you have available and the level of pricing and variation there. This determination is best made by a pricing scientist, which is a discipline within data science that focuses on segmentation and optimization of pricing.

Most price optimization techniques rely first on a solid segmentation model. Once this is in place, most price optimization relies on some form of regression modeling. This uses the grouping of customers and products and the distribution of prices within a given segment to determine the opportunity to change prices.

In retail and digital commerce, elasticity is a key concept in predicting the consumer’s response to a price change. Simply put, it is the change in price over the change in volume.

For example, if I increase the price by 1% and the demand drops by 1.6%, my elasticity of demand is -1.6.  The calculations are simple, but this can be deceptive as you are putting a lot into a small number in terms of predicting volume response and optimizing price to achieve optimal revenue or profit. The key in this is understanding the business well and ensuring you are incorporating the right data and trends into the calculations. For example, is the weather or seasonality a factor in driving demand? What about affinity, availability, or competition? All of this will vary by category and it might be yes for some categories and no for others.

If the answer is yes and you don’t have that in your data set or your algorithms are not considering it, you can get into big trouble fast. This is another area where experienced scientists should be developing the models and ensuring that they accurately reflect the business factors that influence willingness to pay. Elasticity should be considered but not relied upon exclusively.

This is primarily a rules-based AI technique which is used to position your products vis a vis your competitors by harvesting competitive data and using it as an input into your pricing algorithms. Typically the positioning will depend on some classification of products into key value items or key value categories which will be treated differently than products or categories where people are less price sensitive.