5 Algorithms every Pricing Director should know

Peter Sondergaard from Gartner recently coined the term of “Algorithmic Business” to state that data volume does not really matter, but what companies do with that data – how they turn it into proprietary algorithms – is a powerful competitive advantage and therefore the cornerstone of business growth.

Most of Pricing professionals are lagging behind in the digital transformation, and despite an important investment in software and data capture, most of the companies are facing a black box when it comes to understanding how pricing decision is decided and executed.

Mastering the Algorithmic Era in Pricing means creating in glass box situation within the organization, by getting back the control over pricing data and rules and developing and installing sustainable pricing analytics skills.

Where to start?

Here is our Top 5 Algorithms for Pricing:

  • Clustering: Clustering, K-Means are central mainly because segments (both customer and products) are at the very heart of pricing performance.
  • Regression: OLS, Multivariate, Logistic will allow you to identify predictors with or without interaction. Must have for elasticity and cross-elasticity measurement among other.
  • Associated Rules: even if the two first are obvious, this one is often missing. A priori rules are at the center of market basket analysis for example. Very useful when you have a large product portfolio and need to identify product sharing consumption patterns. It can allow you to extrapolate accurately value related insights from market research to your transactional data.
  • Decision Tree: Pricing is all about rules. Decision Trees (CART, CHAID, etc.) allow a representation of pricing decisions relating antecedents and outcomes, as well as a definition of the rules behind them (if / then). The backbone of pricing execution and efficient promotions and discounting for example. 
  • Deep Learning: because pricing is complex and volatile by nature, more advanced tools need to be used in order to deal with complex data or event structures, generate multiple layers of analytics, and learn from continuous data enrichment.

Algorithm and mathematics are at the very heart of the scientific process. And Pricing needs to be treated as a science. If you want to be prepared for the algorithmic era, you’d better start now!