Pricing Analytics

The goal of pricing analytics is to understand how customers respond to different price levels and to use that to set prices that meet the business's objectives. Those objectives differ: some organisations want to maximise revenue, others to grow market share, others to clear inventory quickly.

The project typically involves estimating price elasticity, which is a measure of how much demand changes when price changes, using regression models fitted on historical sales data. Controls for promotions, seasonality, and competitor prices are usually needed. Experiments, such as showing different prices to different customer groups, are used when causal estimates are required rather than correlations. The results feed into pricing rules or tools that support commercial teams.

Python and R are the main tools, with regression and causal inference methods at the core. In e-commerce, price experiments can be run at scale with quick turnaround. In physical retail and insurance, experiments are harder to design and run, so the work relies more on observational data and careful modelling assumptions.

Pricing analytics projects appear in retail, e-commerce, insurance, and financial services. In the Netherlands, large retailers, insurers, and digital platforms all run pricing teams. The project connects to Data Scientist, Data Analyst, and Market Researcher roles, and often overlaps with Market Segmentation work.