Commercial Industrial Manufacturing and Distribution Business.

Using purchase transactions data, a pricing optimization analysis was performed in order to determine pricing elasticity and improve overall profits for their 10,000+ SKUs. Further, a customer and product segmentation was performed to identify which segments were adding the most value to the organization.

Background:

A multi-national distributor of commercial and industrial machinery and parts was struggling to move beyond their historical approach to pricing.


Challange:

  1. To move away from the generic approach of applying the same percentage (%) price increase for all the 10,000+ SKUs in inventory.

  2. To ascertain which customer segments were the most profitable.

The Approach:

The project was separated into two distinct initiatives both based on purchase transaction history.


Customer and Product Segmentation

Recency, frequency and monetary [RFM] value of purchases by product and by customer segments was analyzed and provided clear insights into which product categories and customer segments were adding the most value to the organization.


Price Optimization

A linear transformation OLS model was used to isolate the relationship between price and unit sales, factoring other statistically significant variables such as seasonality and other exogenous (external) effects. From this, AAARL was able to derive the elasticity of individual products and to what extent price changes would increase or decrease overall revenues and profitability based on the marginal contribution from individual SKUs.


Results:

Identified opportunities and made recommendations to improve overall profitability by 5% through price optimization.


Output included product specific price recommendations for over 10,000 SKUs based on the net revenue impact from individual price changes, as well as specific recommendations for each customer group that we identified.