USD 15MM in Incremental Margins through Price Recommendation Engine for B2B Retail

Case Study

USD 15MM in Incremental Margins through Price Recommendation Engine for B2B Retail

Business Objective

Our client is a large retailer-distributor of heavy-duty truck and trailer parts with 250+ store locations. The objective was to identify and implement opportunities to improve margins by setting the right price. The client wanted to target the pricing of those items that contribute to ~80% of the revenue.

Challenges

  • Prices are decided based on in-person negotiations between customers and store associates, resulting in a unique experience which the management wanted to preserve.
  • Standard pricing solutions (COTS) were trialed but did not meet the need.
  • Low trust in existing base price in the system, evidenced by high-frequency overrides.
  • Store associates and outside sales personnel across different stores/regions have different styles/comfort zones.

Solution Methodology 

  • Analyzed transaction data to identify product baskets (group of products that go together or in quick succession).
  • Segmented customers based on the strength of customer relationship (longevity, size of business, etc.), and for each segment, modeled and established the Price-Volume response curves.
  • Analyzed sales patterns across competition zones to detect differences in segment-level price-volume response curves, and store associate behavior with respect to pricing decisions.
  • Established Item x Segment x Zone level price corridors (floor, expected, ceiling)
  • Simulated scenarios for incremental/aggressive changes vs current state to study net impact on margins accounting for reduced volume at higher prices.
  • Incorporated additional business rules to have guardrails and make the change management easier for associates.
  • End-to-end process from extraction to pricing table updates was automated, documented, and handed over to client teams.

Business Impact

 

  • A solution that meets the needs of a unique business model was developed, delivered, and accepted by pricing leaders and store associates. Store associates retained the flexibility that they had and got analytical driven recommendations to improve margins
  • Floor, Optimal, and Ceiling Prices for 10,000+ SKUs in 16 regions contributing to 85% of revenue were put in place
  • USD 15MM worth of incremental margins was realized in 18 months of the solution in production.
Download Case Study

©2017 Tiger Analytics. All rights reserved.

Log in with your credentials

Forgot your details?