Consumer Promotion Optimization: Looking Beyond TPO with a Consumer Lens


Consumer Promotion Optimization | Case Study
  • CASE STUDIES OUR MUSINGS
  • July 29th, 2020
  •   660 Views

The premise of trade spend activities has long been optimizing trade spend for high ROI. What happens when the consumer is brought into the equation?

THE PROBLEM
A popular CPG brand was investing significant time and resources on trade promotion optimization and management with only a 50% success rate. We helped defined their key outcomes as:

  • Maximizing household penetration
  • Building brand loyalty
  • Increasing profitability

  • THE MU SIGMA APPROACH
    Prompted by our unique Art of Problem Solving framework, we explored the problem space and found that their trade spend strategy was missing a consumer focus. Leveraging our longstanding strategic partnership with them and their existing framework for decoding consumer behavior, we developed the Consumer Promotion Optimization (CPO) framework.
    The CPO framework serves as a natural progression of trade promotion management and optimization operations. Using the CPO, their trade planners can:

  • Produce 1000+ possible promotion calendars for a category-retailer combination based on inputs given by the consumer teams
  • Get the final week-by-week promotion calendar with optimal offer constructs, promotion prices, and feature advertising recommendations
  • To avoid overlap with competitors, get recommended promotion timings for competitors’ products
  • Re-plan rapidly and iterate based on retailer negotiations using a scenario planner platform

  • THE IMPACT
    By combining trade promotion optimization with consumer insights for a single product category, CPO generated significant impact:

  • Revenue of this brand for 8 products from this category increased by ~5%
  • The household penetration of a product category from this brand increased by 2%
  • The retail partner saw a profit margin lift by 5%
  • The optimized promotion calendars backed by comprehensive data were quickly accepted by retail partners. These helped grow retailers profit margin by preventing competition conflicts and better household penetration
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