Case Study

Answering a Billion Dollar Question:
Estimating True Sales Lift generated
by Trade Promotion Spends

Business Objective

Trade promotion spending for CPG companies, in the range 15-25% of gross sales, often run into billions for large players. However, except for industry leaders with highly efficient TPM processes and analytics & optimization capabilities, return on trade spend is negative for most of the industry.

For a client of ours, which was operating with a relatively high degree of effectiveness, measuring & minimizing cross-retailer cannibalization impact of trade promotions was identified as a critical problem to solve. This along with four other high impact themes were identified after extensive discussions with executives and business associates from Sales, Sales Finance and Data Science teams of the client.

Challenges

  • Lead times in identifying and procuring necessary data from internal and external sources
  • Linking data from trade promotions planning system to sales (PoS & Syndicated)
  • Separating out everyday pricing vs promotional effects from store x item x week level data without clear markers

Solution Methodology

  • Acquired trade area level sales data for all promoted product groups within the categories of interest.
  • Blended trade area data with retailer PoS data to get a retailer vs rest-of-market picture.
  • Mapped trade promotions spending data to sales and analyzed responses to CPG funded trade promotions run in retailer’s stores (responses to promo within the retailer, as well as what happened in the rest of the market).
  • Identified promoted product groups that are traded-up/down within-box as well as cross-box. Estimated net impact of promotions on consumption volume as well as in dollar terms.
  • Built a scenario planner for use by account executives to design promotions with higher net lift.

Business Impact

  • Established a reliable and repeatable methodology for performing trade impact analysis at the category x retailer-vs-rest-of-market (compared to just one retailer at a time) level for an accurate estimate of net lift.
  • Enabled account managers with insights to plan promotions that have minimal cannibalization and greater contribution to true lift.
  • Provided visibility to trading up and down opportunities for consumers within retailer as well as across retailers evidenced from sell-through data, further validated through consumer insights.
  • Solution being evaluated on trade spends of USD 20 million accounting for revenues of USD 250 million, with a plan to rapidly scale up.
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