Trade promotions are like families: You can't do without them, but sometimes you don't know what to do with them.
Formulating and scheduling trade promotions for maximum impact is one of the toughest challenges in retailing. Many grocers leave this partly or wholly up to their CPG suppliers as part of continuous category management, but this presents its own challenges, for both sides.
The mission of the Promotion Optimization Institute (POI) is to foster collaboration between CPG companies and retailers by leading the emergent collaborative marketing profession through developing, advancing and disseminating trade marketing and merchandising knowledge and education (via POI's Collaborative Marketing Certification). To that end, the seventh biannual POI Summit, held April 6-8 in Chicago, hosted 250 attendees featuring presentations by CPG companies, retailers, academia and service providers about how to predict, plan and optimize pricing and promotions so that suppliers, retailers and their shared shoppers/consumers benefit.
The summit's last day was devoted to a unique exercise designed by POI with Gartner. Five different solutions providers were given historical sales data from actual retailers and CPG companies, which they used to predict and optimize promotional outcomes. The five were given wide latitude in interpreting the data, determining its relevance and deciding what kinds of promotions were indicated. The data were masked as to the identity of the retailers and products, but otherwise, they comprised sales information at every level, down to individual stores.
"Each of the participating solutions providers will have a different and unique approach," said Michael Kantor, POI's founder and CEO, in introducing the presentations.
Differences in the approach included which product categories to pay the most attention to, how to account for "outlier" numbers, how to develop algorithms and verify them with the data available, how to settle on a baseline price and how to determine price elasticity. Each presenter, however, worked with the same data set and was tasked with recommending the best approach to trade promotion optimization.