Consumer goods manufacturers often provide – and pay for — promotions on their products to generate interest and to ultimately gain new customers. A consumer-packaged goods manufacturer had dozens of sales executives negotiating thousands of promotions with retailers throughout North America. However, the manufacturer’s sales team lacked the insight into if these promotions were working to help them gain ground within the market.


Every consumer has had the experience of using a coupon to buy a product. But what most customers don’t know is that when they save money, it’s often the manufacturer who fronts the cost. When you get $1 off your favorite cereal at the grocery store, it’s the manufacturer who pays the $1. For example, if one retailer has 1,000 stores and each store sells 80 boxes of that cereal at a $1-off, the manufacturer will reimburse the retailer $80,000.

They call these monies ‘trade funds,’ and the manufacturer was paying well over $1B in “trade funds.” The root of the problem was with the sales directors, who negotiated these deals. Throughout their negotiations with retailers, the manufacturer failed to provide good predictive applications to the sales directors. Moreover, they did not have access to look-back systems to understand if past promotions worked or not.

Without insight into the success of the promotions, the sales directors continued negotiating the promotion rates, with each year’s trade funds becoming a little larger than the previous ones. As the costs of these promotions reached one billion dollars, company leadership needed to understand the value of this investment.


A team of data scientists performed analyses to understand what was working and what wasn’t. Almost immediately, the team found over $25 Million in inefficiencies. This discovery leads to a collaboration with the data scientists and sales directors: the data scientists built a descriptive tool that the sales executives could use to understand the performance of past promotions.

Moreover, the team built a custom, predictive tool to help them plan future events more effectively.


By using simple tools with sophisticated algorithms, the team of data scientists helped the manufacturer – and its sales directors – see “under the hood” of their promotions. Rolling out the tools, plus training the sales directors on how to think about Trade Spend ROI, lead to over $100M in documented savings, every year. By eliminating the requirement for the sales team to think through each unintended consequence of trade funds, the sales team had the ability to focus on what sales teams should be focused on – selling.