The title was Who Is Driving Trade Promo?, and it dealt with a study by AMR of trade promotion practices related to promotion analysis and optimization, and comparing the practices and results of food/beverage and non-food CPG companies. The webinar is available here and the white paper here, and if you missed them, they are worth your time. There’s a lot of good stuff, but I’ll just deal with a couple of items here.
The first finding, somewhat surprising as the white paper admits, was that the food companies are more advanced in their practices and have better results -- the researchers expected to find that the non-food companies (the biggest of which are bigger and have better margins) were the leaders.
One very interesting result is that the food/beverage companies most often cited by both Wall Street analysts and retailers as best of breed in trade promo practices did significantly better in stock performance than the best of the non-food CPGs. In the six-month period studied, the results were:
Food/beverage firms +0.52%Another important point was that the food/beverage companies were more likely to use ROI measures to determine proper spending levels (48% to 35%), while the non-food companies were more likely to set their spending in comparison to competitors (26% to 14%).
Non-food CPGs –5.02%
Dow Jones Industrial Average –4.72%
The biggest difference, though, was in how well the food/beverage companies use their predictive/optimization software. Quoting from the whitepaper:
Use of predictive simulation and optimization tools is also linked to faster evaluation of promotion performance among food & beverage firms … For food & beverage companies in particular, this is associated with significantly faster promotion analysis times – a mean of 25 days, versus 35 days for consumer products firms who use the same tools. Notably, food & beverage manufacturers that use predictive simulation tools are able to drive post-event performance analysis time down from a mean of 44 days for non-users – a time savings of nearly 43%.I find it interesting that use of the tools makes no difference to the non-food companies in terms of speed of analysis, while it makes a huge difference to the food companies. It was outside the area of this study, but it would be good as a follow-up to determine what the differences are in how the companies use the tools. There are several important summary points, and again I’ll suggest that you access the full webinar and/or white paper, but this one sentence says it all, I think: “The study findings suggest that the use of predictive technologies paired with the proper focus and discipline can help make the use of trade dollars far more effective.”