Most shopper marketing managers recognize the importance of measuring return on investment. But they aren’t sure how to do so, nor do they know which measurements to take and use to replicate successes. For consumer packaged goods marketers, measuring ROI and quantifying shopper marketing’s effectiveness is arguably more important than ever.
Most CPG companies post low single-digit growth but are considered low-risk, and therefore attractive, investments. Any small increase in growth increases their attractiveness and stock values. But topline growth in today’s marketplace is tough for a number of reasons, including the success of store brands at retailers that CPG brands depend on to help promote and sell their products. That forces management to look at bottom-line savings, often leading to cuts to marketing budgets. To avoid the axe, marketing managers must justify their spend and quantify their ROI.
This is where measurements come into play. They are the raw material that’s needed to make the best shopper marketing decisions, bringing about:
Shopper marketers also need a tool to process (and, if necessary, enhance and augment) their raw material. That tool is predictive analytics.
Applied to shopper marketing, predictive analytics is not unlike the use of sabermetric principles applied to baseball (better known as “moneyball”). The goal is to run shopper marketing programs in the most cost-effective way and achieve win after win despite budget limits and other constraints. This gives CPG companies the ability to eke out an extra point or two of growth. They gain an edge over competitors and higher stock values by lowering investors’ risk.
In the process, shopper marketers build credibility by forecasting well and then delivering the anticipated results. When budget cuts are considered, they can use their winning record to keep shopper marketing off the cutting board.