Retailers have privileged and powerful access to consumers – especially through promotional activities and loyalty programs. These instruments are direct purchase decision drivers, but they are also highly targeted communication channels, since both the message and execution can be tailored to the needs of individual regions, clusters, outlets, or even consumer segments. Their effectiveness makes them key elements in any retailer’s marketing mix.
Based on econometrical modeling, MMM enables retailers to measure the performance of their current marketing mix and optimize it across advertising vehicles and other touch points, including unique retail levers such as pricing and promotions.
Marketing mix modeling is geared towards helping executives in charge of strategic as well as tactical retail management – usually the CMO and the commercial director or head of sales – providing them with a fact base to help them make the trade-off decisions they face, especially regarding the allocation of funds and resources across the various marketing and sales levers.
The specific benefits of MMM in day-to-day marketing mix management include increased transparency about the drivers of performance and the return on investment of different marketing levers.
Marketing mix modeling
1. What it is: MMM tells you how to spend it. (It generates a straight-forward yet sophisticated and comprehensive fact base for retailers’ commercial decisions.)
2. How it works: MMM compares the impact of a wide range of growth levers (by modeling their influence on revenues, traffic, and price perception).
3. What it does: MMM has strategic as well as tactical applications. (Its uses range from overall strategic positioning to daily operations.)
4. What it yields: MMM creates transparency across multiple marketing levers (including performance drivers, the ROI impact of different marketing levers, and sensitivity to price perception).
5. What to watch out for: the devil is in the details of the analysis; cross-functional teams help to ensure the integrity of the data, the calibration of the model, and interpretation of its output.