CGT’s New Analytics Study Confirms Continued Significance of Marketing Mix Modeling for Consumer Goods Industry
The recently published 2021 Retail & Consumer Goods Analytics Study highlighted a number of significant findings related to how industries are deploying analytics to compete and win in a redefined market. One of the interesting findings was around the Top 5 areas where consumer goods companies are using their analytics spending. Of the consumer goods companies surveyed for the report, 36% mentioned Promotion Effectiveness and 32% mentioned Marketing Mix Optimization among the 5 priority areas where they are focusing their analytics efforts.
This finding points to the continued relevance of analytical techniques such as Marketing Mix Modeling, which helps brands measure the effectiveness of different marketing and promotional investments and optimize budget allocations across channels to maximize Marketing Return on Investment (ROI).
Marketing Mix Modeling (MMM) has been used by consumer goods companies for decades and is one of the few tools available for holistic marketing measurement with financial guidance. However, with the marketing landscape becoming more complex and brands requiring sharper insights to navigate this terrain, MMM needs to evolve in order to offer brands reliable and robust decision-making support.
Here’s how MMM must evolve to meet today’s challenges.
MMM must deliver Real-Time, Actionable Insights: Traditional MMM required companies to work with third party specialists to generate insights once or twice a year. The process was time-consuming and insights generated were typically based on 6-12 months old data, making them merely “rear-view” looking i.e. they helped identify what worked or didn’t in the past, but were not very useful for making future decisions. What marketers need today are actionable and “forward-looking” insights which can be used to make quick decisions on optimizing marketing spends. MMM must provide real-time insights pertinent to the point in time decisions are being made.
MMM must become More Democratic: Dependence on external specialists for MMM was not just time consuming but also prohibitively expensive, forcing companies to restrict themselves to running it less frequently, and only for their largest brands and markets. Marketers are now looking for automated MMM solutions that can deliver better speed, scalability and cost effectiveness in-house. Such systems would truly democratize MMM. They would allow internal data science teams and marketers to run analytics in-house and on-demand with minimal effort, and cover more of their overall marketing budget without high costs.
MMM must become a Cross-Functional Resource: The CGT report found that in 67% of consumer goods companies interviewed, internal analytics is managed by respective departments. This means MMM and other marketing analytics are typically run by marketing, with other related functions such as sales, finance or operations having limited understanding into the insights generated. This must change so that there is shared learning from MMM insights across the organization. A change in marketing analytics “ownership” will have a multiplier effect on how it positively impacts organizational KPIs such as ROI, sales and profits.
The good news is that MMM is already evolving in these directions. The latest solutions offer marketers the ability to run real-time MMM and other predictive analytics in-house, while providing intuitive interfaces and dashboards that make insights more accessible across teams. Such solutions can give marketers the cost, scale and speed advantages they need to compete and win in today’s redefined market.
ABOUT THE AUTHOR Garth Viegas is a passionate analytics professional with 20+ years of global experience and firmly believes that behind every number is an idea waiting to be discovered. He is General Manager, Americas at Analytic Edge, father to two kids who constantly challenge his thinking, and a long-distance runner who believes running helps him become a long-distance thinker.