In the age of digital marketing, businesses are often faced with the challenge of finding the right mix of marketing channels to get the best return on investment. Marketing mix modelling (MMM) is an analytical tool that helps businesses optimise their marketing spend across various channels to improve ROI.
With MMM, businesses can use statistical models to identify the best marketing mix that generates the highest ROI. By analysing historical data, the models can identify patterns in consumer behaviour, media consumption, promo effectiveness, brand loyalty and marketing spend that help businesses make informed decisions about their marketing strategy.
The models also allow businesses to test different scenarios and compare the results to identify the most effective mix. This helps businesses make data-driven decisions about how to allocate their marketing budget across various ATL channels such as TV, Radio, Print to out of home channels like billboards and e-signages to on-grounds activation events and finally digital spends on paid media and influencer marketing.
MMM has been around for more than 20 years. But over the past two decades, the technology and the capability of the model to solve more complicated and real world problems has increased. For instance, the MMM simulator on the Brandintelle platform is powered with Google’s latest ad-stock attribution model powered with an AI layer which helps the model to self-learn. This cutting edge technology, unlike other older MMM models, doesn’t need to be trained manually every quarter or half year. This has enabled marketers to use MMM to predict almost near real-time future scenarios and hence make better investment decisions.
One of the most common challenges when it comes to implementing MMM is the lack of good quality data. Most brands face the problem of not having rich marketing data in a well maintained and organised manner. Data is usually scattered in silos with multiple teams and internal stakeholders and a lot of the data still resides with vendor partners.
This makes it very difficult to collate uniform historical data, especially from past vendors and even ex-employees! Most organisations use CRMs and ERPs which are not originally built for marketing teams. That is the grass root problem which marketers should fix by implementing a marketing process automation system built purely for marketing teams.
In conclusion, MMM is an effective tool for businesses looking to achieve better ROI by optimising their marketing spend. But it is important for marketing teams to adopt the right tools to capture key data during the marketing operations and feed that to the MM engine for better decision making. If done correctly, it can help businesses make data-driven decisions about their marketing strategy, identify the most effective marketing mix, and make informed decisions about future investments.