Being a part of the digital world for the past nine years, I have witnessed the digital ecosystems constantly evolving. At present, data protection laws and anti-tracking policies are the epicenters of the digital universe. However, the industry has been able to pivot and find ways to combat such situations.

One solution to manage the current state would be moving away from consumer-level tracking to aggregated measurement studies. One such measurement solution is the marketing mix modeling; it is a time-tested statistical analysis thriving on historical sales data, marketing and non – marketing activities, and other variables that can impact an advertiser’s business performance.

It is essential for every practitioner beginning the marketing mix modeling journey to consider the following:

Internal data and analytics

The first and most important aspect would be to check internally on availability of data. Marketing mix models are dependent on quantity as well as the quality of data available. They are not always about the media side of things but includes a wide variety of data across the business.

Practitioners can begin by checking if the advertiser has commissioned marketing mix modelings in the past via 3rd party. On the other hand, if the advertiser wants to move it in-house now and refresh methodologies, a cost and time-effective method would be to look out on any open-source marketing mix modeling codes available.

Have I considered all variables?

Marketing mix models are a mix of art and science. The art is to make sure that practitioners have brainstormed with the stakeholders involved in all the variables that can potentially influence the business.

The models can be misleading or drive inaccurate results if key variables are omitted during the modeling phase. Modeling should not be undertaken hastily or mechanically and should cater to the nature of every business to some extent.

Is it granular enough?

Granularity in simple terms, level of information is vital for modeling to offer essential insights and maximise model accuracy. A practitioner should ensure that they can provide the most profound insights for every channel and break down insights at campaign, product, region levels.

Removing bias

Look at various statistical metrics to assess the model’s accuracy; look for any multicollinearity between the variables; examine the R-squared and adjusted R-squared, MAPE, and Durbin Watson. Do not forget to look at the predictive power of the model: check the out-of-sample forecast quality on the validation data set, as well as associated forecast errors and confidence intervals.

Input for decision-making

Marketing mix modeling is a powerful measurement tool that provides both strategic and tactical insights for advertisers. It enables advertisers to understand the optimal cross-media budget allocation via future facing scenario planner and saturation curves for strategic insights. For tactical insights, marketing mix modelings can deep dive into individual channels and fuel optimisations by using granular information in the models.

Marketing mix models are here to stay. Advertisers should invest in modernising their marketing mix modeling models, making them quicker and automated to understand the overall business performance from a birds-eye view and dive deeper to derive actionable insights.

Key takeaways

• Marketing mix models are a mix of art and science.
• Practitioners should brainstorm with stakeholders in all the variables that can potentially influence business.
• The models can be misleading and inaccurate results if key variables are omitted during the modeling phase.
• Modeling should not be undertaken hastily and should cater to the nature of business to some extent.

Marketing mix modeling thrives on sales data, marketing and non-marketing activities, and other variables that can impact an advertiser’s performance.