Marketing strategies are all about customer engagement, plain and simple. You’ve mastered your brand and spent months researching consumer needs and pain points, All that’s left now is to connect the dots and show people why and how you’re going to change their lives. But if marketing is intended to merge these two lanes - your business identity and potential customers - what’s the best way to determine if the process was successful?
This is where the concept of marketing mix modeling comes (also known as media mix modeling) into play. Utilizing advanced statistical analysis methods, marketing mix modeling (MMM) helps digital marketers to establish connections between their specific strategy and elements with tangible numbers like sales goals and customer retention. But in an even larger sense, MMM can help your marketing team define ROI.
The main function of Market Mix Modeling is to translate the value of marketing efforts into a direct and demonstrable connection to something happening in sales, market share, and return on investment. To do so, the practice focuses on comparing aggregate historical data between marketing and sales metrics.
Throughout the process, marketing mix examples rely on the use of multi-linear regression to identify a correlation between an independent variable x and a dependent variable y, where the value of y can be predicted by measuring x.
Dependent variables represent the hard, financial data that illustrates success. This might mean sales, market share, or stock price.
The independent variable represents the marketing efforts. These are broken into two categories; Above the line (ATL) and Below the line (BTL). ATL activities would include print, radio, TV, and digital ads. BTL activities are temporary promotions, sales, coupons, contests, and direct mail marketing.
By identifying and measuring the discrete factors that led to a specific instance of success, marketers can draw educated and informed conclusions. This makes it much easier to create blueprints for future growth, specifically by:
In general, MMM provides high-level insights into marketing campaigns and strategy, as well as shedding light on the trends that could be most impactful.
While these techniques are mathematically sound, there have also been claims that the MMM is a kind of dead marketing language, done away by time and more advanced tech tools. For instance, there’s the concern that MMM doesn’t provide enough insights on the consumer level, or help marketers to create customized messaging. Additionally, the use of historical data over the course of two or three years means infrequent reporting.
However, MMM can be greatly beneficial when it’s performed once or twice a year as part of a larger marketing strategy. This allows marketers to still benefit from those high-level insights, and then keep those in mind when using more granular data analysis techniques like data-driven or multi-touch attribution. MMM also provides insights into the kind of offline conversions that occur in-person with a sales team.
The truth is, MMM isn’t extinct or out-of-date, it just provides a different vantage point and perspective on marketing analysis. It may have been invented before Big Data was a buzzword, but now it can not only integrate with huge datasets, but can be accelerated by the use of cutting-edge technology. The data used in MMM is aggregate, meaning it’s made up of several years of metrics and numbers. This also requires clean data that is easy to integrate into modeling software. That’s where DemandJump comes in. Our platform allows you to get as in-depth as you’d like with marketing data, and you can alter those timelines to stretch as far back as you need. Ready to get started? Start your Free Trial Today!