Marketing has always tried to understand the relationship between stimulus and response. Before the age of cloud computing, big data and real-time analytics, measuring the impact of marketing efforts was calculated using slide rules. Marketing mix models (MMM) have been around longer than you might think.
The concept of a marketing mix itself goes back to the 1950s, coined by advertising professor Neil Borden. It refers to the controllable elements you can adjust to influence customer behavior, often summarized as the 4Ps: product, price, place, and promotion. This framework helped marketers understand some aspects of their strategies, but a key question remained unanswered: how much is each element actually contributing to sales?
The 1970s saw a revolution in marketing. Up until then, measuring the impact of marketing efforts was more of a guess than a science. University of Chicago statisticians stepped in to change that. They developed the first statistical models, a powerful tool to finally quantify the connection between marketing activities and that all-important metric: sales. These early models were effective but cumbersome to build. We're talking months of work by highly skilled data scientists – a luxury reserved for big corporations with hefty marketing budgets.
In the 1980s, market mix modeling gained traction as companies sought to justify marketing spend and measure its impact on sales. MMMs offered a data-driven alternative to guesswork, especially compared to limited options at the time. However, building and running these models remained a challenge. The complexity of statistical analysis, combined with limited data sources and lack of powerful computers, made MMMs expensive and time-consuming. This restricted access mostly to large companies with dedicated resources, while smaller businesses struggled to leverage this valuable tool.
This complexity, coupled with privacy concerns and the decline of cookie-based tracking, made MMMs less widely used for a while. However, the need to understand marketing return on investment never faded.
A Resurgence of MMM for Modern Problems
Although market mix modeling hadn't really gone away, we're witnessing a resurgence of MMM fueled by advancements in cloud computing and automated data analysis. Platforms like Arima are making MMM tools accessible to everyone. With Arima's self-directed MMM platform, there's no need for a data science degree or a team of analysts. Simply upload your sales and marketing data, and the platform does the heavy lifting. In a matter of hours, not weeks or months, you'll have clear insights into how your marketing mix is performing.
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Synthetic data can also be used alongside MMM within data science platforms to address to complete the loop between planning input and business outcomes.
So, from the days of complex calculations to the era of self-directed models, Marketing mix models have come a long way. It's a powerful tool that helps marketers move beyond guesswork and make data-driven decisions to optimize their marketing mix and maximize their return on investment.