creating a Marketing Mix Model (MMM) to determine market attribution on marketing and sales data to estimate the impact of various marketing channels.
Marketing was always considered more of an art than a science. But with the emergence of online marketing and big data, marketing is more mathematical and methodical than ever. In fact, one of the biggest areas of opportunities for data science and machine learning applications is marketing! This article is going to focus on an extremely prevalent and powerful marketing science technique called Marketing Mix Modeling. This project will cover what it is, why it’s so useful, how to build one in Python, and most importantly, how to interpret it.
A Marketing Mix Model is a modeling technique used to determine market attribution, the estimated impact of each marketing channel that a given company is using. Unlike Attribution Modeling, another technique used for marketing attribution, Marketing Mix Models attempt to measure the impact of immeasurable marketing channels, like TV, radio, and newspapers. Generally, your output variable will be sales or conversions, but can also be things like website traffic. Your input variables typically consist of marketing spend by channel by period (day, week, month, quarter, etc…), but can also include other variables which we’ll get to later.
You can tap into the power of a marketing mix model in a number of ways, including: To get a better understanding of the relationships between your marketing channels and your target metric (i.e. conversions). To distinguish high ROI marketing channels from low ones and ultimately better optimize your marketing budget. To predict future conversions based on given inputs.