Marketing mix modeling, often referred to as MMM, is an advanced form of marketing measurement that uses machine learning statistical algorithms to estimate the relationship between marketing activities (e.g. LinkedIn ads, events) on outcomes (e.g. leads, demos, sales).
The goal?
To find out which marketing activities influence or change consumer behavior (incrementality) and which don’t, and by how much.
Marketing mix modeling works in four steps:
First, we select a target outcome (metric) we want to measure marketing’s influence over.
Depending on your business model, sales cycle, and data, that could be closed/won new business, sales qualified opportunities, or leads.
For example, since MMM requires volume data to estimate incrementality with confidence, if you drive 2,500 opportunities in a given time period compared to only 100 closed/won new customers, opportunities would make for a better target metric.
Or say you have a long sales cycle (12 months).
Since there’s a significant time lag between marketing activity and closed/won, it’s best to choose an earlier stage target metric like leads, not closed/won new sales. Too much time lag between activity and outcome introduces changing dynamics that the model won’t be able to measure accurately.
Once we choose our target outcome, we collect and organize all of your historical spend and performance data across all of your marketing channels: online and offline, paid and organic, brand and performance.
What does historical mean? At least two years of organized data.