An analysis technique allowing marketers to better measure the impact of their ad campaigns and determine the varying degrees to which different elements are contributing to success.
Why would I need this?
Insights gained from MMM offers an additional set of aggregated data for marketers to use to refine their campaigns and create forecasts for what tactics might have the best impact in the future.
The factors within an ad campaign that can be better evaluated with a constructive implementation of MMM are wide-reaching and varied, from consumer trends to external influences like seasonality.
How does it work?
MMM relies on multi-linear regression to determine the relationship between a dependent and independent variable, for example, revenue and price or market share and distribution. MMM can use both linear and non-linear regression models to provide a reliable indicator of those factors’ combined influence on marketing impact for a particular campaign. To ensure the insights are robust enough to act on, several models using at least 2-3 years worth of data should always be evaluated to account for factors like seasonality.
Another important factor that influences the degree to which MMM will be successful is the quality of the data being inputted to measure. This is why it’s important for organisations to have access to clean, aggregated data that, where possible, isn’t overly reliant on third-party sources.
Real world examples