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Multi-Touch attribution (MTA) models are designed to assign credit to the touchpoints that affect user journeys on the path to conversion. MTA models are built to measure the contribution of each touchpoint so that marketers can measure how marketing investments are improving their key business metrics. MTA models leverage user-level paths for converters and non-converters, comparing the characteristics of each path to determine what combinations of touchpoints and features within them explain the likelihood of a conversion.

Marketing Mix Models (MMM) (also known as media mix models) are used to measure the contribution of various media investments as well as external drivers on business outcomes. The data used in marketing mix models is generally time-series, as the relationships between the drivers and the business outcomes are derived from the occurrences in the events at same time (temporal relationships). For instance, media mix models enable advertisers to measure the contribution of TV investments on sales by comparing sales in periods where TV was on vs. sales in periods when TV was off, while controlling for other external drivers and marketing variables. Marketing mix modeling enables marketers to measure the impact of media channels where there is no data available to know whether a particular user was exposed to the campaign. It enables marketers to measure the impact of channels like TV, print, or out-of-home, for which individual exposure data is not available.

Aligning the insights of the MTA and media mix modeling solutions offers an opportunity for better decisions at the strategic and tactical levels. Using the two models together enables marketers to trust the insights and develop coherent and consistent action plans.

The key to success in integrating both approaches is a two-way communication that ensures coherent and consistent recommendations from both models. MTA models can provide timely updates for digital media performance based on actual observations of individuals and marketing mix modeling can provide updated readings on baseline demand and impact of online media using more factors.

 

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