Why machine learning is critical to multi-touch attribution

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Until six or seven years ago, econometric models offered the best way to measure multi-touch attribution. These methodologies, like MMM (marketing mix modeling), turned statistical analyses into predictions and answers to high-level questions: How much revenue is generated from each channel? How much do I need to spend in each channel to optimize my mix? Econometric models rely on complex information and assumptions by human experts, and these models did (and still do) provide valuable insight into big-picture forecasts.

Two recent shifts, however, have necessitated a new way to address multi-touch attribution: big data and user-level analysis. Both require processing power far beyond traditional modeling — beyond, in fact, what humans are capable of on our own. This is where machine learning comes in.

[Read the full article on MarTech Today.]

Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.

About The Author

Alison Lohse is COO and Co-founder of Conversion Logic. Alison spent the last 18 years focused on digital strategy for a number of Fortune 100 companies across many industries including telecom, retail, travel, B2B, CPG and tech. Her expertise and focus on client service, advanced analytics, media planning and optimization lends Alison a unique ability to drive digital strategies that scale brands helping them reach a wider audience. Cutting her teeth on digital starting in 2000, she worked across the interactive media practices at Starcom IP, then Avenue A, Razorfish and SMG with a focus on sophisticated media buying through analytics and optimization. Most recently, Alison was the Regional VP of Visual IQ, Chicago where she worked with Conversion Logic’s CEO, Trevor Testwuide. Alison earned an MA from the University of Manchester (UK) and holds a degree in art history from Lawrence University.


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