Why machine learning is critical to multi-touch attribution
Columnist Alison Lohse notes that in today's complex marketing atmosphere, marketers need tools that can quickly and accurately make sense of myriad and disparate data -- and machine learning does just that.
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.
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