Channel Attribution Modeling is not the best way to decide on Media Mix Optimization (Channel ROI). Here I present results by formulating the problem as a Marketing Mix using two models:
(a) Linear Regression Analysis
(b) Log-linear Multiplicative Model
How is Real-Time Analytics Different from Traditional OLAP?
Modeling The Market Mix Modeling Problem (Media Mix Optimization)
1. Market Mix Modeling:
Search For The Perfect Mix
5th Annual Big Data Conference (2017), Santa Clara
Amit Satsangi
More on MMM coming
soon. Slideshare.net
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6. Assisting Channel
(SEO)
Originating Channel
(SEM)
Assisting
Channel
(Email)
Activity Marketing
Spend
Day1:
User searches for a beauty
product, and clicks on Google Ad
SEM
($16)
Day 5:
Organic Search by user, lands on
the company website, provides
email, abandons cart
SEO
($5)
Day 6:
User receives email and clicks on
it, but still does not buy
Email
($0.05)
Day 7:
User sees ad on Facebook, clicks
on the ad, and makes a purchase
Revenue Generated $100
Facebook
Ad
($2.0)
Converting
Channel
(Facebook)
The Channel Attribution Problem
9. Issues With Multi-touch Attribution
Modeling
9
Soccer
How should we distribute the prize money of $10 Million amongst these players?
• Give 100% to the goalkeeper(First Click Attribution)?
• Give 100% to the player who hit the ball in the goal (Last click Attribution)?
• Something else???
Does This Sound Totally Absurd?
So, should Attribution Modeling
22. • Channel Attribution fails to provide even a minimally acceptable
solution to the problem of Media Mix Optimization (marketing
budget allocation)
• At best Channel Attribution can be used to determine the most
popular “channel paths” that the customers take (using Markov
Chains)
• Using MMM one can go to various levels of model complexity
depending on the effects that are desired: current vs. carryover
vs. shape etc. (see Tellis)
• With MMM the costs of building and keeping the model up-to-
date go up with complexity (accuracy)
• Any MMM model (even the most basic one) takes time and
effort to build – check for Heteroscedasticity, Collinearity,
Normality. Auto-correlation etc.
Learnings
22
23. It is not the answer that enlightens, but the question.
…Eugene Ionesco