Predictive methods. Understanding customer preferences. Agenda. Introduction to predictive analytics Logistic regression Case study: Japanese car manufacturer exporting in the US Modelling interdependent consumer preferences Causality estimation
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Understanding customer preferences
0 = “Stop research”
1 = “Continue research
0 = Female
1 = Male
Very strong in predicting Japanese cars purchases
but weak in predicting non-Japanese cars purchases
Area under the curve:
Gini coefficient = 0.5
The residuals represents what is not explained by the model
Adding a group dummy which is equal to 1 if the individual fits in the group and 0 otherwise
Asian – 26-40
What is the truth?
What’s the causal effect ?
Custom Attribution Algorithms
Mathematical Attribution Models
Rules Based Attribution
In this example we drilled into the AdWords > AdWords path to see the specific ads that were clicked on en route to purchase.
To further increase the accuracy of attribution, an advertiser is able to choose the maximum log window.
Consumer’s decision is a function of
Our communications, Consumer Search, Competitor communications,
Paid Search, Banner Ads, e-mail, On-site Promotions, Comparison shopping, Affiliate ads
Site visits to us
We are barely scratching the surface of the potential of path data with the attribution models!!!