November 2020
Beginner to intermediate
352 pages
8h 43m
English

As we discussed in Chapter 7, “Designing Your Data for Analytics,” it’s ideal to design your data by running a planned experiment or quasi-experiment. But in many business situations this simply isn’t possible or practical. Moreover, as we learned in the previous chapter, you may not be able to control for confounds—particularly when the confounding variables are things like customer intent or interest, which are hard to measure.
The good news is that across sectors and circumstances, companies and customers unintentionally create situations that mimic experiments. ...
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