Question 1 (2 points)
In “Why Experiments”, we discussed cases in which data correlation does not imply causation. In fact, a specific data pattern (e.g. a correlation) can often be supported by multiple underlying stories. An important part of the data scientist or quant marketer’s job is to pin down which story can best describe all different patterns found in the data. As a manager, when your experience with data grows, the ability to guess what is driving the data pattern will become your second nature. You will automatically question the credibility of a causal claim when it is based on data correlation.
The following examples will train your mind to think about possible explanations of a given data pattern. In each case, a data pattern is shown, and one (causal) claim is provided. If you find the explanation provided is not the only explanation, describe one other possible explanation/story that could drive such data patterns. If you belief the claim is the only explanation for the data pattern, briefly discuss why you support it. You are encouraged to think a bit outside the box for the potential causes of the data pattern as far as they are sensible. The grading will be based on whether your explanation you give can produce the data pattern observed.
Optional background reading: “Correlation is not causation: why the confusion of these concepts has profound implications, from healthcare to business management.”
Data Pattern: (This is an example. No grade is assigned.) Conversion rates are higher for longer sales calls.
Claim: This means keeping a longer conversation with the customers is a good strategy to get them to buy, so the telemarketing salespeople should push for longer conversation time if possible.
Your explanation: Customers who are more interested in purchasing the products stay longer on the line. This is why customers who eventually purchased have a higher average call time than customers who did not purchase.
Data Pattern 1 (0.5 points): An automaker runs a display ads campaign for a new car model, and they summarize customer conversion rate into purchasing based on their reactions to the auto ads
The data shows that consumers who did not click on any of the auto maker’s ads have a <1% conversion rate into purchasing the new model. Among consumers who have clicked on the dealer ads alone, the conversion rate is 3%; among consumers who have clicked on the carmaker ads alone, the conversion rate is 5%; and among consumers who have clicked on both dealer and car maker ads, the conversion rate is 14%.
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