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The most popular use case seem to be recommender systems of different kinds (such as recommending shopping items, users in social networks etc.).

But what are other typical data science applications, which may be used in a different verticals?

For example: customer churn prediction with machine learning, evaluating customer lifetime value, sales forecasting.

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Also, there seem to be a very comprehensive list of data science use cases by function and by vertical on Kaggle - "Data Science Use Cases"

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Satisfaction is a huge one that I run into a lot. Huge referring to importance/difficulty/complexity.

The bottom line is that for very large services (search engines, facebook, linkedin, etc...) your users are simply a collection of log lines. You have little ability to solicit feed back from them (not a hard and fast rule necessarily). So you have to infer their positive or negative feedback most of the time.

This means finding ways, even outside of predictive modelling, to truly tell, from a collection of log lines, whether or not someone actually liked something they experienced. This simple act is even more fundamental (in my biased opinion) than a/b testing since you're talking about metrics you will eventually track on a test scorecard.

Once you have a handle on good SAT metrics then you can start making predictive models and experimenting. But even deciding what piece of log instrumentation can tell you about SAT is non-trivial (and often changes).

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It depends, of course, on the focus of the company: commerce, service, etc. In adition to the use cases you suggested, some other use cases would be:

  • Funnel analysis: Analyzing the way in which consumers use a website and complete a sale may include data science techniques, especially if the company operates at a large scale.
  • Advertising: Companies that place ads use a lot of machine learning techniques to analyze and predict which ads would be most effective or most remunerative give the user's demographics that would view them.
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