Better Way to Use Predictive Analytics

Good thoughts about analytics using proxies.   And the ever present issue of the lack of needed data.  We often did something similar, but not as sophisticated.   The use of proxies for many predictions is a common thing.  Just make sure your proxy operates in the same way in changing contexts.  And make sure you included it in your documented assumptions.   I have seen examples where that was somehow forgotten. 

Podcast and transcript:

Beyond Clicks: Getting the Most out of Big Data
Wharton's Hamsa Bastani discusses her research on a better way to use predictive analytics.

In the deep ocean of big data, it’s hard for companies to know what’s true or even relevant to their operations. The latest research from Hamsa Bastani, Wharton professor of operations, information and decisions, can help companies navigate the waters by offering a better way to use predictive analytics. Bastani spoke with Knowledge@Wharton about her paper, “Predicting with Proxies.”

An edited transcript of the conversation follows.

Knowledge@Wharton: This paper focuses on predictive analytics. How do companies use predictive analytics today?

Hamsa Bastani: A lot of companies across a variety of applications are starting to use predictive analytics to guide their decision-making. For example, in e-commerce, companies like Amazon or Expedia use customer-specific data to try to predict what sorts of products a customer might be interested in and then use that to make personalized product recommendations.

Knowledge@Wharton: This process often uses something called a proxy outcome. What’s the difference between a proxy outcome and an actual outcome? And why do firms settle for proxies?

Bastani: It’s often the case that the data that we want are available in a very limited quantity, and this is what I would call the true outcomes. What we have instead is a large amount of data from a closely related outcome, which is what we call proxy outcomes.

In the e-commerce example again, a company like Amazon typically will have very little data on customer purchases for a particular item, but they’ll have lots and lots of click data. If you think about it, clicks are a pretty good proxy for purchases because a customer will typically not click on a product unless they have some intent of purchasing it. Of course, these two outcomes are not exactly the same. What I’ve found in some of my research looking at, for instance, personalized hotel recommendation data for Expedia is that these outcomes can be different along a few dimensions. ... "