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- On Sunday, September 30, 2018
- By Read More
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Here’s the chronology of events: Month 1: Cold Start on a winter night Our first task AUTOMATING MACHINE LEARNING MONITORING: Imagine if what viral polite grandma was thinking when she was typing in her search query was actually true: that there is a human operator on the other side of the screen accepting queries on his/her whim, who then manually searches through websites and returns a list of relevant results.
It would be in such a pre-automated world where you could also imagine data scientists manually monitoring a long list of machine learning models for a long list of clients.
Feature Engineering: At Retention Science, we want to capture all sorts of variability in customer behavior in order to model behavior such as calculating purchase probability, predicting customer lifetime values, and optimizing which discounts are most appropriate for which customers.
For instance, with user data, we’re able to derive their average order value, location, age, which items users browsed recently, and so forth.
The data science team at Retention Science uses Welcome Purchase Probability to help sort out the good customers from the bad so marketers can spend less effort courting bad customers and more time engaging the good ones.Part 1 of the Retention metrics explained: Welcome Purchase Probability post explained why we use WPP and how it works.
- On Tuesday, June 2, 2020
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