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Feature Engineering: A Closer Look, Part 2 [RS Labs]

September 22, 2016

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. Once we acquire the raw data from our clients, we derive thousands of relevant features from that […] Read More

Feature Engineering: A Closer Look, Part 1 [RS Labs]

August 24, 2016

In simple terms, feature engineering involves feeding knowledge into a Machine Learning model. As a refresher, a machine learning model is an algorithm that takes features as input and produces as output a prediction or classification. A feature itself is the item that… Read More

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Evaluating Machine Learning Predictions: Customer Churn & CLV [RS Labs]

August 11, 2016

At Retention Science, we are committed on making machine learning and artificial intelligence more accessible and understandable. This blog introduces our process of evaluating the accuracy of two crucial predictive models, Customer Churn Prediction and Customer Future Value (CFV). These two predictions provide invaluable insights on how to keep customers engaged. Our evaluation framework purpose […] Read More

Retention Metrics Explained: Welcome Purchase Probability (WPP), Pt. 2 [RS Labs]

November 12, 2015

62% of customers churn immediately after signup, which means once these customers go through the registration process they will not make a purchase. The data science team at Retention Science uses Welcome Purchase Probability to help sort out the good customers from the bad so marketers… Read More

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