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Scaling Recommendation Engine: 15,000 to 130M Users in 24 Months

January 16, 2017

Delivering users with precise product recommendations (recs) is the creative force that drives Retention Science to continue to iterate, improve and innovate. In this post, our team unveils our iteration from a minimum viable product to a production-ready solution. Here’s the chronology of events: Month 1: Cold Start on a winter night Our first task […] Read More

Automating Machine Learning Monitoring [RS Labs]

November 2, 2016

This blog takes a small dive into one of our internal monitoring tools that overlooks our entire ETL pipeline and helps us stay on top of our machine learning models. Background: Imagine if what viral polite grandma was thinking when she was typing in her search query was actually true: that there is a human […] Read More

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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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