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Serverless with AWS Lambda: Reducing metrics reporting lag from hours to seconds at ReSci

Author: Avi Sanadhya, ReSci Platform Engineering Team At Retention Science we deliver personalized marketing campaigns powered by machine learning to drive a deeper level of customer engagement. Our AI engine, Cortex, is responsible for billions of predictions daily and hundreds of millions of personalized emails each month. As this number grows, it becomes increasingly important to report […]


Solving Cold Start Product Recommendations in e-Commerce

Recommendation algorithms have evolved in the last decade to provide a personalized experience to every shopper. In most cases, these algorithms rely on some behavioral (implicit) or transactional (explicit) inputs of the user. Pure collaborative filtering methods and matrix factorization methods are well known techniques to perform recommendations that have shown high returns. However, new […]


Female, Male, or Neutral? Filtering Based on Gender

Background In predictive recommendation systems, sometimes events will occur that appear counterintuitive. As a marketer, you may notice some male users in your customer list receiving recommendations for some women’s skin care products. Sometimes, this is simply because those male users were shopping for their mom, wife, girlfriend, daughter, etc, and their browsing history indicated this. […]

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Behind the Data Science: Sending Item Alerts to the Right Users

The Problem Recommendation algorithms usually try to optimize the best set of items (products) to show a specific user. In certain situations it is necessary to obtain the best set of users that might be interested in a specific item. Many digital marketers face the hard problem of targeting the right users not only for […]


Machine Learning Predictions for Subscription Companies

With the rapid acceleration of Subscription business models, several native e-Commerce companies like Amazon, Starbucks, and Sephora are moving towards adopting the subscription model. Machine learning can help marketers of subscription e-commerce businesses by providing predictive insights. ReSci’s (Retention Science) new lifecycle marketing product Subscription Cortex aims to harness the power of AI to the marketers. […]


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

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 […]

fortune 500

3 Major Recommendation Algorithm Mistakes Fortune 500 Companies Make

Several recommendation algorithms power email-marketing campaigns as well as on-site product recommendations. With Amazon’s success in driving revenue and engagement from product recommendations, several companies leverage these algorithms to cross-sell/up-sell products to users. The data science team at Retention Science has helped power onsite/app and email recommendations for more than 75 e-commerce companies. With over […]


Automating Machine Learning Monitoring [RS Labs]

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 […]

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

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 […]


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

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…