Retention Science, the global leader in Retention Marketing, is the best way to understand, engage, and retain your customers. Our AI marketing technology predicts customer behavior and helps you create one-to-one campaigns via email, mobile, and web. We make sophisticated data science driven marketing accessible for all marketers.
We power retention focused marketing campaigns for Target, Dollar Shave Club, Honest Co., BCBG, Microsoft, Wet Seal, Gamefly and many more innovative brands.
We’ve been featured in WSJ, CNN, and Forbes, and Inc. Magazine just named us the “Most Innovative Company in Los Angeles” in 2015.
SCALING RECOMMENDATION ENGINE: 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
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. Google would need one hell of a customer service department to cater to their 40,000 searches per second, and we data scientists at Retention Science would not have enough time to refill our coffees.
One word: Scalability. Well, actually two words: Scalability and Laziness. You want to do the same thing you do for one client across a 100 clients? Automate. You’re lazy and hate monotonous labor? Definitely automate! The latter turned out to be an earlier (and bigger) motivator for us at ReSci to start automating most of our daily and weekly monitoring tasks.
In ancient times, each data scientist would get assigned a diverse list of clients and the task would be to monitor all kinds of models for them once a week. We would go into our database, see textual reports for all models running for each client and probably run a script to draw a chart or two to help our curious eyes.
However, when you have such a sophisticated Artificial Intelligence platform with over 50 models running across many clients like we do at Retention Science, it screams of work. And with that list of clients and models increasing, that pile is just going to get stacked higher and higher.
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. Once we acquire the raw data from our clients, we derive thousands of relevant features from that data. For instance, with user data, we’re able to derive their average order value, location, age, which items users browsed recently, and so forth. These features are then fed into our models to generate predictions and analyses, which in turn power our dashboard visualizations and marketing automation.
Evaluating Machine Learning Predictions: 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 is twofold. Internally, it helps us choose the best performing predictive models for the prediction problem at hand. Secondly, it serves as a reporting tool for the marketer to examine the prediction accuracy of models.
Retention metrics explained: 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 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. In Part 2, we dive deeper into the model and examples.