Case Study
How Spotify Boosted CRM and Retention Rates Using Machine Learning
Client:
Spotify (Founder’s work experience)
Services:
Lifecycle Program Launches & Management, Growth Experiments Planning, Launches & Reporting, HTML Email Template Development & QA
Duration:
6 months
The Challenge
Spotify wanted to improve the performance of their CRM using machine-learning model optimization. Just like their ML-powered in-app recommendations, they believed that making push notifications and emails more relevant to the user’s content preferences would make the product stickier. They saw success using machine-learning to optimize the in-product experience, and up until this point, CRM was mainly driven by manual campaigns and journeys using stale business logic.
Our Solution
Grohaus founder, David, began by working with an Insights team to gather qualitative and quantitative research on people’s perceptions of various types of push notifications and emails. With fresh research in-hand, David worked with UX copywriters to develop 100+ messages, and he worked with ML engineers to ensure that the personalized experience for each message was feasible. He created a large inventory of notifications to feed the machine-learning model with a rich set of inputs to learn from, and to ensure that almost every user would have a diverse set of relevant notifications at every step in the messaging journey.
The Results
Spotify learned that users exposed to the ML-powered notifications saw a significant improvement in engagement and a sustained increase in retention over time. This helped the business get closer to its Monthly Active User growth goals, which was key to their overall business performance for the year.
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