Churn Reduction

A top bay area SaaS company uncovered why customers leave and predicted who will churn next. Then actively pursued users before they cancelled.


rows of data

5 days

time to value


prediction accuracy


increased retention


Determining the value of each customer, including likely future spending and the resulting profit margins, is a vital factor in deciding where to aim personalized marketing efforts. Lifetime customer value models determine the likely profit each individual customer will generate over the entire customer relationship, as well as when a customer is likely to churn; allowing marketers to focus their efforts on those likely to be most profitable eventually reducing churn and increasing revenues.

The company's Business Intelligence (BI) team was looking to build predictive models for customer churn, up-sell and cross-sell. However, the in-house team had limited background in data science and were pre-occupied on larger projects to undergo data science training.

Consultancies were tough to find as no one understood the business well. That’s when they turned to Obviously AI.

The Solution

The company conducted extensive market research and chose Obviously AI’s predictive analytics platform for their needs. 

The BI team used Obviously AI’s platform to train a model to analyze their customer’s demographic and behavioral data to answer the following questions:

1. Which customer is likely to churn in 3, 6 and 9 month period?
2. What is avg. LTV per customer based demographic details?

The Results

The company saved 3% of their new customers from churning.

Obviously AI’s platform allowed the Unlimited’s existing BI team to build the AI model within 5 days, versus 4 months it would require a data scientist, and at a significantly lower cost.

Most notably, Unlimited empowered its existing BI team with the capability of building state of the art AI predictive analytics models, without the need for an on-call data scientist.

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