top of page

Identify and Reverse the Risk of Churn

For VPs of Customer Success, CSMs, Account Managers, Support Representatives, or any number of customer-facing roles, identifying accounts likely to churn and taking immediate action is critically important. Customer Intelligence offers a clear path to doing this, minus the guesswork.

Reverse churn with CI

Why churn matters

Churn is a lagging indicator that the value your organization works hard to create isn’t being realized by certain customers. By analyzing customer data to understand why this is the case and how to change it, you can protect revenue and, more importantly, unlock that value for your customers.

Using involve.ai to identify churn-risk and optimal actions

involve.ai insights are based on organizations’ combined customer data, weighted appropriately for each organization (if you’re curious, you can read about our AI Models and how we train them for unparalleled accuracy). The Customer Health Dashboard, used in conjunction with Workspaces, allows customer-facing teams to collaboratively identify and support struggling accounts.

The dashboard, when sorted by the involve.ai Customer Health score, displays customers in order of their overall health. From there, users can open account details for high-risk customers to review their health score history, summaries of past interactions, and insights, and can convert the most relevant insights directly into to-do items in their teams’ Workspaces. Review a detailed how-to article.

Collaborate with your team and surround at-risk accounts with love

As a cross-functional team, it’s important to set aside regular time to review your at-risk customers and the Workspaces tasks that have been created from insights. Ensure that action is being taken on each task and assess how those actions are impacting the relationship and the customers’ health score. Then watch those health scores, and revenue, grow!

19 views

Recent Posts

See All

How we test accuracy and performance

Our AI predicts customer behavior with greater than 90% accuracy. How do we know? We test and measure the performance of our models regularly, in a variety of ways. Model training test accuracy - 94

Comments


bottom of page