Account health score is a hot topic in Customer Success circles. With more data available than ever to teams across your organization, building a churn risk model allows your Customer Success Managers to quickly identify and proactively engage at-risk accounts.
What is churn in customer success?
Churn is the percentage of customers who stop using your product or service within a given period of time.
Churn mitigation can oftentimes be thought of as a reactive motion: CSMs kick into high gear when things go awry. But with a gold mine of your customers’ product usage data, billing history and support tickets in your data warehouse, you can easily flip the script to make this a proactive motion and engage accounts before reaching an inflection point.
By implementing churn risk flags, you can:
- Identify at-risk customers all in one tool
- Automate alerts for account owners as soon as your customer reaches the churn risk threshold
- Trigger automated playbooks based on those signals
- Re-engage customers before engagement becomes unsalvageable
- Improve forecasting by identifying which accounts will renew ahead of time
- Boost Net Recurring Revenue (NRR) and reduce churn by being proactive and taking action to mitigate churn
Many startups develop sophisticated AI models for health scoring. But the reality is that you can start with a basic model including only the key factors that contribute to churn.
For example, one of our early stage SaaS clients knew that “customers are 50% more likely to churn when their product usage is lower than 50% of the license limit and the customer has late payments”. Their data team created a simple model from this logic and passed it to their CS tool. Inside the CS tool, the team created automated playbooks for their CSMs to reach out.
With a proactive churn reduction strategy enabled by the data warehouse and Census data activation, your Customer Success team will be equipped to turn things around before churn is inevitable.
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