Does your risk score actually drive action, or is it just a number on a dashboard?
Customer success (CS) teams often rely heavily on risk scores to identify at-risk customers and prevent churn.
However, many organizations find that their risk scores don’t actually lead to meaningful action or reduced churn rates. This disconnect often stems from a misunderstanding of how to use these scores effectively. To truly leverage risk scores and turn them into actionable insights, CS leaders need to understand the limitations and opportunities of their current approach.
The Limitations of Traditional Risk Scores
Traditional risk scores aggregate various metrics—such as product usage, support ticket volume, and customer satisfaction surveys—into a single number that indicates the likelihood of churn. (think Blood lab report at your doctors office) While this can provide a quick snapshot of customer health, it often fails to tell the whole story. (Think Blood Lab report with number, but not ranges for what is healthy or not, or any methods to improve the results) Here’s why:
- Lack of Context: Risk scores typically lack the context needed to understand the underlying issues. A high-risk score might indicate a problem, but without detailed insights, it’s challenging to know what specific actions to take.
- False Positives and Negatives: Risk scores can produce false positives (flagging healthy customers as at risk) and false negatives (missing genuinely at-risk customers). This can lead to wasted efforts and missed opportunities.
- Static Nature: Risk scores are often calculated periodically (e.g., monthly or quarterly), making them slow to reflect recent changes in customer behavior. This delay can result in missed chances to intervene and address issues proactively.
Turning Risk Scores into Actionable Insights
Traditional risk scores aggregate various metrics—such as product usage, support ticket volume, and customer satisfaction surveys—into a single number that indicates the likelihood of churn.
(Imagine a blood lab report from your doctors office) While this can provide a quick snapshot of customer health, it often fails to tell the whole story.
(Now imagine how the blood lab report looks like with number, but not ranges for what is healthy or not, or any methods to improve the results) Here’s why:
To make risk scores truly effective, CS leaders need to transform these metrics into actionable insights that drive meaningful intervention. Here are some strategies to achieve this:
- Deep Dive into Data: Go beyond the risk score and analyze the underlying data. Identify specific behaviors and patterns that contribute to the risk score, such as a drop in product usage or a spike in support tickets. Understanding these patterns allows you to tailor your interventions to address the root causes.
- Define your risks: Qualitative (things you can see) vs quantitative (death by a thousand cuts). There are 7 deadly risks all saas customers have and a few more that are custom to your company. Define them so you and your team recognize them and can apply them in the same situation. (not as easy as it sounds). Without definitions, risks change weekly and by the end of the quarter, they don’t mean anything.
- Customer Segmentation: Service Segments are when you segment your customers based on various criteria that impact service (not marketing). Service Segments often revolve around the unique ways customers use your product (including integration). This enables you to identify common risk factors within each segment and develop targeted strategies to mitigate them.
- Proactive Communication: Use customers at risk as a trigger for proactive communication. Reach out to at-risk customers to understand their concerns and provide timely solutions. Personalizing your approach based on their specific situation can significantly enhance customer satisfaction and reduce churn. (Standardized responses that are scalable for you but feel custom to the customer)
- Cross-Functional Collaboration: Collaborate with other departments, such as sales, product development and support, to address the issues contributing to high risk scores. For example, if a particular feature is causing frustration, work with the product team to improve it, but only AFTER you quantify the problem with an at risk report featuring “Product Gaps”.
- Regular Monitoring and Adjustment: Continuously monitor your risk definitions and adjust your strategies based on real-time data. Implement a feedback loop where you regularly review the effectiveness of your interventions and refine your approach as needed. (If you’d like to see examples of the 7 deadly risks, reach out to me)
Examples of Effective Actions
- Usage Decline: If a customer’s product usage declines, investigate the cause. It could be due to a lack of understanding of certain features or a change in their business needs. Provide additional training or suggest alternative ways to leverage your product. (set automated triggers, but not too many)
- Support Ticket Surge: A surge in support tickets might indicate unresolved issues or a need for better onboarding. Analyze the tickets to identify common themes and address them proactively through improved documentation or direct assistance. 0 report tickets may not necessarily mean your customer is fine, when combined with usage, it may indicate indifference.
- Negative Feedback: Negative feedback from surveys should be addressed immediately. Reach out to dissatisfied customers to understand their grievances and demonstrate your commitment to resolving their issues. This may seem obvious, but requires a closed loop resolution process with accountability.
Qualitative risk reporting is a valuable tool for identifying at-risk customers, but they must be used effectively to drive action and reduce churn.
By diving deeper into the data, segmenting customers, communicating proactively, collaborating across functions, and continuously refining your approach, you can turn risk reporting into a powerful instrument for customer success.
This proactive, data-driven strategy will not only help you retain more customers and enhance their overall loyalty, it will also demonstrate the value customer success delivers to the company.
About the Author:
Daniel Hoesing is the creator of the Predictive Customer Behavior Index™ a comprehensive set of 175 standards, indexed to the size and growth trajectory of the company, used to create and implement Customer Success capabilities, data management, reporting, and best practices for SaaS B2B Customer Success.
Daniel also specializes in leadership development using the 90 day Customer Success Accelerator™ – a leadership training, mentoring and development program that drives results. .
