- Focus on customer health to reduce churn, grow recurring revenue, and increase dollar retention.
- Analyze quantitative and qualitative signals together with value delivered to evaluate accounts and form a predictive model of customer health.
- Create standardized actions & playbooks based on customer health insights and triggers to address churn—and to benefit from upsell or cross-sell opportunities.
With economic headwinds intensifying and uncertainty deepening, many SaaS companies are focused on protecting their recurring revenue base. Market priorities have pivoted from fast-paced growth to profitability, and metrics like gross and net dollar retention (GDR and NDR) have come into focus.
One of the surest ways to fortify these metrics is to improve customer health. This is not only a defensive measure to strengthen retention, but strong customer health also unlocks cross-sell and upsell opportunities that are often easier to capture.
Measuring the strength of a customer relationship along with objective measures such as utilization or value delivered gives companies a proxy for customer health and likelihood to expand, renew, or churn. Traits and behaviors of existing customers can inform predictive models that trigger early account actions. While not a new concept, many companies don’t have signals on customer churn that are effective or actionable. Additionally, once established, the approach and efficacy should be revisited for continuous improvement otherwise it risks depreciating into a loosely accurate measurement that checks the box and is not operationalized.
The customer success (CS) function is a core driver of defining and tracking customer health. While CS is the typical sponsor, customer health is a cross functional effort that requires partnership with sales, rev ops, finance, and support to not only get an accurate measure, but also the buy-in and change management needed to deliver the desired value-based outcomes.
There are many ways to measure customer health in varying levels of complexity. This can be a basic model using the data readily available in CRM or using more advanced tools for measuring utilization and success based metrics. While not exhaustive, there are four factors to consider in building such a model:
Elements of a customer health model
- Define what good and bad health look like
- Identify the signals that matter the most
- Score & segment the customer base
- Continuously track health
Define what good and bad health look like
Start with the data available on customers for the last 2-3 years, including the ones that have churned. Identify the customers that have expanded and the customers that churned. It may be helpful to screen out justifiable churn (e.g., M&A, bankruptcy).
We often see GDR > 90% and NDR > 100% as markers of healthy retention metrics. However, understanding GDR and NDR for cohorts that fit your ideal customer profile will provide a more inspiring overall target.
Identify the signals that matter the most
Identify the archetype of customers that expanded as a proxy for the healthiest customers and the customers that have churned as a proxy for unhealthy customers and determine the signals that most correlate with each. It’s important to be practical and simple, but worth considering a broad set of metrics to test before landing on the select few that matter the most. Below is an example of possible signals:
Mission Criticality / Value Delivered
Relationship Strength / Satisfaction
- Objective measures of ROI
- Product utilization & engagement
- Product adoption & active user count
- Net Promoter Score (NPS), Customer Satisfaction (CSAT)
- Duration as a customer or renewals
- Expansion and meeting history
- Sales rep input
- Proxy if ROI achieved
- Fully implemented & value delivered
- Sales rep input
- Attendees of last Quarterly Business Review (QBR)
- References / testimonials
Score & segment the customer base
Using the most relevant signals, companies can build a training model to score and predict customer health. A critical step is to pressure test the result by iterating with sales & CS teams along with other relevant functions and by back-testing against historical customer data to ensure model usefulness.
In our experience, results and impact from this approach can be seen in months. We worked with one of our portfolio companies to put a predictive customer health scoring model in place, and when paired with a “red account playbook” to focus on highest risk accounts, we saw NDR increase from 105% to 107% across all customers and from 107% to 110% among large customers in less than 12 months.
Continuously track health
Understanding customer health requires moving beyond one-time manual measurements to live, automated tracking. The scores and approach can get stale without continuous improvement as accounts expand and churn. As accounts expand or churn, companies can continuously improve models to ensure an up-to-date understanding of customer health.
We’ve observed this with another one of our portfolio companies where we automated reporting, which was an ongoing manual process. This created the space to talk about insights and actions and shift in thinking as teams put more focus on finding ways to maintain a high GDR and expand NDR through pricing, and other initiatives.
With perspective on customer health and early warnings of churn, the next step is to operationalize this knowledge. Actions might include:
- Standardize actions to address accounts with different levels of customer health: Standardizing actions and plays to common scenarios with defined functional roles and responsibilities can help ensure consistent, expedient action. Several software options can also help automate and scale the actions.
- Implement a Customer Lifecycle Management program: Proactive engagement is important to ensure your customer’s value and experience perception is positive and drives good CSAT. Consider asking your RevOps team to establish an engagement framework to ensure that CS reps seek feedback and engage proactively in a structured way with playbooks aligned to their respective health score.
- Segment action based on account size: For small accounts, a digital or in-app messaging approach may suffice. Large accounts might require outreach from the account manager or customer success manager, and large strategic accounts might require a cross-functional team that meets monthly and an executive sponsor assigned to each unhealthy account.
- Aggregate & individual account reporting: Consolidating metrics into a 360-degree view of each account provides a powerful tool to inform customer meetings and QBRs. Meanwhile, tracking progress on customer health in aggregate provides insight into customer success efficacy and impacts to GDR and NDR.
Finally, strong health is more than turning a ‘red’ account ‘green,’ it’s about ensuring customers are getting the ROI and value of your solution, which will lead to increased adoption and expansion within existing and new customers. In practice, effective and actionable customer health scores are not easy to achieve.
Often, companies can fall victim to checking the box, over-engineering, or winding up with too many red and yellow accounts to be actionable. Keeping a practical lens along with operationalizing customer health is critical to protecting revenue and driving growth.