Kirk Kappelhoff is the Director of Strategic Finance at Drivetrain and a member of Vitally’s Success Network.

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Remember that time you confidently told your friend, "It definitely won't rain today" right before a surprise downpour? 

That's sometimes how revenue forecasting can feel today. Relying on spreadsheets, historical data, and a whole lot of finger-crossing🤞while presenting revenue forecasts to the board can be overwhelming, leaving more questions than answers. What many FP&A teams don’t realize is that there is a goldmine of data sitting right there with their Customer Success (CS) teams. 

CS data such as usage patterns, expansion signals, and recurring revenue provide evidence-based predictions. What if revenue prediction could be less "fingers crossed" and more "data-backed confidence?” How do we make that happen? Let’s dive in. 

Your CS Team Knows More About Revenue Than You Think

CS teams are literally talking to your customers every day. They're watching how people use your product, catching the early warning signs, and celebrating the wins. But when it comes to revenue forecasting, we often don’t see the connections between this information and revenue, much less how it might inform our predictions.

Customer success data includes several signals that are relevant to revenue forecasting, including:

• How customers use your product
• How engaged they are
• Overall customer health scores 

This isn't just nice-to-have information. This data provides insights that help you spot potential churn before it happens, identify promising upsell opportunities, and understand which customers are likely to stick around. 

Your CS data basically helps you understand early warning signs and opportunities so you can predict revenue more realistically. However, collecting this data is only half the battle. The real challenge is getting your CS and Finance team to work together.

When both teams collaborate, Customer Success data can flow directly into revenue forecasts. This helps leaders have a much clearer (and more accurate) picture of where the business is headed.

Key Customer Success Metrics for Forecasting

One major metric that serves as a comprehensive indicator of account stability and growth potential is Customer Health Score (CHS). Typically measured on a 0-100 scale, this is a key metric that enables you to predict renewals, churn, and expansion opportunities. 

CHS reflects various factors that influence whether a customer is likely to renew or churn. The score is often divided into bands (green: >70, yellow: 30-69, red: <30) to signal urgency. 

There are a few key metrics that feed into the customer health score:

1. Net Promoter Score (NPS)

The NPS indicates if customers see enough value in your product and if they are willing to promote it. 

It asks a simple question: “On a scale of 0-10, how likely are you to recommend us to a friend or colleague?” A higher score indicates stronger customer advocacy, and a lower score can signal churn. 

2. Customer Satisfaction Score (CSAT)

The CSAT gauges immediate satisfaction. After every key interaction, the customer success team asks the customer how satisfied they are with their experience. 

Lower satisfaction can point to future problems like churn, and a series of higher scores can represent greater satisfaction. 

3. Customer Effort Score (CES)

This score measures how easy it is for customers to interact with your company. High effort scores in important workflows like onboarding or feature adoption often translate to lower expansion rates and higher churn risk.

4. Product Usage Metrics

Product usage metrics like the number of logins, login frequency, session length, feature adoption rates, and daily or weekly active users show how customers engage with your product. 

Usage is the most objective signal of future revenue potential since customers with high usage patterns are often ripe for upselling, while low usage can be a red flag for churn.

5. Customer Feedback and Support Metrics

Feedback and support metrics help you understand what your customers like and dislike about your product. 

Metrics like the number of support tickets, ticket resolution times, and the number of critical issues (like open P1 tickets) are strong indicators. Frequent, unresolved issues can frustrate customers and put renewals at risk. At the same time, positive feedback and timely resolutions strengthen customer relationships.

6. Relationship Strength

This metric includes evaluating customer sentiment, whether your solution meets their needs, and the scope of use cases delivered. A strong relationship is often a solid predictor of long-term retention, while a weak relationship signals potential churn.

Steps to Integrate Customer Success Data into Forecasting Models

Integrating Customer Success data into revenue forecasting models can be challenging, especially when companies rely on different systems and software. However, with financial forecasting tools like Drivetrain, Pigment, Anaplan, teams can seamlessly collect, standardize, and analyze this data to improve forecast accuracy.

Step 1: Collect and Standardize Customer Success Data

Customer Success data, such as health scores, usage patterns, and support metrics, is often scattered across different platforms. This data, whether housed in a Google Sheet or CSM tool, needs to be pulled out into a centralized platform or aggregated into a dashboard. 

To ensure consistency, standardize the data by defining key metrics — such as Net Promoter Scores or product usage rates. You need to make sure everyone speaks the same language.

Step 2: Analyze and Integrate Data into Forecasting Models

Once the data is collected and standardized, it can be integrated into financial forecasting tools. These tools can analyze customer success metrics to generate insights. 

For instance, you can assign renewal probabilities based on health score bands: 90% for green, 70% for yellow, and 20% for red. For expansion opportunities, evaluate account-level data and flag customers as likely to renew or expand using a 0 or 1 scale.

Benefits of a Data-Driven Forecasting Approach

By leveraging real-time Customer Success data, such as health scores and usage patterns, businesses can refine their revenue forecasts. These forecasts are considered to be more reliable than those based solely on historical sales trends.

Another benefit of a data-driven approach is better resource allocation. With insights from customer health metrics, companies can prioritize resources — focusing support, marketing, and sales efforts on high-potential accounts or at-risk customers to maximize retention and growth.

Conclusion

Gone are the days when revenue forecasting could rely solely on pipeline data and historical trends. In today's subscription-based economy, customer behavior signals are just as crucial as traditional financial metrics.

The companies getting it right are the ones breaking down walls between CS and Finance teams. They're using revenue forecasting software to turn customer signals into accurate predictions. And the results speak for themselves — instead of reacting to surprises every quarter, they're proactively managing their revenue based on real customer behavior.