From Lagging to Leading Indicators: A Proactive Approach to Account Health Scoring

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With customer success managers (CSMs) juggling anywhere from 10 to 300 accounts, it can be difficult to know where to focus your efforts to have the greatest impact on customer retention. Customers who are having trouble will eventually start to make noise — but too often it’s too late to prevent churn once they do.

Rather than reacting to key customer health indicators, what might it look like to proactively shape your customers’ journey using predictive indicators of success?

At Heap, we’ve built leading indicators of success into our account health scores so that our CSM team is able to act on advanced signals to prevent churn and boost account expansion. The path to redefining account health scores based on predictive insights isn’t easy, as you’ll see below. Although it takes work, it’s totally worth it.

But first, let me explain what I mean by “lagging” versus “leading” indicators of customer success.

Lagging vs. leading indicators of customer success

Lagging indicators of success are critical to every business and are typically the most important metrics that CS teams report on: how many renewals did we win, how much churn did we have, what did our net and gross retention look like? Teams often focus on lagging indicators as they’re the easiest CS metrics to measure. For example:

Lagging indicators of churn:

  • ARR Decrease
  • Low NPS Score
  • Missed Quarterly Targets

Lagging indicators of expansion:

  • ARR Increase
  • High NPS Score
  • Exceeded Quarterly Targets

While these are a great starting point for metricing your CS team, lagging indicators are not very prescriptive for CSMs looking for a clear path toward improving them. They tell you that there is a problem, but not what you can do to fix it. By the time CS teams notice lagging indicators, they’ve already lost the valuable time they could have spent improving the customer experience for at-risk accounts.

While leading indicators of success are harder to initially identify (since finding them requires thoughtful consideration and precision; see more below), they are much more indicative — even predictive — in nature.

Leading indicators also tend to change much more over time compared to more static lagging indicators. Which is why leading indicators are much more useful for measuring the impact of the CS team’s performance.

Some good leading indicators: changes in depth or breadth of product usage, decreasing or increasing use of specific features, or achieving positive business outcomes. For example:

Leading indicators of churn:

  • Incomplete Onboarding
  • Declining Monthly Querying Users (MQUs)
  • Low Depth of Adoption

Leading indicators of expansion:

  • High Breadth of Adoption
  • Increasing Monthly Querying Users (MQUs)
  • Achieved Positive Business Outcomes (customer increases revenue, decreases costs, etc.)

Focusing on adoption over revenue

From our analysis on predictive indicators of success at Heap, we’ve learned that our leading indicators of both churn and expansion are largely centered around adoption. As the process by which customers start using your product to accomplish their goals, adoption offers predictive insight into the health of your accounts.

By working with customers early on to boost their engagement with product features tied to high renewal rates, you’re much more likely to move the needle on increasing retention and even expansion of your accounts.

Goaling your team on leading indicators like rate of adoption rather than the lagging indicators listed earlier, is a much more effective way to drive results as a customer success organization. When your CS team is focused on adoption rather than revenue, you’re able to create flags earlier in the customer lifecycle to ultimately get ahead of potential churn risks.

Put another way: paying attention to adoption indicators allows CSMs to take action earlier in the customer journey to improve engagement, rather than trying to influence it when there’s not enough time left before renewal decisions.

Scaling our account health scoring

At Heap, we haven’t always followed the leading indicators of success when measuring account health. We went through a process of evolving how we look at customer health and the product behaviors that influence it.

When I began leading the Customer Success team at Heap, I quickly realized that our account health scoring was not scalable. We had developed a spreadsheet with guidance for CSMs on four different components: Product Adoption, Relationship Plan, Value Plan, and Financial Status.

Creating an overall health score meant the CSM had to manually pull data from several sources. This was cumbersome, which meant that our CSMs tended to refresh health scores on an adhoc basis, or right before their quarterly QBR presentations.

In addition, we weren’t effectively utilizing our resources, as every customer was getting a fairly similar experience and level of service. There was a lot of opportunity to improve the scalability of our customer success organization as well as to create a more personalized experience for our customers.

Smarter segmentation

The first step to optimizing our customer experience was to define our segments based on factors that informed potential growth and support needed from our customers. We looked at the most important factors for our company and for our customers so we could provide the best customer experience.

As we looked at the data, we developed four key segments (Strategic, Growth, Renew, and Nurture) based on two axes: potential Heap users and digital footprint. We found that customers with a higher digital footprint tend to have more sessions, which means they have more data they’re trying to analyze, and more team members trying to access that data (Strategic + Growth).

Customers that have smaller digital footprints tend to have fewer potential Heap users, so would need less high-touch servicing from our teams (Nurture + Renew). Customers that fall in between the two (low digital footprint, but higher potential Heap users, or vice versa), would also require slightly different servicing support. Our Growth and Strategic customer segments require our most strategic resources with high servicing, while our Renew and Nurture customer segments require fewer touchpoints from our teams.

This exercise got us clear alignment with sales, and helped our customer success organization become more efficient, which in turn led to a better customer experience.

Looking for leading indicators

The next step we took in optimizing our account health scoring was to evaluate our dataset to identify our leading indicators of success. As an analytics company, we have tons of data on our customers.

To figure out which product metrics were most predictive of renewal, we used Heap as the product data source and Zuora as the revenue data source in this analysis. We worked with our data science team in a consultative capacity to discover the extent to which certain user behaviors taken during the window of time right before the renewal would impact the renewal.

We found that the product metric that is most predictive of renewal is Monthly Querying Users (MQUs) (i.e., the number of users building queries each month to answer questions and discover new insights from their data). Customers that have at least five monthly querying users are 35% more likely to renew than customers with fewer than five.

Secondary product metrics that work nearly as well as MQUs at predicting retention include the number of pages viewed, the number of reports viewed and saved, and the number of events defined. Since these secondary metrics are closely correlated with MQUs, it’s rare for customers to score high on those metrics and low on MQUs, or vice versa.

We also learned that we could get an even more accurate sense of a company’s health by considering their size — three MQUs might be enough for a small company, but we’d expect well above seven for a large enterprise client.

After going through this exercise ourselves, we began providing a similar product metrics consultation as a service to our customers since it’s a great way to quickly get value out from Heap data for a common SaaS use case.

With Heap’s Account Health Analysis & Scoring package, our Solutions team works closely with your CS team to identify your leading indicators of churn and which product usage metrics should feed into your Account Health Score. Your CS team will walk away from this six-week engagement with predictive account health scoring that will help you quickly identify and rescue at-risk accounts.

Creating data-driven account health profiles

In 30 days, we were able to set up our new account health profiles in Catalyst (our CS tool of choice) based on our new segmentation, and all powered by product usage and customer data from Heap and Salesforce.

Thanks to Heap’s Salesforce Data Connector, we didn’t need engineering resources to have customer behavioral data sent into our CS tool of choice. And because Heap captures everything, we can be very specific about which metrics we want to include in our customer success workflows. If we need to adjust which data is being sent over, we can self-service that directly in Heap in a matter of minutes. We send our top five leading indicators of product success directly from Heap into Salesforce.

In Catalyst, we were able to easily set up health profiles by segment based on specific thresholds of our product measure of success — Monthly Querying Users. For example, our Nurture accounts are “At Risk” when Monthly Querying users are below three, which is a different threshold than that for our Strategic accounts.

We now have an automated health profile defined by customer segment and backed by data. Our CSMs now have valuable time back to spend with customers as they no longer need to munge data together in a spreadsheet. This data is also channeled all the way up to our board in our revenue forecasting and risk assessments, allowing us to have a very accurate forecast for churn and demonstrate a more efficient customer success organization.

An example of leading with data

Customer Success Managers at Heap generally start their day with a cup of coffee and a quick look at their “Good Morning” dashboard in Catalyst. Powered by behavioral data from Heap, these Good Morning dashboards help our CSMS get a quick pulse on the leading indicators of health, help them prioritize their near-term tasks, as well as keep up with other updates or key activities on their accounts.

For example, one of our CSMs might identify that one of their accounts has moved from being healthy to being at-risk. After spotting this flagged account, the CSM then considers the play that they should run in order to effect change for this health metric. Think: How do I get this account back to a healthy state?

In order to dive deeper to learn more about this at-risk account’s activity in the product, the CSM logs into Heap to double-click into the CS dashboard. They notice that certain features have strong adoption, but they do see that the customer stopped defining events and using funnels (core features used in organizing data and running reports in Heap). Since the customer has a marketing use case and cares about conversion rate optimization, they should be using funnels in their analysis.

This is an opportunity for the CSM to use a play to help the customer adopt funnels. They also notice that this customer has two new marketing users who haven’t engaged much with the product yet — which is also an opportunity for the CSM to engage with new users.

The CSM decides to send a targeted email campaign using Heap data to create a meaningful conversion rate optimization funnel analysis for the customer. This positions the CSM as a strategic advisor as they encourage the customer to click into Heap with a clear CTA for adding value to their marketing strategy.

The result of targeted outreach based on specific usage data? We’re able to much more effectively measure the success of our Customer Success Managers.

We want our CSMs to be focused on the right accounts, getting the right customer data, and taking the right actions based on that data. Our goal overall is to make sure CSMs have those playbooks to prevent churn and to promote adoption ahead of it being too late to intervene.

Heap powers the proactive CS team

Customer success teams have a lot of responsibility: ensuring a healthy customer onboarding, identifying expansion opportunities, and preventing customer churn.

High-performing Customer Success teams rely on Heap to provide the data they need to understand product usage and account health, so they can retain more customers, increase customer satisfaction, and grow revenue. (Check out our Success Guides for CS Teams to see what this looks like!)

Ready to take the guesswork out of customer success? Drop us a line if you’d like to learn more about how CS teams use Heap to improve the customer experience with data.

Thanks to Lydia Koehn and Ben Lempert for their help in editing and refining this post. Also available on the Heap Blog.

Senior Director Customer Success @Heap, Formerly IBM, App Annie. I am an advocate, leader, builder, career mentor, and passionate champion for Customer Success.

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