Top Five Metrics to Track on Your Customer Success Dashboard

Alex Farmer
Alex Farmer Member Posts: 62 Expert
First Anniversary Photogenic First Comment GGR Blogger 2021
edited January 2021 in Metrics & Analytics

Customer success leaders need to be more commercially literate. This has been a big talking point in the CS community for several years and I’m all for it. In fact, I ran a webinar on the topic in April, sponsored by the Customer Success Network (login required, sign up here: and COVID-19 has only further exacerbated this need. One of the first steps in proving your commercial value is measuring and reporting customer success performance against commercial KPIs. 

So what are you tracking on you customer success dashboard?

Before I jump in, its fair to say I track a lot of data. And I'm sure a laundry list of customer success metrics that might not make sense for your business won't be particularly useful, so I've picked out the top five metrics on our suite of dashboards that I think will help you measure the commercial impact of customer success.

1. Current Financial Year Net Retention Rate and Gross Retention Rate Forecast

This one might seem obvious, but I am surprised at how many customer success organizations don't actually own the renewal forecast. The customer success team should have their finger on the pulse of their customer base, so they should also be the team that owns and delivers the most accurate churn and expansion forecast to the finance team, right? Yet CFOs make broad assumptions about how much churn they'll experience in a financial year all the time without asking for detail from those that know customer health best. That approach won't fly for sales forecasts, so why should it suffice for churn forecasting?

CS leaders: share your churn forecast at least quarterly with your colleagues in finance. COVID-19 has likely increased the pressure of finance teams to accurately predict revenue, so they'll thank you for it.

2. Forecast Churn Split by ARR Band, Ideal Customer Profile Fit, and Churn Category

Once you've gotten a handle on your churn forecast, it's important to start using data to understand why. Is there a specific industry, type of customer, or purchased product that churns more than others? Businesses cannot learn from churn if they can't dissect it. To accomplish this we split our churn forecast by various data points – these three have been the most impactful:

By Churn Category

We split churned customers into four categories with varying degrees of attention dedicated to preventing them in the future:

  1. Regretted - This is what we focus on the most. This is churn that was the most preventable and where we need to focus our customer success roadmap to reduce in the future.
  2. Non-regretted - customers who have been acquired, legacy products we choose to sunset, or old contracts that are not commercially viable -- in an ideal world we would keep these customers, but some extenuating circumstance prevented it.
  3. Trial - split out customers who didn't convert into full customers after a trial or pilot. This helps us show our business the cost of selling trials.
  4. COVID-19-related - customers who have had to downsell or pause due to the economic impact of the pandemic.

By Ideal Customer Profile Fit

It's critical all teams across the business are clear on what your ideal customer looks like. To track how often we're selling to bad fit customers, we split forecast churn by ideal customer profile (ICP) fit - good fit, stretch fit, and bad fit. This helps educate the business that churn is directly influenced by bad sales qualification and even the best CSM can't save something that was a bad fit to begin with. Naturally, we're looking for the percent of bad fit customers to decrease over time as product-market fit is refined and sales qualification is improved.

CS leaders: If you haven't yet defined your ideal customer profile, I suggest a simple customer overview dashboard that shows customers by geographic territory, industry, and their annual revenue band (25-99m, 100-499m, etc). Which segments are most successful? That's a good start.

By ARR Band

To oversimplify, let's say your ICP is enterprise customers. In early start-up days, its likely you were selling to businesses of any size that had a need your product addressed. But as the ICP is refined, you start moving away from SMB (who have smaller ARR contracts) and entrenching in the enterprise space (bigger contracts), which means logo churn may increase, but average contract value also skyrockets. Sales and marketing teams restrict their targeting to better-fit potential customers and customer success teams focus their resources on better-fit existing customers. Those who aren't close to the data might conclude that we're losing a lot of customers and that's bad. But actually, you're course-correcting while increasing average contract value and likely preventing future churn by decreasing bad-fit customers.

We split our forecast churn into ARR bands (for example <20k, 20k-50k, 50-100k, 100k-200k, 200k-500k, and >500k ARR) to analyze this, grouping customers within each band into four categories - churn, downsell, retain, grow. This allows us to understand which ARR bands are predisposed to contraction or growth, ie: for all customers who started the year in the 50-100k band, how much of their total starting ARR churned, downsold, or expanded?

Understanding which ARR segments contract or grow helps segment customers into the right appropriate experience (yes, segmentation should take into account more than ARR contribution). In the example laid out above, I'd expect that customers in the <20k ARR band churn or downsell more often, while customer in the 100k+ ARR bands expand. This insight should justify the push to enterprise as defined by your ICP and might demonstrate to your CFO that you need to reduce the customer to CSM ratio for customers in the >100k ARR bands to capitalise on their predisposition to expand.

3. Expansion Leads by Source with Win Rate

In some more complex, high-touch businesses, CSMs are responsible for generating leads from the customer base, but account managers are tasked with closing them. If that account managers report into the sales function, they likely have sophisticated dashboards that go to finance to show how much revenue is being being generated from the customer base.

But do those dashboards connect any of that expansion revenue back to the efforts of the customer success function? Account managers are responsible for closing customer leads and customer success managers are responsible for generating them, just like marketing is responsible for generating leads for new business sales. So, just like marketing tracks MQLs, are you tracking CSQLs (customer qualified leads)? You may not be responsible for converting them, but CSQLs should be a key metric to demonstrate the revenue impact of your CS program. And if the CSQL win rate isn't great, it might even help build a case that CSMs should close their own CSQLs for some account segments, which would reduce cost of sale (as account managers are usually paid commission while CSMs are not). If the CSM does own expansion, then it's even more important to track lead source for expansion opportunities.

CS leaders: Track expansion pipeline by lead source (CS qualified, sales qualified or customer-led) measuring total amount and win rate.

4. Percentage of Customers Willing to Participate in Advocacy

Many SaaS business don't break even on a customer contract until the second or third year, once marketing costs and commission are factored in. Understandably then, customer acquisition cost (CAC) is a critical metric for scale-ups and one that CFOs watch closely.

Customer referrals and customer advocacy are crucial tools that SaaS businesses can use to reduce CAC and customer success should be providing them back to the business. To influence them directly, one of my favorite customer success KPIs to track is the percentage of customers willing to be advocates if asked. It encourages teams to keep building the reference/customer advocacy pipeline and driving customer acquisition cost down. 

CS leaders: Are you capitalizing on your customers' success and making demonstrable reductions to customer acquisition cost? Track the status of committed customer advocacy (in progress, completed, published) and total number of customers who have agreed to provide sales references, speak at events, or participate in case studies. Feed this back to your sales team so they have access to your current reference customers.

5. Red Accounts Grouped by Reason with ARR Totals

So we're reporting on where the churn is coming from and will come from in the future, which is good, but that's reactive. Using data to influence the business to prevent churn is probably even more important. To do that, we spend a lot of time dissecting our struggling accounts in our 'red account process'. Every three weeks, a cross-functional team reviews all red accounts on our customer health dashboard. We group red accounts in reason for red categories – service escalation, low engagement, exec sponsor departure, product issue, etc – and sum each reason by total ARR at risk and review the trends month on month. This allows us to answer important questions like "what is our get well plan and what are our blockers?"

But, even more importantly, why did they become red in the first place? What areas do we need to invest in across the company to prevent future customers from "going red?" So if for example product bugs is the biggest issue, it allows us to present data that influences more investment in tech debt/bugs to prevent future churn. This data is also useful in annual budgeting – where do we need invest more next year?

CS leaders: Are you using data to influence different departments to fix red account root causes to prevent churn?

Delivering Customer Data to Decision-makers

We use Salesforce to deliver all of our customer success dashboards. I’ve created almost all of these objects, fields, reports, and dashboards myself – there isn’t a need to pay expensive consultants to build most of this. The majority of data being reported on is stored on the customer success plan record, which sits on the account object. But while its great to have pretty dashboards, if we don’t use it to drive decision-making, then it’s a bit of a vanity project. 

Getting customer data in the hands of decision-makers is even more important than creating reports to measure it. As customer success leaders, it’s our job to ensure the business grows and innovates in a customer-centric way – to ensure that when decisions are being made, they take customer data into account. I’ll cover how to accomplish that in a future article.

If you have questions about how or why we chose to measure certain aspects or think I’m missing something, feel free to leave a comment. Thanks for reading and happy reporting!