Customer Retention

Net dollar retention - a SaaS metric juggernaut

By
Joel Passen
March 15, 2021
5 min read

The SaaS industry is still roaring towards ubiquity. Blissfully’s 2020 SaaS Trend Report notes that overall spend per organization on SaaS-based products is up 50%. However, the report also notes that this is down from previous years, and the growth rate seems to be slowing. This gradual slide has the industry turning its attention to optimizing for customer retention and leveraging existing customers for substantive growth.

Anymore, churn is just SaaS slang. Churn as a metric is confusing and ambiguous. There are too many ways to calculate customer and revenue churn. Analysts and investors have been increasingly skeptical of churn rate calculations for years. Anymore they just want a raw data dump from companies so they can run their own math.   

“There are too many darn ways to calculate churn. That makes it ambiguous.” - Dave Kellogg

The focus is on net dollar retention (NDR)? 

NDR has emerged as one of the top SaaS metrics that matter. NDR takes into account upgrades, downgrades, and churn to quantify how much recurring revenue from current customers you retained across a defined period of time. There are two hugely important questions that NDR can answer.

  1. Is your product delivering the value promised during the sale? 
  2. Are your customers growing with you or without you? 

What makes net retention so powerful is that for most companies, it’s cheaper to sell to existing customers than to sell to new logos. This makes net retention the most cost efficient way to accelerate revenue growth.

Calculating net dollar retention.

If your NDR is over 100%, this means that an increase in revenue is attributable to your existing customers. Here’s how to calculate NDR. 

(Starting MRR + expansion — downgrades — churn) / Starting MRR  = NDR

Let’s say you start the month at $100,000 in recurring revenue (MRR). Over the month it added $25,000 in expansion revenue, has $10,000 in downgrades and another $5000 in churn. ($100,000 + $25,000 — $10,000 — $5000)/$100,000 = 110% NDR. Your MRR is $110,000 with an NDR of 110% This is good. Essentially, your upgrades / upsells lifted your revenue despite losses. 

What good looks like.

At least 100% is considered a good NDR rate for SaaS businesses selling to the SMB market. Selling to smaller accounts naturally yields a lower NDR. SMB clients are less financially stable, ripe for acquisition, and have smaller budgets.  A good enterprise NDR is 130%. As with many SaaS metrics there are other things to consider. For example, Workday’s NDR is 100% but gross retention is 95%. Either Workday is very good at selling the “whole” deal or their product footprint presents limitations. 

Here are some examples of net dollar retention rates for some interesting SaaS and SaaS-enabled companies.  


Caring about net dollar retention.

NDR provides a revenue-based view of customer retention. NDR is increasingly important as you scale from a small to medium-size business and beyond. For example, a $5m business that churns 20% can replace that $1m with net new business when it’s growing +50% a year. But when a $30m business needs to replace $6m this becomes insurmountable especially if the growth rate is slowing.

The effects of NDR compound with time. It’s either additive or punitive with every customer that you acquire. This means that small upticks in NDR can add up to very large differences in total revenue over multiple years. For example, assume a business had $10 in revenue last year and consistently generates 20% of revenue from new customers. Improving the NDR from 95% to 105% may sound meager, but over five years the business will gain another $5m in revenue. 

Lifting NDR and a plug for Sturdy as a solution to help.

How can you start identifying more opportunities to grow and deliver value? Here are two ideas that sound great in articles and when delivered by panelists at conferences. First, hire a great team of CSMs who are well enabled and know your customers intimately. Second, develop more premium services to sell your customer base. Frankly, these are right answers but they take a lot of time, resources and change management to create an enduring impact. 

Now consider this. What if you had a “tool” that could analyze customer emails, tickets and conversations for important signals that are typically related to predicting churn? Maybe something that can listen for suggestions about features and products that might accelerate value capture and lift revenue? What if you could get started with such initiatives without major upfront investments in data infrastructure or change the way your teams work? We might know of such a tool. Hit us up. We’d be just as happy to talk about NDR and our experiences over the years tracking this SaaS metric juggernaut.

Similar articles

View all
Customer Churn

The Four Horsemen of Customer Churn

Joel Passen
December 4, 2024
5 min read

Our data scientists have combed through mountains of unstructured customer usage data to crack the code on proactively identifying accounts that are a churn risk. After analyzing thousands of signal combinations, we found that four key indicators—Budget Issues, Unhappiness, Value Issues, and Urgency—are the ultimate predictors of revenue risk.

Nearly every B2B tech and services company sees the same pattern: when these signals align, it’s time for action.

Hold on, what is unstructured usage data? It’s the raw, untamed data that tells you what customers are *really* doing and saying—not just what they’re willing to admit in a survey or conveyed by numbers of daily average logins (also critical but lacking context). Here are the harbingers of risk; when combined, they are what the team needs to act on right now. 🧯

1️⃣ Budget Issue: This signals a customer struggling to justify the cost, possibly due to tighter budgets or a perceived lack of value.

2️⃣ Unhappy: Customer dissatisfaction can stem from unmet expectations, unresolved issues, or lack of engagement.

3️⃣ Value Issue: If a customer doesn’t see the ROI, they’ll start questioning the worth of your service.

4️⃣ Urgent: An urgent flag indicates an immediate problem that requires rapid action. They are expressing a need to engage with a teammate now.

Customer Retention

Improving Revenue Retention in 2025

Joel Passen
November 15, 2024
5 min read

If improving revenue retention is a key priority in FY25, here is some food for thought. If you believe data is the essential foundation for improving retention, imagine the possibilities with 50-100x more data about your customers. Here’s the thing: Every business has this customer data, but 99% of businesses are sleeping on a data set that could change their business. It’s the unstructured data that’s sitting in ticketing systems, CRMs, chat systems, surveys, and the biggest silo by volume - corporate email systems. Most of us still rely on structured data like usage, click rates, and engagement logs to gauge our customers' health. However, structured data provides only a partial view of customer behavior and revenue drivers. Unstructured data—like customer emails, chats, tickets, and calls —holds the most valuable insights that, when leveraged, will significantly improve revenue outcomes.

Why Unstructured Data is Essential for Revenue GrowthImproving Customer Retention: Unstructured data helps businesses identify early warning signs of dissatisfaction, allowing them to create proactive interventions before customers churn. Repeated mentions of poor experiences, response lags, product-related frustration, and more in call transcripts, cases, and emails indicate potential churn risks. By identifying these trends while they are trending, businesses will improve retention.

Fueling Product Innovation: Let’s face it: Our customers bought a product or service. Post-sales teams don’t develop products and are limited in what they can directly impact. Product teams need more unbiased, unfiltered contextual customer data, and they need it consistently. Unstructured data provides real-time feedback on how customers use products and services. Businesses can analyze customer feedback from multiple channels to identify recurring requests and pain points. This data fuels product innovation and informs customer-led roadmaps that lead to higher engagement rates and more profound value. Developing products that directly respond to customer feedback leads to faster adoption, better advocacy, and a competitive advantage.

Identifying Expansion Opportunities: Unstructured data reveals customer needs and preferences that structured data often overlooks. Businesses can uncover untapped expansion opportunities by analyzing email, chats, and case feedback. These insights help identify additional products or services that interest customers, leading to new upsell or cross-sell possibilities. To drive immediate improvements in revenue retention, the key isn't pouring resources into complex churn algorithms, chatbots, or traditional customer success platforms—it's being more creative with the data you're already collecting. Start listening more closely to your customers, identify the patterns in their pain points, and share this knowledge with your peers who can improve your offerings. This is the year to start thinking outside of the box.

Customer Retention

Burton's Broken Zippers

Steve Hazelton
November 15, 2024
5 min read

Last year, I bought a pair of ski pants and the zipper fell out on the first chair lift. I called Burton, and they offered an exchange. New pants, first chair, same problem. Support informed me that I was required to return the pants for repair. The repairs would be completed after ski season. For the inconvenience, Burton offered me a 20% discount on my next purchase of skiwear. The next time I am in the market for skiwear that I can't wear during ski season, I will use that coupon.

I started my first business over 25 years ago. Since that day, I have lived in an almost constant state of fear that somehow, somewhere, things would get so broken that we'd treat a customer like this.

Let's be clear, no one who runs a business wants stuff like this to happen. Yet, it happens all the time.

If you run a software company, your engineering team will have usage tools and server logs to tell you when your product is "down" or running slowly. They can report which features are being used and which ones aren't. You'll learn that certain features in your product cost more to run than others, maybe because of a bad query, code, or something else. And you'll know what needs to be upgraded.

However, every time a customer contacts a business, they are "using" (or "testing") your product. If you sell ski pants, your product is ski pants, and your customer service team. If you sell software, your product is your tech and your customer service.

Yet, your customer-facing teams have very poor usage data, if any at all. Which feature of our service gets used the most (billing, success, support)? What are the common themes? Is one group working more effectively than the others? Does a team need an upgrade? 

(BTW, what costs more, your AWS bill or your payroll?)

The reason your customer-facing teams don't have usage data is because this data is "unstructured," and it is everywhere. Imagine if your engineering team needed to check 50 email inboxes, 1,000 phone recordings, a CRM, and a ticket system to get your product usage statistics. 

That's where your customer-facing teams are today. Until you can get answers from these systems as easily as an engineer can, you’ll continue to churn, annoy customers, and try to hire your way out of a retention problem. It won’t work.

How many customers will you have to lose before you try Sturdy?

Schedule Demo
A blue and gray logo with a black background
A number of different types of labels on a white backgroundA white background with a red line and a white background with a red line andA sign that says executive change and contact request
A white background with a red line and a blue lineA number of different types of logos on a white backgroundA pie chart with the percentage of customer confusion and unhappy
A number of graphs on a white background