Customer Retention

How to increase net dollar retention

By
Joel Passen
November 1, 2022
5 min read

Churn. We've all heard about it before, especially if you're building a SaaS business. There's no shortage of thought leaders who proclaim the all too simplistic mantra: "Decrease churn! And increase profits!"

Yet, for many, churn as a metric is confusing and ambiguous. For example, customer churn is different than revenue churn for example and there many ways to calculate churn leading to confusion across your company.

If you're tired of the over-reliance on churn, you're not alone. Analysts and investors have been increasingly skeptical of churn rate calculations for years.

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

So if churn isn't the magic pill many businesses want it to be, what should you be looking at?

It all starts with, net dollar retention.

What is net dollar retention (NDR)? 

Net dollar retention (NDR) aka net revenue retention (NRR) has emerged as one of the top SaaS metrics that matter and for good reason.

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. Why focus on a single metric such as churn, that doesn't actually give you the complete picture of the health of your business?

While no one metric is going to transform your business overnight, net dollar retention does help answer two incredibly important questions for businesses (especially SaaS businesses) looking to grow.

Net dollar retention can help answer:

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

Having answers to these two questions can dramatically improve your business across the board.

What makes net retention so powerful is that for most companies, it’s cheaper to sell to existing customers than to sell to new customers. This makes net retention the most cost-efficient way to accelerate revenue growth. Instead of investing tens of thousands of dollars in a new marketing campaign, you can strategically use net dollar retention to improve the qualities and services of customers who have already trusted you enough to make a purchase. Yes, acquiring new customers is part of the business game, but all too often businesses neglect one of the most important revenue streams that already exist: current customers.

How to calculate 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

Here’s an example.

Let’s say you start the month at $100,000 in recurring revenue (MRR). Over the month it added $25,00 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. 

Without understanding your net dollar retention rate, you might be under the impression your business is sinking without a solution in sight. But with the knowledge that current customers are helping keep your business afloat, you can continue to invest in your marketing and business strategy without making rash business decisions.

What is a good net dollar retention (NDR) rate?

A minimum NDR rate of 100% is considered good 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. 

An excellent enterprise NDR rate 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.  

Why you need to care about net dollar retention.

NDR provides a revenue-based view of customer retention. NDR is increasingly important as you scale from a small to a medium-sized business and beyond. For example, a $5MM business that churns 20% can replace that $1MM with a net new business when it’s growing by +50% a year. But when a $30MM business needs to replace $6MM this becomes insurmountable especially if the growth rate is slowing. Understanding net dollar retention from the start will allow you to stay the course if your NDR rate is in line with or above average. Similarly, a low NDR score means you may have bigger challenges within your business you need to address before further investing in scale.

As with most things in business, 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 $10MM 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 $5MM in revenue. 

One of the biggest challenges within a business is knowing those small actions that have life-sized effects. Monitoring and tracking your NDR rate is invaluable in helping you build a sustainable business over the long term.

How to increase your Net Dollar Retention.

Net dollar retention is an important metric to track. So the question is... how can you start identifying those opportunities to grow and deliverable value at scale?

First, hire a great team of CSMs who know your customer's needs and pain points inside and out.

Second, develop more premium services to sell to your customer base.

While on paper, this sounds straightforward and doable. But frankly, this takes a lot of time, resources, and buy-in from management to create 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 could listen for suggestions about features and products that might accelerate value capture and lift revenue.

What if you could start such initiatives without major upfront investments in data infrastructure or change the way your teams work?

We may be biased, but here at Sturdy, we created that exact tool. Connect with a member of our team to learn how tracking NDR and other critical metrics can help take your business to the next level. 

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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.

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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.

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The question is no longer about whether you will use AI; it’s when. And no matter where you are on your journey, navigating the ethical implications of AI use is crucial. Ethical AI is not just a buzzword but a set of principles designed to ensure fairness, transparency, and accountability in how businesses use artificial intelligence. In the case of Sturdy, we’ve made ethical AI a core commitment. These principles guide our every move, ensuring AI benefits businesses without crossing the line into unmitigated risk.

What Is Ethical AI?

Ethical AI refers to developing and deploying AI systems that prioritize fairness, transparency, and respect for privacy. For businesses, this means using AI to make smarter decisions while ensuring that the data and technologies used do not cause harm or reinforce biases. The importance of this cannot be overstated—AI has the potential to either empower or exploit, and ethical guidelines ensure we remain on the right side of that divide.

Sturdy’s Commitment to Ethical AI

Sturdy's approach to AI revolves around several inviolable principles:

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  2. No Ulterior Motives for Data: The data collected by Sturdy is knowingly provided by our customers, and the company doesn't use this data for any purpose beyond what's agreed upon. This ensures transparency and trust between the platform and its users.
  3. Privacy and Protection: One of the most critical aspects of Sturdy’s approach is its commitment to not allowing any entity—whether a business or government—to use its technology in ways that violate privacy. If a client were found to be doing so, Sturdy would terminate the relationship.
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Human Oversight and the Role of AI

At the core of Sturdy’s AI principles is the belief that AI should not replace human decision-making but augment it. Our Natural Language Classifiers (NLCs) are built to detect risks and opportunities based on the probability that a conversation indicates a particular issue. For example, when a customer complains about a "buggy" product, Sturdy’s AI might tag it as a "Bug" and label the customer as "Unhappy." However, humans remain in control—analyzing the situation and deciding the best action.

Final Thoughts

Sturdy's approach to AI exemplifies how businesses can responsibly use technology to drive growth and improve operations while safeguarding ethics. They demonstrate that AI doesn’t need to infringe on privacy or replace human decision-making. Instead, AI should be a tool that empowers teams, ensures transparency, and upholds ethical standards. Navigating the ethics of AI is not just a challenge—it’s an ongoing commitment, and Sturdy is setting a new standard for how it should be done.

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

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