Customer Churn

The four types of SaaS churn and how to calculate them

By
Alex Atkins
August 31, 2023
5 min read

Customer churn is a term often used in the SaaS world, but what does it actually mean?

Simply put, churn is the rate at which customers are lost. These are customers that have canceled your service and aren’t coming back. It can be calculated for individual customers (B2C) or for an entire company (B2B). Four different types of churn are commonly measured: customer churn, revenue churn, gross churn rate, and net churn rate. Let's take a closer look at each type.

Customer churn rate calculation

Customer Churn

Customer churn is the most commonly used type of churn. It is the percentage of customers that stopped using your company's products or services during a specific time frame. You can calculate your customer churn rate by dividing the number of customers you lost during that period — say a quarter — by the number of customers you had at the beginning of that period. 

Let’s pretend for a moment that you work on the growth team at SaaS.io, a new (you guessed it) SaaS startup. Over the last few months, SaaS.io has continued to grow hand over fist with little to no customer churn. However, customer acquisition has begun to slow, and your boss is asking you to calculate the customer churn rate in October. This equation is relatively straightforward. At the beginning of October, Saas.io had 54 customers. However, by the end of the month, two had churned. That means your customer churn rate in the month of October was 3.7%.

1. Total customers at the beginning of a period: 54

2. Number of customers lost in period: 2

3. Customer Churn Rate = (2/54)*100 = 3.7% (that is a great number, by the way)

Revenue churn rate calculation

Revenue Churn

Revenue churn is similar to customer churn, but instead of measuring customers leaving the company, it measures the amount of revenue lost due to customers who have left or downgraded their plans. To calculate revenue churn, divide the total amount of revenue lost in a certain period by the total revenue at the beginning of that period. 

If we head back to our SaaS.io example, it’s important to note that the October revenue churn is much scarier than the customer churn. Yes, only two customers churned, meaning there was a 3.7% customer churn rate. However, one of those customers (Customer 2) accounted for 11% of MRR (monthly recurring revenue). Customer 1 generated only $6,000 in MRR, whereas Customer 2 generated $22,000 MRR. That means that at the beginning of October, SaaS.io’s MRR was $200,000. By the end of October, the revenue churn was .14.

1. Total revenue at the beginning of a period: $200,000

2. Net revenue lost in period: $6,000 + $22,000 = $28,000

3. Revenue Churn Rate = $28,000/$200,000 = .14

Gross MRR churn equation

Gross Churn Rate

The Gross churn rate takes into account both customer and revenue churn. It measures the total number of customers and revenue lost in a certain period, divided by the total number of customers and revenue at the beginning. This gives an overall picture of how much business is lost in a given time frame.

If we apply this to SaaS.io, the MRR for October was $200,000, and users canceled $28,000 worth of contracts. That means the gross churn rate will be 14%

1. Total revenue at the beginning of a period: $200,000

2. Net revenue lost in period: $6,000 + $22,000 = $28,000

3. Gross Churn Rate = ($28,000/$200,000) x 100% = 14%

Net churn rate calculation

Net Churn Rate

Net churn rate considers both customer and revenue churn. However, it also includes new customers and expansion revenue acquired in a certain period. Expansion revenue is the additional revenue you generate from existing customers through upsells, cross-sells, or add-ons. That’s why net revenue churn gives an overall picture of how much business is being gained or lost in a given time frame. 

A month has passed since those two customers, and 14% of gross MRR was lost. Saas.io is currently at $172,000 MRR in November, as no additional sales have been made. Unfortunately, November has also seen $12,000 in contract losses. Luckily for Saas.io, a few existing customers have upgraded their plans, generating an additional $10,000 in revenue. Your boss asks you what the net churn rate for November is. First, you must subtract the customer upgrade revenue from the revenue lost in downgrades and cancellations. Then, divide that number by the revenue at the beginning of November.

1. Total revenue at the beginning of a period: $172,000

2. Net revenue lost in period: $12,000 - $10,000 = $2,000

3. Net Churn Rate = $2,000/$172,000 = 1.1%

Leaky bucket equation

Leaky Bucket Equation

At the beginning of this post, we noted that four types of churn could be measured. That isn’t entirely true, so here’s a bit of a bonus round. SaaS angel investor, Dave Kellogg argues that the leaky bucket equation “should always be the first four lines of any SaaS company’s financial statements.” Kellogg continues, “I conceptualize SaaS companies as leaky buckets full of annual recurring revenue (ARR). Every time period, the sales organization pours more ARR into the bucket, and the customer success (CS) organization tries to prevent water from leaking out”.

Kellogg defines the leaky bucket equation as “Starting ARR + new ARR - churn ARR = ending ARR”.

If we apply this to our Saas.io example, we can determine that the starting ARR in the fourth quarter (Q4) of 2022 was roughly $400,000. The new ARR in Q4 ‘22 was $56,000, and the Churn ARR in that same time period was $45,000. In other words:

1. Total starting ARR: $400,000

2. New ARR: $56,000 & Churn ARR: $45,000

3. Ending ARR = $400,000 + $54,000 - $45,000 = $409,000

Churn is an important metric to track for any SaaS company, as it can be used to identify trends, measure loyalty, and assess the effectiveness of customer retention strategies. Calculating churn rates can help companies identify which customers are more likely to leave and which types of customers are the most valuable. By understanding churn, businesses can take steps to improve customer retention and keep their business running smoothly.

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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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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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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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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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