our insight
AI tips & trends
Join CX, product, ops, and engineering leaders worldwide that receive The Signal - Sturdy's Blog Newsletter.
The four types of SaaS churn and how to calculate them
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
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
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 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
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
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.
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
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
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 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
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
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.
Our articles
Sturdy Now Analyzes Customer Slack Channels
We’re making it easier than ever for teams to tap into the power of customer conversations. With this integration, Sturdy’s AI-driven insights—trained to spot key behaviors and trends unique to your business—are now right where your team works. That means more proactive decisions, better collaboration, and a serious productivity boost.
Here’s how Sturdy works with Slack.
- Get the right insights, right in Slack. Sturdy delivers AI-powered Signals where your team already works, flagging risks, expansion opportunities, and other key moments in real-time. No more digging through conversations—just actionable insights when you need them.
- Stay on top of every conversation. If your team works asynchronously in Slack channels, it’s easy for important feedback to get lost. Sturdy keeps you ahead by surfacing critical insights before they slip through the cracks.
- Act fast, not after the fact. Whether it’s a service risk, a feature request, or a potential upsell, Sturdy helps teams spot and respond to what matters—without disrupting their workflow.
Seamless sync with your tools. Sturdy doesn’t just stop at Slack. Insights discovered in customer Slack channels automatically flow into Jira, CSPs, CRMs, and other systems, ensuring the right teams get the right info—without extra work.
.png)
He doesn’t talk much, but when he does, you’d better listen.
He doesn’t talk much, but when he does, you’d better listen.
Quote from C-3PO, Star Wars: A New Hope
A few days ago, I spoke to a business leader, and they asked, "How would Sturdy work for customers who never contact us?"
"Do you know who those customers are?"
"No idea."
"Would you like to?"
“Dark Customers.” It is almost impossible to source this list. Your customer might be dark to five silos, and bright in just one.
(By the way, there is a little-known filter in the Accounts page of Sturdy that lets you sort by “Last Inbound.” Check it out. You can see the last time any customer sent you an inbound message.)
Let’s be fair. In a recurring-revenue business, a lack of inbound contact isn’t necessarily bad. Sometimes your customers don’t feel the need to chat with you, but they like you just the same.
But, here’s the cool thought. What should happen when a Dark Customer suddenly reaches out?
For example, Acme Corp sends an email to your CS team for the first time in 18 months. What needs to happen next?
I would want to know. So, we’re working on that. Naming such a signal is a bit tricky, if you have ideas, let us know.
Software is no longer the end product—intelligence is
The future doesn’t belong to systems that store data and automate workflows—it belongs to those that synthesize information, surface insights, and drive action.
The days of bouncing between screens, hunting for information, and manually aligning teams? Numbered.
Every day, we are getting closer to a workplace where:
-Knowledge workers won’t be glorified data entry clerks. Technology will finally do the heavy lifting, freeing them to focus on strategic work. These people will be responsible for outcomes without being encumbered by the tedium. As a result, we will need fewer people to acquire and keep our customers.
- Every team continues to work on their screen of choice, but the data they have access to will be aggregated across every system. They may be on different screens, but everyone will be on the same page. The next era is about alignment, automation, and AI-driven decision-making.
- There will be a fundamental shift in the tech business model. Businesses won’t pay for ‘seats’—they’ll pay for intelligence. The old model of software—charging for logins, licenses, and user seats—is dying. No one wants to pay for access to another tool; they want outcomes, insights, and automation that drive real impact. The solutions that deliver intelligence over any interface will define the next era of technology.
The shift is happening—those who embrace it will lead, while those who resist will be left behind. The future belongs to businesses that trade inefficiency for intelligence, that replace busywork with impact, and that empower people to think, create, and drive outcomes—not just enter data. Innovation doesn’t wait.
Usage data alone won’t predict churn
I've seen a slew of new AI companies doubling down on analyzing usage data as the silver bullet for predicting churn. It’s an attractive idea—track how often customers log in and how many features they use, and you’ll magically, often with some proprietary algorithm, you'll know who’s at risk and who’s primed for expansion.
That’s not how reality works.
Usage data alone is riddled with false positives, often creating a distorted view of account "health." A customer heavily engaging with your product isn’t necessarily satisfied—they might be struggling and frustrated. A drop in product usage doesn’t automatically signal churn risk—perhaps the customer has completed implementation and is now deriving value without needing to log in frequently.
🚨 High Usage ≠ HappinessCustomers with high usage might actually be frustrated and, therefore, a risk. Why are they opening support tickets and emailing their CSMs?Are they engaging because they love the product—or because they can’t figure something out? What are they saying? What’s the context?
⚠️ Low Usage ≠ Churn RiskThe modern technology landscape isn’t about engagement for engagement’s sake—it’s about delivering value with minimal friction. ✔️If your product makes life easier, customers shouldn’t need to use it constantly.
✔️Instead of measuring time spent, measure outcomes.
✔️Instead of chasing logins, track behaviors.This requires context—something raw usage data doesn't provide.
📉 Usage ≠ RenewalsIn SaaS, high usage doesn’t guarantee a renewal.Renewals are driven by:
✔️ Perceived value (or lack thereof)
✔️ ROI & business impact
✔️ Alignment with evolving needs
To truly predict and drive retention, track the right contextual signals like:
✔️ Contract issues
✔️ Bi-directional responsiveness and closed-loop resolutions
✔️ Budget and procurement discussions
✔️ Expansion/contraction language
✔️Change order requests
Look for specific context beyond sentiment.
🔍 No Context, Limited InsightsUsage data doesn’t explain why something is happening. Why did usage drop?
⁉️ Did the customer stop needing what you sold them, or are they trialing a competitor?
⁉️ Have users given up on your solution and found a workaround?
⁉️ Is usage dropping in specific customer segments (e.g., corporate accounts)?
You won’t find these answers in product telemetry alone.
Companies that get this wrong focus heavily on usage metrics and then wonder why their churn predictions fail.
The ones that get it right combine usage data with contextual signals—the insights that explain the "why."
Real-world signals tell you how customers feel and what they need, not just which buttons they click and how often.
If your account management strategy is built purely on tracking usage and opinions, you’re looking at a puzzle with half the pieces missing.
The Four Horsemen of Customer Churn
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.
.png)
Improving Revenue Retention in 2025
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.
Burton's Broken Zippers
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.
Navigating AI Ethics
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:
- Business-Only Data Use: Sturdy’s AI systems focus solely on improving how businesses make decisions. They don't delve into personal data or manipulate information for other purposes. The data processed by Sturdy comes from business sources like support tickets, corporate emails, or recorded calls—never from personal channels.
- 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.
- 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.
- No Deception: Our product is engineered to prevent deception. It never manipulates or deceives users, ensuring that the insights drawn from AI are used to enhance business practices rather than exploit loopholes.
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.
You have been paywalled
(The image attached to this post is not entirely accurate but read on, and I’ll explain)
I’ve been spending a lot of time on Sturdy’s brand message lately. Part of this process entails interviewing folks from various walks of life about the current state of their businesses, their teams, and the companies they invest in.
The recurring theme: Sales Leaders aren’t having a good time right now. But you knew that already. I want to talk about what you don’t know.
After one of my interviews, I received a text with a quote by the former CEO of Swedish Airlines, Jan Carlzon.
An individual without information can’t take responsibility. An individual with information can’t help but take responsibility.
There are many different “things”’ that impact revenue: bad service, confusing products, poor response times, overselling, bug reports, price, whacky renewal processes, etc. You already knew this.
You know a lot about economic conditions, because that information is widely, and publicly available. You probably know a fair amount about “Sales Things” because your team is talking about “percent to goal” in almost every meeting, and there are a lot of discussions about what’s working and what isn’t. And, you likely review almost every deal in your pipeline.
What would happen if the opportunities in your pipeline were randomly placed in your ticket systems, CRMs, and a smattering of email inboxes? Knowing what was working with sales would get more difficult, if not impossible.
Today, the issues that affect service, product, marketing, etc., are randomly smattered across every customer-facing system in your business. The only way you “know” they happen is if someone else decides they are important enough to log or forward.
How do you get the information you need to make an impact?
Where is the information your product team needs to know?
Where is the information that your pricing team needs to know?
Where is the information that your renewals team needs to know?
If your Product Marketing Manager wants to know how their new pricing plan is working, what would inform that? A pretty good source—I’d argue the best source—of that information is sitting in emails, tickets, and call transcripts. But, if you are a Product Marketing Manager, you don’t have access to tickets, call transcripts, or customer emails.
You’ve been paywalled.
If you want to know what features to fix, there’s a data point in your Support Chatbot. When your Renewals Manager needs information on an account , they need to scroll through tickets and ask a few people, “What’s going on with this account?”
As a result, every business has smart people who rely on other people to log things, categorize things, and forward things. This is why our teams have logins to systems they seldom use - so they can find a “thing” they might need.
The irony is that the information you need to know to do your job effectively is harder to source than the information about things you can’t control.
You probably know the inflation rate. If you don’t, you can discover it in one search.
Your VP of CS probably doesn’t know “What’s the most common source of customer frustration in the last 90 days?” Why? Because that information is splashed across your business in a host of silos that VP can’t access. Imagine trying to do that job, without that answer.
Imagine if that VP could answer that question in one search, using what customers are actually saying to every person in your business.
This paywalling has made our businesses fragile and slow. The hints of the B2B slowdown were arriving at our doorsteps in emails and tickets for months. “We’re cutting costs”. “Procurement wants a discount.” Why didn’t we see this coming? Because we weren’t looking for it, and couldn’t find it.
Time to get faster, and sturdier. You have smart people who can take responsibility. Bust the paywalls and give them information they need to react and act.
Do that hard things,
Steve
How about Ethical Software?
There has been, and should be, a lot of talk about Ethical AI. Over the last several weeks, I have been revising Sturdy’s Ethical AI policy. I am trying to convey that we don’t do shady stuff and won’t let our customers do it, either.
(If you are interested in Ethical AI, we have a webinar coming up at the end of the month; the registration link is in the comments)
Writing the policy, I realized we need to talk about ethics writ large, not just as it relates to AI.
Consider the case of Allstate and Arity, as reported in a June 9 NYT story, “Is Your Driving Being Secretly Scored?” Allstate apparently owns Arity. Arity builds phone apps for things like finding gas stations. Their apps also track how you drive, although they bury that minor detail in their “consent” pages (that no one reads). They then share this data with Allstate.
Not a lot of gray area here. This is unethical.
My co-founder, Joel Passen, coined this mantra at our first startup 20’ish years ago:
“Build what you’d want to use, sell it how you’d want to be sold, and service it how you’d want to be serviced.”
I don’t think anyone downloading a Gas Station finder app wants their driving to be sent to Allstate. I would not. And I would not build it.
So, instead of an “Ethical AI” policy, I’ve decided we need an “Ethical Software Policy”. It will encompass our use of AI, our platform, and how we expect our software to be used.
Here’s a bit of a summary so far…
Sturdy’s Ethical Software Policy (WIP):
- Our product is only be used to improve how businesses make decisions so they can be better vendors to their customers;
- We will not support use cases that do not directly relate to our problem set. The use cases for our product will be obvious;
- We do not have ulterior motives for our customer’s data or their users;
- We will not let any entity, business, government, or person use our product in a way that violates a person’s privacy;
- We will not, nor will we allow our product to score or rank human beings;
- Our product will be engineered to prevent deception and must never be used to deceive people;
- Finally, If we feel that one of our customers is using our product in a way that violates our principles, we will terminate their service.
The problem is that many “Ethical Policies” are only as good as the paper they are written on. They are a checkbox on an RFP. None of us want to live in this world. Maybe it's time to try and live in a better one.
At some point, somewhere along the corporate food chain, executives need to say, “No.”
It is hard to say “no” to revenue. Do the hard things.
Let me know your thoughts.
Steve
