Customer Churn

The Scary Six: Executive Change

By
Steve Hazelton
January 26, 2024
5 min read

At the end of last year, I shared a regular expression (regex) that identifies "contract requests." That's a scary signal for people who like to keep customers.

Today, I want to discuss the scariest of the Scary Six, "Executive Change."

At my last company, Newton, this signal had the highest correlation to churn and initially resulted in a loss about 50% of the time (for many of Sturdy's customers, this is also true).

So what is it? Let's say you sell accounting services, and this happens:

"Hi, I am the new CFO, and I would like a quick rundown of your capabilities."

The response is often,

"So nice to meet you! LMK when you have 30 minutes for a quick call!"

(By the way, usage will be high during this time, and their Health Score will be green.)

On to the regex…

The first two are specific to HR services/tech, so replace "hr" with "e-commerce," "accounting," "logistics," or whatever business you're in.

Here's what they do:

1. The first detects when someone says, "Hey, we have a new VP of HR coming on board soon."
2. The second, "I will be taking over the Admin role for this account."
3. The third, "Hey, I wanted to let you know that I will be leaving at the end of January."

Remember that they return a fair number of false positives (FPs). FPs are not included in the churn rate calculation.

The frequency of "Executive Change" varies depending on the industry and segment. In the SMB cohort, it occurs in about .1% to .2% of customer conversations. In huge enterprises, around .04%.

Interestingly, this signal is much more common in the HR space, firing at .3% per conversation.

There is also a lot of variation in the severity. Still, the correlation to cancellation is the 2nd highest of any signal we currently detect at Sturdy ("I want to cancel" being the highest, obviously). For SMB customers, the churn rate for this signal, if untreated, will approach 70%. It will be lower for enterprise customers.

Another critical point is that this is a leading indicator. It often occurs long before the cancellation event.

Why is this signal such a strong indicator? At the beginning of the post, we showed a sample trigger-sequence that ended something like, "Let's do a quick demo!"


What's wrong here? I think it is because one or all of the following is happening:

1. The value of your service can't be communicated in a "quick demo."
2. The new contact has undoubtedly used and trusts a competing solution.
3. The person conducting the demo has not been trained to sell your product, overcome objections, and destroy your competition's product.

This is a perfect recipe for failure. Here's a scenario...

Acme Corp sells HR Software on M2M and yearly contracts; it receives:

10k emails and tickets per month (items).
10k items equals to about 2k conversations (1 convo = ~ 4.4 items)
.3% detection per conversation = 6 Exec Changes
Two false-positive (30% FP rate)
50% churn x 4 = 2 losses

If untreated, Acme loses two customers to this signal per month.

The good news is that, in my experience, treatment will save about one of these customers each month. How?

1. Train everyone who touches customers, billing, CS, and marketing to identify the signal.

2. Immediately send the signal to your sales AND marketing teams.
Someone should attempt to discover the product the new contact used at their former company.

3. A salesperson must schedule a demo as soon as possible. (At Newton, our KPI was to conduct the demo within ten days). The seller should come armed with useful information, like usage data candidates hired (e.g.), and be prepared to sell against the new contact's previous solution.

4. In parallel, the marketing team checks LinkedIn to see if the previous contact has landed a new job. If not, someone should reach out and see if they need help in their job search (after all, you sell to companies that hire these people). If the person has landed somewhere, send them a note, a gift basket, or whatever you think is appropriate.

5. Send the previous contact to the Sales team as an SQL.

(Shameless plug: Sturdy has AI-language models that find 1, automatically route 2, and can tell you if 3 and 4 happened.)

The result of this process is a successful "double-dip". You may save a customer and gain a lead for your sales team. Ironically, if your competition is not tracking the Executive Change signal, your chance of closing that deal is very high.

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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.

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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.

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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.

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

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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.

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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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