Quick summary: When a sponsor leaves, you lose the person who made your case internally. One CS team traced 31 percent of churned revenue to leadership change, then cut that to 8 percent.
A SaaS business I worked with this year was losing 31 percent of their churned revenue due to leadership change.
When they dug into why, they found they weren’t getting in front of their executive sponsors in a meaningful way. When a sponsor left and somebody new took the seat, they had nothing to build an argument on besides “Your previous leadership really liked us, so you should keep us around. We’re important.”
By July, they had gotten in front of an executive sponsor at almost 60 percent of their accounts, and the introduction play that got them there ran a 60 to 70 percent reply rate, not just opens. As a result, they cut churn from leadership change to 8 percent.
Here’s how they did it.
First, the best practice that helped them.
This CS team tracked specific churn reasons on every opportunity record and not just on the accounts they lost. That is something a lot of CS teams don’t do in a meaningful or consistent way. Most teams have an open-ended form field to capture churn/lost reasons. With no fixed list, the field could be left incomplete, and the reasons given can be clouded by emotion or too vague to show a pattern.
This team worked from a strict set of options. Every opportunity record, including upsells, had its churn fields so closed-lost reasons on expansion were captured the same way. When a CSM closed out a churned account, they had to pick from a fixed set of reasons: didn’t get value out of the system, got acquired by another company, went through a leadership change or consolidated to a CRM.
That constraint kept the cause visible.
Of course there were judgment calls. Say the CCO left and a CRO came in focused on cost-cutting, so a bunch of things got slashed. A CSM could reasonably log that as a budget issue. But the actual cause was a leadership change. If the CRM logged it as budget, the pattern would never have surfaced. The driver was leadership change, with a new CRO now pulling the strings.
Because they logged it consistently, they could determine that 31 percent of churn wasn’t due to product. With that number in hand, the team set two goals: cut that rate by at least half and get in front of an executive sponsor at every account.
Finding out before it’s too late.
Both goals depended on knowing a sponsor had changed while there was still time to do something about it.
Usually, this CS team would get this information through word of mouth or by seeing it on LinkedIn. Oftentimes, they would get word weeks after the person was gone. In this case, they still heard word of mouth, but once they put ChurnZero’s AI agents on it, the change came directly. The agents read call notes, email threads, support tickets, survey responses and usage data, which already flowed through the platform. They flagged the shifts a person would only catch by luck, like a contact who stopped using the product, a disconnected email address, or a passing mention on a call that the person was no longer there.
Harbinger was the agent doing that work for them on the relationship side. It read engagement patterns, sentiment shifts and behavior, along with closed support tickets, against the account’s own context and the signals that came before it. It recognized early warning signs that there had been a reorg at the company.
Each risk signal carried a type, urgency and impact, plus the supporting context and a prescriptive next step. The recommendation weighed things like renewal timing, budget and org changes instead of telling the CSM to follow up. It also checked what had already surfaced to prevent repetitive warnings.
The agents ran on the set cadence, so the check happened whether or not anybody remembered to look. The signal landed in Command Center with the actions sitting on it, ready to send or assign. In the past, that context might have been stuck with one CSM, or lost in an email chain. From then on, it sat on a record anyone in the business could open.
Everyone started from the same picture.
Before even communicating, the VP and the CSM were looking at the same account thanks to Snapshot AI.
It read call notes, surveys, health scores and usage data to quickly produce a summary the VP could share across the organization. It also included the customer profile which details a real-time view of usage, health trends, contact details and account history.
The VP cut his time to understanding because of the shortened lag between getting pulled into an account and knowing what was actually going on. He used to have to reach out and ask for more context or find out what had already been tried.
After the change, what happened and what was talked about was already in the record without him having to ask for it. The VP could get face time much quicker and never walked into a first conversation uninformed.
The same context wrote the emails.
Previously, when an executive sponsor was identified, the follow-up with this company would be a generic email from the VP of customer success to introduce himself and offer support from a leadership standpoint.
After the change, two emails queued up inside an automated play, and the AI email step wrote them. Instead of a static template with merge fields, the play carried what the message should accomplish, what context to draw on and what to avoid. When an account reached that step, ChurnZero read the live customer record and drafted the note in the sender’s own voice. By default, an email was only sent after a human reviewed it.
One came from the VP of customer success, the other from the CSM. Both landed with the sponsor directly and said roughly the same things regarding their partnership and goals.
It also gave both their executive and the CSM direct lines to that person. Neither of them had to do cold outreach if a save scenario came up.
They then added a standing alert on top of the introduction. Their VP got flagged on any account above a set revenue threshold that hit a big risk or a big opportunity signal. This was more for their enterprise accounts and determining how to tackle the issue and whether an intro is needed. Sometimes it ran the reverse by noticing if a customer was interested in a product, then suggesting how they could help. Either way, they got a lot more executive engagement.
The need for human interaction.
The AI found the trouble, drafted the note, and ran within the play. It connected the necessary people at the right time so they could discuss the issue or explain the value.
AI worked up to that line, but past it, humans had to step in, and that part still matters.
