Co-CEO of Accoil Analytics. We help CS teams and support AI (like Fin) actually use product data.

Recent client 2x'd revenue, then got acquired. Not bad.

Most B2B SaaS companies have product analytics. Mixpanel, PostHog, Amplitude - the data's all there. But CS teams can't get to it (or it's just too damn complicated to make sense of). 

And your support AI? It has no idea that your customer just used your product 47 times this week before opening a ticket.

Accoil fixes that. We connect the product signals to Intercom so Fin knows what's actually happening, and your team knows who needs attention today.

You don't need to know SQL. It doesn't take a 6-month implementation. And you don't end up with health scores that still miss churn.

Before Accoil, I worked with my co-founders on a startup that sold to Atlassian. We grew it using product data the same way Accoil helps others do it now.

I write about: product signals vs sentiment (here's a take: NPS is a polite lie), why the tool won't save you, and building Accoil in public.

DM me if you're running Intercom and want Fin to actually know your customers.

Peter Preston

In SaaS, Setup Tourists are a real thing. So are Configuration Cowboys.

These are your users who sign up, jump in, smash 🆇 on every onboarding popup, and leave all that configuration nonsense for later.

Shoot first. Config later.

What's wrong with Configuration Cowboys?

These are like power users that skip activation and onboarding steps. They're not always the bad sign, but they can be a sign that you need to improve things.

And really what you have on your hands with these cowboys is users who may miss the most important parts of your product, which means they may not stick around for very long.

You can see this in low activation, but high engagement.

They did not follow your onboarding steps. They have not taken the key steps that you think they should. But they keep coming back and they keep using the product, maybe in ways that you didn't expect.

Here's one way to find these cowboys and then what you can do about it:

1. 
Look at your product data. Find highly engaged power users who have not activated in the way that you think they should. They haven't hit your activation milestones.

2. 
See how many of these you can find across your accounts? Look for as many power users, these highly engaged people who come back and use your product but are not activated.

3. 
Look for patterns in their usage data. What are they doing? What are they using? More importantly, what are they not using?  if you find patterns here that may give you some ideas on how to improve things or what to change. 

4. 
And of course, as always, reach out to these people. Try to have a conversation. See what you can learn. What problem are they trying to solve? Is it working? Maybe they've got some unique use cases you're not aware of yet.

And lastly, make sure that any new insights that you get from this are looped back to the product team, to your onboarding team, and anyone else who has influence over that early stage of your customer lifecycle. 

If you have product analytics data, you can do this pretty quickly. If you don't have access to that data, pester your product managers. 

And when your product managers get tired of answering these questions, maybe come try Accoil and just get these answers delivered to you right in Slack 🤠

(i was told to include pics, but couldn't be bothered leaving my desk. thanks for the cowboy hat, figma)

Product signals ​> NPS scores | Recent client 2x’d then acquired | Helping CS teams and AI actually use customer product data | Co-CEO @ Accoil | Same team built & sold to Atlassian

"SaaS stocks are getting crushed" and yet we're all still here putting in the work. 

"AI will take all your jobs" and yet there's still so much to do.

"We can't 'just do more with less' dammit" and yet some people are...

It's never been a better time to build stuff.

Customer Success has an existential problem and it's not the ol' "proactive vs reactive" debate.

It's unit economics. Or is CS making money -> Y/N?

A lot of CS LinkedIn focuses on how to do the job. How to keep customers happy. How to establish relationships and structure QBRs and how to "Take a seat at the table."

I've spoken to 100s of Saas teams in the last two years. When churn and retention come up, it's usually from the IC level. That makes total sense. It's their job to know who's going to stick around and who might leave.

But when I talk to the CEOs, CFOs, CROs, or CCOs they're speaking a different language: it's the money honey.

Not that they don't care about the tools and the ways of working. That's all a means to an end.

When a Seed or Series A team is staring down the barrel of another fundraise, the tools don't matter as much as the metrics.

Net Revenue Retention (better be well over 100%)
Gross Revenue Retention (better be as close to 100% as possible)
Big accounts that surprised us by not renewing (losing big logos sucks)

The unit economics that I'm thinking about this week:

*Cost per retained dollar.*

CS spend / Retained ARR (+Expansion if CS owns that too)

*LTV extension from retention work.*

As LTV goes up, original CAC works even harder. CS work is super important to the LTV:CAC ratio, even if it feel distant from it.


Proving that CS is profitable isn't as straightfoward as sales. Sales is sexy b/c it's easy to see how much it costs and how much it generates.

Clear input. Clear output. Everybody can see the ROI.

I think we're about to see more and more leadership teams pay attention to retention.

NRR as both a % and a hard $$ amount are being talked about in the C-Suite and board room.

The closer you can tie yourself and your team to those numbers, the better. 

So absolutely get your customer health signals dialed in. Build strong relationships. Deliver value to the customers in your book of business. 

But don't forget to focus where you're going to have the most impact on the business --> big hairy at-risk accounts that have the ability to move your KPI.

As much as we all want to look after each and every customer

if we spend our time making sure the $ customers are happy while ignoring the $$$$$ customers...

NRR crashes and so do the unit economics of the whole CS team.

Signals aren't just for sales teams. They're for CS teams too.

A good signal is one that A) you trust and B) you know what to do about it

It's so easy to overcomplicate this 
but this is what Accoil users tell us makes it simple to know what to do next

A good engagement score is both a moment in time and the change over time, or trend. 

If you're a CEO and the board asks "yo, what's up with Database Yard?" you can tell them "they're crushing it"

If you're on the frontlines as a CSM, you can answer confidently when asked what you're working on this week "Thought Focus is doing ok, but Operation Stable Net is definitely needs my love and attention."
 
(You can either build engagement scores in spreadsheets or Accoil - ask and I'll give you the spreadsheet if you're into pivot tables and whatnot)

CS teams spend too much time rooting through dashboards and tickets trying to answer "which accounts *should* I focus on right now?"

Sales gets signals meant to drive actions and growth. CS can have them too.

Sentify

Senior Consultant

HCLSoftware for U.S. Federal

Lead Atlassian migration and consolidation for a multi-national software organization, integrating Jira and GitHub to optimize engineering workflows across flagship products like AppScan, Domino, and IBM acquisitions. My focus is on security, collaboration, and development velocity, ensuring teams can innovate while maintaining compliance and efficiency.

Secure Build Lead

A9 Group, Inc.

Advancing the Atlassian Platform on behalf of our customers with data and API-driven solutions

Founder

eBay

Transforming Jira Business Process with Big Data, Machine Learning and Artificial Intelligence

Technical Solutions Architect

New Relic, Inc.

Principal Solution Developer

KIOXIA America, Inc.

Atlassian Systems Architect

Marqeta, Inc

Lumentum

Lead and Develop all aspects of Technical and Functional Delivery of Atlassian Jira Software, Confluence and Portfolio.

Netskope

Atlassian SME

[24]7.ai

Consulting Solutions Developer for Atlassian tools use and implementation. Focus on API, platform migration, scaling.

I’m currently advancing through the Atlassian Solution Partner track as an independent consultant, with a focus on large-scale data strategy, AI integration, and Jira process mining. My goal is to augment the capabilities of established partners — bringing deep technical execution and customer-aligned insights into how work actually gets done in enterprise environments.

At HCL Software, I’ve led secure DevSecOps programs, cloud migration of Jira Data Center instances, and AI-enhanced workflow automation. At eBay, I architected ML pipelines that ingested Jira data to power intelligent prioritization and issue classification — the same kinds of use cases driving Rovo and Atlassian Intelligence adoption today.

My niche is helping teams close the loop between technical architecture, governance, and work visibility — especially when tooling is fragmented or legacy constraints are high. I’d love to collaborate with partners who need to go deeper into platform telemetry, ML integrations, or compliance-heavy environments.

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