Nearly two decades in B2B SaaS. Scaled CS orgs across Enterprise, Mid-Market, and SMB. Worked at orgs <$10MM – $200MM+ ARR. Built playbooks, teams, and strategies that drive adoption, renewals, and expansion. I now focus my time building out customer orgs for the AI Era.
I help B2B SaaS companies keep their customers and grow revenue by building world-class Customer Success organizations.
I’ve spent nearly two decades building and leading Customer Success organizations across B2B SaaS.
Today, I’m focused on what comes next.
AI can create the playbook. It can analyze individual customers, recommend interventions, personalize communication, and help execute the work. That changes the challenge.
The questions now are: What signals matter? What should happen next? What can AI handle? Where does human judgment create value? And how do we know any of it actually worked?
That’s the work I’m interested in. Maybe those were always the questions, but how we answer them, that is certainly different.
I combine customer signals, AI, automation, experimentation, and human judgment to improve retention and expansion at scale.
How I Work
AI has changed the operating environment. The information, analysis, context, tools, and execution needed to solve a problem can increasingly exist in one connected place. That changes how I approach the work.
Start with a problem and bring it to AI. Provide the objective and context, then work through it together. Challenge assumptions, explore the evidence, find the gaps, and consider different approaches. Once the direction is clear, turn the thinking into a plan and start moving.
For years, scaling Customer Success meant standardization: segments, journeys, health scores, triggers, and playbooks. Those systems existed because humans couldn’t reason individually about thousands of customers. AI changes that constraint.
We can increasingly ask:
Given everything we know about this customer, what should happen next?
My approach is simple:
Problem → Signals → Understanding → Decision → Intervention → Measurement → Learning
AI can participate at every stage. The operator’s job is to design the environment, set the guardrails, and determine whether the decisions actually produce better outcomes.
Experience Matters. So Does Evolution.
The technology has changed enormously during my career. The fundamental business problems haven’t.
Customers still need quick time to value, to adopt products, stay, and grow.
I’ve spent my career building teams, scaling organizations, managing recurring revenue, improving retention and expansion, and solving those problems across startups and larger B2B SaaS companies.
This site is a record of that work.
You’ll find years of writing here about Customer Success, leadership, scaling, retention, expansion, AI, experimentation, and the lessons I’ve learned along the way.
Some of my thinking has stood the test of time. Some of it has changed.
I’m keeping both.
Because experience isn’t about having been right about everything.
It’s about knowing when the world has changed and changing how you operate with it.
What happens when AI can reason about every customer individually? When should AI act, and when should a human? How do you personalize across tens of thousands of customers without creating enormous complexity? How do you know an intervention actually caused the outcome?
And what does a customer organization look like when AI is part of the operating model from the beginning?
I don’t think anyone has all the answers yet.
That’s what makes the work interesting.
I want to provide more information on what you can get when hiring me, that can shine through the bullet points of a resume. The site includes my customer success philosophy and what I would do in different scenarios most common in the B2B SaaS space.
Every company is different, as is every industry, but I hope this site will help allow as much of my experience to shine through as possible.
