I've spent the better part of the last decade watching customer marketing evolve. Eight years at UiPath, growing the company from $15 million to over $1.6 billion in ARR, taking it through a successful IPO, and building a customer marketing function essentially from the ground up.

Through all of that, one belief has only gotten stronger: customers are the most powerful growth engine available to any company right now.

More powerful than campaigns. More powerful than demand gen. More powerful than any paid channel you're running.

If you work in customer success or customer marketing, you probably already feel this intuitively. But feeling it and being able to prove it, measure it, and build a system around it is where most teams still struggle.

So let me walk you through how I think about this, what I've actually built, and where I'd suggest you start.

From programs to a system

Customer marketing has always had a clear identity. We build brand ambassadors, foster communities, and elevate customer voices to drive thought leadership. None of that has gone away.

But the role is changing in a meaningful way. It's moving from a collection of programs to something more structured, more deliberate, and more measurable: a system built around signals, scoring, activation, and measurement frameworks.

That shift matters because it changes how you show up internally:

  • Running programs usually means asking for budget and goodwill.
  • Operating a system means showing impact on revenue, expansion, adoption, and retention.

That's a very different conversation to have with your leadership team, and it's an exciting time to have it.

Start with signals

Customers are constantly leaving signals across your organization. The challenge is that those signals are scattered, and most teams aren't collecting them in any structured way.

I've found it helpful to group customer signals into three broad categories:

  • Commercial signals: contract renewals, upsell activity, changes in spend
  • Product signals: adoption rates, feature usage, health scores
  • Market signals: NPS, review site activity, engagement with advisory boards or community events

When you look at these signals together, patterns start to emerge. You can begin to identify which customers are most likely to advocate for you, which ones are ready to engage right now, and which ones have a compelling story that aligns with where your company is headed strategically.

That last point matters more than people realize. It's not just about finding happy customers. It's about finding customers whose success story ladders up to the narrative you're trying to tell and the strategic priorities your company is focused on. Relevance is part of the equation.

Scoring for propensity, readiness, and relevance

Once you've got signals coming in, the next step is using them to prioritize. This is where scoring comes in, and it's also where AI can genuinely help.

I've used AI to score customers across three dimensions:

  • Propensity: the likelihood that a customer will actually advocate with you
  • Readiness: whether they're in a position to engage right now, given everything else going on in their world
  • Relevance: how well their story fits your current priorities

But here's something I feel strongly about: humans need to stay in the loop. The model doesn't set the weightings. Your team does. Someone needs to review the scores the AI surfaces, apply judgment, and then activate those relationships with actual human effort.

AI helps you prioritize at scale. Relationships still close the gap.