Ask most CSMs what makes them good at their job, and they’ll tell you some version of the same thing: they know their stuff. They’re well-versed in the product, its use cases, industry benchmarks, and workarounds. That knowledge took time to build, but AI has made it table stakes. This doesn’t mean CSMs are less

As I write this, four astronauts are doing something no human has done since 1972: looping around the Moon on Artemis II, 252,756 miles from Earth, navigating genuinely uncharted territory. One of them—Victor Glover, the mission pilot—is a Cal Poly San Luis Obispo alum. Same as me. Glover didn’t get to pilot a lunar mission

Churn doesn’t usually come with a warning. When a customer skips a QBR, stops responding on Slack, or sends the dreaded “we’re evaluating our options” email, their decision is often already set. The signs showed up months before, but you missed them. Eric Gilpin, President of Go-To-Market at G2, has spent 25 years building two-sided

Your NRR hits 115% and the board approves. Then two enterprise accounts pause expansion. Suddenly that number feels less solid. Strong net revenue retention looks like validation—until a few large accounts stop growing and the total hides what’s really happening. This article explains what NRR means, how to calculate it, what good looks like by

The traditional Customer Success (CS) model was built for a different era of software companies. The shift to usage-based pricing, the rise of non-technical users, and the rapid expansion of AI tools are forcing CS leaders to rethink the fundamentals: who owns what, how teams are structured, and what “delivering value” actually means now. Replit

The conventional wisdom around AI and Customer Success (CS) goes something like this: automation handles the repetitive work, and CSMs get to focus on higher-value activities. Fewer manual tasks, more strategic conversations. Efficiency scales up, but headcount doesn’t have to. The most forward-thinking CS leaders are taking it a step further by asking a harder

Most companies treat an AI rollout the way they’d treat any other software deployment: buy the licenses, enable the accounts, send the onboarding email. Then they wait for adoption to follow. But what happens when it doesn’t? Christina Meng has watched this play out across some of the world’s largest enterprises and shared her insights

If a customer asked, “What happens next?” would every team give the same answer? After the demo. After the contract. After onboarding. After the first login. In many SaaS companies, each team owns a piece of that story. Marketing owns awareness. Sales owns purchase. Customer Success owns onboarding and renewal. Product owns adoption. Community and

NPS scores, health dashboards, adoption reports. CS teams have long used these sentiment and engagement metrics to show value. But we’ve entered an era where retention has become existential. Those signals are useful, but they rarely make Customer Success mission-critical inside a company. What does make it essential? Delivering outcomes that impact revenue. On a

AI is powerful, but without real customer context, it’s just guessing. Most AI tools can draft emails, summarize notes, or answer generic questions, but they don’t understand your accounts. They don’t know which relationships are weakening, which stakeholders just changed roles, or where renewal risk is quietly building. And they definitely don’t see the full