Saad Ullah
The graph showed something most growth teams never see at scale: users who had abandoned a product months earlier were suddenly returning. Kamradt's breakdown of this "resurrection effect" went viral, sparking conversations across product and data communities about what could cause such an unusual pattern.
What the Chart Shows
Every so often, a piece of data makes everyone in tech stop and take notice. That's what happened when Greg Kamradt, a former Salesforce growth strategist, shared what he called an "unreal" retention chart on X.
The retention chart tracks user cohorts month by month, showing whether people who joined in a given period kept coming back one, two, or three months later. Normally, these lines slope downward as users drift away over time. But this chart was different.
"Ladies and gentlemen, this chart is unreal," Kamradt wrote. "The curve upward on the right-hand side of the lines is nearly unheard of. It means that users returned after churning out."
Instead of the usual decline, the right side of each line bent upward. Previously churned users were coming back—and not just a few of them. This "resurrection rate" appeared across multiple cohorts simultaneously, suggesting something systemic changed. Maybe it was a major product update, a viral marketing moment, or renewed market relevance that pulled people back in.
Even more impressive: retention wasn't just recovering, it was improving. Later cohorts were staying longer than earlier ones, with three-month retention jumping from around 75% to 85% between cohorts. The product wasn't just winning users back—it was getting stickier.
Why This Matters for Growth
In growth analytics, retention is everything. You can acquire users all day, but if they don't stick around, you're just filling a leaky bucket. A chart like this signals that the product has found something special—strong product-market fit that's actually strengthening over time.
Kamradt pointed out that while many small improvements likely contributed to this success, his experience tells him that 80% of the impact probably came from 20% of the changes. "If I had my hands on this data," he wrote, "it would be time to break it all down. The answer is always a bunch of small things, but 80/20 analysis will show you the key feature or use case—video, image generation, something."
His point: among all the tweaks and updates, there's usually one or two core improvements driving most of the outcome. Finding those is where the real insight lives.
What Could Cause This?
Kamradt didn't reveal which product the chart came from, but the pattern fits certain scenarios. AI tools like ChatGPT or Midjourney often see return spikes after launching major new capabilities. Creative platforms can drive reactivation through viral projects or shared content. Community-driven SaaS products might experience synchronized returns following big marketing campaigns or key integrations.
Whatever the specific trigger, the simultaneous rise across all cohorts suggests a combination of good timing, meaningful innovation, and a product that genuinely resonates with its users. In other words: the rare combination every product team chases but few achieve at this scale.
This chart represents what every growth analyst hopes to see—proof that product improvements can not only retain users but actually bring back the ones who left. It's a reminder that when you nail the core experience, users notice. And sometimes, they come back for more.
Saad Ullah
