Marcos Rivera analyzed 500 companies and found most have incomplete pricing data because flat-fee models never required tracking usage. When firms switch to usage-based pricing, they finally invest in fixing the data debt they've ignored for years. AI can help clean it up, but only if the underlying data improves first.
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I have cracked open and looked into the pricing and packaging model of like 500 companies in my career.
No one has had perfect data.
Nobody. I look at it, I score it, and I'd be like, you're missing all this stuff. This doesn't even make sense. You have tons of null values here. Like, why do you have null values in your price? I don't, I don't get it. Why is it this bad? And the number one answer I always got was, Marcos, we just never invested in it. Then why don't you invest in it? You know it's important. And here's what they would say. They would say, well, Marcos, here's the thing. We charge on a flat fee. We charge by the seat. Didn't really need to track usage that much because you just paid by the seat, or it was just, you know, one flat fee for the year, or it was an enterprise deal and it was all in and you just paid one fee per year. So there really wasn't a forcing function to drive them to track all that level of detail, that telemetry, all those things. So If something's not burning and coming in front of you, then you're just not gonna pay much attention. And that's how you accumulate debt. Think about any debt, money debt, technical debt, right? This is simply data debt. But the point is, is that the pain is not, is something you can defer. It's not forcing you to make the change and it slowly accumulates over time, the problem. And then you're facing this huge gap in your data. Now it's a bigger issue to solve, which takes resources, time, energy, people, which are busy doing other stuff. And therein lies the cycle, right?
Ethical diagnosis makes complete sense. Do you think that usage-based pricing and maybe sort of AI kind of represent catalytic moments for companies to care about this earlier?
100%. And, and I would even go as far as to say is that some of the usage-based pricing, um, modeling and and business strategies that we've seen, think of like the Snowflakes of the world and others, have really, I think, opened up and driven urgency to try to clean up and improve data, which by the way, has opened the doorway to guess what? Companies that can come in and help clean up your data, right? We've seen that. Talked to a few AI startups just 2 weeks ago that are, that's their thesis is like, we think we can clean up data with AI. And I'm like, yeah, more power to you, right? No, there's plenty out there. So there, I think it is a catalytic moment. I think it's driving the urgency. It started with usage. I don't think AI is starting it, but AI is pouring a ton of gasoline on top of it. And because you have to have a good, just a good handle on your data and the quality is AI is still garbage in, garbage out, baby. Like nothing, nothing else changes there. But I think from an urgency perspective, when companies are laying out their resources for the year and their bets, Data cleaning and data hygiene is usually at the bottom. It doesn't make the cut. I think now it's start to making the cut.
