Ramesh Johari Q&A On Key Considerations When Transitioning To A Scaled Marketplace

Rob, do you wanna come on? Yes, thanks, Mike. Ramesh, thank you for presenting today. I'd really love your quote about no marketplace or no business is a marketplace when it starts, but all are marketplaces when they scale. I'd just love to hear your thoughts about when companies are making the transition from not being a marketplace to scaling. What are some of the investments or things that you'd recommend for companies to have more of that marketplace DNA in terms of like people or skills, or what do you think has been be effective for companies to kind of make that transition?

Yeah, I actually think it's interesting, you know, even for companies that sort of imagine themselves in marketplace, I really want to double down on the data science of marketplaces here as that investment in particular. There's a great article that Eric Colson, who used to be Chief Algorithms Officer at Stitch Fix, recently wrote. I think he wrote it maybe 2 months ago in O'Reilly about sort of data science as more than just You know, people that analyze data. And this is a really important way that I think that that comes up. And here's kind of what I'd get at. A lot of the mental shift that has to happen is that suddenly you move from a world in which you can evaluate the health of your business entirely through the behavior of one type of individual, right? Like I said, Conversions, daily active users, retained users, like these are things you won't stop looking at those. Even if you imagine yourself to be a marketplace, as you're scaling up and you're focused on those things, it can be easy to forget that you're getting into a world in which the health of the opposite side is actually the driving factor on the health of, let's say, the user side. Data scientists are actually, especially kind of data scientists that I think come from sort of a multidisciplinary training. So, you know, this means sort of data science that involves like econometrics or economics background, operations research background. Actually, to be totally honest, even sort of folks that are trained in applied physics or neuroscience, cognitive neuroscience, like what you see in those environments is an ability to think sort of more at what I call a system level. where you're basically asking yourself, what are the main drivers of health? So I might imagine my primary metric actually is retention on the user side. There's a difference between thinking about mechanically as just let me just track that in a dashboard and think about it mechanistically, which is what are the things that are driving user retention, right? And what you'll see is if you break out that metric, you're gonna come to a point at which, well, One of the things that's driving retention is, are you growing liquidity on the opposite side? So I think that you want people who take the system-level viewpoint, who are willing to break metrics down and always interrogate, are we actually sort of causally affecting the knobs that are most influential for that top-line metric? So in a way, you know, Rob, I think I'm cheating a bit on your question. What I believe is important is actually data science broadly is kind of that core competency that becomes fundamental when you scale up into a big marketplace. So that's where like this kind of liquidity-driven data science is happening. But I think that the point underneath that is you want what I would call kind of system-level data scientists. You don't just want data scientists who are just purely about implementing statistical methodology and nothing else. You kind of want people who are willing to constantly ask that, interrogating question about causal mechanism. And so that's why I said physicist, right? It's like the physics of a marketplace. Like you need to keep asking yourself, when am I crossing over that threshold? So, you know, if you're J&J and you're starting out as a Johnson& Johnson type business, you're going to come to a place where suppliers matter. And when that's the bottleneck, you want somebody to have identified that so that you can start shifting your mindset quickly into imagining these kind of platform types of questions rather than just single-sided optimization. One thing I want to bring up there, I'll close with this kind of anecdote, right? You know, I won't name platforms or people in this story for reasons that you'll see in a second. So I was working with someone and, you know, we were working on sort of helping a PM make a decision about liquidity management. sort of feature. So liquidity management, meaning like, you know, something which kind of determined what aspects of the opposite side of the market would one side see. And the, you know, I asked the guys, or I saw the memo and I was like, hey, you know, like I saw what you did here and you used some really advanced methods. It's really cool, but you're kind of not really recommending anything. You don't have like a punchline. You don't have a, you know, here's what I think is happening. Here's a decision we should make. Here's the, Scenario planning around different things we could do. And the guy's response was sort of like, well, hey, look, my job as a data scientist is to take the methods we know, use them on our data, and implement them and make sure that everybody knows what method I used. That's where my buck stops. Everything after that is the PM. It's the PM's job to take in what I wrote up, figure out what the implications are for decisions, and make those decisions. And I just want to say, I categorically disagree with this viewpoint. Like, that viewpoint is disastrous for data science and it's disastrous for the PM. Like, it is the data scientist's job to be the one who's interpreting that, right? And so I think that's where I was going with my earlier comment, Rob. You want data scientists who don't just stop at, let me measure the metrics you're telling me to measure. You want data scientists who are willing to do things like break those metrics apart and say, here's where we're hitting a liquidity constraint. You know, versus not. So I think hopefully that kind of answer makes sense, but I really feel like that's the type of competency. It's a human resource competency, I think, that you want to pay attention to as you're moving through that transition.