In this video, we're gonna talk a little bit about product management in general and a lot about analytics around enhancing existing products. Joining us is Kiran Kadambi of eBay. Kiran, thanks for joining us. Alex, great to be here. And Kiran has been at eBay around five years. He's a senior product manager there and also, a Darden alumnus. I can't help but mention. So, Kiran, at a place like eBay, I'm sure you've got a lot of strong validated learnings, and there's a lot of existing infrastructures made the business successful. Can you talk a little bit about a new PM starts at eBay, what would you recommend they do? How do they sort of understand what's made eBay work and how to think about the product and the system? Great question Alex. The first thing that I'd recommend a new hire at eBay or at any other company for manufacturers is to go to use the product, and in one form or the other, or if you're in a company where the end users are enterprises, then go talk to your end users, and that applies to consumer or business. And so use the product, get your hands dirty go through the inner workings of the product, find issues with it, talk to people why those issues exist. Is it because it's a bug in your product? Or is it because it's a government mandated reason that causes it to be there? Get the nuts and bolts on. And until you do that, you really can't be a good product manager until you use your own product. And second is, what I recommend to somebody who turns new is to go and observe how the whole system interact. So the first point that I would advise was to look at it from the outside. And the second point is to look at it from the inside, go see how the things work, try to get an understanding of the whole flow, the end-to-end process. What happens in the good scenario? What happens in the bad scenario? Is that something you would do through actually just using the product or talking to engineers or technical product managers about the infrastructure itself? Correct. So you want to go have these conversations in addition to using the product on your own. Try to read through previous diagrams or previous documentation, and try to get an understanding of the end-to-end slope. And what I generally would do is I personally would lay things down on a flow chart or create a block diagram at high level to help me understand the different touch points. And given that you would probably do work which would involve a subset or a whole set of touch points. It'll be a great place to start. Yeah. And one thing we've been working on the course is how you amplify the product market fit for an existing product by expanding or extending new features. Can you talk a little bit about how you approach that in your work at eBay? Sure. I'll give you an example. As one of the projects that I worked on in the past is how do we improve the eBay market place? So eBay is a market placem where there are tons of buyers and sellers who come online, who come on eBay and do any kind of transaction. So what happens is is that sometimes, or period of time for various reasons, you have junk inventory that kind of buys up on site. This is inventory that nobody buys and nobody kind of even looks at it from a source perspective. So the question comes down to is how do we look at this problem? And how do we can fix it? So in order to look at this problem, we first need to have a good definition of what the problem is. So in this example, what does underperforming mean? So they could mean different things to different people, depending on your vantage point. So the first thing that I had to do is understand how do we define the specific problem, and that meant that look at different aspects of it, and then decide on how we progress further. And how did you do that in this case? So one of the things that we did in this case was to look at the listings or the items on eBay and see their aging, so how long they have to be on site. And then eBay is a company that's really big on how we use data to drive a lot of auditions and asks. So what I did was look at our data around search conversion, as well as how many times this live listing basically got shown in search. And then how much of those sightings that resulted on a click through, and then approach it. So we try to pull all this data and then try to identify what would be a good baseline. So how would we define aging of a listing? So let's say that, hypothetically, if a listing is one-month old, do we see what kind of conversion rate do we usually see, and what kind of impressions do we usually see and similarly extend that to three months, six months, and so on. So the idea is by the end of this, we've identified what is clear cohort group of listings. What are the characteristics in which we can then form a definition, which we then use to share across our organization? And this kind of helps drive various activities. For example, not just on why should we even wait until listing becomes underperforming? Can we do a better job at recommending changes because eBay is a marketplace, and we have all this data? How do we look at other converting or other items, similar item with better impressions and see what's different? How do we suggest this to sellers? So it can feed a lot of those visions so the best how we approach this and look at it that would define more underperforming listing list. So you define the underperforming listing, set a baseline, and then you'd create some ideas or hypotheses about how you might improve them. Correct. And then what do you do from there? Like you mentioned, right. One of the things we do is, gonna be to be hypothesize and come up with different proposals and solutions and ideas, and see how they impact different categories or verticals. And then what we do is the next thing that every project manager should do, which is go test them. And so some of that things that I did in this specific use case or example was we ran some A/B test, where for the test group, we ended up not showing listings that were a defined underperforming listing threshold that we've identified. And for the control group, we let the organic search results show up. Now, what we found as a result of this was that there was increased conversion for these sellers for the test, buyers in this example, where items that had a better impression ratio performed better, and it goes without saying. And the thing to observe is that it also helped the other sellers, who would have not shown up otherwise. So let's say in the control group, as a buyer, you used to see items from let's say 15 sellers on the first page of your search results. For the test group, three of those sellers were not shown because they had stale listings. So that helped in planning conversion for the whole group of sellers, but also more importantly, it helped our buyer experience. So you'll be more inclined to come back to eBay because, "Hey, I got what I want within my first search. I didn't have to wait through lots of bad listings." Yeah. And you make your conclusion on this test by the performance of these A/B results, and that allows you to run a nice disciplined program, where you're closing the loop on your ideas. Is that accurate, reasonable to say? Yes, it is. So based on the test result, what we got was we got bankable data, to say that, "Okay, if we go with this proposal and apply the definition of underperforming listings at this cutoff, and this kind of impression ratio then, that makes sense. That helped us go back and say that, "Okay, how do we institutionalize this?" So what that means is how do we provide capabilities across the value chain in eBay, where sellers can focus on this particular value, in terms of all the listings and converting. What's the impression ratio? And then optimize their business to make sure that they reach a threshold value, which is category dependent, and which would help them convert better. Go ahead. So I was just making sure I understood. So you kind of solve this problem of getting the right listings in front of the buyers, and then you took those warnings to the other side of the market to help the sellers who were having those deal listings or just to generally show them that you learn in a form that's actionable for them. Correct. So at the end of the day, you want a more efficient marketplace, where you want your conversion ratio to go up. So how would you do that is if you have data which identifies what matters to the buyers, and if you can feed that back to our sellers to make those specific improvements, and then what happens is that the seller can focus on building their business, which is go focus on sourcing the right inventory at the right point and then selling on eBay for the right price to our buyers so that the buyers can get what they want on eBay, which makes them come back to eBay and that cycle continues. So that's what we ended up achieving with this. That's great, that saying. So for the product manager who wants to do better, to learn how to take this analytics-driven approach to product improvement, what are your top three tips? The product manager who wants to succeed, especially in today's world where the key differentiator in my opinion will be how your company utilizes the data because everybody collects data now. The question comes on to how you get insights from that data and make sense of it. So it distance down to asking the right questions. So I think that will be one of the top skill that will come to mind in a data-driven world. So asking the right questions and knowing what to make sense or knowing how to make sense of the data, all the data that your organization has collected. So that will be something that I would say would sit high on that list. And the second one is don't be afraid of data, entities, and the directions that it takes you. So sometimes you might find that you're really passionate about a product. This definitely has happened to me, where you pursue and go down on a line, and then you realized that the data shows otherwise. Yeah. Right? So that I believe is something that you shouldn't be afraid of, and the sooner you do it, the better because you can fail sooner, and later you can come back with better learnings. So that will be something that is probably the second tip I would give to somebody who wants to come into this role. And the third one is to primarily, get more. Make this part of your daily routine. Make it a more hands-on experience. I know that a lot of schools are focusing on taking SQL skills. Go ahead and take them because what I found is that similar to the case of Darden, the more you get your hands on, the more experience you get, the better you get at identifying patterns, the better you get at asking the right questions, and it creates a good feedback loop for yourself So to make a little extra time to acquire those skills on the job for yourself? Definitely. That's great. Well Kiran, thank you so much for joining us and for these insights about how to run your program with analytics. Thanks, Alex. Thanks for having me. It's always fun to come and talk to you.
