Video
===

Jeff: [00:00:00] Welcome to LaunchPod, the show from LogRocket, where we sit down with top product and digital leaders. Today, we're talking with Pravath Nanasetty, Global Head of Industry for Retail Data, Technology, and Quick Commerce at Snowflake.

It was a blast getting Pravath's take on why it's not the model. It's the input you use to get the model that's important. And why you have to dig into the core details of the most important of those inputs. How you look beyond just ROI to see what really drives the business. The packaging lesson every product manager should know.

And why honing his product skills at Procter Gamble, where product management was invented, set him up for his whole career. So here's our episode with Pravath Nanasetty.

Prabath. How's it going, man? Thanks for coming to the show.

Prabhath: Absolutely. Pleasure to be here.

Jeff: This is going to be a fun one. I say that every time, but I have yet to have a non fun episode of this for me, at least. I hope it's fun for everyone else.

Prabhath: I hope I can keep the the streak going.

Jeff: Yeah. All right. But I want to start way, way back at the beginning cause I feel like we always run into this where it's the joke about everyone has a weird way to come into product, but you have [00:01:00] a master's in chemical engineering from from Carnegie Mellon.

That's quite the journey from there to digital product. How were you inspired to take on, what is objectively a very hard major and then what caused you to pivot there?

Prabhath: Yeah. Well, let's say it wasn't always by by choice. I think when I was graduated with my undergrad, it was 2002 right after September 11th. So, there weren't really jobs. For a lot of engineers at that time. So I ended up going back for a fifth year masters. And the thing about chemical engineering is a lot of other engineering.

Professions really are actively building things right here. I'm always jealous with the mechanical engineers building robots and other stuff. And then we've got electrical engineers working on bread boards and circuits and then chemical engineers. It's like, well, I need a billion dollars to go build a distillation column.

That's not going to happen. So we can do things in our lab scale. So it tends to be a [00:02:00] lot more simulation, a lot more math heavy. Which, as I found through my career, really works well in the marketing and kind of digital media profession.

Jeff: Yeah, that's fair. I actually saw you have, and we'll get into this in a sec, but you have a big kind of consumer behavior analytics deepness to your career. And that makes a lot of sense. 

Prabhath: I'm a bit of a data geek, so I've got an interesting fact for you. So, the census, the U S census does a study, I forget if maybe it's every three years or every four years, they send out a survey to graduates, and They ask you questions about your career how much you make, all of this stuff.

And if you look into the data, it's got some great data about degrees and then how much that degree matches your job in different years. And I looked at all of the engineering professions and the engineering profession that has the lowest percentage of people that still work with anything related to the degree is chemical engineering. so I think, I, so I've almost proven [00:03:00] my my theory that really it's a, it's an applied mathematics degree rather

Jeff: yeah 

Prabhath: you know, something that leads you into something relevant to chemistry.

Jeff: I mean, I've always told people that at heart college is either kind of vocational training or it is learning how to learn and you can go on to do something else. So. But you graduated and got your master's and then went on to Procter and Gamble and you actually for a little bit, like applied the role.

You were a senior scientist you know, working on, I think it was it the bounty product line or like, tell us a little bit about how you walked into that.

Prabhath: . So I actually internshipped at Procter and Gamble in their manufacturing plant, so it was a plant in Mahupany, Pennsylvania. So Northeast corner of Pennsylvania. And it's it was eyeopening experience, honestly, because these machines that they're working on are just massive. And any little change results in.

A massive change downstream that you know, you could be wasting tons of literally tons of paper \ [00:04:00] and other things. So it was a great learning experience and applying what I knew and the math I knew to downstream the impact of it. When I started full time at PNG, it was in a role where I was given a task basically end to end.

It was a form of product management. It was, hey, we've got some ideas that consumers have thought about or we've thought about. Can we take these ideas, actually go demonstrate them on a, on the lab scale, right? Like literally, Printing paper individual paper towel rolls you know, could be colors.

It could be patterns. It could be additives that we add to the paper. Let's try a bunch of things and then let's test them with actual consumers and see where there's actually a physical, real performance difference. And where there is. Let's go try to scale that up and see if this is a viable product.

So that was the the task and I led the product management or the process management side of it. 

Jeff: [00:05:00] I feel like very few people who are in the digital kind of, you know, software realm have this big background in CPG. But you're already talking about minimum viable products and like testing quickly and all sorts of stuff.

So it's really cool to hear that, you know, some of

Prabhath: you know, it's another interesting fact. I'm just full of like random trivia. Do you know where product management was actually invented?

Jeff: was it Procter and Gamble?

Prabhath: It was Procter and Gamble in 1931.

Jeff: So there's no better place to learn it potentially than the OG of PM. That's

Prabhath: yeah, apparently apparently that, that is true. So it was invented by a I forget the role, but his name is Neil McElroy. And basically they wanted to build a new product. And so he basically scoped it out and said, here's the resources I need. Here's what we think this product will do. Here's the business case for it.

And so it's credited as the original place where. The philosophy of product management was created.

Jeff: of awesome. But speaking of which you moved on from MVP development [00:06:00] and kind of understand this and building this out and building physical kind of lab scale stuff and you moved on to a much more kind of like consumer insights role.

I get a little bit of the background, right? When we talked about, you know, the math involved in chemical engineering stuff, but how'd you make that role jump career wise?

Prabhath: Yeah, it's a weird jump, right? So in the process of creating some of these products, right? You'd have to create it in a lab, then you'd have to take it to one of their factories or their plants and actually go and try to build it and make sure it runs and then actually runs well enough to actually go create millions of these these products that appear on shelves well, you're working with a multifunctional team.

There's people responsible for marketing. There's people responsible for the financials around it, there's packaging, all of this stuff. And one of the things that was really fascinating to me is the actual consumer testing side of it. So we would take these products you know, make sure that they're safe and, you know, minimal viable for what we're trying to build.[00:07:00] 

And then we would actually ship them out to consumers around the country and say, Hey, we would love for you to give us feedback. And they would use it for a little while and give us you know, information, whether it's a survey or other things around it. And understanding that was really just fascinating to me because I love how we could turn that into real data that we could then use to analyze it was even more fascinating was you know, there were a few folks in this group that would then take that data and then they could forecast how good this product Would perform based on a bunch of historical data.

And I just love, like, if there's one thing I love about math, it's the ability to go predict things in the future based on things that, you know, today, and they had just a fascinating set of models to go and predict consumer purchase behavior that led to how good we think this product is that then our finance person could say, yep, this is a viable product and profitable one for us.

Jeff: And [00:08:00] I think I read somewhere in your background that one of the kind of big early accomplishments you had in your role here is like director of consumer insights was an extremely high. Total forecast accuracy which I think maybe at a software level where to produce marginal goods is basically zero cost, less important.

But what's the import at the CPG kind of side of it? Why is that so vital that you're hitting that target?

Prabhath: The reason why it's important is that you know, a lot of these products that you're developing just require obviously a ton of investment in R and D, but the bigger issue is that to create, you know, a new roll of paper towels, it seems like a commodity kind of thing, but the amount of equipment that's needed and the amount of energy and and products that are used to go create that is it's huge.

It's very high. And so. These machines have to be running, you know, very efficiently. And [00:09:00] they have to be running all the time to make all this stuff pay out. The problem is when you create these new products, you're inherently like throwing a wrench in the system, right? You're doing things that haven't been done before.

You're taking a risk on a bunch of product that maybe nobody wants to buy. And it's. You know, you've just spent millions producing it. And so it's very important for us to know well ahead of time, how well is this going to sell? And then, you know, if it does sells poorly, then we'll ton of waste.

How are we going to pay off this this product? And then if it sells really well and over delivers, then all of a sudden, now you've got to figure out how I'm going to, like, what other machines am I going to have to, Change to do this, and there's just lots of issues and in between. So, you know, it was really important in a in an industry like paper to get that right more so than maybe something that's Maybe less mass produced or less commoditized from a material standpoint.

The insight there in how to get [00:10:00] more accurate is like all good data modelers or forecasters, you realize it's not necessarily the model. It's the inputs that you use to get the model. And luckily we had a, an amazing team and have great mentors and other things other folks involved in this.

But really one of the big insights here is really making sure that we involve a lot of these multifunctional individuals in Providing those inputs. And typically, you know, the relationships were, yeah, send me this Excel file. I'm just going to plug it into the model. And part of what I'd love to do is actually, I just love to understand how other people work.

How are you getting this data? How are you you know, having the conversation to even get this data in the first place. And so I spent a lot of time with our marketing. Folks on, you know, how are you supporting this product? What kind of advertising are you going to do? Why did you choose TV and not radio and digital was like, just, I mean, just in its infancy

Jeff: I was there early [00:11:00] days.

Prabhath: early days, right?

It had phenomenal ROI. Anything you did digitally was like. 300 ROI, but you were only spending like less than half a million dollars in it, which is a big difference now. I'd love, I'd love to understand those. And that just made me a better forecaster. And I could, you know, essentially sniff out the BS essentially.

And when it wasn't thought out and then the most important thing is our sales and distribution side, right? So, in a fast moving category like paper if. You know that a certain retailer is going to take this product or not going to take this product or they choose a small size when you thought they were going to take a big size.

Those are all big. Differences and in how you forecast. And so though really diving into some of the why's was was really part of the reason for the success. And it carries through the rest of probably my career where, you know, I encourage a lot of PMs, right, really take the time to understand.

This like [00:12:00] this network of influence of your product. And it's a key thing to think about and take the time to do that early in the, in your career leading that product because it's like that Chris Pratt meme, you know, like the longer it takes, the more it's more awkward to start asking those questions when you're much later.

Jeff: I feel like there's so many things that Success goes back to understand deeply the core most important pieces and not just a surface level, but dig in and understand and like you said, be able to sniff out the BS. We, a few months back had a guy named Justin Amovic on the show who he made the opposite journey.

I'm going to. But he started as a sales executive and actually moved into running product. But it's really funny kind of talking about this focus of yours of get into the details, sniff out the BS and understand what's real. You could have made a great like chief revenue officer you know, go into sales forecasts and figure out where it's not, you know, where it's not really going to happen or not.

[00:13:00] You know, that said, maybe you get more math to use going into product and consumer insights, but it's just so many fundamental things back into, if you don't understand the fine grain details of the most important bits, you just can't do it. 

Prabhath: It's interesting. This person that you mentioned about coming from sales, I think it's actually a phenomenal profession for product and the longer I've been in my career the more respect I have for sales leadership and and the challenges of selling.

And I used to think, you know, way back in my career, I'm like, who are these like salespeople that show up at my meeting? They're basically like, no, I can't sell this. Or. Now we're having difficulty of this retailer. And I used to be like, what, like, what are these guys doing? Like, I feel like they're just, you know, they're being nonchalant, but I really just got a ton of respect and the role after the one we just discussed where I actually went lived in Bentonville or actually Fayetteville, Arkansas for three years and worked very closely with the sales [00:14:00] organization of PNG.

And I realized, boy, these guys. This group of people, it's, they are very data oriented. Just process it and consume it in a very different way. But you know, they have to manage people relationships. They've got to manage orders and all of this other stuff. And it's you know, I'm actually in my, even my current role, I'm working with a sales organization.

And it's it's fascinating to see. , where it all comes together in you have to be able to blend all of your disciplines, like whether you're deep in tech, deep in math, great with people, all that stuff. You, it's a great melting pot of all of that to say, all right, how do we actually drive action?

And it's it's really cool and it's a great skill for product managers to have and one of the things that I think the best product managers are, you know, half technology and half. They have to be able to represent that product and speak to it better than anybody else because, you know, they set the tone for it.

Jeff: Yeah. I really, it's funny. I shared the same kind of belief early on [00:15:00] and, but having worked with several really orgs, you realize that there's, almost no function that wouldn't benefit from having better sales skills, like the truly high performing sales skills. Just, it takes such an incredible kind of intellect and horsepower to do that, but that kind of shared across anything done really well I've found is interesting and probably more to it than you ever thought.

I do want to. So one question I had for you is I saw a different bit where it talked about on top of really accurate forecasting. Your group at this point also showed I think the highest marketing ROI. Of product lines at the time. And I've always just been curious.

Like we always hear especially for public companies, you know, on forecast, hitting forecast, you know, based on, you know, expect, you know, hitting expectations, what's more important. Is it hitting that forecast accuracy or is it being more efficient and growing more,

Prabhath: Yeah. That

Jeff: it a little bit of both?

Prabhath: yeah, I love [00:16:00] that because that's the heart of it. And before I answer that, I think one of the diatribes that I usually have is, you know, I love the marketing profession, right? I've spent a long time in it, but I think there's this tendency to use. Poorly descriptive language in the marketing profession, right?

It's just rife with acronyms and all of these. And I'm still convinced that a lot of marketers have not taken the time to actually dig and ask the questions of like, all right, why does this work? Why doesn't this work? And so you get questions like this one that that are simple on the face of it, but it's a great test to understand like, wait, do you actually understand?

This concept of R. O. I. And you know how it can be good and how it can be bad. But so I think with anything like with measuring R. O. I. By the way, I used to teach this, right? I'm

Jeff: We got the right guy to ask them.

Prabhath: like, super passionate about it because I used to do, you know, with [00:17:00] forecasting, used to do market mix modeling kind of the early days of that.

But you're right. It's not a It's not an either or so there's with ROI, you've got a couple things, right? It's basically how much did you gain? In terms of revenue sales for how much did you spend? But that's simple on the face of it. But if the goal is, Hey, I just want to have a high ROI.

Well, great. You can spend 10, 000 and you've got a great ROI, but you're not moving any product. But if you keep investing, your ROI goes down because, you know, maybe every new, like dollar you spend is going to give you just less. But is it still growing your business? Like go keep driving that.

Where it gets really interesting is what's I think what P and G at least called profit ROI, but there's different words for it, like marginal ROI, but really you want to drive certain vehicles until, you know, they've, they stopped being a net positive for you, right. the [00:18:00] ROI. That you're getting outweigh the costs that you're spending in there.

And so there's a point where you can keep going and it's still a great decision to go past this point where your ROI goes down, but you're still driving cases of product or, you know, installs and whatnot. Now, the other consideration is what are you trying to do as a business? Are you trying to get acquire customers because you're still new?

Or are you well known and you're really trying to keep like mindshare? And I think , these are all considerations. And so if you are trying to acquire customers, let me tell you, like trying to acquire customers for say, like a mobile app or something can be. Not great returns for a while because you're trying to break through this this this cloud of, you know, like attention in many cases.

And so in some cases you know, you're going for a different purpose. Maybe it's, we're just going for driving awareness or we're driving some sort of positive influence on the product. And [00:19:00] so it's really important as a marketer working with a marketing organization to show them kind of the portfolio of like.

Business strategy and actions and really start thinking about it so that they can first be educated on that. And then when they get asked by their CFO, like, wait, hold on, why is this ROI going down? You can articulate it as, Hey, this is still good revenue. But let's not get caught up by a grade that somebody's arbitrarily written.

Jeff: Right. A 30 percent ROI might be better than 50 percent if it allows you to keep growing and your goal is we need to grow as fast as we can. And maybe you would have spent less to get 50 percent ROI, but if you grow a little bit less, then that's probably not as good.

Prabhath: The simpler way to say it is, if Heisman trophy winners were like so great, why aren't they more successful? Well, it's got to do more than just like the badge that you get or the the award that you get. Right. So there, there's more to it 

Jeff: but I do want to be clear here. You had an incredibly successful run, it seems like in [00:20:00] PNG and you were there for what, a decade? But it wasn't, all roses either. the thing I always tell my team when things get hard is, the book about Airbnb is up and to the right, but along the way in reality, you know, there's a bunch of dips and turns and hard times, and I'm sure, periods where they were, almost insolvent, even when they came back.

Because, it's how you bounce back from the hard things that kind of define a company or a career or anything like that. And so on your end, I mean, it sounds like there's a few things that you push through to make successful, but one specific story I found out about was this kind of push to roll out like early on some deeper BI dashboards and better understanding of numbers and give people the insights maybe you had and how that kind of adoption.

Maybe didn't start out as great. But I'll let you tell a story cause I'm going to butcher it.

Prabhath: This was actually a little later into my career. I was on our Walmart team. So in in most consumer goods companies you know, one of the things that people don't always know, [00:21:00] I mean, it's obvious, but it's deceptively obvious that the P and G's of the world, while they're very consumer oriented for the most part, they don't actually sell to consumers.

Retailers. So the Walmarts and the targets are their customers. And so it's important for these companies to actually have teams that are co located with some of our large retailers. So that's why There's a large contingent of employees for actually most CPGs sitting in Bentonville or Northwest Arkansas.

So while I was there you know, one of the challenges that we we saw is that retail typically operates on an hourly daily basis but The challenge is that most of the data that you're buying and that your company buys is actually bought through maybe a headquarters organization. And the timelines for a brand manager or brand leader, these are, you know, years or like 18 months when it takes, you know, a year or two to [00:22:00] actually get a new product out to market.

You don't have the urgency for like real time data that maybe somebody on your sales team has. , so The pain points on that role is using data that was like you know, call it a year or two quarters behind. And you're trying to go to somebody at Walmart and say, yeah, here's how your product category is doing.

And they're looking at you like. Six months is like, you know, like last decade for me. I need to know what happened last week. And so from getting a few of these scars after a number of conversations, we ended up, creating a different Service essentially and buying other data sets to be able to fill the gaps.

A large part of this wouldn't have happened without again, reaching out and understanding what does this I. T. guy. Do at in like Northwest Arkansas. He's a PG employee. Like, what exactly does he do? And then going to another function called category management. These are the people that you know, analyze all of the [00:23:00] products that are on shelf and they're the ones designing where the product should go on the shelf, right?

Do I put all of the bounty brand together, or do I take all of The premium bounty and the premium other competitors and put those together. And then, you know, do it that way. Like, how do you decide all those? And that's the role it's called category management. And really understanding what they do and coming up with some ideas on, you know, what are the biggest pain points that we are experiencing as a team?

Is it the speed of data? Is it the source of the data? Is it just a question that we always get asked and we have no idea how to do it? And it came from a realization that each of our kind of functions never actually talked to each other. Or we would talk, but we weren't actually sharing like the actual data or the actual things together.

So, we got together and started to brainstorm, right. If we had to answer this question from somebody at Walmart, how would we do it? Like if there were no barriers, like here's what I would put together. And then it [00:24:00] became a. Well, why couldn't we do that? Like, how do we get from here to there?

And so it was awesome. It was, I mean, it was product management without the kind of the structure, I would guess, and we essentially created a whole set of BI dashboards. That's what was hot at the time. All the the Tableau and BI dashboards, but we organized it around kind of topic areas that, you know, we knew that in the course of a year.

In this quarter, this is typically what you would do in this quarter. This is typically what you know happens. There's annual planning and another quarterly activities. And so we organized it around those. And for each of those, we had dashboard set up to answer questions during that time. And so we created a whole set of dashboards.

It was a Huge success, both internally at P and G. And as you can imagine with Walmart, cause they could now actually just go in and ask a question where like pull it up on a screen. And here's your answer, Mr. Buyer. So [00:25:00] it it ended up doing really well, but I think the struggles we're through that process of really making sure that we could even get data or get budgets to go buy our own data.

In some cases, it was the same data that we were already buying. Just, we were buying the same data, but maybe it was just a faster version of it. So there's a lot of under the radar things that we did and getting feedback that were challenging, but we had some good experiences there but learned a lot from how to work better and more collaboratively across like functions to do it,

Jeff: And I think one of the things I've seen when I've worked on, you know, ops teams and built dashboards like that maybe is building for not the user, but you know, either the power user who isn't necessarily going to be the one consuming every day and or like the executive stakeholder who's paying, you know, sign the check.

And once this drop down, that drop down this, you know, little feature and this knit and that in reality. The actual user wants like one chart or two charts and all the extra complexity makes [00:26:00] it maybe even less usable. Did you guys run into anything like that?

Prabhath: We we did and you know, luckily by that time, it wasn't my first rodeo with BI. And so I think the danger with building to a single stakeholder or the budget holder is that you have to make sure that you're planning for not only what but also what they need to actually Like solve the problem that they're trying to get to.

Right. Sometimes you know, you see it all the time. Right. It, thinking back, it would actually be a great way to understand a stakeholder. It's like, Hey, send me your proudest Excel document that you've done. And if there is like, if there are fonts and all this other stuff, you're like, okay, you're an over designer, you know, like it would be a great test to understand stakeholders.

But sorry, I digress. I think there's a tendency for the person that's Your stakeholder trying to drive the action to overdesign certain things. And so you can take two approaches. One that [00:27:00] I've done is make sure that you expand that stakeholder network, right? So it's not just the budget holder.

It's maybe it's the user that could be different than the budget holder. And then who's the outcome. Stakeholder, right? Who's actually receiving the end result of whatever you're delivering. So that's one way to do it. And that's a, it's a very effective way because it also helps you discover features that you maybe wouldn't have thought to build.

But you can discover them just by understanding how people are how people are using it. And the big part there is you know, and I know most people kind of subscribe to it is, it's actually. Try to do as much passively rather than actively asking, you know, like, show me what that PowerPoint deck that you created was or show me like the actual, after you filtered a dashboard or something, show me what, like, what did you tell that?

You know, that stakeholder, how did you receive it? What did that stakeholder do with it? After that, then piecing together that whole chain. So [00:28:00] that's, I think that's one way to to really assess it. I think the the other way is a bit more of an iterative process, right?

You try to get people to use it. Quickly in an MVP state, that's probably the MVP design. But it takes courage to push that through. Right. And that's not an easy conversation on a budget holder, because there's always a tendency to to perfect and to, you know, in some cases over design. And so, those are sort of the considerations.

But yeah, with specifically with this Walmart one, we knew what we I think collectively understood and we would push out early iterations with buyers where, you know, we weren't anywhere close to our vision, but we would push it to the buyers and then we would actually discuss like, okay, how did that go?

Was there something that, you know, on what we've planned that, Would actually have helped and it helped us not only designed to a better product, but also helped us prioritize. Like, Hey, we thought this thing was, you know, much [00:29:00] later stage, but like three buyers have already asked about this.

Maybe we do need to pull this one forward and prioritize. And you're not going to get that kind of learning without actually doing it. Like everything else is sort of what people say. And you know, the iterative process helps you get to what people really need.

Jeff: Exactly. So. After this, at this point you've been a PNG for like 10 years and, you've gotten, , your master's degree in chemical engineering from Carnegie Mellon.

You've now gotten your honorary master's in uh, it seems like product management and consumer insights from your time at PNG and then you moved on to InfoScout, which is now called Numerator.

I wasn't familiar with the company until I, you know, started researching for this. So maybe do you want to just give a quick, you know, the 10 second TLDR on what InfoSight and Numerator does.

Prabhath: Yeah. So InfoScout was one of the original apps that did the receipt scanning on using a mobile app. So, I think it was founded in 2011 and very quickly there was an app that you could you know, sign up for an [00:30:00] account and whenever you went shopping, you would take a picture. Of that paper receipt in your hand, and then we could digitize it and turn that into data about who's buying what.

And at the time, the reason why we did that is that there were a lot of other sort of consumer panels and purchase panels that were out there. But they required a lot of complex, like, you know, like an actual barcode scanner where you went shopping, you came out and then you'd have to lay everything out on the table and scan everything.

And I knew from a lot of my consumer behavior that it's important, you know, this is like the survey science and qualitative science that people tell you is, the way you collect the data is just as important as the data itself. And so if you're collecting the data and there's lots of friction, You're probably not getting the best data in there.

And so we created an app that did that. Later, we also created the ability to connect it to like your email inbox to get all your receipts [00:31:00] automatically and then even connect it to different kind of retail or accounts directly. So you could just get the data. So, you know, the types of data was there, but we were the first company that could actually paint a picture of.

Consumers in the U. S. Across almost every thing that they bought. So it's not just your typical grocery items and retail. But where did you go? Like, get that cup of coffee? And did you visit a nice restaurant? And did you go to a hair salon? And piece together this, like this map of Purchase behavior, which ended up becoming very valuable for a lot of companies to understand who their competition was.

What's my competition when I put it all together and can actually tell for a retailer, you know, they can figure out like where else are people going? Like I sell this product. Why didn't you come and, you know, you come here every week. Why don't you buy it? At my store, why did you go to another store to buy that product?

And you can [00:32:00] start to ask those questions. So yeah, it was a great product. And it expanded very quickly. And now you know, I think we've changed the industry a bit because at that time for that kind of data, it was a lot of phone calls, right? And Excel sheets, you had to call a and ask them, Hey, give me an analysis on why people switch away from my brand and work on it for a week or two, and then come back to you.

And none of that was ever put into a SAS product before, like you couldn't self serve on that data and ask questions of it and tease it. So we were one of the first companies to build not only the different data collection mechanism and data, but also moved the industry to a SAS based way to go and like query data, build audiences and all,

Jeff: And it seems like the value here is fairly unquestionable. The early problem you guys ran into wasn't almost one of product market fit. It was packaging, right? It was, is there was a bit of [00:33:00] friction around people loved the product, but couldn't get their head around how much you all were charging for it.

I guess where did that misalignment come from? Like, is this just a matter of let's push the envelope here a little bit. We have something. Brand new on the market and we can get our value for it. Or was there something more than that?

Prabhath: Yeah, this was in the just the first maybe 18 months of us even being a company. I think we realized that this was a completely new proposition, right? Like to get this level of understanding, understand, you know, how people are buying stuff on Amazon or even other places that you couldn't get data for from anywhere.

And so, At that time this was actually before we even had a SaaS product. We were primarily just delivering like projects just with data and a bunch of consultants. And you know, I think we got a little ahead of ourselves in terms of, Oh my gosh, this is so valuable. We're going to like shoot this price sky high and start.

And I think there's always this feeling [00:34:00] of, you know, it's better to start high. Because it's easier to like reduce your price later versus start low and then try to increase your price. And there might be some truth to that, but I think we took that a bit too far in the early days. And you know, I think You know, while the data was valuable and, you know, we had a lot of inbound demand for it. I think we also didn't do a great job of saying, yeah, when you're buying this analysis, that should come with you know, this kind of analysis this follow up analysis when you have questions about the data maybe some.

You know, consulting hours to do some custom type of things, you know, not typical SAS product thinking, but we had to build a SAS platform and this was our way of. the data for it. One of the, one of the big things that we aired on is that, you know, when we, when you share such valuable data and like data, that's just going to like change somebody's mind.

You want to be involved in like the solutioning or like, you [00:35:00] want to solve their problem. I blow in their mind. It's so good. You also want to make sure that they've got some flexibility. So you're not immediately going to like nickel and diming, like, Oh, that's a followup question.

That's another, you know, X. I think we did a bit of that because I think we were like, Oh yeah, this is so valuable. And I think what I've learned over the years when you create something like that. The people that you are selling to and that have taken a risk to even talk to you, right? Like as a, you know, it's like a, who's the startup there?

I've got new data and I've taken this risk in there. I think I've realized a lot that , we needed to really think differently about some of the early adopters and. Allow for a lot more flexibility. Right. Cause in that case, you know, you're still trying to drive the business.

You're still trying to you know, show value to your early investors, but at the same time, your earliest customers are some of your like biggest risk takers. And so, you know, we had to make that right. So [00:36:00] we, you know, we did the things to make sure we. Did well by that that customer and, you know, other customers like it because there's other ways for them to give you value.

It's not always revenue. It's, you know, could they jump on a webinar and advocate for you and advocate for this thing that you're trying to do. And so I think we, it took us the early six to 12 months to really figure out that, look, this isn't just a data business. And, we're not just selling a software.

We're like trying to change an entire industry on how they think and how they even use this data. And so I think once we understood that we've had a lot more fun along the way. And you can even read ton of the thought leadership that my former company does. So it's it's a different.

Form of like, you know, as a product leader, you have to know what you have, but you also need to know what is truly valuable for that product. And if it can start to unlock future opportunities, you've sometimes got to take that risk as well.

Jeff: It's funny it reminds me of early on when chick fil a which is now massively [00:37:00] successful the supposed, you know potential inventor of the chicken fried chicken sandwich When they were starting out, it was a total fluke that brought them to selling the fried chicken sandwich. And it was not something that people were used to a, you know, chicken came on a bone and you ate it with a fork and knife.

And it was it was you know, meal. And when they came out with this in the early iteration, the person who ended up going out to found what is now chicken, chick filet would literally have to walk around the dining room and tell people how to eat it. It's, you know, no, you pick it up with your hands.

You don't take it apart, the rolled part, and you just bite it and you go, and it's a sandwich. But it's, Amazing. I think you can have the best product, but if you are doing this massive change, part of success early on is product delivery. And that includes at times, like you said, you know, advocating that people use it right.

And making sure that they're getting what they need out of it. You can have the best thing, but people aren't enabled to get that value out of it. Might as well sold them a rock. 

Prabhath: That's a really good point because it does come up and [00:38:00] this is where I do encourage a lot of product managers that are on the kind of more call it digital products right to actually go and understand how physical product management is done because sometimes it's a it's a proxy in some ways.

But you're right. Like, physical packaging is so important and the distribution of it. And I've heard a lot of stories from companies where they create a new product and they shelve it in different spot. Maybe at the store and then it fails because nobody thought to go over there to to buy this.

It's like, a lot of like plant based meats you'll find that I think nowadays there's like a plant based meat section, but even to this day, I was walking through my local grocery store. They have a plant based meat section, but then over by like the breakfast sandwiches, they have their plant based.

Breakfast sandwiches sitting next to all the sausage and bacon ones and I was sitting there going I wonder [00:39:00] how they made that decision because if somebody's actually looking for it, wouldn't they be going to the plant based? Like section versus serendipitously finding it, but maybe there's a reason for it.

Jeff: maybe it crossed the chasm enough now where it's popular enough where it's just a normal purchase decision to be made. I mean that it parlays straight into the importance of. In building software products UX, right? Like you can have , analytics or you can have all sorts of things in your product, but if you don't build, it's why certain kind of navigation paradigms exist and why things end up.

Looking somewhat similar to some levels is people want to know how to find the thing that they think of where, you know, a certain thing should be in a certain place often. So if you don't call it the same thing, or you innovate too hard and don't enable people to be successful, it can often cause negativity.

Prabhath: I'm a huge fan of designers like actual like product designers. And one of our biggest successes that we had as a company was probably because we had just a phenomenal designer that [00:40:00] helped us. And I'd say one of the things that We did, it was a part of this product is we changed how people selected items in an analytics product.

Typically in an analytics product, you're looking at filters, right? It's like, select your filter here, select your filter here, select your filter here. And it's sort of like this wizard style where you go. And we did a ton of research and we realized, you know, the problem with these filters is that if you're trying to answer the question business question A versus a completely different business question B, well, like the act of doing it is exactly the same.

You're like, Oh, select this option here. And pretty soon you're like, what am I doing? Like, how do I like, I've lost the context. And so we look for ways to get that context. Serendipitously, we found this this great idea for, remember when Apple bought Beats 

well, Beats Audio had this really great app where you could basically fill out a Madlib. It was a Madlib, like, I am [00:41:00] feeling You know, some emotion while some activity and I forget the other pieces, but you basically selected and then it would basically give you a set of songs and we just got so inspired by this Mad Lib format that's how we actually structured our product.

It was a business question that you were trying to answer. And you were just trying to replace the words in that business question with. And it turned out that was one of the most favorite features for a lot of our early customers are like, this is so intuitive. Like, this is the question I'm trying to answer.

And I know like when I'm trying to replace this word, exactly what it's trying to do. So little things like that, like, I mean, the, Make such a big difference in adoption and usage. You

Jeff: It seems like the kind of common thread through all this is, it's not just what you build, but it's how you deliver it. It's how you bring it to markets, how you market, right. You'd like, you said it's where you put it in the store. You can have the greatest CPG product in the [00:42:00] world, but if you put it in the wrong section, where they're not, you know, early on, I'm sure if you would put a plant based meat breakfast sausage in the breakfast sausage section, it would have not worked at all.

People were just not looking for that. It needed to go in the, you know, vegetarian meat replacement section, but now the world has changed and you can do that. So, You know, understanding not just the product you're building and the problem you're solving, but how do consumers buy that product, whether it be.

B2B software product where, you know, there's a two year brain cycle, maybe that goes into understanding this segment before you even think about buying it versus paper towels. It's still requires like how people think about it. How do they understand it? How do they make these decisions? And if you do that wrong, you can have the best like, lab level production product that goes on to the great.

Consumer product from a product level. If it can't be found or doesn't, isn't sold the way they expect, you're going to run into friction.

Well, Prabhat there is so much more we could talk about. I have so [00:43:00] many more questions for you. I think we will hopefully have to have you back again at some point because There's a whole world of questions I want to ask you, but I also don't want to take your whole day as fun as this has been so we can check it off.

It was another fun one. I deeply enjoyed this. I hope you did. But as a follow up, if people do want to maybe ask most questions that I didn't get to where's a good place to find you? 

Prabhath: Yeah, LinkedIn is probably the easiest to just send me a question. Happy to always talk. I love answering tough questions and I'm just super passionate about it. So yeah, LinkedIn is easy.

Jeff: Awesome. Well, Prabha, thank you for joining us. It was a blast. I feel like I learned a lot about CPG and just in general consumer understanding. So I'm stoked. Yeah, hopefully we get to try again, man. Thank you so much.

Prabhath: looking forward to it. Thanks, Jeff.

Jeff: Thanks.