Sreenath is the founder and CEO of a technology company called Intentwise. As the likes of Amazon and Walmart share more data than ever before, at Intentwise their goal is to help brands and agencies make the most of all this data.

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> Here’s a glimpse of what you would learn…. 

In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley converses with Sreenath Reddy, CEO of Intent Wise, about leveraging data to scale e-commerce businesses. They emphasize the importance of data from platforms like Amazon and Walmart for gaining a competitive edge. Srinath shares insights on organizing and visualizing data, tracking key metrics like customer lifetime value and repeat purchase behavior, and identifying true competitors. He advises creating a roadmap of critical questions, ensuring data quality, and using technology to streamline data management. This episode provides actionable strategies for e-commerce entrepreneurs aiming to scale to eight figures and beyond.

Here are the 3 action items that Josh identified from this episode:

Special Mention(s):

Related Episode(s):

This episode is brought to you by eComm Breakthrough Consulting where I help seven-figure e-commerce owners grow to eight figures. 

I started Hadley Designs in 2015 and grew it to an eight-figure brand in seven years.

I made mistakes along the way that made the path to eight figures longer. At times I doubted whether our business could even survive and become a real brand. I wish I would have had a guide to help me grow faster and avoid the stumbling blocks.

If you’ve hit a plateau and want to know the next steps to take your business to the next level, then go to www.EcommBreakthrough.com (that’s Ecomm with two M’s) to learn more.

Josh 00:00:00  Welcome to the Ecomm Breakthrough podcast. I'm your host, Josh Hadley, where I interview the top business leaders in e-commerce. Past guests include Kevin King, Aaron Cordova, and Michael Gerber, author of the E-myth. Today I am speaking with Srinath Reddy, the founder and CEO of Intent Wise, and we are going to be talking a lot about how you can use data that Amazon and Walmart and all the marketplaces are giving you in order to create a competitive advantage in moat around your business. This episode is brought to you by Ecomm Breakthrough, where I specialize in investing in and scaling seven figure companies to eight figures and beyond. If you're an ambitious e-commerce entrepreneur looking for a partner who can help take your business to the next level. My team and I bring hands on experience, strategic insights, and the resources needed to fuel your growth. So if you or someone you know is ready to scale or looking for an investment partner, reach out to me directly at Josh at Ecomm Breakthrough dot com. That's e-comm with two M's and let's turn your dreams into reality.

Josh 00:00:49  But today I am super excited to introduce you all to Srinath Reddy. Srinath is the founder and CEO of a technology company called Intent Wise, as the likes of Amazon and Walmart share more data than ever before intent wise, their goal is to help brands and agencies make the most of all of this data. So with that introduction, welcome to the show strength.

Sreenath 00:01:06  Josh, thank you for having me.

Josh 00:01:07  I'm excited to have you on. Now, I'm not a data nerd by any means. Like if, my favorite, one of my favorite, like, profile or like, personality assessments is the Colby index data could not be more lower than it probably is right now. I think it's probably like A12. It's like, just give me the gist. Like, I don't want to dive in, but I've got a strong, member of my team that's like a nine or a ten in data. So him and I, I've really, really well together. So selfishly, I'm really excited for this podcast to then turn it over to him and say, pull this apart.

Josh 00:01:38  And how do we implement this in our business? So I'm looking forward to this. That's awesome.

Sreenath 00:01:41  Yeah. No, absolutely. I've, I mean, I have swung I swing between, you know, that, you know, the nerd levels of data and all the way back up on a regular basis. So so we'll have a good conversation.

Josh 00:01:51  This will be great. All right. So Srinath I love you know, before we hit the record button on this podcast, you mentioned that, you know it was just maybe 24 months ago where we didn't have as much data as we have now. And you're a strong believer that people that use this, the data that Amazon is giving them right now can be a massive competitive advantage, especially as we're talking to listeners that want to grow to eight figures and beyond. So talk to me more about that. Srinath. Like why do you feel like data is important? Why do you feel like that can become a competitive advantage?

Sreenath 00:02:20  No. Absolutely. So I think a little bit of historical background on me.

Sreenath 00:02:23  Right. So before starting this business, I used to run the data teams and online marketing teams at a company called Orbitz, the online travel company, which then got bought by Expedia. So, so in that role, I have battle scars from dealing with massive amounts of data as a consumer internet site with 20 million people. So I lived the story of what do you do with data? How do you really leverage it? All the challenges it comes with to operate and grow a business. So that's just the background in which I have started and built this business. You know what has happened in this space? First of all, if you're a seller that has reached a certain scale, you know, the basics of having a compelling, differentiated product that appeals to some portion of the customer segment. You check that box already, right? Now you know, how do you grow? How do you differentiate? what has happened in support of that? I would say over the last, like like you said, last 24 months is if you look at, you know, dial forward, actually I'll go back three years, right.

Sreenath 00:03:11  Dial back three years. You had sponsored ads, you had DSP. And if you're a seller, you had an AWS API. And if you're a vendor, you had no API, right? So that was the state of the universe. If you look at it now, ads and DSP have remained and they've continued to, expand. And they have all kinds of levers that are now part of those products. But Amazon is rapidly expanded, what I call their measurement footprint or the data footprint. And to give you very specific examples, you know, we used to have selling, AWS API that has now changed to what's called selling partner API. They've really leveled up the ability to access the data, as well as the amount of data that you can get from that area. Right. You always had your ads, data and sponsored ads in DSP data, but now you have hourly data on both advertising as well as retail. That's new. Right. And the the most, The biggest innovation they've done on the data side is you've heard the term Amazon Marketing Cloud, right? now Amazon talks about it a lot.

Sreenath 00:04:02  The way it's unique and different is for the first time, you have data at a very granular level on every shopper, right? Josh, if you click saw sponsored ads, clicked on an ad the next day, and then three days later came back and bought, or maybe three days later you added it to a wish list, and a week later you came back and bought. Every one of those events is tracked in Amazon Marketing Cloud, and you have a unique ID associated with you. It's called user ID, right? So that's Amazon Marketing Cloud for you. Why is that helpful? Well, you can start to answer questions you could could not answer before. Right okay. What is the lifetime value on this customer. How are multiple campaigns interacting with each other. And there's a number of use cases we can dig into. My point being all this put together, it is giving you access to data that can answer super important critical questions that you couldn't answer before. Right. And we'll get into what those are.

Sreenath 00:04:49  But if you are someone thinking about growth, what are you doing all day? You're asking important questions, right? How do I do this How do I do that? How did my Prime Day go? Did I spend my money the right way on Prime Day? These are all questions you're asking. and the this data, these data sets, these expanding data sets are your pathway to getting those critical answers and making those moves. As a business, I can help you, you know, drive growth. And also it's not going to be easy, which also makes it a competitive differentiator. Right. You can't just think that I want to do this and it happens. You got to commit and invest over a little bit of time to make it happen. And then I think it really can be a differentiator.

Josh 00:05:21  Awesome. Srinath. So here's what I think most entrepreneurs probably listening to this, they're like, oh my goodness. Like more data. Like I don't love data. So how do I, you know, how do I do this? And what's the value of this data.

Josh 00:05:34  And you know initially you before we hit the record button, you all you gave kind of like three really good questions. that we should be considering. But I'm curious. Srinath like, what questions should sellers be asking and trying to find the answers to of the data that is going to make the biggest impact in their business?

Sreenath 00:05:49  A great point, and I actually think it's it's daunting to sit and even listen to me talk about all this data. Okay. What am I going to do? I don't have time to do my basics. Where am I going to find time to do this? I think it's a valid reaction. I actually think that as much as there's tons and tons of data, and you can get into the weeds on where it's coming from and all that, that's not even the starting point. I'll make the starting point extremely simple, right? Which is if you're a business that is wanting to drive growth, it is important to have what I call a roadmap, a roadmap of questions you want to answer.

Sreenath 00:06:17  Right. and I'll give you specific examples. And I, you know, roadmap is a fancy word, but I just say like, do you have a one pager with the ten questions you're trying to answer that you think will change the trajectory of the business or you think will reduce anxiety? Or do you think we'll get you some time back? Right. It's just a list of questions. And if you don't have one today, I would sit down for 30 minutes, bring some important team members together, construct one. But what are these questions? I've seen a lot of sellers in agencies right over the last 4 to 5 years. To me it boils down to three simple buckets. The first bucket is an operational bucket where you're just trying to understand what happened to the business yesterday, last week, last month, year over year. Okay. as crazy as this sounds, this in itself is not easy for a lot of people. A lot of time goes into it, and there's always a lot of gaps.

Sreenath 00:07:00  And by the way, I'm I'm going to get very specific in terms of examples as well. That's bucket number one. Bucket number two is diagnostics. Let's just assume that something happened to the business. Your top seller, which is doing phenomenal for the first six months of year, suddenly started to tank. Why did it happen. Right. That to me is diagnostics. you know, one of the things you can tell about, like the health of the business is when such a thing happens, do they have a set process to go figure it out or does everybody lose it? And they're running around with, like, chicken with their head cut off, like. And I've been in both environments, not just in this business but in my previous jobs also. But diagnostics is just can we get to answers quickly when something good happens or bad happens. Right. And then the last part is the strategic one, which is these can be transformative questions. Right. Who do I really compete with on this product.

Sreenath 00:07:42  What is that? What are that. What is that competitive set doing that I should react to? Right. those are the types of questions that fall under the, strategic bucket. Again, operational diagnostic and strategic are the three buckets. just I'm happy to get very specific on questions in each of the buckets. Yes, that's but but I'll let you lead that. But I just want to go back. Yeah. Sorry. Go ahead.

Josh 00:08:01  No, go. Go ahead and finish that up.

Sreenath 00:08:03  I just want to go back and say I think the reaction to this is too much data. It's daunting, is normal. I again strongly recommend that's not where you need to start. You just start from what do you want to know or what is painful today in the business to monitor? Let's get that list and then we can go back and say, where can we find these answers? Some answers comes from Amazon Marketing Cloud. Some answers can come from the stream data, but that roadmap is perhaps the most critical thing in executing a data strategy.

Sreenath 00:08:29  Yeah, I.

Josh 00:08:30  Love that you really simplified things to say, hey, you know, there is a lot of data out there. Don't get overwhelmed by the data. Start with the basics. What questions are you trying to answer? So here's what I would love to do. And yes, I do want to get specific with these questions. But the more questions you can give or like ideas that you can give to our sellers to say, like, these are the type of questions you could or you could or should be considering asking. To be able to find the answers in the data and maybe even work in some case studies to say like because we found the answer to X, Y, and z, we now were able to make this action that we were able to grow sales or recover sales, whatever it may be.

Sreenath 00:09:04  Makes sense. So I'm happy to dive into each of the buckets. Right. the operational, bucket is I mean, I'll start very simple, like product level acres and tacos.

Sreenath 00:09:14  I'm assuming that your audience is familiar with the cost in tacos definitions, but just basically bringing organic sales and ad sales all together and have a clear visibility of ACOs. Tacos, right. Here's why this is important. You see, many times where you could be spending up on ads on products. A cost is flat, maybe even looks better, but your top line is hurting, right? Or top line is flat. So just having a clear, consistent view at an individual product level of across tacos, advertising or organic sales in one place. As simple as this sounds, most people don't have. That's a great example. Another example right Profitability is a fundamental question. Every seller has. The gap I see all the time is, for example, between advertising and profitability. It is especially true if you are a seller that where you have a team managing ads, it could be an internal team or an agency that is a little bit disconnected from the operational aspect of the business. Your item level profitability does not align with your ad spend, right? So unless you bring these together in a view, you're not going to know it.

Sreenath 00:10:13  So I think profitability by product is another example that you want to track on a very regular basis. So these are all things that just explain what happened. Right. another example I see is like, do you know, I keep going to ad spend because we see a lot of people, like a big chunk of time is spent in ads when it comes to growth. So I have a lot of these examples tying inventory levels and ad spend. Okay. Are you spending money on products that you know may be low on inventory, or are you spending more on products that have enough inventory, like why are you doing that? These are questions you can ask if you tied ad metrics and inventory metrics together. I don't know if you picked up the theme here, which is getting ad metrics by themselves is not hard. You can go to the ad console. You probably are pulling in the data. You have reports. Getting organic stuff is not hard. You could get that from sellers. I always believe that when you bring two data sets together that come from different places, you unlock some magic that otherwise you don't, you don't have.

Sreenath 00:11:05  So that's where inventory and ads, profitability and ads, organic sales and ads. Right. So I hope the theme is clear. So those all fall under the operational bucket right. Yeah I think you've.

Josh 00:11:15  And I just want to double down on that to say how important those those questions are to be able to answer. Because if you I think you made a great example here with, let's say you're running out of stock on a product and it's not going to be back in stock for another week or maybe two. Right. Why would you spend more money? Right. It's like, hey, let's let's our ACOs is great. Let's spend more money. You're only losing more money to go out of stock even faster, right? Those are some of the things that we've made for with our within our own team, because we do kind of like pair the two data sets together if we know we're going out of stock and we know we're going to have to rerank this product anyways when we come back into stock, because it's out for two weeks, three weeks, four weeks, whatever it is, then that means I should pull back on the ads now.

Josh 00:11:55  Might as well be profitable. But I also love another thing that you touched on too is like how often the ad metrics look great. you know, hey, the ACOs is looking really good and consistent taco's it's fluctuating by a couple percentage points, but if you really look at it, you're just eating into your own organic rankings exactly right. Or your organic sales. But if you pull back maybe just a little bit on the ads, you see that organic percentage bump back up and you're like, okay, because I do believe there's some cannibalization that goes on. If you're just constantly advertising, you want that top of search placement. and you're also, you know, ranked in the top five organically. You're getting a lot of people that just click that first thing. And there's there's a here's the thing about Amazon advertising. You ask 100 people about their strategy. You're going to get a hundred different answers. Right? So, but at the end of the day, having that as part of your strategy to say guys like Amazon is for is really focused right now to capture more of your money.

Josh 00:12:48  So there's going to be more ad placements on that on that page. So you've got to get really good at being able to actually identify and make sure you're making a profit at the profit skew level, which you mentioned. And I do think that most sellers overlook that they're not pairing the few data sets together. Srinath, do you have any other examples or maybe case studies in this kind of like operational bucket of insights that you've seen people find and changes that they made to their strategy?

Sreenath 00:13:11  I think the most obvious one is like this happens a lot when you again, I said this before, which is you've got a separation of ad execution and retail, oversight. the especially like let's say you have an agency running your ads and they're doing their thing and, and for the right reasons, optimizing on a cost or whatever the metric is. The thing is, every time and there's a number of examples of this, but every time you brought a lens of profitability into the product, in fact, what I recommend actually is, there's a cost and there's Roas, right? Return on investment.

Sreenath 00:13:44  It'll be great to It'll be super important to have a profit on ad spend metric, right? it has altered the level of spend you will allocate to products, and this has happened multiple times. So, so that, you know, you know, in multiple cases, you have certain set of products on which ad spend was allocated. But when you bring the profit lens, that allocation has shifted. Right. so that's one example. Inventory is a common one, right. This is and there is some issues depending on your I assume all the audiences are as sellers. Is that is that fair. Correct. Yeah. Right. So so that's less of a challenge. There's enough data sets there where, you know, just proactively reallocating dollars to products that are not running out of stock, has definitely resulted in, you know, certainly better deployment now, but lesser pain later when you're actually out of stock. So that's another example. also the we have right now we are dealing with a specific example where again, I go back to ad metrics are looking great, great.

Sreenath 00:14:37  But top line is not right. And when we dug in and looked at numbers ad performance is up 5,060% at the same cost as before. Top line is down 2,030%, and we keep digging further and learn that something shifted about their, nodes or categories that they are listed in several months back. that probably has hurt their organic volume. So but again, bringing in this case, it was bringing bce's ad performance and organic sales together to at least establish, hey, there's a correlation here about something about the categories of products changing and hence that's impacting your organic sales. But that's only possible when you bring these three together. Otherwise you won't see. Yeah.

Josh 00:15:12  I think those those are great examples. So I know we may jump the gun here a little bit, but before we move into like the diagnostic, questions would be how in the world do you pair all these data sets together then.

Sreenath 00:15:22  Yeah, no, I think so there is only one way to do it, right, technically speaking, which is you collect all the data, you organize it well, and you have dashboards or views to sit on top of it so that as an end consumer, you're looking at it in a beautiful chart, or you have an analyst that can go get the data and analyze in a spreadsheet that they have.

Sreenath 00:15:41  So those three have to have and a collect, organize and visualize. Okay. Now we are in the business of having built a product that does throw three pieces. This is a plug for ourselves here. But I'm just saying that like a technology company like ours is building tech, so you don't have to worry about those three. That's what we have done, because building these things from scratch is not core to your business. You'll spend a lot of time and money, and I've known even agencies as well as some brands that have done that. So I would avoid spending money on doing those three things. I would make sure you, you know, partner with someone that solves those three pieces. You focus on the fourth piece, which is what questions matter to you, right? Because if you have that there is enough service providers or companies, including ourselves. Well, we'll get the bottom three pieces nailed and help you accelerate the topics. Even if you have an internal team, analysts or whoever. Right? You don't want them focusing on those bottom three pieces, which is collection better organization.

Sreenath 00:16:30  Those are all those can take a lot of investment. So have them work with someone that can get that in order. And by the way, we are at a place where that can be in order within days. Right. and I'm talking about like collection not just from seller central seller central sponsor ads, DSP, Amazon marketing, Cloud stream, all that. Pipes are already built. Data is already coming in. There's a couple more things you have to do as a brand. Now you unlock and start unlocking insights, right? so I would say don't take that project on. Yeah, yeah. But focus on rapidly extracting value, which starts with you knowing what questions you want to answer. Yep.

Josh 00:16:59  Makes a lot of sense. So I want to go back I want to double down into intent wise, then a little bit here and ask you the question, like what? What type of data are you bringing in. So you're getting Amazon Marketing Cloud. You're getting like I would imagine the the brand reports that you can download through Seller Central and advertising.

Josh 00:17:14  are you factoring in organic ranking? Are you also getting a snapshot of the BSR at the category level, like what all data sets, you know.

Sreenath 00:17:23  Do you have. So yeah, so you're right. There's tons of data. So I break it down into five buckets just so that we can wrap our head around this. One is the advertising data and AI in that for sponsored ads in DSP, if you're doing DSP ads, just pulling down all that data, right. two is retail data, which is seller central data or rapid retail hourly data, which is all the retail metrics. and if you get the go get use the catalog API, you can get BSR, you can, you know, you can get price data. So all the retail metrics, is, is the second bucket. The third bucket is what I call, shopper insights, which is where from Amazon Marketing Cloud, you can start to pull down certain interesting things like, hey, which products, drive the most new to brand acquisitions for me? Oh, it turns out it's not the same for all products.

Sreenath 00:18:05  So these ten products drive a lot more new brand acquisitions. I need to treat them differently so you can get those types of insights from Amazon marketing called the competitive Insights, which is like, where am I ranking organically? Who has what badge? That's the other. I call it competitive insights that include yours. But you should know what exactly what keywords and what products you have badges on so you can defend them. Right? You should know that. Right. And how is that changing? So that's the fourth piece. And the last piece is actually things that are specific to you, the seller. Right. I'm talking about there's a way you categorize products, right. You may want to know acres and tacos by those categories not individual license. Right So that mapping only you know Amazon doesn't know that right. So that's your data being ingested into this mix. unit economics your landed costs or you know, cost data on products which you need to figure out profitability. That's an input from you. Yeah.

Sreenath 00:18:51  Right. So these five things ads, retail operations, competitive data, shopper insights from Amazon Marketing Cloud and the sellers own specific metadata. These five have to come together. Now a lot of magic can start to happen. And I go back to those three buckets of questions. There's a lot of questions you can start to answer. By the way, collection is one thing. We have built out APIs. We connect to them, we extract them. That's the easy part. What's hard is that those APIs break. There's data quality issues, right. You know, we raise a lot of tickets with Amazon, but you need to be on top of this to maintain data quality high. That's the dirty part of this, right. Very unsexy. But you know, you know this, right? Every other day, like an API upgrade happens or some report is late or some new metric has been defined that we don't understand. So there's all this stuff that goes into collecting. Right. So that's one component.

Sreenath 00:19:35  The other component I'll say is I go back to the. There's this data explosion. What the heck do we do? This is where better organization matters. If today you want a single view at an Asian level that has advertising performance, organic performance, inventory levels, perhaps some review insights all in one view, like a 360 degree product view. It is extremely painful to construct. Why? Because this data sits in 20 2530 different reports. So we spend a lot of time not just collecting the data. And our customers have access to the raw data, but we also better organize into a slightly different e-commerce friendly data model where you'll have a single view bias in. You get all these metrics in one place so that your analyst, whether it's yours or ours, when they build this dashboard, they're not touching 20 or 30 different tables, trying to figure out what is where. It's all in one view. Just build a nice chart, pie chart, bar chart, whatever. And we have a bunch of templates for that.

Sreenath 00:20:24  So that data organization completely, underestimated in the space is very important. If you as a business owner wants to get insights very quickly. Right. Otherwise, or you will have a question, it will take three months for someone to get you an answer. That better organization of data is so critical. And I'll just add one more example for complexity. Now, if you are a seller operating in five different marketplaces, right, you're looking at cost. You want all these currencies to be normalized to your dollars, right? To understand what's going on, you need currency conversion data somewhere, right? Well, that's part of our data field. So that's what I mean by better organization and enrichment of data. otherwise it things slows a lot of things down downstream. but yeah. So collection and better organizations. I hope I'm a long winded way of answering your question. I hope that answers your question.

Josh 00:21:05  Yeah. No, it sounds super helpful. I mean, that's that's I think like the the missing crux of all of this is that you need to have something that's collecting the data really well, as you mentioned.

Josh 00:21:13  And you gave the five really core pieces of data you've got to bring in, and then you've got to have something that's visualizing it so that you can extract the insights. And so anyways, thanks for for diving down into that. Let's go back into the diagnostic questions then. So give me some specific examples. What are diagnostic questions that I should be asking and trying to find answers to in the data.

Sreenath 00:21:30  Yeah I mean I have a, you know, a funny way to frame this. Let's just say you're operating a cellar account. Acres went up 25% for whatever reason, right over the last one week. How long do you or your team or your agency partner takes to isolate? The reason I say it should be 15 minutes. Okay. I don't think it is 15 minutes. In most places where I'm going at this is this is what I mean by diagnostics, right? When things go wrong. Do you have a standard operating procedure to go figure out what may be causing it? No, it's it's very difficult to understand exact causality.

Sreenath 00:22:06  Right. But it is not that difficult to understand correlation. Okay. Hey, maybe you lost a badge that you had before, right? Maybe a competitor is aggressively bidding on your brand term. it's Prime Day, and you're not able to keep your sponsored brand position at the top because somebody is willing to spend $45 a click, and yet, you know, and you can't defend it at that. So the point is, whenever a problem happens, the problem and you know the problems, the sellers know the problems better than anybody else. Like, hey, my margin sucks. my cost has gone through the roof. you know, whatever those problems are, and I can list a couple that show up a lot. Do you have a standard operating procedure to nail the diagnostics? So I'll take a simple example. Right. I go back to the exam. You're spending a hundred grand a month, and your cost just shot up by 25% last week. You should have a process that says okay, very quickly okay, I have account level.

Sreenath 00:22:56  It courses up 25%. Let me let me break this down by portfolios. What portfolio or what product group is causing this or what campaign type is causing it, or what campaign is causing it? What specific products are causing it? What keywords are causing it? Is it a non brand versus brand problem? So there's about 8 to 9 dimensions with which you want to look at the business and quickly isolate the problem areas. Right. That's what I mean by diagnostics. What went wrong. Why is my specific metric up or down. And then do you have a SOPs in place to diagnose to troubleshoot that. and so I'd say simple things like when your order growth is hurting. That's why right when a cost is up, why? Let's just start with those two questions and ask yourself, do you have a process to get to your answers within? I say 15, but let's say 30 minutes. If not, I would say work with your internal team or agency partner to have a process in place to solve this.

Sreenath 00:23:43  Otherwise, what happens is you just lose a lot of valuable time, right? Like how often? I'm sure you've seen this. How often do people sit in, like, just figure out what the heck is going wrong and you can't get to at least points of correlation. Forget causality. So I hope that helps. Yeah, I love that.

Josh 00:23:58  I think those are fantastic examples. And I think, again, being able to even learn when that 25% increase that happens to your cost, right? Let alone being able to to troubleshoot it. Like you've got to be able to see your metrics on a week over week basis. Right. And that's something that our team, we've spent a lot of time. We actually built out a lot of our tools in power BI and dashboards to be able to visualize a lot of this, and we look at it on a daily and weekly basis to be able to say, like, how quickly are changes happening? And, you know, if we activate this type of ad campaign, what happens, right, good or bad, and make changes accordingly So I love that.

Sreenath 00:24:32  And you and you said something interesting. This is where the first and the second bucket of questions intersect, right? Operational tells you my cost is up, but the diagnostic process tells you at least gets you closer to y Y Y is always hard, but you can at least figure out, hey, it's because, you know, it's correlated with you spending more money on sponsored brand video last week on these five products, right? Whatever the correlation is, right, you want to be able to get to it very quickly. so I would I would say back to the roadmap frame, the 2 or 3 questions you struggle with all the time, quickly invest in figuring out a diagnostics process that can get you the answer very quickly. Yeah. In fact, what we have done in our sorry, I don't plug my, So just a quick note, what we have done in our ad product, right? We literally introduced a feature that says what changed? And for this reason, it's funny. Like there's a lot of conversation that happens about ad management, PPC management.

Sreenath 00:25:23  Here's the thing. I see 70 to 75% of PPC management is actually analyzing. What happened is it's not even moving the dials. Right. So how do you shrink that time. So we introduce this capability where you can say hey compare last week or prior week or last week or same week last year, come back and tell me the things that drove the biggest change and we assign an impact score and say, hey, here's one, here's two portfolios and the five products and the seven keywords you should pay attention to with all the metrics, because that's the algorithm we built. But again, that is an example of why, you know, where we have invested in making diagnostics easy and simple, because we just spent inordinate amount of time in that bucket and it's not efficient.

Josh 00:25:59  Yeah. That's that's brilliant. And yes, you can go ahead and plug it, plug yourself away. Because I think you're solving the pain point that entrepreneurs don't want to build that out. Right. They don't want to have to think through what are all the questions.

Josh 00:26:10  How do I correlate this with that. So that's great that you guys, it's not only just like a visualization. Like you guys have that like, hey, you're you're raising red flags and saying like take a look at this, this this. Yeah.

Sreenath 00:26:20  It's it's a it's especially during events like Prime Time day is over. So I've so many requests I've seen in the system where they've said like two days this year of Prime Day versus last year Do the compare. Show me all the changes and what drove the difference. So the most frequently used, let's call it request in the last, last month is exactly that.

Josh 00:26:39  Okay, so here's another question. Before we get to our third and final, competitive, strategic questions, which is going to be are you using AI as part of your guys's software to be able to identify all of that data?

Sreenath 00:26:51  And it's a great question, I think. Look, I've lived and breathed this AI stuff for some time in my career. So here's I just want to clarify the point of view, right.

Sreenath 00:27:00  If you look at the software space, there is no PowerPoint or no pitch today that does not have the two letters AI. Right? And by the way, it has been the case for the last five, seven years. If you if you remember right. AD management. So any software right. What has happened is a lot of machine learning techniques which like if you think about regression analysis, right, which has existed for the longest time, where you know, in any scenario where multiple factors influence an outcome and you have a ton of historic data, you can put it in a machine learning model to let it find the pattern. And now you can let it predict the future, right? So that has existed for a very, very long time. And simple examples are like weather forecasting. I know it's not perfect, but weather forecasting has been around for the longest time. That's not new, right? So leveraging machine learning in bid management is old. It's table stakes. Right. And we've been doing this for a long time.

Sreenath 00:27:50  Right. What we today call refer to AI when we all say AI is something that ChatGPT introduced about 20 months ago, which is generative AI. And the simple way to for us to, I think for at least the way I articulated the change is we have had AI and machine learning techniques on numeric data and structured data for a very long time. That's not new at all. What we have not had is that level of sophistication on unstructured data like text, like images and video and even that, right? Like, remember, we all are on our mobile phones. We are texting all the time. Notice those auto suggested keywords. And these days I rarely type a word. The auto suggests I have gotten better and better and better. That is machine learning and I getting better at understanding unstructured information like text. So I just want you ask me a question about AI. I just want to break these two apart and say, look, AI is always existed on numeric data. Yes, we use it in our bid management solution.

Sreenath 00:28:41  Yes, we use it in part in assigning an impact score on the what changed analysis. So that's one kind that's that's that's existing. What's new is AI on unstructured data. Because we are talking about data. I'm going to just call it a couple of 2 or 3 use cases where we you, we we see we see AI working beautifully. And by the way, there's just been too much noise and fluff around this. and in fact, there is a, there is a individual by the name Amara. There's a law named after him. It's called Amara's law. You can look him up. It says, all tech innovations are overestimated in the near term and completely underestimated in the wrong, long term. And that's exactly what's happening with generative AI. Too much noise, too much fluff has happened over the 18 months now. Noise has settled down. But I really do think there's like valuable applications of it. For example, today, internally for our own business We take texts in live chat of our own software, send it through a generative AI model, and ask questions like, hey, which conversation did not go well and why? We'll come back and tell you that in the absence of AI, we would just it's like trash, right? The digital exhaust, we all trash.

Sreenath 00:29:41  We don't use it. But Gen I think is helping us understand if if you're a seller, right. If you have if you take calls from customers for support, if you have live chats, if you have review content, any unstructured content, I can unlock insights that you could otherwise. you were just throwing it away before, right? So sorry.

Josh 00:29:59  I love that.

Josh 00:30:00  We could dive into that whole AI rabbit hole. And I'd be very interested to get your predictions on the future there, but let's bring it home here. Going back to our strategic questions now, that's kind of our final third bucket. Give me some specific examples of strategic questions we should be finding answers to in the data.

Sreenath 00:30:15  Yeah. So the most basic question and I see this all the time. Most brands and sellers have a hard time understanding who their competition is right because of the scale. Like if I'm selling 500 products, who is your competition? It's at the product level. It's not at a brand level, it's at the product level.

Sreenath 00:30:31  So I think simple question who do I really compete with? Let's start there. Right. Once you know that let's bring it back to operational tracking on those. I want to see my BSR against those seven products. I want to see badging. I want to see share of voice and ads on those products. Just so I understand, how am I doing relative to those guys. Right. That's an example. Another strategic example understanding repeat purchase behavior. Or I go back to Prime Day. You did Prime Day, deals. you want to be able to track the cohort of users that bought your products, your products and deals on Prime Day and watch what they do. Do they come back and buy again? Has it been worth it for you? Right. That's another example of a question, by the way. You can answer this without Amazon Marketing Cloud, but because Amazon Marketing Cloud is there, you could actually answer this. You could literally create a pool of people that bought your products on Prime Day and watch what they do in terms of purchase behavior over the next 12 months.

Sreenath 00:31:24  So again. And this ties into repeat purchase behavior. Lifetime value. bring it back to subscribe and save is another situation where you can actually understand, you know, what is really happening or enough people opting in. How many customers do you have that have not opted into subscribe and save? Right. So these are all these fall into these strategic bucket where ultimately what you're trying to do is think about it from an end customer's lens and differentiate from competition. Expand share of wallet on that customer that. So to me those are examples of questions that fall into the strategic bucket.

Josh 00:31:53  Yeah I love I love that. And I think especially if you have a consumable product where there is going to be repeat purchases, I think what you just touched on is extremely valuable because how in the world, like for example, I know that for most Shopify stores that are if you're selling like a supplement or some type of subscription, their whole premise is I'm going to go in the red generating ads, right? I've got all these ads, I might be paying $100 customer acquisition cost, but I break even after two months because of the subscription.

Josh 00:32:26  That's the entire model. And then they just have to bridge the gap between outweighing the cash between today and two months from now when I break even. And this is kind of a this has been a long established process in the DTC game, but this is fairly new in the Amazon game because we now have access to this data. And so I would just double down on like anybody that has anything that's consumable, subscribe and save like repeat purchases. Like if you're not doing this, I promise you you are at a massive disadvantage because sellers like myself or those that have this understanding, they're going to be willing to go in the red for a month or two knowing that, like I'm just gobbling up more and more market share and this snowball is going to get bigger and bigger and bigger over time. I'm buying the customers. I'm willing to go at a loss during Prime Day, because it leads to a massive increase in the number of subscribed and saves. It's not profitable up front, but it is three months down the road, so I think that's so important 100%.

Sreenath 00:33:15  And I would just again, to frame this very simply, I would just ask the question, do you track retention performance? If you don't, let's start that process. We don't have to. Over complicated. Let's start with some very simple questions. What is repeat purchase behavior? What is my lifetime value now? sellers people and seller central. There are some FBI reports that have, anonymized buyer IDs with which you can get to some understanding of lifetime value and repeat purchase behavior. Problem with that data set is there is no, connection with ads. You have no idea what's coming from ads versus not in that data set. It's still useful. Gets you started. Amazon Marketing Cloud, on the other hand, can pull those two pieces together, and you can start to see the impact of ads on those metrics also. So yeah. And the strategic bucket, like continuing to build your muscle around measuring retention performance is going to be so crucial. And to your point, it drives so many dishes upfront decisions.

Sreenath 00:34:05  What a cost goal do you set on those products. Right. How would you answer that without knowing this? Yeah.

Josh 00:34:10  So I love this. This has been a fantastic conversation. Srinath. as we wrap up, I'd love to leave the audience with three actionable takeaways from every episode. Here are the three that I noted, but you let me know if I'm missing anything. So action item number one is have a roadmap of questions that you want to be asking or finding answers to in the data. I think it is so important for those that have PPC agencies that are operating in their own silo. They don't know what your inventory looks like. They don't know what your profitability looks like for the business. And they also probably aren't tracking your organic ranking. BSR Competitive Insights. And so it is extremely important for you to have a roadmap to say, what are the questions I need to have answered and how am I going to get those questions answered. So action item number one is just simply having a roadmap ten questions that you want to find answers to.

Josh 00:34:56  Then action item number two is if you don't have a way to visualize all of the data to see how your inventory stacks up with your ad performance, to see how it stacks up with your organic rankings and competitive insights and profitability, you need to leverage a tool whether it's something you build internally because you're a data wizard and can build out Tableau tables and SQL and all that stuff on power BI, or you leverage a tool such as intent wise that's able to help get to that level of data. And then last but not least, my third action item is are you actually leveraging this data? Do you have processes in your business of somebody, even if it's yourself to begin with, that is looking at the data that is trying to find correlations and then utilizing that data to then make additional strategic decisions in your business because the Amazon is becoming a more competitive. That's not news to anybody. But guess what? The people that are going to win over the next 5 to 10 years are going to be the sophisticated brands, period.

Josh 00:35:50  And so if you're not and you're just doing the stuff that you're a solo entrepreneur and you were able to make a quick buck and you got to seven figures back in 2016 and you're just kind of rolling along. Now. I promise you, if you do not implement these things, you will not be surviving in this new Amazon world because the purge is in action. And over the next 5 to 10 years, a lot of the small mom pop shops that aren't doing the sophisticated stuff, you ain't going to be making millions of dollars anymore. You'll still make a little bit of money, but it's not going to be millions like it's been today. Srinath. Anything else you would add to this?

Sreenath 00:36:18  No, I think you I think you nailed it. I would just add that, like, there's another phenomenon that happens, especially if you're kind of slightly scaled in your scaling, trying to scale up further is, you end up working with a bunch of service providers to do a different set of things right in the process.

Sreenath 00:36:32  What happens is you don't pay attention to owning your own data, right? That is very critical. Like, hey, don't put yourself in a position where, you know, you switch a tool or you change a service provider, you lose data. So that's another like caution, I would say, just make sure you are on a path to own your data all the time, no matter who your service providers are.

Josh 00:36:54  Great insights Srinath. This has been a lot of fun. Here's the the last part of our podcast. I asked the final three questions. So number one, what's been the most influential book that you've read and why?

Sreenath 00:37:04  Great question. I for me, I mean, I do a reasonable amount of reading. I think the one that has certainly influenced me in this journey as an entrepreneur myself is, I would say, made to stick. It's a really tiny short book by Chip and Dan Heath I'll tell you why it was important, right? It's, you know, I'm a entrepreneur that came from the background of being in the weeds on data and building.

Sreenath 00:37:24  That's where I came from. I'm not a sales guy. I was not a marketing guy. Right? I was none of those things. And so one of the things that book taught me is the importance of messaging in messaging that resonates with your prospects or shoppers or whoever, customers, whoever. And they put a framework around how to think about messaging. Right. so that's what I really love. It's kind of really changed the way I talk about stuff now. I know I can get a lot better, but I just feel like that was a fundamental shift for me. I recommend that book for everybody. In fact, I've bought and given that off as gifts to kids and friends. And so many times I need to become an affiliate for those guys. So yeah.

Josh 00:38:00  That's fantastic. Made to stick. Great recommendation.

Sreenath 00:38:02  Tiny book. You'll finish it in an afternoon.

Josh 00:38:05  That's awesome. Next question. What is your favorite AI tool? I know you have a lot of opinions on AI and lots of information on AI, but what's your favorite AI tool and how are you using it?

Sreenath 00:38:14  Yeah.

Sreenath 00:38:16  I don't know that I have a favorite, but I'll tell you what I use a lot. I use ChatGPT all the time. I use it to do research. You know, I had some Olympics going on, and I was having a debate with friends, so I asked ChatGPT to plot medal tally for the last 50 years by a few countries. And there it is. It has become this assistant that, it just really like I wanted to, you know, in our space, there's a lot of acquisitions. I want to understand, like top five agencies, they're all public companies. I just want to know revenue, EBITDA, multiples of valuation on EBITDA and top line and all that. I just for sentences. This thing came back with a beautiful table and charts. So it's become my research personal assistant.

Josh 00:38:52  I love that.

Josh 00:38:53  And I think like using it even more so than Google. Right. Shifting that to be that's your new Google.

Sreenath 00:38:58  Yeah it is and it's just and you can go back and forth like oh I don't like that chart.

Sreenath 00:39:00  I change these colors to blue and red and you can go back and forth. It's really like someone working for you, right? and I also do it for internal purposes. Like I take a live conversation from my intercom and I want to just know some details. I put it in and say, hey, find these things for me, and it comes back with outputs that are super interesting.

Josh 00:39:16  I love that.

Josh 00:39:17  All right. Final question here. Srinath is who is somebody that you admire or respect the most in the e-commerce space that other people should be following and why?

Sreenath 00:39:24  Oh, there's a lot of people. But I guess if I'm forced to name one, I'd say I mean, I'm a big fan of, Rick Watson. I don't know if you follow him, but, you know, I think he does. it's for me, it's very educational. Like, he's in the weeds on performance of companies and earnings calls of the Amazons and the Walmart. And he breaks this down at a level of detail that, frankly, I can't find in very many other places.

Sreenath 00:39:45  So it's like a source of education for me. so I would follow him.

Josh 00:39:50  That is a great recommendation. This has been an awesome episode. If people want to learn more, they want to follow you. Srinath. Learn more about content wise. Where should they go?

Sreenath 00:39:58  in Wacom, I'm very prolific in terms of putting out content that's largely educational on LinkedIn, so just follow me there or srinath.com if you want to message me directly.

Josh 00:40:08  Awesome. Well, hey.

Josh 00:40:09  Thank you so much for your time today and jumping on the show.

Sreenath 00:40:12  Well thank you Josh, this has been this has been a blast.