E522:🎙️MAXIMIZING ECOMMERCE CUSTOMER VALUE! | VALENTIN RADU - OMNICONVERT | THE ECOMMERCE EDGE Podcast

This is the E Commerce Edge podcast with your host, Jason Greenwood. Hello everybody, and welcome to another episode of the Ecommerce Edge podcast with me, your host, Jason Greenwood. I have another incredible guest to introduce to you today and he is someone that I have been friends with for a very long time in this industry. We met quite a few years ago via LinkedIn, funnily enough. And it is my absolute pleasure to welcome Valentin Radu from OmniConvert to the podcast. Welcome, Valentin.

Hi, Jason. And hi everyone. I want to congratulate that you are making yourselves better and improving your businesses by listening to this podcast.

Thank you very much, Valentin. I really appreciate that. And look, it's been, you've been in this space for a really long time, this e commerce space, and you have been building and fighting and scrap, scrapping with OmniConvert to build it up into the software business that it is today for nearly 13 years now. So this is not your first rodeo by any stretch of the imagination. But for those in my audience who maybe aren't connected with you on LinkedIn or haven't been following you as long as I have, what drew you into this space of E commerce and particularly trying to build software that helps e commerce brands do better and be better for their customers?

Yeah, so I've started as an entrepreneur back in 2000 and my third company was an e commerce company, so we were selling here in Romania and we, we've, we did a lot of things and I was struggling to improve my conversion rate. It was flat. Google Analytics, that old version, it was the conversion rate was that blue line which was flat and I wasn't able to bring enough money home. Even though we've got a lot of revenue, so like millions in revenue, we couldn't make profitability happen. And that led me to improve the conversion rate and to understand the mechanics of growth. What other levers of can I use besides traffic and paying for. For traffic? And I found that conversion rate was crucially important and customer lifetime value was crucially important. So after one year of improvements and running a B test and building our own software back in the days, because in 2011 there weren't too many options and it was only Oracle Maximizer and whatever, which was pretty expensive. And we've said let's build it and I was on the back of our developers forcing them to change the website so that we can squeeze more conversions out of the website. And at some point we've said, let's build something for this mad entrepreneur. That's what they've said let's build him something to change the website. So that was the ancestor of our current technology. And after one year of intense efforts we've managed to improve the conversion rate by 60% which was amazing for us. We, we've made all sorts of things like geolocation testimonials, we've made fake loading progress bar so that we can show the price afterwards. We've made the, we were selling car insurance. So basically we've made the website show the exact car model that you had. So we've played with a lot of experiments and once I've seen the impact I've said, you know what, I could go into this adventure because I had six years in that company back in the days and I've said let's focus software, building software and helping other e commerce companies to make progress. And that's how we've built OmniConvert. And we've started initially as a conversion rate optimization platform. We've specialized in E commerce and then we've launched other products in the around e commerce and around helping e commerce companies make progress like a segmentation tool and a customer analytics tool and a customer experience tool. So nowadays we have four different products, we have clients all over the world in 85 countries and basically that's where we are. But it's still a long road ahead so there is a lot to cover.

Yeah. And look, I think what's admirable about you is that you have really made an effort to create an entirely new category of software. And anytime you go into the category creation process, you're in for a hard road, you're in for a challenge because obviously if you tail off of existing categories of software that's out there, it's easier because people know where to put you mentally. They have a mental model of where to put you in the commerce stack. Whereas if you're creating a brand new category or categories of software, that's a much harder road. But the upside of that is that if you can carve out a strength in this new category of software that you are creating and you create a sense of uniqueness in what you do, then automatically that creates a level of a moat around your business that you can't really get any other way. And so before we get into specifically what OmniConvert is and does, I'd like to talk a little bit more about what you've coined a new terminology to go along with the new category of software that you've created called cvo Customer Value optimization. And this is not your traditional sort of clv, aov, CLV conversation. It goes beyond that. So maybe help me and the audience understand a little bit more about how you decided, hey, along with a new category of software, I need to create some new concepts and that the industry can wrap their head around to understand why we built what we built in the first.

Yeah, for sure. So it all started out of the struggle. Like any proper progress on this planet, it must be preceded by struggle. And we were struggling to grow in the CRO category and our clients were struggling to, to make profit happen. And it allows the, this struggle led to a lot of research and I think research is one of the least, the most important and undervalued activities in the whole marketing realm. Right. Because we, it's easy to pay money for ads and to think that you're growing, but it's, it's even easier when you think about the resources to do customer research and understand what the customers are all about. So our customers were after profit, not after conversion rate. And that difference made a whole impact in my head because we, we had one of our customers selling shoes, online shoes, and they, they've got, they, they've got a lot of competition. They were the market leader here in Central and Eastern Europe and they suddenly couldn't make the ends meet because there were a lot of new German players in the market and they couldn't make profit happen. And they thought, hey, let's do a 2 million euro TV campaign. And me and the E commerce manager and the CMO were like, what? How can we invest such a lot of money into a TV campaign? What's the reasoning behind that? Yeah, because we have competitors, whatever. And I was an advocate of the research and we've said, let's do an analysis of the customer behavior and see if your customers are migrating to the competition first. And let's see if you have a retention problem or you have an acquisition problem, because if you don't know what to fix it, you can easily spray and pray. And they've said okay. And we applied the RFM methodology. RFM standing for recency, frequency and monetary value. We've segmented their customers and we've been able to see that even though they thought that they had an external problem, they also had an internal problem, which meant they launched brands like private label brands coming from China. They've launched these shoes branded, like Italian amazing shoes, but they weren't that good. So basically out of their best customers that churned, most of them had this freaking brands and it wasn't easy to see it from a BI perspective because those new brands, they sold amazing and they thought that these are good products. But the reality was that they were churning their customers in for having a better margin. And that was one insight. A second insight was that while they were investing a lot into all sorts of cities, their best customers, which we call the soulmates according to the RFM were coming from cities with no shopping malls, like cities with under 50,000 inhabitants. And that CLV was there and the customer acquisition cost was here, which was pure nirvana for them. And they doubled down on this. So we've met a lot of initiatives towards increasing their profitability by driving not with the conversion rate, but with the customer lifetime value. And that's, that was the moment when I've said, you know what, there is no process, proper methodology around increasing the customer lifetime value in E Commerce. So we've continued to work with that client. We improved their lifetime value by 30% which was like pure profit, right? Lifetime value means profit per customer in, in the future. And they were that happy. And we've said, okay, let's build the tools around this process around customer value optimization. So basically after that I wrote this book. It was quite an endeavor for me to outline the process. We've worked with dozens of companies since then and I still think it's an undervalued activity. It's bringing so much impact. But not many leaders are aware about this lever.

And I think you make a very good point which is that oftentimes, especially in mid size or larger, let's say $100 million plus E commerce brands, they oftentimes will have a conversion rate optimization specialist, they might have a retention specialist, they might have an outbound specialist, a performance like they've got these marketing specialists within the business. They oftentimes are very siloed in their very niche in the roles that they have within the business and they're laser focused on one or two things and they go deep in that without having enough breadth to understand the other influences on conversion. And I'll just give you one example and I'd love to get your thoughts on this. When I was still working brand side. What we we realized through BI was that whenever we had a situation where say for example our top 20% most popular products were out of stock, whenever those items were out of stock, it didn't just dramatically impact the sales of those items, it had a massive impact on multi cart abandonment and multi product cart abandonment. And what we realized through this research was the fact that our average Cart size was like 2.5 items, say for example. And what we realized was is that 9 times out of 10 a customer would buy one one of those top 20% items along with their other favorite items, they would add it to the cart. And one of the reasons why the items were 2.5 items was because generally at about 2.5 items, considering the unit value of most of the items that we sold, that would get them into free shipping threshold, that would get them into a much better loyalty points award level. There was so many benefits to being at around that AOV level that we were targeting that, that anytime somebody could not achieve that, they wouldn't just, they wouldn't just remove that one item from the cart that was out of stock, they would abandon the entire cart and they would move that entire cart to say, for example, a competitor so that they could get the free shipping, so they could get the loyalty, all that sort of stuff. And so what we found was that our average percentage of in stock rate had a, was one of the biggest single levers that we could pull to improve not only our conversion rate, but also a customer lifetime value. Now, oftentimes brands don't do that level of analytics and they certainly don't track the average percentage of their catalog that is in stock at any given point in time. On a rolling basis they don't. They'll look at individual items, but they won't look at it as a total percentage of the catalog that is in stock or out of stock. And I think that it's this level of deep research and analysis that brands must do to understand the psychological triggers that cause their customers to buy from them. And it's hard, like it's a hard thing to start looking at all those different levers. And as I understand it, this is exactly why you developed your platform, was to start to understand all of the influences to buyer behavior, not just okay, let's change the color of this button or let's change the positioning of this thing or this message that we have in cart or, or our, our personalization technology for cross sells and upsells and substitutions. I think from what I can tell, you are trying to get a much more holistic picture of why customers behave the way that they do. And then we can address the root causes of say for example, abandonment or why our CLV might be low, for example.

You are perfectly right, Jason. And I think this, this has a lot to do with the, let's say, openness of the leadership towards changing things as the companies go grow they get specialized tenured leaders which do what they've always did. But E commerce is evolving very quickly. At this moment we have large players investing a lot into their digital digital channels and that means the competition is higher. So they can't afford anymore to leave it to chance. It's not is it was three, four years ago back in the pandemic time.

Or even 10 years ago when it was way.

Yeah, that's one of the reasons why. And as you've said, the org chart is the problem. So they don't have someone responsible with the future profitability per customer, which is affected by a lot of things. It's affected, as you've said by the inventory, by the stock availability, it's affected by the marketing, it's affected by the customer experience, the delivery, fulfillment, all these departments together and they are having this siloed approach. And I think is the job of the CEO or the CFO to care about this and to come up with the game plan in order to improve the profitability. Because if you don't begin with this end in mind, why would you invest? Why would you shoot in the dark? Why would you invest 200 or 100 to acquire a customer if you don't know how to is the profitability? What is the CAC payback time? Yeah. And what is the if you don't track this CLV to CAC ratio on a year over year basis, how do you know how much you can invest? Because you can either over invest or under invest which is not a logic.

Thing to do either. Either way it's a bad thing.

Exactly. And I think that's the, that's causing the problem. And of course the solution is not easy. We've seen on our own skin that it's not easy because we demand companies to change. But not to change a single component or a tool is to change their mindset around what success looks like. Is it successful to convert more customer? Yes. But wouldn't be more successful to be more profitable? Yes, but how to change that? Because that means let's change the KPIs for the marketing manager, let's change the KPIs for the operations and whatever. Let's add, let's blend this with the other KPIs which is requiring change is always hard. And unfortunately the best companies that are getting a lot of impact from this endeavor are the largest and the largest companies are hard to change. So basically it's an uphill battle to change things. And that's why our sweet spot right now is not very large companies, very small companies are the mid sized companies which are agile enough to change things around and to challenge the leaders by baking this type of data driven customer centric approach into their growth mechanics.

And I'd like to talk to you about another scenario that I see all too often within merchant, let's just call it broadly merchant analytics that I think is oftentimes either missing altogether or they are not bringing it together in a meaningful way that then they can operationalize on the backside of it. Because analytics are great insights, are better actionable analytics that then you actually take action on and change. That's the holy grail. Right? And so I see that most businesses, they start down here in this quadrant which is gathering a bunch of data, but not having a meaningful way to pull insights out of the data that they're collecting. Then the next step is once they're starting to pull insights out of the data, do they actually take action to improve the KPIs or to structure their business in such a way that the analytics are showing improvement and that the insights are showing new things to be able to change over time and then they take action to make those changes. So for example, oftentimes within an E commerce brand, if they took, if they looked at two customers and they said okay, this customer spent, these two customers spend about the same amount of money with us every single year. They stay with us about the same length of time before churning. So in theory, if I looked at that, I would think that those two customers have about the same value to my business. So I might target cohorts like those two people with the same amount of the same amount of focus. Right? I might create for example, two lookalike audiences based on those two customers. And they might actually be in terms of net revenue or net profitability from those two customers, they might be wildly different. And based on things for example, like one customer might return products at twice the rate as the other product. One customer might contact customer service an average of two and a half times per order, whereas the other customer might Only contact us 0.5 times per order, whether it be pre purchase, post purchase, whatever the case may be. And so all customers aren't created equal, even if the revenue, even if the top line revenue and the top line retention rate looks identical. Right? And so we have this concept of cost to serve or cost to service. And many brands do not take into account cost to serve, return rates, et cetera. When they are starting to calculate the value of one customer to the brand versus another customer. If you're not taking into account all these facets, then you will never be able to understand the net value of a customer to your business. And therefore you might be spending money to go out and acquire customers that are super expensive to get revenue out of and super expensive to service when you could actually be going after the customers that, that are much cheaper to service and have a much better net value. And finally, I'm going to say one final thing about this. When you carve out your cohorts, for example, of super high return customers, that may not just be indicative that the customer is shitty, it may actually also indicate a problem either with those specific products. Meaning, okay, cool, maybe the quality is bad or maybe we need to have a better sizing guide for that specific product because the sizing guide is wrong and therefore people are oftentimes buy the wrong size. It may actually indicate a problem with the product, not just a problem with the customer. And so when I say brands don't dig deep enough into their analytics, these are the types of scenarios I'm referring to.

Yeah, I think you're totally right. And there are three, let's say free traps for companies which are focusing on customer lifetime value. One is the one that you've just said about the fact that they don't look at the total costs, so they don't look at the cost to serve and they thought, they think that it's all about how much they buy. Another thing which is happening is that they don't take into account the residual customer lifetime value. Maybe you have businesses where you have advocates, right? You have this type of customers which are using their social network, they are bringing you more customers due to the fact that they love your product and you can measure and you can get amplified by this type of customers. So even if you are an acquisition driven brand, you still need to look at lifetime value because you have customers which will promote your business. And if you focus on this getting the amplification through your existing customers through a referral program to whatever mechanics, then you'll be on the safe side when it comes to profitability because you can't improve what you don't measure, right? And if you don't measure it, then you simply go to spend money to acquire new customers that will never come back. So that's the second, the second trap for companies into this CLV space. And, and the third trap is that they don't look at, let's say, repeat purchase rate by the first bought product. You have products which are not born equally right? So if you have, if you have customers and we've discovered this like four or five years ago we were working with the company selling digital cameras and photo and whatever they had B2B and B2C customers. Well, and we looked at the revenue itself and the profit itself, but we thought we've scratched our heads and said what if customers are starting with the, the A product which has the same freaking AOV as another one. But what is the lifetime value if they start their journey with product A versus product B? And guess what? We found this type of anomalies and you can't find the find them if you're not looking for them. But now with AI and tools like okay, ours, but other tools as well, you can generate this kind of insights. And we've discovered that we've, they've got newly acquired customers that were professional photographers and they've built their own studio and they've, they've started their journey by buying some lights. Their AOV were the, the same as individuals which were buying this type of Polaroid cameras. They've got pretty much the same AOV but the lifetime value was 6 times higher for these customers. So basically you need to look at what they are buying first because they've got a high potential down the line. So you can't treat them the same. Right. And you can't treat those products the same way because that means your acquisition budget for products with the same AOV should be different if that product carries a lot of lifetime value down the line. Yeah.

But the problem is most brands, if they see the same AOV item, let's say it's a hundred dollar item, two items the same, they'll put the same budget into acquiring customers to buy those two items. When in actual, in actuality the LTVs are wildly different when those products are.

Purchased through the brand. And that requires this type of CLV driven approach that requires to blend in the time and the total profit per customer according to what they are buying first.

And do you find that usually these brands to get the kind of value out of not only the methodology that you expound, but also to get value out of your platform, they need to be in business for say for example, 24 months or something like that to where there's, there's actually enough purchase history data to be able to start running this kind of analytics. Because in the first six months, for example, it just feels like you have no idea who your repeat purchasers are going to be. You have no idea where your incremental revenue is going to come from. You have no idea which products or which experiences are causing churn. It feels like you need to be at a certain level of maturity, as I like to call it, before you can really start to benefit from this deep analytics.

There is a caveat here Jason, which is the purchase frequency and the average days between the transactions according to what you sell. So if you are selling groceries for instance there you can optimize really quickly because if you don't buy groceries for three weeks, you are starving, right? We don't have people which are not eating on this planet. So for grocery retail you can optimize for CLV faster and you don't need two years of data, but you need many transactions. Which brings us back to having enough time. So I, I personally think that you need at least 30,000 customers to benefit from this. So you need to look at a large enough sample of customers for B2C to do this type of modeling. For B2B then the threshold is different because there you have higher purchase frequency and lower numbers. So basically for B2C it's larger while for B2B is lower. But still you need at 13 months of data to have a complete cycle, right? To make sure that you're on the safe side when you're coming up with these assumptions. That doesn't mean that you shouldn't track it from day one day because you need to look at these type of things and see how they stack up so that you invest wisely because you can't throw money on the window right now. And acquisition is so freaking expensive and you need to do everything. It's possible for you to decrease the and increase the profit and profit Few are the companies that woke up to this reality. Profit means profitability per customer, you know.

And also I think also what brands and I'd love your take on this and to see whether your platform does this and I'm sure it does. They also are not only looking at. First of all, most sellers are not looking at profitability per customer. Then even if they're doing that, they're not looking at profitability per customer per acquisition channel because these are wildly different. Like sometimes if I acquire a customer for example via meta, a meta ad, for example, that profitability in that channel overall might be wildly different versus if I acquire them via a Google Ad or via organic social or profitability per customer per channel is the level of depth we actually need to get to. Because at the end of the day then if we don't do that, how will we know which channels are worthy of more or less investment. Unless we're running some sort of software or something like that where we can understand market dynamics by channel marketing, mix modeling. And so I'd love your take on the. What sort of maturity level are most of your clients at when they start to understand, okay, I need to not only understand my profitability at the customer level, but I also need to understand it with the added dimension of channel or level.

Yeah, that's, that's totally true, Jason. And one, one thing which is, which is happening is that when they cluster the data like this, they. Not only that you can see the lifetime value per channel, but you can see the distribution of the most loyal customers because you can run RFM per channel because basically is the distribution of your best customers per channel or per brand, category, location, whatever.

Whatever.

Yeah, so this is how you need to look at it because you might be. When you look only at the lifetime value, you can be affected by outliers. So that means you need to get rid of the outliers. Like maybe on, for instance, let me tell you, we have companies which are incentivizing their first buyers with the ridiculously low price, lower price on a certain channel, and you can dismiss that channel altogether. But maybe it's not about that. It was a campaign in itself. So you need to have your hygiene data there. However, the best is to look at the lifetime value per channel and also at the segmentation and the distribution per channel. And what we found out by working with larger and larger companies is that lifetime value is very hard to move, is like an average in itself. So we need to find ways to look at what success means and how you track the success of. Let's say we had a company, another shoe retailer, they've put flip flops during the summer, branded flip flops for any shoe, pair of shoes that they've sold. And they wanted to see is this moving the needle in terms of customer retention? It did, but how much? Then that meant they needed to look at the cohort that got this incentive. So they needed to look at the transition of the newly acquired customers into loyal customers. So they needed to split cohorts to, to, to see the differences under the same circumstances. Because that's experimentation, right? So at the end of the day, you end up in order to move the needle, not looking only at the blended CLV like an average, but you run experiments. And that's where our solutions collided. And we woke up to this reality one year ago when you, we unified our approach towards running experiments, towards improving Conversion rate and customer lifetime value. And in order to do that, you need to look at the transition of customer segments from one group to another. So it's not about RFM in itself, recency, frequency and monetary. But how able are you to move customers from an ex loyal customer state to a active loyal customer state? And if you look at these transitions, because you have many transitions, right? You have new customers that are churning, new customers that are sticking around, former good customers which are returning, endangered customers which are getting into the safe zone. So when you have such an approach, you end up improving the behavior of the customers by looking at these transitions. And I think this is all that matters to at the end of the day to improve lifetime value in order to track it, right? You have conscious initiatives and actions toward onboarding new customers better, reactivating old customers better, and so on. And in order to do that's, that's what you need to track these transitions from one state, from one bad state to a good state. Right?

And I think you hit the nail on the head here. One of the things that I see that most brands also do not do is they do not track propensity to discount at the customer and acquisition channel level. Now what that leads to is it leads to sometimes going out and courting customers and trying to acquire customers that will only ever buy off of you if something is on sale, right? Which means by definition the net margin that customer gives you on average will be lower. Right? And there might be two things at play here. One that I've seen, and again, they don't track this at all. So they certainly don't track it in through these two dimensions. One is they don't track the propensity to buy at a discount over multiple purchases. So they don't look at it and say, okay, 75% of the time this person purchased something when it's on special, when it's discounted, or we acquired this customer during a massive discount period, like a Black Friday, a Cyber Monday Christmas sometime when we're running a massive promotion, like maybe a site wide promotion or something like that, we don't segment these customers out by when we acquired them and how we acquired them. And so I think that if you're not tracking net margin by customer or net, yeah, net net profitability by customer. Because if you're only looking at net revenue, that skews how you look at that customer, right? You have to actually look at your net margin or net profitability by customer and by channel. And so that. So not only do they not look at cost to serve, not only do they look at return, not look at return rates, not only do they look at basket impact by inventory availability, but they also don't look at the propensity to discount at customer and channel level. And unless you're looking at all of those things, you're going to be missing something for sure. And you might say, okay, cool, the net margin of this customer looks really good. But unless you're tracking propensity to discount, then you are probably likely to go out and try to. And this is why I say if you, if your major customer acquisition is happening during Cyber weekend, for example, you need to segment those people out of your standard cohort analysis because you acquired them at a time when you were probably running something like a stupid site wide 50% off sale. And so I think that brands don't understand the long term brand impact and customer acquisition impact of extreme discounts.

That's correct. We I'm of the mentality that I have this mentality of experimentation, right? So I see if you're running a discount, you're actually running an experiment. And if you're running an experiment you need to have a hypothesis. And in order to have a hypothesis it needs to start and end and have a success criteria like why are you running 25 off? Or 20 or 15 because you are hoping for a break even. Where is the breakeven point? I don't know. Why wouldn't. How could you run this experiment without knowing where is the break even point? Like how many extra new customers you should acquire during the discount period in order to break even for the loss of the margin that you are sacrificing for this experiment. And if you don't have this set up correctly, what you are doing is you're shooting in the dark. Let's run 15% sidewise, let's do this, let's do the other. So the opposite of this thinking is to pre calculate where is the breakeven point by category, whatever, to run the experiment for a set amount of time so it starts and it ends and then to look if you are above or, or, or not in terms of this threshold. And another thing that you should be doing afterwards, as you've said, you need to get those bargain hunters and treat them differently in order to make them to come back. Because treating all the new customers the same, it's a bad idea. So we know that you are here because we are selling our hand creams for 15 off. But guess what? Because you've bought these creams, we also have 10 off for our lipsticks or whatever. So you keep them coming until you get them into the buying habit. And once they are into the buying habit, maybe they will stick around. But if you let them go and all that you're doing is destroying your margin, that's not a good idea. And that's why these experiments to increase the profit down the line. Because when you run discounts, when you're running a campaign or whatever, what you are doing is an experiment. And many times you're not even properly running that experiment. No hypothesis, no prioritization, no success criteria, no proper reporting when the experiment is done.

And I think that this extends into the type of campaigns that most brands run are spray and pray. They're site wide, they apply equally to every single customer, regardless of cohort. And the challenge with that is, let's just stick with the point we were talking about before, which is, okay, one customer comparing two customers, they look like they're spending the same amount with us, they look like they stay with us for the same amount of time. But one customer has a propensity to discount of 80% and one has a propensity to discount of 20%. So this customer over here only buys things on discount 20% of the time. This customer over here buys things when they're on a discount 80% of the time. Why would I want to run the exact same discount campaign to both of these customers? Why would I want for the customer that buys things when they're on special 20% of the time? Why would I want to run a massive discount campaign to that person when I know that most of the time greater than 80% of the time, they're willing and happy to, to pay full price for my products because they love my products? Why would I run a stem campaign or a discount campaign to that customer when maybe all I need to do is just to let them know that I've got these new products in that have attributes that are interesting to. If this customer has a 20% propensity to discount, if 80% of the time, let's say I'm a fashion brand and 80% of the time they buy a product that's red, that has the red attribute, if I just let them know I've got this new product in that is in red, then it's highly likely they're gonna, they're gonna have a really good chance of converting on that with or without a discount. So why would I want to discount to customers who haven't shown any kind of price sensitivity? And it Is this kind of thing that most brands cannot do at scale because they don't have the technology to be able to pull that data together in a meaningful way?

Yeah, that's, that's correct. It's, it's, it's something which is happening in the market which is this lack of data literacy. Literacy, because I think that's the problem. You need to be data literate to look at the things that you're telling about this, let's say comparison between customer A and customer B, which they are doing this, they have this propensity to discount differently and you're trading them the same, you're giving out the same perks or even though you have customers which are having, imagine this type of customer, high propensity to discount many returns, let's say 60% returns from all the orders that they've placed and you're giving out the same kind of treatments then you give to another customer which looks the same. So there are many things to blend in and once you have those, you have a solid approach.

And how do you find that when you first go into these brands and you first start working with them, do you find that it's that there comes a point where they have a light bulb moment where they go holy shit, we did. They have the aha moment where they go, okay, we were using GA4 or whatever it was and we were trying to do the segmentation, we're trying to do this reporting. We were trying to create these cohorts, we were trying to. Or maybe we were using Klaviyo for example, or maybe we had a CDP or whatever it is we had. And we're trying, we were trying to create these default segments or cohorts or whatever it is. And we just didn't have the analytical capability or these platforms didn't provide enough out of the box default functionality reporting for us to turn this into action and to where we could actually operationalize this data within the business and within our marketing team and within our CRO team and within our fulfillment team and within our procurement teams. And because at the end of the day, what I found is that when I was an E commerce manager inside of a brand, something like 80% or maybe even more of the impact on my KPIs were completely outside of my control. They sat within other areas of the business. They sat within customer service or they sat within logistics or they sat within. They sat completely outside of my control. For example, I wasn't the one setting prices on products. I wasn't the one doing Procurement and treating my suppliers differently depending on what their performance was like working with us, there were so many levers that were outside my control that it almost felt like I was fighting with one hand tied behind my back. Do you find that when you go in that the aha moment maybe is something like this? Hey, in order for us to operationalize the insights that we're getting from this technology, actually every single department in the business has to be involved in this analytics. It can't just be marketing, it can't just be someone who specializes in retention or CRO or whatever it is. It actually requires the entire business coming together and understanding which departmental levers that they need to pull to contribute to the performance of.

Yeah, the aha moment usually happens when they, when we show them how much they are losing. So that's the best thing. So you need to amplify the pain, make them aware about the pain and amplify the pain. And they need to be the right person. So if they don't care about their company and everything they do is to defend their seats, they are not the good person to talk to. But if they do care about the company or they are, let's say, personally invested into it, let's say they are in the leadership of the company and they become aware about the fact. By the way, an easy thing. An easy. I recall this by the way, you've just acquired 400000 customers this year. And they were like, yeah, kudos. And also you've lost 350000 customers this year. So basically the net growth is 50 000.

It's 50, 000.

And when I, when we also ask them, because we don't know how much you've paid to acquire those 400000 customers, then they're almost their entire budget, their entire marketing budget was towards acquisition. And that's dumb. That's simply dumb. Because you can't neglect the fact that you're a big company right now and most of your revenue comes from your existing customers. Because that's the mathematic of growth. That's how a company grows. At some point in their existence, returning customers are generating such a big amount of the revenue that they can't be neglected anymore. But the I, I think the companies are hypnotized by this acquisition mode. It's always because it's sexy. It's. Nobody brags about hey, we made 25% more customers return this year. Kudos. It's. But they always brag about hey, we acquired 100,000 new customers this year. Kudos. So it's all about novelty new customers. But basically this is the, the cancer which is not that easy to see. And many companies are dying without even realizing that they are dying or they are stagnating because of this lack of proper growth mechanics with their customer lifetime value.

And do you also help them understand the net cost of acquisition when you take into account churn? So for example I acquired 400,000 customers over a 4 of 12 month period. So then I think my CAC is X. But if I look at that through the lens of 50,000 net when I take out my churned customers right then my CAC is total. That's my real CAC. My real CAC is the total customer acquisition spend over 12 months divided by my net customer retention. My net that 50,000 number for example. Now my CAC looks wildly different and that's actually the lens we should look at the net net retention of customers. The net acquisition divided by basically retention. So what are my net new customers that I'm going to be able to realistically keep and likely to get them to purchase at least a second time through my business. That that starts to give you a very serious reality check. Because CAC brands already think CAC is high based on that 400,000 acquisition number. They already think that the acquisition cost is through the roof. But if they knew their net acquisition cost based on net retention, oh my God, they would probably pass out if they realized how much it was costing them to acquire customers on a net basis. Is that something that is a massive wake up call to these businesses?

It usually is Jason. And that's happening when, when let's say we have more people at the table. Unfortunately they we need an internal champion and he needs the right, let's say partners in this rodeo. So you need the cfo, you need customer you and you need marketing, you need the head of product, you need the people. Yeah you need ops, you need the supply chain and so on. So basically the when they are doing nowadays what we do, we stop selling. Hey, we have this technology amazing one is going to do the X, Y and Z. What we do is it's like a free AKG we do just give us access to your data. We're going to say you where you are, we're going to tell you what's your potential, how much you are going to lose in the next 12 months, how much you've lost, how much you can gain and from there onwards is going to be your decision. And usually they when they see this kind of data and when they are realizing that they are simply their model, their growth model is missing components because that's the problem. They have a function of company growth which is lacking elements. And like in math, you can't have the right result by ignoring parts of the equations. And basically that's when we make them move. And another thing which is crucially important is to come up with the low hanging fruits. Hey, let's do this. For instance, a very simple growth initiative which is low touch from them is to generate the feedback from their customers. Use our tool to do nlp, show the main issues that their customers said say they have and then treat the objections in real time with people that they already hired, which are the people at the help desk, right? So we arm them with the prevention instead of activity. Like we grab the NPS pre post delivery. If it's post delivery and is lower than 4 out of 10, they have an automation and they need to take into account the segment of that customer. So you have a highly heavily important customer which is pissed off, he just received their product. Call them, you have 20 minutes, that's it. And it's like they use the entire org chart that they don't have to hire someone else. They just need to to add a small new task into the help desk solution that they have with their existing employees which is bringing back a lot of their customers. So that's one company that we did. They are a B2B office supplies company. And they realized after doing this RFM they realized how impactful are their top 25,000 customers, right? So basically 65% of their revenue and 80% of their profit came from these 25,000 customers which were only 7% of their customers. And they've said let's stop here, what do we do? And we've said let's do this simple thing, let's do an automation. And if you are at the help desk, you are a customer support rep. You can't process new orders if you have a red, red countdown timer. So you have three minutes to call this customer which is in your territory, he's pissed off, do something. And they've got an NPS of 93 and now their compound annual growth rate is 25%. So basically every year they grew compound with 25% because of that. So they are now eight times higher than they used to be because of this simple thing. Obsess about your best customers which are bringing new customers, right? Because they've started to advocate for this in the world of Office supplies. There are these acquisition managers, they know each other, they call each other, they go to conferences and they've said work with this company, that that was their growth. Growth initiatives, never exactly a simple one. If you think about it and you.

Point out something that I think many brands overlook. It's, it's easy to scale or it's easier to scale the unscalable with technology. And I'll give you an example. And, and I buy, I'm weird in that not only do I work in the industry, but like 90% of my purchases are online. I, I don't really like shopping, I don't really like going to malls. I don't even really like going to supermarkets. I just, I don't really enjoy the physical shopping experience. So if something can be bought online, I will buy it online. So I know I'm an outlier, but there are other people out there in the world like me. But what's interesting is when I left New Zealand to move to Mexico, there were a ton of suppliers or brands that I bought on regularly. Like I remember there was a liquor, an online liquor store that virtually once every month I would make a fairly significant because I love bourbon and I love some high end spirits. And I would buy with them month in, month out, month in, month out and have it delivered to my door. Now all of a sudden, and this, I was a customer of theirs for three years, okay? And buying very regularly, spending an obscene amount of money with them. It was actually embarrassing when I look back and I realized how much money I'd spent with them over three years. Now all of a sudden, randomly, one day, one month, I disappear off their radar and never buy with them again. Did I ever get an email from them? Did I ever get a phone call from them? Did I ever get any communication from them? Especially for me, I was on their mailing list. So I received their standard maile but I never ever got the question, hey, we have really appreciated serving you for the last three years. We really value your business and we would love to have a conversation with you about why you no longer purchase through us. Was it because we don't have the range you want? Was it because of our pricing? Was it because, oh, and then they would have discovered if they would have made that effort, they would have discovered, oh, he's moved to Mexico, that's why. It's not anything we did wrong. He's just not in our region anymore. And so that's fine. We can chalk that up to a churned customer because they Moved out of the country. Okay. But I feel like technology now because customer service reps are very rarely busy 100% of the time. Sure, they got to respond to emails, sure, they got to respond to live chat, sure, they got to respond to phone calls. But there's always, when I walk into customer service centers and even in the brands that I've worked inside of before, there's always lulls, there's always downtimes, there's always these little periods where they're quiet and it's certain hours of the day at certain times of the day, it's certain holidays where things are quiet. It feels to me like these brands would get a whole lot more value out of these customer service reps if they could have a short list of customers like me that appear to have churned and they just need to pick up the phone and have a con. Instead of automating the shit out of absolutely everything, let's focus on the people that matter that can actually give us super valuable market and customer intelligence. And let's shortlist them almost like a call list in a CRM that says, okay, customer service team, for this cohort of customers, you are going to reach out and actually have a conversation with them. Instead of just pushing out automated emails, you're going to have a conversation and you are going to be doing customer intelligence and research with these conversations. I, I have yet in the last 25 years of working in this industry, I don't think I've ever had a brand pick up the phone and actually call me to have these conversations. That to me seems freaking ridiculous.

Let me tell you a funny story from, from one of our customers. So this company is called Kazidomi. They are a grocery retailer. They sell bio, whatever, stuff from Belgium. And imagine this. So they wanted to be completely hypnotized about customer centricity because they had low chances against the giants in their industry. So these guys made an experiment to see if the customer lifetime value because in, in this type of thing, it's not about the initial purchase. If you are completely obsessed about things like Manuka honey or whatever, you are buying B.O. stuff, B.O. coffee, B.O. whatever, then you'll buy them every now and then. So, so they looked at their onboarding and they looked at their customer retention and it wasn't nowhere where it should have been. And they've said, let's try for a few weeks to pick up the phone and to call every freaking new customer. How do you like our coffee? How do you like whatever they've bought and it was expensive indeed. But they made this experiment and then once they realized that this is moving the needle, they realized that, you know what, we should do this. But our customers are speaking French. What can we do? And guess What? They've hired 12 people from Madagascar where they are speaking French and the average wage wages 15 times lower than in, in, in Belgium and France. And now they continue doing this. They have this amazing customer support in French for their, for their customers because they found this growth lever to be extremely warm with their, with your new customer so that they feel like it's against all this shopping that I'm doing online. There is one company that freaking called me and asked me how it was and this type of human touch is still there and it's still moving the needle.

Yes. Amazing. Listen, Valentin, an hour has absolutely flown by. I really appreciate your time with me today. It's been a pleasure, fascinating conversation. You've been in this a lot longer than I have. I've seen the, I've seen the results of brands not doing all the things that you recommend and not using a technology to allow them to scale these types of efforts within their business. So I really kudos to you for building something that allows brands, it democratizes access to this kind of technology in a way that's difficult to do any other way. So I think you're doing a great thing in our world. Now, what is on your radar? Is there anything your customers are asking you to do that you don't do today? Or is there anything on your radar that you say, hey, we don't do this thing today, but in 12 months we will be what is high on your radar, High on your priorities?

Yeah. So at this moment, Jason, we have these four technologies. One is doing a B testing, rule based, a B testing we have. One is doing customer analytics and tracking the things that we've talked about. One is doing customer experience and nlp. We've just recently released it and we're going to put it at, at a ridiculous price on Shopify Marketplace is going to be OmniConvert Pulse and another one is OmniConvert Engage, which is another tool that we're going to put into to the Shopify marketplace. It creates audiences based on your existing customer. So based on your rfm, you can push this audience to ads. What we are building right now is an AI benchmark. So basically we are grabbing the data from a website, from Google Analytics, from our customer data and it's going to come up from the qualitative feedback from social media From Help Desk, from Google Maps, from whatever source of open answers that the customers are using to say how they feel about our brand, about the particular company. And this is going to be baked in and basically we're going to provide an AKG and a leaderboard for all the companies on the E commerce for the top 10,000 companies. So basically that's what we are building. It's like it's called the CRO benchmark and it's gonna provide.

Benchmarking is so cool.

Yeah, that's the thing that we're, we're building. We're weeks away from releasing it. So basically we have 255 criteria that we are analyzing in eight minutes and we're giving a leaderboard like on the fashion B2C on Shopify in UK. This is the list. That's where you stand as an E commerce. So that's the thing that it's very exciting for me right now because it's revealing what's wrong with the business like this. You don't have to wait and to do a lot of things. It's using the data that it's already there.

Yeah, I love it. Absolutely incredible. Now how should people get a hold of you? I'll drop a link to OmniConvert in the show Notes. I'll drop a link to your LinkedIn profile in the Show Notes. Other than that, if people want to get a hold of you, understand your CVO model, understand your technology and platform better, about how they can leverage it to get better at all the things we've talked about today, what's the best way?

Yeah, I'm a LinkedIn person so I'm posting there every day. Hopefully I'm trying my best and yeah, get a hold of me on LinkedIn. I'm really open to helping, helping out. I have a lot of conversations and I think the for the listeners out there, I've learned this on my own skin. So I'm a expert guy from Bucharest, Romania and I've learned my way through and I've started to create and build by asking questions. And I think this is an underrated method of growth. So if you have a business or if you're working for an E Commerce or you think I can help you out, just send a message not only to me, to whoever you want. There are so many people which went through the hell that you're passing through right now.

I love it now as we do come down to the end of our time together, this is where I get to flip the script, I get to Hand the microphone over to you. I get to let you ask me one question. Any question you like can be personal or professional. Mr. Valentin Radu from OmniConvert. What is your question for me?

Yes. So, Jason, how. What is the greatest growth hack that you found throughout your existence that you wish you had when you were like 20?

Wow.

One more. Sorry to stop, Jason. One more. The objective is to live a fulfilled life, not to be an amazing business person or consultant or whatever. So your conversion, let's say you're optimizing for a fulfilled life.

Cool. Don't go into debt. Stay out of debt at all costs. I think that this is the thing that I've only had debt for very short periods in my life. I have an almost allergic reaction to debt, which is odd considering I was born and raised in the United States and Americans normally love debt. But I have had an almost allergic reaction to debt for most of my life and that has served me in very good stead. One of the reasons we moved here, one of the reasons we bought here in Pueblo City, Mexico, was because I could actually pay cash for this house and I didn't want to have a mortgage. I didn't want to be in debt. I didn't want to give money to the banks. I wanted to spend that money to enrich my family's life instead and to enrich my business instead. And I think that most people in the world today, it's. It's a little bit less so in Mexico, for example, where it's the norm to pay for a house in cash, in Mexico, it is not normal to go and get a mortgage. Here, it's just not. Whereas in America, it's completely normalized to have student debt, to have credit card debt, to have personal loan debt, to have car debt, to have to have a mortgage. It is completely and utterly normalized to where your credit score becomes a key focus of your entire existence in the United States. And for me, this is one of the things that will handicap you from setting yourself up for success, is if a significant portion of the money that you make is going to just service debt, it means that there's lots of other things you can't do as a result. And so for me, I think if people can get out of debt and stay out of debt, they will have a much greater quality of life and they will have much more optionality. One of the reasons I was able to become a digital nomad, one of the reasons I was able to move around the world, one of the reasons I was able to do all the things that I've done is because I didn't have the cinder blocks of debt wrapped around my ankles that I then. And for example, I could switch jobs. If a job didn't work out or it wasn't working for me, I could just leave. Whereas people that have massive amounts of debt, they stay in jobs, in roles, in verticals, in. In industries that they absolutely hate because they are being choked to death by debt. And then they can just base. They. They're gonna have to work until they die, basically. And so for me, I think debt is so freaking insidious. That is one of the things I'm actually. Even though it's not directly related to E Commerce, I think it holds true for businesses too. I think businesses that are operating with significant amounts of debt, it really radically reduces their agility and ability to pivot with new market opportunities when they are spending a significant amount of their revenue just to service debt. I think it's a. I think debt is a fucking trap and I wish more people would stay out of it.

I'm totally with you, man. So basically it's. I still remember how happy I was. Those were. That was one of my. The best lives of my life. When I. When I closed the loan back in, I think it happened nine or 10 years ago. It was like, now I'm free, I thought I have a house, I have a wife, I have kids, I'm happy. And I don't have a freaking loan anymore. It was like amazing feeling and I wish everyone could stay, indeed out of bed, out of that.

Yeah, I love it. Thank you so much for your time. It's been an amazing conversation. Kudos to you for doing what you do. Kudos to you for trying to help the industry do better and be better for their customers and for the world. Can't wait to speak to you again soon. Thank you so much.

Same. Thanks a lot, Jason. And thanks everyone for listening.

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