🌟 Join us on the Jonny Ross Fractional CMO podcast for an insightful dive into the future of retail marketing with Lewis Rothkopf, Chief Revenue Officer of Pairzon. 🚀

🔍 In this episode, we unravel the complexities of connecting online advertising with offline sales measurement and the incredible potential of AI in reshaping marketing strategies. Lewis shares his extensive experience in digital media and ad tech, offering valuable insights into how retailers can leverage data to supercharge customer acquisition, retention, and overall ROI.

📈 Discover the secrets behind effective online-to-offline marketing strategies and learn how to measure the real impact of your marketing efforts with precision. Don't miss this opportunity to gain expert knowledge on using AI to solve today's biggest marketing challenges.

👉 🔗 Guest Links:

Connect with Lewis Rothkopf on LinkedIn: https://www.linkedin.com/in/lewis/

Discover the power of Pairzon: https://pairzon.com/

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Grab your headphones, and let's get futuristic with marketing! 🎙️🌟

#JonnyRossCMO #RetailMarketing #AIInnovation #DigitalMedia

Timestamps:

The problem of measuring marketing effectiveness (00:01:53)

Lewis discusses the challenge of measuring the effectiveness of marketing efforts and the limitations of click-through rates as a metric.

The need to bridge the gap between online and offline marketing (00:03:20)

Lewis explains the difficulty of connecting online ad exposure to in-store purchases and the limitations of geolocation as a proxy metric.

Using AI to understand the customer journey (00:06:24)

Lewis discusses how AI and machine learning can track the entire customer journey, from online interactions to offline purchases, and eliminate guesswork in measuring advertising effectiveness.

The consumer experience and wasted advertising spend (00:11:20)

Discussion on the negative experience of seeing repetitive ads and the wastefulness of advertising to consumers who have already made a purchase.

Targeting and anti-targeting based on consumer pools (00:12:23)

Explanation of how marketers can use consumer pools to target or anti-target their ads based on reach, frequency, response frequency, and monetary value.

The role of a customer data platform (CDP) and alternatives (00:14:12)

Comparison between Pozen's marketing data platform and traditional customer data platforms (CDPs), highlighting the benefits and cost-effectiveness of Pozen's solution.

The power of AI in understanding user search behavior (00:23:23)

AI can predict what users will search for next based on data analysis and autocomplete suggestions.

Using lookalike audiences to drive customer acquisition (00:24:59)

Lookalike audiences help expand the customer base by targeting similar individuals based on first-party data.

The importance of data integration for actionable insights (00:29:19)

Having all the data in one place allows for immediate actionable insights and better decision-making in marketing strategies.

Bridging the online and offline sales gap (00:33:36)

Lewis discusses the importance of using data and AI to bridge the gap between online and offline sales.

AI-driven success (00:33:36)

Lewis talks about how AI can drive success in marketing by leveraging data and optimizing the consumer journey.

The future of marketing with AI (00:33:36)

Lewis and Johnny wrap up the episode by discussing the potential of AI in marketing and encourage listeners to use their data effectively.

Keypoints:

• Problem of bridging the gap between online and offline advertising
• Use of AI algorithms to analyze data from retailers' POS systems and transaction logs
• Eliminating guesswork and providing accurate insights into the effectiveness of marketing campaigns
• Poor return on ad spend for big retailers and the need to focus on accurate measurements of campaign success
• Tracking and analyzing consumer behavior to optimize marketing strategies
• Targeting and anti-targeting consumers based on defined pools
• Preventing consumers from seeing ads for products they have already purchased
• Pozen's compatibility with retailers
• with loyalty programs and ability to act as a customer data platform for
• retailers without loyalty programs
• Differentiation of Pairzon from other companies in terms of cost-effectiveness and quick implementation
• Use of AI in predicting consumer behavior and the effectiveness of lookalike audiences

Welcome. Thanks for joining. Thanks for listening. Thanks for watching. Perhaps you're with us right now. We are live. Welcome to Jonny Ross, fractional CMO, where we dive deep into the mind driving business and marketing forward. I'm Jonny Ross, and today we have the privilege of chatting with Lewis Rothkopf, the chief revenue officer of Pozen. With a rich tapestry of experience woven through the digital media landscape. Lewis has been at the forefront of adtech innovation, steering global businesses to success. We live on Facebook, we live on LinkedIn. We are live on YouTube. Lewis. From the days of Double Click to the AI powered horizons of Pozen. Right now, Lewis has been navigated through the evolution of advertising and customer data like a true visionary. So sharpen your pencils and ready your minds as we unpack the intersection of AI, marketing and the consumer journey with a master of the game. Stay tuned as we explore how today's marketeers can harness AI to transform data into actionable insights and robust sales strategies. Welcome, Lewis.

How are you?

I'm very well. It's good to be here, Jonny. Thank you for having me.

Thanks for joining. So you your company specializes in retailers working with marrying online and offline to get the most out of that. You're typically working with some reasonable sized retailers. We're talking sort of millions of transactions per month. And what let's start with what's the problem here. So you know CMOs of these retailers, what's the the problem that you come along and solve.

So you mentioned in your intro that I've been doing this for a perverse amount of time. It's not the words you use though. I appreciate that. And so going all the way back to the late 90s, really when digital advertising began, it was in its infancy. And when we began to sell advertising to marketers in 1998, 1999, how do you know if that marketing worked? Well, there's a click through rate. So there was a click. Then, you know, it worked. And if the click through rate was 10%, then fantastic.

If the click through rate was 1%, then not fantastic. The problem is, as folks in the industry very quickly learned, is that there's basically no correlation between click through rate, people who click and people who actually buy your products. In fact, a couple of studies actually showed that people who click on ads tend to not be the people who buy your products. So clearly a better approach was needed. And several of those approaches evolved. Initially there were surveys and studies. So you saw it out online. Mr. consumer, do you remember seeing that ad would you do after you saw that ad, did you go and you buy the thing very easy in that context to measure conversions online? So now marketers weren't only playing with proxy metrics like click through rate, but they were able to see, okay, we ran this ad online, person clicked on it. We've got tracking. We have advanced systems that measure how many people clicked, convert it, etcetera. And now I've gotten the closed loop online.

And so life was good for the e-commerce players, the online marketers. However, many of these online marketers, if not most of them, also have a retail presence and offline presence. And there was no way to bridge the gap between online ad exposure and in-store purchase, not proxies took place. Right? One of the proxies that is still in use today is geolocation. So let me measure advertising that the consumer has seen. And then what do they do? They drive to this location and it's where the store happens to be. And we're able to say okay, that's a conversion. The ad works. The problem with that approach is that geolocation in 2023, using the technology we have today, is not as precise as it would need to be to draw a 100% accurate correlation between the advertising that was seen and the action that was taken in store. Yeah, I can tell you if somebody came within the vicinity of that store, but what if it's a mall, right? What if it's a shopping center? And then what did they buy? So we know they came there, but did they buy anything? And what do they buy and go down to this new level.

Look to understand okay. The advertising is working. The advertising is working here. It is working these products. And here's what we need to do to make it work better. That solution really didn't exist. So our company Pairs On was founded by two retail veterans of which I am not one. And the idea was, let's take all the data that marketers have in their POS systems and transaction logs. Let's take all that data and let's run it through a AI algorithm that was purpose built for what we do. And let's have the algorithm tell us two things. Number one, which. Should. My consumers are most likely to convert from the advertising that we're about to show them. So let's target them in this advertising. And the second thing it shows is what happens when a user sees the ad, either online or SMS or email. What do they do? What's the offline interaction that takes place because of that ad exposure? That's the problem that we we solve. It's deceptively simple, but it's the problem that has been bedeviling the industry from its inception.

So, so this is understanding exactly what happens when a consumer season ad interacts with an ad, engages with something, receives a text message, receives an email. And what you're saying that you that you can do using the power of the machine and the AI is to then follow the entire customer journey into the on, into the offline world where they go in and visit store and you then know exactly what shoe they've bought. And more importantly, you can predict as well.

Correct. And it takes the guesswork out, right? It takes the, you know, the sort of hocus pocus out of deciding whether or not your advertising is working. There's a famous quote that you and your listeners have heard many, many times from Wanamaker about how the advertising works. I don't know which half. There's no excuse for that. Right? That is a problem that still persists today with both online and offline marketing. And it is inexcusable in 2023 because we have the tools to close the loop and understand which half are ideally, which 90% worked well.

Has there been? So having a look at the data and working with the number of organizations that you that you work with, because you we were talking earlier, there's a significant number you work with. Has there been any surprises in terms of now that you've got this data and you understand behavior and customer journey, has there been any surprises or any particular learnings across the the sector that sort of actually, you know, everyone thought we should be doing remarketing or whatever it might be. I have no idea. Um, I'm just curious, are there any are there any particular surprises or any particular trends that sort of say, actually this is something that everyone should be doing?

I see this with a tremendous deal of respect for the market and our customers, and sort of what tools that historically been available to them. The surprising thing is, when we do a pilot with a prospective customer will ingest their data or perhaps run a campaign with them and see how our performance comes out relative to not working with us.

And the most surprising thing was how terrible the return on ad spend was in many cases in many campaigns for many big retailers. It's horrifying right? And it's hard because if you are a marketer and you know your CMO cares about click through rate, video completion rate, and that's what they're grading their agency on, well, then what does the agency say? The agency says I want supply that clicks well and video that completes well. And the publisher then says okay, gotcha. No problem. But if you're setting something flawed, like click through rate as the core metric that you're optimizing your business do, it's just not going to work. And so we encourage our customers to sort of move away from those old proxy vanity metrics, some would say, and into accurate, deterministic, what actually happened? To whom should I be marketing with which messages? And then what happens after they saw the ad?

Give. Give me a practical example of how this works. So, you know, I'm a consumer, um, you know, maybe it's a grocery shopping, maybe it's maybe it's a clothing fashion, whatever it might be.

You know, I've received an SMS this week. I've got an email this week. I've, I've, I've seen an advert tell me exactly how this is going to. Piece this together.

Yeah. So you're a consumer and you are in market for a pair of sneakers. And so you do some searching around and you look for sneaker sneaker sneakers. Could do could.

Do have a new pair actually.

As could I ask can I am under no more sneaker in the house prohibition until I throw some old pairs. So if my wife is watching, I'm very sorry. I'm sorry. Yes. So you're going to buy. You're looking for some sneakers, you're researching online, and then of course, what happens? You see ads for sneakers and you're exposed to this ad. You click on this ad and it's like, huh, these are really nice sneakers. But you know what? I hate buying shoes online. Let me go into a store. And so I go into the store and I say, hi, I want those shoes.

I want them in a size nine and a half. And I don't really want to look at other stuff, but I want those shoes. Oh, and by the way, while I'm here, let me pick up those socks because they're on sale. And you know who doesn't need socks when they need some shoes? The person makes the purchase. And in sort of the previous world, right before folks like us existed, that was that. Right. So all the marketer would know is that the user clicked on a, was exposed to and or clicked on a sneaker ad. They don't know what happens after that. Did you go into the store? All they know is it didn't convert online. So now you're saying, oh man, well, that's a crappy consumer. Let's not target them again because they didn't buy the thing after they saw the ad, when in reality like, no, that's that's the best kind of consumer. Because not only do they get into the store, they added on to their purchase beyond what they came into the store for.

Now, what experience have you had so many times when you search for something online, you see that same damn sneaker ad everywhere you go for the rest of time. It's crazy. Like if you ask anybody, what do you hate about internet shopping? Well, I hate that I was shopping for, you know, these, this pair of jeans. And now everyone and their mother thinks I'm a gene aficionado because they keep showing me the same as the same genes. And what's wrong with that? If you're a consumer, it's a terrible experience. What's wrong with that? If you're a marketer, you're wasting money. You're spending money to advertise to somebody who already bought the product. Like, that's crazy. Why would you do that? So, you know, we help marketers both target as well as anti target based upon pools of consumers that they define whichever methodology they want to use. We want to use reach frequency response frequency and monetary value. So they want to do media mix modeling. It's all baked in.

But the critical thing here is consumer no longer sees ads for things that they already bought. Retailer knows what impact the offline. Excuse me the online had on the offline behavior. And they have another piece of data now which is when people buy these sneakers they also tend to buy these socks. So why don't we put those two items together in the physical store? And why don't we advertise them together and say, hey, people who bought these sneakers, they also bought these socks. Would you like them? And that's one thing the platform does. It's sort of it melds together all of the pairs. So we get pairs on of consumers and the products that they purchase, but also the other items in their cart that they completed as part of the same transaction.

And this is I assume it's based on a unique identifier, something like their email address or their phone number. Would that be fair.

Email address or phone number? Where it works really well is if you're a retailer who has a loyalty program.

So you go into the store, you swipe your loyalty card, all your data is entered automatically by the purchase loop is closed. Even for retailers who don't have a loyalty program. All they need to do is collect an email address or phone number, and then we can actually SMS or email them a digital receipt. So you've effectively created the same sort of scenario where consumer information has been collected. The loop is closed. In this case it's closed with a digital receipt. And you can make all the inferences and decisions that you can, that you can based on the data that's generated.

So there must be a fair amount of competition out there. What options have CMOs got if they're not going to you. What what else is out there to be able to do this there?

Sorry. There are very big companies who do similar things. There are customer data platforms that are large and sold by large companies and are monolithic. And it's interesting we used to market ourselves as a customer data platform because we are we are a CDP, and for several of our customers, we we're their prime.

We stopped doing so and instead switched to talking about our business as a marketing data platform, because you can have an existing CDP in place, but not have the capabilities that we're able to bring to the table. So we complement the CDP. Or for those customers who don't have a CVP installed, we act as the CDP for them. So what could you do? If you don't want to work with us, you could go work with one of those very large companies. It's probably going to be much more expensive than us. It's probably going to take you a long time to implement it, to train on it, to learn how to use it, whereas we get customers up and running in, you know, a month's time. I don't want to suggest that we're the only ones who do what we do, but think for for those reasons, we're the most appropriate solution for those who are looking for either a solution or a solution that complements their current capabilities, looking to understand and predict where their customers are and where they're going to convert or how they're going to convert.

And whilst you're global, you're very much looking at the US and Canada market. Is that right? That's right. But customers globally and the and as I said earlier in terms of well in fact you said it yourself, it fits really well with retailers that already have loyalty schemes because that's just a really easy way to to close the loop very quickly. But as you say, you could you could soon simply ask for an email address to, to send the the receipt to anyway. So there's just talk to me more about the prediction side here. So I think you, you sort of already talked about how you how you can predict that if there's a certain buying pattern, for example, you know, people are buying those socks with that trainer and therefore we should put them together, etcetera. But what other predictions can this modelling make and why, you know, why is it important. And and I don't know if you've got any real examples you can share. I don't know if you can use names or not, but but in terms of, in terms of predictions, what can your system help marketers predict?

So we ask our customers for two years of transaction logs.

So let's take your historical logs. Let's feed them into the AI. And let's begin to understand what your customer journeys have looked like historically. And then we say, we need you to give us the live data as well. So some of our customers have a always on link where we get transaction data in real time and we process it in real time, and that informs how we build and prune and optimize audiences. Or you can send us your data once a day, once a week. It's obviously not going to be quite as precise, but it's still pretty darn good once we have all that data fed into the AI, the AI says here are the categories of consumers that you have, and they range from your best. Consumers can't lose them. These guys spend a lot of money in the store. They come in very frequently and they spend a bunch of money on individual products, or they spend a bunch of money on the total basket. One way or another, they're spending a bunch of money all the way down to lapsed customers.

So here's somebody who has not been in the store in the past year, doesn't buy anything when they come into the store, only comes in like once a year. And that's somebody that you would effectively consider to be a new prospect that you have to acquire and activate. And then there are multiple tiers in between those that allow you to create audiences that are geared specifically for what you're trying to do. So if you're running a reactivation campaign, which is effectively a prospecting campaign, you're probably going, but you're definitely going to target those in lower tiers of disengaged customers, disengaged former customers. If you're looking to reward your best customers or try to squeeze a little bit more business out of them, you know, for Black Friday or for the holiday season, you're probably going to target your very best customers and maybe even spend a bit more no on the advertising, knowing that your best customers are going to walk into the store and walk out with $100 worth of merchandise on, on the average, those sorts of insights that the AI is plucking out and giving you to act upon, they're the sort of thing that you would otherwise have to have a team of analysts, like our biggest competitor is a spreadsheet, right? A lot of companies have a team of analysts on spreadsheets kind of pouring through the data.

I would lose my mind. I don't know how people do it, but even when you do and you spend all that money and you have all that toil and all that time, are your predictions going to be as good as a model that is built on? Millions of transactions over the course of years, and is able to pick out signals in the data that individual humans generally aren't able to do.

What some of the, um, are you able to share some of the the figures here in terms of ROI and what the sort of difference it can make to an ad campaign? You know, on matter, for example, the, the metrics instead of, instead of that, um, CMO just looking at, you know, click through rates and have they watch the full video through and all of that sort of stuff. What are the what are the metrics and what are the what's the some of the ROI that that you've been able to achieve?

Yeah. So with apologies to people who are watching us instead of listening to us, my eyes are going to go over there so I can actually give you real numbers.

If you're looking at me, it is not much of a pretty picture, so just bear with me one moment. Um, we had a campaign that we ran with a pilot program with one of our current customers, and we ran an AB test of using the audiences that are baked into the walled gardens. So Google audiences, Facebook audiences, they have this product, it's built in and then lets you target based upon audiences. And we sense the audiences they think are most likely to convert. Um, we said, all right, let's take half of this campaign and run it on us. Give us a few weeks, give us half of the budget, and let's see what we can do. And, you know, in one of the campaigns for this, for this retailer, there was a 200% lift in return on ad spend for the campaign that used some of our audiences and for the bigger campaign that ran after that, where they were using, I believe, 60% of our audiences and 40% was the control group, a 333% lift in Roas.

And if you think about like the numbers behind the numbers, right, like it is, it is generally terrible if you are not getting the return on spending. Well, first of all, it's terrible to not know that you're not getting the return on spend that you expect to. And it's terrible to not get that return on spend. And so that alone should make you worry as a marketer that like, oh man, like we are leaving money on the table here, but you know, it even goes beyond that, right? It goes to the point of, what are you doing if you are not maximizing the spend, why are you wasting money? Why are you not spending? Maybe you should invest more somewhere. And so the idea is we take a look at the basket size, we take a look at what the cost of the advertising was, and then we figure out what the Roas is. And, you know, we have one, one client who ran a pilot with us whose Roas was negative.

It was costing him $100 to acquire a customer and their, excuse me, $150 to acquire a customer. And their average basket size was $100. It's terrible. It's awful. You're dying. And so we help make that problem a thing of the past.

And I assume that you're also looking at lifetime value as well. Over over, over. Yeah. And the by the way, if you're listening, if you're watching, perhaps you're on the recording. It's great that you're here. Thanks for being here. If you are live with us. That's brilliant. You're very welcome to comment. We've had some comments actually. Erin Sparks, can I build a predictive model for what the consumers are going to search for next? Finding the gaps where our clients need to be would be invaluable. I sort of think you might have answered that question.

Yeah, absolutely. Well, thanks for the question. Absolutely. No question. And, you know, I always sort of catch myself a little bit when I talk about AI because it has been used to describe everything from these large language models all the way down to like the crisper drawer setting on your refrigerator.

And it sounds a little bit cliche, but the power of AI is that you're taking so much data that individual humans would never be able to grok, and you're making it really, really understandable. And so to the commenters question, you know, are you able to sort of figure out what the user's going to search for next? Absolutely. And the way that you can see this is go search for something on Google, then go back and start typing something else. And there's a really good chance that the autocomplete suggestion is going to be what you're looking for next, if they're just that smart. So yeah, I really matters, but I think we are already at the point where predicting it's exactly what we do, right? Predicting the consumer's behavior is paramount to not wasting money on advertising.

So where this fits is, is. Pretty much across the entire funnel. What you're what you're not focused on, though, is necessarily bringing new people into the funnel. This is around really getting the most out of the people that you know or the people that you have data on.

But you have talked about lookalike audiences, though, and so I'm just wondering how that works. And and you know what? Yeah. How how does this system work with lookalike audiences?

So the thing about lookalikes is, you know, is there's sort of a continuum between precision and scale. I think some systems even literally have a slider, like, I care more about being precise, or I care more about reaching more people. And, you know, it's entirely up to the marketer to determine what their tolerance for a loss of precision is. In search of scale, we do lookalikes again based upon the marketers first party data. And you're correct in furtherance of not bringing net new customers to to this to this retailer. It is not a prospecting platform except to the extent that you have lapsed customers or lost customers who have not come into the store have not purchased in a long time. That absolutely is user acquisition, right? That is getting people who otherwise have been dormant for the past year or more, or however you define, lapsed in your business.

And we use lookalikes to help make that list bigger. Drive more of your existing customers to buy more, drive your non existing customers to start buying again, and sort of tying it all up into a process that can be replicated. And critically, all the insights that resurface are immediately actionable. Like we've all used systems where it's like yeah, so we know there's a correlation between people who buy Mercedes cars and those who like chocolate chip cookies. Like, that was all the rage, like ten years ago. It's like, did you know? And it's like, no, I didn't know that. And what am I supposed to do with that? Like, do I do I like, sell chocolate chip cookies at the Mercedes dealership? Well, no. That's crazy. So, you know, you sort of like you look at the correlations on a much more restrained and rational basis and you say, all right, you know, these guys, they were shopping for socks. Bet you they're going to like some shoes.

I mean, it's it's scary, but it's also very clever. And why wouldn't you? If you've got the data, why wouldn't you go to this level of detail? And, you know, ultimately that's where loyalty card started was, was, you know, okay. Yes. It was to give value and to, to to to buy that loyalty. But ultimately it was to buy the data and um, you know, and yeah, I think, I think it's a, it's endless really. Um, so. Is there? Is there a.

The questions completely.

Got on my mind. Lewis and I do loads of live.

Stuff and it's completely gone.

But I think I was just trying to to sort of conclude and summarize, to be perfectly honest. And what message would you be giving CMOs? You also, you know, I know in the green room we talked about it doesn't have to be just retailers. You know, potentially you can look at bringing this into hospitality. You can bring this in into to any, any venues where you've got the the online marketing and you're trying to bring them into an offline, you know, we're looking at a serious number of transactions.

It's not just sort of five people a month. Um, but but where you've got the footfall and you've got the online and offline, you're trying to marry them together. It doesn't have to just be retail. Although that's where you, you specialise. And so what would be the message to to CMOs. What's the you know what if they continue to ignore this and don't join the dots. I mean and and is there any particular um, learnings that you can that you can give them anyway?

I mean.

It's, you know, what is the consequences of ignoring anything in your business, right? Like it's going to be bad. The message that we have for CMOs, of course, is just give it a try. And the just give it a try really comes from a place of this is your data, right? You should be as familiar with your data as the walled gardens are, right as the buying platforms are. So, you know, you can use one platform, right? One system that fills the role of a customer data platform.

You've got data scattered and things like DSPs. We pull it all together. And that's why a lot of our customers, we didn't go out to do this. Call us their morning dashboard because everything is there, right? I wish I could show you the thing right now, ranging from how much was spent yesterday. By which consumer groups has it been purchased? What are the the payment methods? How many of those transactions were matched right to to a known user? So all the things that you as a would want to know, just like that, off the crack of the bat first thing in the morning, it's all there. You stand to lose nothing by giving it a try. And I will say there are certain industries and certain sections of industries that are even more concerned with data protection and privacy than than the typical marketer for them. We also make the platform available as an on premises solution. So none of your data leaves your walls at all. We just install the thing for you. No loss of capability.

I've never sold something like that before. It's very cool, no loss of capabilities, and it lets you address a very common objection that CMOs and CTOs often have, which is, yeah, I don't care what kind of security you got. It is not leaving my four walls. And as a consumer, I respect the hell out of that.

You know, I was going to ask that question. And it's great to know that you've got an on premise solution, because I can imagine that some of these larger retailers that would be be an issue just to finish. Is there anything in particular on the roadmap that sort of suggests were the future, what plans you may have in terms of where you might take this or what you think think the future in with in terms of AI driven marketing may go.

We want to.

Be that morning dashboard for every retailer. We want them to be able to come in and say, aha, the shoes are selling really well with the socks, so let's sell the shoes with the socks and like, think about what you would have to do in an organisation if you didn't have all that data in one place.

You'd have to call your merchandising department. You would say, what's selling well? What have you put next to these two things? You have to call your individual store managers and say, well, how's the walkway from the shoe section of the department? Right. So like bridging all of those data points together and making them actionable and doing an increasingly good job of predicting that's what we want to do.

Here's a question for you. Have you integrated things like the weather yet?

We have.

Not. That tends to historically be the domain of the media buying platform like a DSP. But we'll use a signal on oh, it's raining out. Show the raincoat ad. We don't get involved in the actual media transaction. We are solely in the realm of of audience. And now I thought of something brilliant to say and a completely went out of my head. So we are equal. Fair turnabout is fair play I suppose.

One all like it. Well.

This has been brilliant, Louis, thank you so much.

And and think it's just a real reminder that if you're sat on this data, it is a gold mine. And it's about. Uh, getting the most out of existing customers and really increasing that retention and, uh, and the lifetime value. And I think that's, you know, that's what we're all after ultimately it's around how can you know, why are we in? Why are we in marketing? It's to help organizations, to help businesses make more money, sell more, sell more, increase profits and increase customer retention. So it sounds like this product, you know, if you are a of a large retailer, you need to be speaking to Lewis Lewis. Would you hang out online? Where should where should they be going to?

You are if you're.

A website person, you can go to peers on zone. If you are a social media person, it's at Lewis NYC on Twitter or X, and you can find me on LinkedIn as well. And you can find our blog posts and a whole host of resources on our site.

Not just about by our product, but sort of the trends that are impacting the market today. And suggestions that we have around them.

And all of those links will be in the show notes as well. Thank you so much, Lewis, and thank you so much for you, for listening, for being here, for turning up and for listening to what Lewis has shared with us. And to really get you thinking about how you could be using your data. We've been talking about AI driven success, the bridging the online and offline sales gap with Lewis Rothkopf today, and it's been really good. So listen, thanks for joining us. We look forward to welcoming you on the next podcast. Please do click that subscribe. Tell your friends. Share it. If this has been helpful, let me know. Find me on social and let me know. But for now, that is all. This has been the Jonny Ross fractional show and we will see you soon. Take care. Thanks, Lewis.