This week’s guest is programmatic media expert Lewis Rothkopf, CRO at Pairzon. Without relying on third-party cookies, Pairzon’s customer data platform facilitates precise audience targeting for omnichannel, DTC, and physical retailers. Lewis explains the importance of first-party data and how Pairzon’s platform tracks consumer actions after ad exposure. We also discuss the role that machine learning and AI play in Pairzon, illustrated by real-world examples and case studies.
Adrian Tennant: Coming up in this episode of In Clear Focus,
Lewis Rothkopf: We’re able to tell marketers literally how many consumers saw your ad and then went into the store and bought something and what did they buy and how much did it cost, and critically, what was your return on ad spend What did it cost you to get this consumer to go into the store and buy something?
Adrian Tennant: You’re listening to IN CLEAR FOCUS, fresh perspectives on marketing and advertising, produced weekly by BigEye, a strategy-led, full-service creative agency growing brands for clients globally. Hello, I’m your host, Adrian Tennant, Chief Strategy Officer. Thank you for joining us. In digital marketing, third-party cookies have long been the backbone of online advertising. These small text files stored on users’ devices have for years been tracking our behaviors with the intention of serving up more relevant and timely ads. As we’ve reported over the past couple of years, the landscape has been changing. With mounting privacy concerns and evolving regulations both here in the US and internationally, the days of third-party cookies are numbered. This shift has been reshaping programmatic advertising, which previously relied on cookies for targeting, retargeting, measuring campaign performance, and much more. The industry is now moving toward first-party data, that is, information about customers collected directly by retailers and brands, which it’s hoped will lead to a more privacy-conscious approach to targeted marketing. To explore what this transition means for marketers working for retailers and direct-to-consumer brands, our guest this week is Lewis Rothkopf, an expert in digital marketing with approaching 25 years of experience. Having held senior leadership roles at organizations including DoubleClick, MediaMath, BrightRoll, and Pubmatic, Lewis has witnessed the digital ad industry’s evolution firsthand. Now, as the chief revenue officer at Pairzon, he’s championing the move away from third-party cookies. To discuss how Pairzon is helping its clients change their approach to retail and DTC brand marketing, Lewis is joining us today from his home base of New York City. Lewis, welcome to IN CLEAR FOCUS.
Lewis Rothkopf: Thank you, Adrian. It’s great to be here with you.
Adrian Tennant: Well, Lewis, as I mentioned in the introduction, you’ve been at the forefront of digital media, working at many of the companies that created the programmatic ecosystem. So, how did you first enter the industry?
Lewis Rothkopf: It’s a funny story for me. I’m not sure if it’ll be a funny story for your listeners or yourself, but I’ll tell it anyhow. I graduated college with a degree in communications in 1999, going all the way back to the time when dinosaurs roamed the earth! And I was a communication major, I was focusing on advertising management. I learned how to buy full-page ads in magazines and how to advertise in newspapers. And this notion of the internet, really wasn’t much of a thing until my latter years of school. And so I graduated in 1999. Once again, for folks who’ve watched the industry, it was really the birth and the explosion of online advertising, and I got this phone call from a recruiter who said they saw my resume on Monster.com. And they were interested in seeing if I could come to work for them in an entry-level job. And I said, “Well, does it pay at least $30,000 a year? Because I have this apartment that I have to pay for. I just moved in.” And they said, “Yeah, I think we can take care of that for you.” And then, you know, same old story, one thing just led to another, and I’ve wound up in this industry for the past 24 years, coming up on a quarter century.
Adrian Tennant: Lewis, what have been some of the most dramatic changes you’ve witnessed in marketing technology during that period?
Lewis Rothkopf: A few things. So, you know, marketing and advertising, in particular, have really increased their use of automation and augmented or artificial intelligence and efficiency tools to make the business more accountable and to drive better results. And the challenge is that for all the changes that have taken place in the industry over the last 20 years, a lot still has not changed, and that’s a problem, right? We still have advertisers that are running ads and not knowing how or where or why their ads are performing. In many cases, they’re still measuring performance by old, outdated proxy metrics like click-through rate or cost-per-click and not looking at the actual impact that their advertising is having on their sales. That has to get better. And, having spent a very long time in this industry, it’s frustrating to me personally that we haven’t seemed to get to the place yet where marketers are only evaluating success based upon actual real-world metrics that matter. But the smart ones are doing it, and they’re doing it really well.
Adrian Tennant: Today, you’re the Chief Revenue Officer of Pairzon. Could you tell us briefly what Pairzon is and what your role as CRO entails?
Lewis Rothkopf: Yeah. So Pairzon is a company that is focused on building artificial intelligence-enriched marketing data platforms that help marketers understand who their best consumers for conversion are and whether their advertising is having a result on in-store sales. So it’s historically been very difficult. You mentioned third-party cookies a moment ago. Very difficult to, you know, recall an ad, to close the loop. A marketer runs a campaign on Publisher XYZ. Consumers see that campaign, and then what? Do they go to a store? Do they buy online? Very difficult to track online performance in an offline context. It’s also difficult to understand which of your customers are most likely to convert based on their previous shopping behavior. So, we do both of those things for marketers. We predict which of their consumers are likely to convert, and we use those predictions to help focus audiences and advertising targeting. And we close the loop from online advertising to in-store purchase or in-store action by virtue of having the transaction logs from the POS ingested into our platform, the algorithms are run against it, and we’re able to tell marketers literally how many consumers saw your ad and then went into the store and bought something and what did they buy and how much did it cost and critically, what was your return on ad spend for that campaign that you ran? What did it cost you to get this consumer to go into the store and buy something?
Adrian Tennant: Well, given your extensive background in digital advertising, what attracted you to Pairzon?
Lewis Rothkopf: You look at the last couple of decades I’ve been in the space, I’ve worked for some fantastic companies that have solved some really important problems, but the challenge of understanding offline behavior based on online advertising and understanding what consumers are doing when they put down their phone or turn off their computer, has really been bedeviling the space since the beginning. And there are sort of probabilistic solutions that you can use – like you can measure the geolocation of a user and see if maybe they saw the ads that are coming to the store. The problem with methods like that is they’re inherently probabilistic, right? So we know that a user went to this location after seeing an ad, but what if this location is a mall, right? Or what if it’s a strip mall? So you’re still unable to connect those dots with 100 percent certainty, and we’ve created this platform where you can do just that, where you are able to connect those dots and understand down to the individual purchase level, down to the individual SKU level, what is my marketing doing and how is it converting offline?
Adrian Tennant: Let’s explore what Pairzon is In more detail. First of all, what problems is it designed to solve, specifically?
Lewis Rothkopf: Yeah, so first and foremost, we predict the consumers that are most likely to convert. So, I’ll take a moment on that. When we work with a client, they connect their transaction logs to our platform. We then ingest those logs and make certain determinations as to which consumers are most likely to convert based upon their prior shopping behavior with that particular store. And here’s where the notion of first-party data and third-party data, third-party cookies really comes into play. All of the data that we leverage to help make these determinations is a hundred percent first-party data, right? There are no third-party segments that are being purchased here. There are no third-party cookies that are necessary. We take the data from the marketer’s own POS system. We analyze it, predict which audiences are likely to convert, and then we pump those audiences directly into the media execution platform of the marketer’s choice, whether it’s Google or Meta or TikTok or so forth. And we do that over an established API connection. What we’re not is a demand-side platform. We don’t buy media. We don’t get into the media buying process. We’re not in the supply chain. We simply create the audiences based upon our expertise, and then those audiences again are pushed into the media platform for however the advertiser wishes to advertise, wherever they wish to advertise. Our role in that process is to create the audiences and help the marketer activate them. Now, once the campaign is over or while the campaign is running, you’ve got to see if it’s working or not. And so we’re able to provide real-time insights into how these campaigns are performing, campaigns with our audiences. Would you like to A/B test and use our audiences for part of the campaign and some different audiences that you generated elsewhere for another part? Absolutely, you can do that. We don’t presume to tell marketers how to advertise or where to advertise. We just tell them what’s working. And that gets to the second part of our value, which is to understand offline conversions. I talked about this a little bit a moment ago, but I’ll expand on it a bit. If you want to know the impact that your online advertising is having on offline behavior. And it doesn’t just have to be purchases, right? You could be in a doctor’s office. Or you could be an auto repair chain. But you’re looking to understand what is happening once the consumer hears the ad, are they going into the store? And then what are they buying when they go into the store? That’s incredibly powerful. I mentioned some others of these platforms will use proxy methods like geolocation to understand did the consumer go to the store? Which is well and good, but you know, a big part of the challenge here is understanding, well, what did they buy and how much of them did they buy? And what items did they buy alongside one another? So now we have some more intelligence we can use to make recommendations to marketers about where they should be merchandising specific items basically in-store and how they should be recommending additional items for purchase with consumers that are on their online property. We do all that, and we do it with a pretty straightforward and easy-to-use user interface. And our goal is to really be a critical – but invisible to the consumer – part of the marketing process.
Adrian Tennant: One of the terms that we come across sometimes in this kind of space is a CDP or a customer data platform. So, first of all, for anyone listening who’s unfamiliar with the term CDP, can you just explain to us what it is?
Lewis Rothkopf: Yeah, so a custom data platform very simply is a platform that houses all of a marketer’s user data, first-party data, as well as information about how their consumers interact with them online and offline. It’s interesting that Pairzon can function and does function for many of our clients as the CDP. In other cases, though, marketers have existing CDPs that they’ve had in place for years, and there’s a sort of big monolithic effort internally to get them changed out. And so we work alongside the CDP so we can plug into not only those advertising platforms I mentioned a moment ago but also into any marketing automation tools like a CDP that clients are accessing today. The value that we add at that point is predictive analytics and the closed-loop marketing.
Adrian Tennant: Got it. You’ve indicated that the platform is designed for omnichannel retailers, but also for brands that sell direct-to-consumer. Pairzon has a specific approach to pairing in-store transactions with customers’ online identities. Lewis, can you explain how this process works and how it benefits your clients?
Lewis Rothkopf: Absolutely. So, certainly, if we are working with a customer who is online only, if they’re direct to consumer only, they don’t have physical locations, then understanding the effect of their online advertising on offline behavior is irrelevant because they don’t have offline behavior. We are still able to help those that are DTC or online only with all of the tools we’re able to provide around conversions and predicting which audiences are most likely to convert. The same as we would be able to do if those conversions were taking place offline. It’s actually a little bit easier even for marketers to understand the impact their online advertising is having on their online behavior. Where it gets tricky though, and where we add value to these DTC marketers, is in that predictive analytics step, right? So, understanding here’s the behavior from these anonymous users that has taken place over my properties over the past 12 months, and based upon what we’ve seen, here are the audience recommendations that we’re going to make. And we actually break the audiences out into whichever marketing methodology the marketer wishes to use. So, we have media mix modeling built into the platform. We have RFM: the recency, frequency, and monetary value, built into the platform so that marketers can see directly, where are my customers? Where are my consumers who are really good at the top of the spectrum? They come into the store, they go online frequently, and they purchase frequently. And when they do purchase, they tend to spend a lot of money. All the way down to the other tiers to has not bought online in a long time, has not visited the website, doesn’t respond to emails. And so here’s an opportunity to essentially reactivate those consumers and move them up in the model. We do all that regardless of whether the purchase takes place online or offline. And again, that’s where you get into this notion of first versus third-party data. the data that we analyze and give the marketers to execute is based 100 percent on what they know. We’re not buying third-party segments from some data syndicate. We’re simply helping marketers take the data they already have and put it to use.
Adrian Tennant: How does Pairzon ensure the accuracy and reliability of that paired data?
Lewis Rothkopf: So, let’s go back to the offline example for a moment. You are in a store, and you are at that store to purchase a pair of socks. And you purchase those socks, and at checkout, they say, “Hey, are you a member of our loyalty program? “And, the consumer will say, “Yes I am.” And we would then be able to use that loyalty card data to tie the purchase from anonymous user 12345 at the store to anonymous user 12345, who was exposed to the advertising online. Now, what if they say, “No, I’m not a member of your loyalty program,” or what if the store doesn’t have a loyalty program? And that’s okay too because we’re able to say to that consumer in the moment, “Can we have your email address so that we can send you some discounts, and so we can send you marketing materials?” or “Can we have your phone number so we can send you SMS, sale opportunities?” and so forth. And if they say “Yes,” then great! Now we once again have that bit of personal data, which is hashed – hash data is simply put, email address firstname.lastname@example.org gets changed into a long string of numbers and letters that are otherwise unidentifiable without having that same hash to decode it. That bit of personal data, which is hashed, is used by the media execution platforms like Meta, like TikTok, to tie that consumer’s offline interaction into information they already have on that consumer. So you do have this one-to-one match, this one-to-one pairing of user and, you know, offline and online. The same takes place if you’re an online-only merchant, right? You still have consumers who are likely logging in and signing up for email lists. I think it’s relatively rare that you would do a complete checkout as a guest and not provide any personal information because, after all, you have to have the product you purchased delivered either electronically to your email address or physically to your postal address. Now, in the cases where the consumer just does not provide any information, they are a completely blank slate, they’re not telling us anything or the marketer is not collecting anything, then we can’t do anything. We need to understand who the individual user is so that we can pair it with the user on the internet. if we don’t have that information, there’s nothing we or anybody else can do.
Adrian Tennant: Let’s take a short break. We’ll be right back after this message.
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Adrian Tennant: Welcome back. I’m talking with Lewis Rothkopf, Chief Revenue Officer for Pairzon, an AI-driven customer data platform that pairs in-store transactions with online identities, enabling retailers and DTC brands to optimize marketing campaigns across digital and brick-and-mortar environments. Well, on this podcast, we track developments in artificial intelligence, especially use cases that support marketing and advertising functions. So Lewis, how does Pairzon leverage AI?
Lewis Rothkopf: We make use of the incredible amount of data that marketers are generating from both their online and in-store purchases, but also their online advertising So, you know, you talked about omnichannel and the need to make sure that we’re covering our bases at all points in the funnel. There’s a lot of data that is generated from that process, way more than any analyst would be able to crunch on their own. And in fact, we had a conversation with a prospective customer a few weeks ago. And we kind of did our 90-second elevator pitch before getting into the demo. And the person we were pitching said, “This is a dream come true, you know, what you are describing, we have literally a team of analysts doing manually using Excel documents, and it’s just wildly inefficient.” Of course, it is. And so, using the incredible power of machine learning and artificial intelligence, we’re able to take all that data, do the crunching in real time, and then provide the company with actionable insights that they can use to improve their campaigns in real time. There’s nothing worse than running a marketing campaign, getting your results six weeks after it ended, and finding out that people really don’t like blue banners. And so being able to provide those insights and those learnings while the campaigns are in flight is incredibly powerful. On the flip side, with the AI in process, you’re able to understand which of those consumers is most likely to convert based upon the information that you have from them, that the AI is able to analyze and crunch and make available. So let’s say you are a consumer who has bought white socks, gosh, every week for the last 20 years of your life. I don’t know why you would do that, but hey, let’s let’s just pretend! And then let’s say that you wake up one morning and you say, “I’ve wasted 20 years buying white socks. I’m going to stop buying socks altogether. I’m just going to go sockless from now on.” Now, if we kept you in the purchase category in the audience segment of “guy who buys white socks every week,” we would continue to send you advertising messages that are targeted to an existing strong customer. But now you’re no longer in that audience. You no longer meet the criteria of somebody who buys white socks every week. So, in real-time, it is a living, breathing audience that the AI will add or subtract based upon the behavior of the consumer. So they see, “Okay, Mr. White sock guy, he came in every day for 20 years, but now he’s not coming in at all – let’s move him into the lowest tier and, identify him as somebody who we have an opportunity to reactivate, but is not somebody we can any longer count on, for being a white sock aficionado.”
Adrian Tennant: Got it. Well, beyond targeted marketing, what other reasons or use cases might retailers or brands have for considering Pezon in their Martech stack?
Lewis Rothkopf: You have got to know what’s going on, right? You, as a marketer, as a CMO, have to know with certainty what is happening to your advertising after it leaves the advertising agency and makes its way into the consumer’s eyeball or ear. if you don’t know that, your marketing is really likely to be ineffective or at least inefficient and suboptimal. And we talked a lot to your point about marketers and retailers and stores, but this applies to anybody who is selling anything or is driving any sort of results online. To use a quick example, let’s say that the local government wants to increase education about the new ‘flu vaccine that came out, and so they want to understand who is the audience we should send this to. That’s relatively easy, but then what happened when we actually ran this campaign? What did people do? When they got the information, did they search for a pharmacy they could get their shot at? Did they do some research on what the shots are made of? Or did they go into a physical pharmacy and interact and got the shot in the arm? That’s a sort of example that doesn’t exist in our business today, but easily could as one that is possible and has nothing to do with what you’re buying in stores or online. It simply allows marketers and advertisers to understand whether their campaign is changing behavior in the right way or the wrong way.
Adrian Tennant: Does Pairzon potentially replace existing tools, or is it additive to a stack?
Lewis Rothkopf: We are more than happy to be your CDP. We have all the functionality of the CDP. We have customers who are using us today as the CDP, but as I mentioned at the top, we would not restrict, and we don’t restrict ourselves to those who are looking to buy or replace a CDP. What we do is complementary to that of a CDP for our customers who already have one in place. It’s the predictive analytics and being able to tell and show here are the people who are most likely to convert that supplements the data warehousing that a CDP and a marketing automation platform typically make available to customers.
Adrian Tennant: As I mentioned in the introduction, one of the objectives of removing cookies from the ecosystem was really around privacy concerns. How does Pairzon address concerns related to data privacy and compliance, especially with the European regulations, GDPR, and California’s CPRA?
Lewis Rothkopf: Yeah, so we do everything that we can to not ever see unhashed, personally identifiable information. So when a transfer is made from the marketer’s database, their POS transactions, to our platform for ingesting, and then we take that data and we push it out to the ad execution platforms, the social platforms, that data is hashed. And so, we then ensure that anything we have in the platform is protected. We are able to operate both as a SaaS platform, we also have customers that use us on-premises, and this is really good for those companies that have specific data privacy concerns. Those that maybe are banks, those that deal with sensitive personal data, so they don’t have to worry at all about their data leaving their home, their office, and going into the cloud somewhere. They’re able to run it all on their existing infrastructure, and we still do for them all the things that we do when you purchase on the SaaS platform.
Adrian Tennant: What does the onboarding process look like for a new retailer or DTC brand integrating Pairzon into their MarTech stack?
Lewis Rothkopf: So the first thing we need is your transaction logs from the past year. Two years are even better. One year is fine when we’re training the AI on what is the makeup of this marketer’s audience? What are they buying? How frequently do they come into the store? What items do they tend to be buying together? And so now we’ve got this model that we’ve made of the consumer behavior from Adrian’s store. We then work with the marketer to incorporate the CDP with our platform if they are using a CDP. If they’re not using the CDP, we become their CDP. We have relationships and integrations in place with POS providers so that when we integrate a marketer’s transaction logs into our platform, We’re not building the wheel from scratch. We already have integrations into many of these platforms. So that is a completely transparent process to the marketer. And that’s really it, because on the other side, we have these direct integrations into the media execution platforms, the social networks, and so forth, as well as SMS providers and email service providers. So it’s not the first time we’re doing any of this, and it makes the integration process go very smoothly. Like any other integration, testing and troubleshooting are probably the most important parts of ensuring that we’re ready to go live, but it really is a very straightforward and simple process. And where it gets exciting is, now that we’ve got all this historical data and we start to see some new real-time data streaming in, well, now we’re able to take those understandings and assumptions, validate them against data that’s coming in the door, and then operate in real time to shift audiences, to shift consumers in and out, and even to recommend products to be sold together next to each other, in-store and online, based upon what we know of past behavior at that market or shop, as well as the real-time information coming in on their active transactions.
Adrian Tennant: As teams are getting used to using the platform, what kinds of support resources does Pairzon provide?
Lewis Rothkopf: So we have the customer success team that’s responsible for exactly that, ensuring that they know our customers’ businesses inside and out, ensuring a smooth integration and a smooth launch process. And then being this customer data marketing expert who sits alongside our customers and helps them get the most value out of the platform. Our platform is like a gym membership: If you buy it but don’t show up and don’t use it, you’re not going to get stronger. We think about it the same way. You have to actually make use of the data so that it becomes beneficial to your process and to your sales. it’s not a set-it-and-forget-it type tool. You take the actionable insights that come out of it, and you work with them. And our team works with our customers to understand, “Great, so I see that everybody likes the purple banner because when they see the purple banner they go into the store and they buy $100 worth of merchandise. Have you thought of maybe changing all of your banners to purple because maybe that would increase sales even more and increase your return on ad spend?? Those sorts of insights are commonplace when you are spending all day, every day, living in the system, you know, helping dozens of customers and finding those points of similarity between how multiple customers execute, using the data that we make available to them and build audiences based upon what we predict as being, they’re most likely to buy.
Adrian Tennant: Lewis, I know you’re ramping up operations here in North America, but what kind of ROI or other business effects do existing clients in other markets typically see from deploying Pairzon?
Lewis Rothkopf: Yeah, so where we get credit or not, where we do a good job or not – and fortunately, we do – is the return on ad spend (ROAS). So that’s a really important metric for marketers to understand. How much was I making off of each ad that I was running before working with Pairzon, and how much am I making now? And, you know, everybody likes to give the most exciting examples, and so I’m happy to share case studies or other examples with your listeners if they like. We have one campaign with one marketer that had a 20x increase in return on ad spend simply by using our audiences, and they did run it as a test. We’re happy to go into head-to-heads with our competitors. They were initially running audiences that were generated by another partner. We said to them, “Don’t get rid of that other partner entirely. Go ahead and put us in for, call it, 40 to 50 percent of the advertising to your audiences.” And once they did, they saw a 20x increase in ROAS simply from targeting consumers in these audiences that are most likely to convert. When you buy audiences from a third party, right, using third-party data, well, now you’re getting the benefit of knowing with a reasonably high likelihood that this consumer is in-market for a luxury auto, or this consumer likes to buy chocolate chip cookies at the grocery store. And that’s going to get you some ROI improvement. That’s going to get you a little bit better return on ad spend because you’re now targeting those users who ostensibly are most likely to be good customers of yours. But it’s not exact. If there’s a lot of probabilistic room in the margins there for consumers to be placed in segments in which they really don’t belong. That tends to happen when you’re buying third-party data, perhaps when somebody is buying that data from another third party versus using your own data. I mean, you literally have nothing to lose because you are targeting your audiences. And so the ROI they tend to see is quite strong. We also are a humble company in what we do, and we try very hard to make our products one of the least significant cost components of what they do as part of their marketing stack. We want to make it easy to work with people and they’ve seen exactly that in the markets in which we’ve served thus far. And we do have a couple of pilots going here in North America. North America, as you know, is a tough market to crack but it’s exactly where we want to be, and we are growing, and we believe that as more and more marketers see our solution and see – most importantly – the impact that our solution has on their real-world sales, we think we’re going to take a good chunk of market share here. At least, that’s what we hope. We’ll see!
Adrian Tennant: Well, we love examples here on IN CLEAR FOCUS. So I’m curious: after implementing Pairzon, have any retail clients completely changed their marketing, sales, or customer service strategies that you know of?
Lewis Rothkopf: They’ve changed their audiences in several cases to run exclusively on the audiences we generate for them. And again, I can’t stress enough how important it is for marketers to evaluate the differences between first and third-party data. First-party data is the most precious and unique thing they have, and so why would you trust some other third party to make data decisions, to make audience segmentation decisions based upon some probabilistic algorithm, versus, “No, no, no, like – I know Adrian like he’s one of my consumers, and I know what he did online, and I know what he did in the store, and so I know with deterministic certainty that I’m doing the right thing with this right consumer and the right advertising on the right media property,” and so forth.
Adrian Tennant: Lewis, are there any upcoming features or new tools in the product roadmap that you’re particularly excited about?
Lewis Rothkopf: So we’re always adding. Part of being a small early-stage company is that we can be incredibly scrappy and agile. I’ll share my favorite feature in the platform. So in the Pairzon user interface, you have a complete view of your business – you’re using your data. So purchases and ROAS and ROI and all the way down to the individual SKU level. What is happening with this product? What is happening with this product online? What’s happening in stores? We have a really cool feature in the platform. It’s new, and it visually represents which products are linked to one another in terms of being bought alongside each other. So we see that folks who buy white socks in-store almost always buy white T-shirts as well. And so maybe it makes sense to put the white socks next to the white T-shirts in the store display. And maybe it makes sense to recommend the white T-shirts when somebody buys the white socks or when they add them to their cart. I love that. It’s a very cool visual representation of what are the relationships between this product and that product. And what are the actionable takeaways that we can glean from that information? I love it, but I love all of our platforms. So, obviously, I’m biased.
Adrian Tennant: Well, if IN CLEAR FOCUS listeners are interested in learning more about the Pairzon platform, what’s the best way to get in touch with you?
Lewis Rothkopf: I’d love for them to email me directly. It’s Lewis – L, E, W, I, S at Pairzon – P, A, I, R, Z, O, N dot com. They can also go to our website, which has some really good information, and they can view some blog posts we’ve made. We’re happy to share case studies so that marketers who’ve listened to this know that they’re walking into an organization that understands their concerns, understands the challenges that they have as marketers, has solved these problems, and has addressed these challenges for dozens of marketers already.
Adrian Tennant: Well, Lewis, I’m off to buy some white socks and some white T-shirts!
Lewis Rothkopf: Good man!
Adrian Tennant: Thank you very much for being our guest on IN CLEAR FOCUS.
Lewis Rothkopf: It’s been my pleasure. Thank you so much, Adrian.
Adrian Tennant: Thanks again to my guest this week, Lewis Rothkopf, Chief Revenue Officer for Pairzon. As always, you’ll find a full transcript of our conversation, along with links to the resources we discussed, on the BigEye website at bigeyeagency.com – just select “Podcast” from the menu. Thanks for listening to IN CLEAR FOCUS, produced by Bigeye. I’ve been your host, Adrian Tennant. Until next week, goodbye.