#6 Dr. Markus Wübben | CRM Democratization | Customer Lifetime Value Mastery | AI Integration Challenges

Ready to revolutionize your understanding of Customer Relationship Management? Join us as we welcome Markus, a true pioneer in the CRM realm with nearly two decades of expertise. He shares his transformative journey from orchestrating loyalty programs to crafting a state-of-the-art customer data platform. Discover why CRM was once underestimated in newer businesses, and how Markus endeavors to democratize CRM tools, making them robust yet accessible for marketers everywhere.

Learn the intricate dance with Customer Lifetime Value (CLV) as we explore its multifaceted challenges. From B2B complexities to the pressing need for precise predictive analytics, we dissect the nuances that make CLV a critical yet often misunderstood metric. Markus brings clarity to the often murky waters of CLV, highlighting why a tailored approach, grounded in solid data, is essential for predicting future customer actions and achieving long-term success.

Balancing immediate sales targets with nurturing lasting customer relationships presents its own set of challenges. We take a closer look at how AI can be a game-changer for CRM, while also addressing the hurdles of fragmented data systems and the evolving regulatory landscape post-GDPR. Markus offers insights into how businesses can optimize customer journeys through strategic data management, ensuring that AI and CRM integration isn't just a possibility, but a powerful reality.

Transcript

Speaker 1 0:05

Hey there and welcome to Inside CRM. I'm your host, Jessica Janssen, and I'm so excited to have you join us today. On this podcast, we dive into the world of CRM with experts from all kinds of companies, from fresh-faced startups to billion-dollar unicorns and solid, established corporations. Our goal To share practical insights and strategies that you can actually use. Each episode is a quick 20-minute chat packed with tips and tricks to help you get the most out of your CRM. So grab a cup of coffee, get comfy and let's get started.

Speaker 2 0:45

Let's get started. I think for me, you were one of the persons that, quite early, were pretty proactive and also loud around CRM and its impact, talking a lot around customer lifetime value, and I think back in the days I really appreciate it. I still think we need to be louder, but I think you were one of the first movers that really pushed it and tried to get attention around it. First of all, grateful for that, and with that, maybe not everyone is aware what you have done in the past, what's your background, so it'd be great if you can give like a little introduction to yourself. Sure.

Speaker 3 1:23

Would love to Thanks. Thanks so much to yourself. Sure, I would love to Thanks. Thanks so much for the warm intro. I'm Markus.

Speaker 3 1:28

I've been working in CRM or with data for about 19 years now. I started 2005 with a PhD. Currently I'm actually consulting companies on CRM strategy. But what I've been doing, at least for the last nine years. Before that, I was running a company called CrossEngage. We're a customer data and prediction platform. Last nine years before that, I was running a company called Crossengage. We're a customer data and prediction platform. The last year to. A Dutch company Now uses the CDP in their entire ecosystem basically.

Speaker 3 1:52

But many years before that I started in a loyalty program called Deutschland Card. Like we had a physical card and you would check how many glasses of marmalade somebody would be buying and people were really like excited, oh, you're really like you're lasering the customers and you really know everything about me. Okay, a couple of years later there was Facebook and Google and all these players and what it was insane what people were actually, how afraid they were about these things. But started my career there pretty fast, knew that corporate is not mine, so I went to a company called Rocket Intranet where Sri Florian Heinemann hired me. He's now running Project A as well and I was building up the CRM departments of all the rocket ventures back in the day. So mostly were e-commerce ventures Dafeeti in the South Americas, lenio, jumia, lazada, zalora, the iconic Lamoda in Russia back in the days, zalando, of course, all these things, oh, actually.

Speaker 3 2:44

So the one thing why I'm so passionate about CRM is I did my PhD in basically CRM and we found out back in the days already that the value of a customer is driven by the retention time, so as the time the customer is with the company. So why don't we do everything around this? And what I noticed actually also back in the at the rocket times, it was all about quick sales quick sales, right, and CRM was it sent out a newsletter seeing how many revenues we were making and back in the days it was actually viable, right, because we could just pour in new customers so easily. It was so easy to buy new customers. We put them in the funnel.

Speaker 3 3:15

We were also doing work with Groupon. It was just insane like how many people like we're losing on the newsletter side just because we didn't do any great CRM, but they were able to pour in new customers. The first fascinating was actually that CRM really was undervalued and one of the reasons, I thought, was that back in the days, it was the data platforms that were really bad. We had something called MailChimp in 2012. I don't know if you still use that, yeah.

Speaker 4 3:41

I still do.

Speaker 3 3:41

Yeah, right, it's still big, right. It's just, if you compared that to like what Facebook and Google could do in terms of segment targeting was just a joke. I remember we were in the rocket office and the people were like, on the acquisition side, we're running these kinds of campaigns on these kinds of segments, like super interactive and what they purchased and what they did on the website and all this stuff, and it's like a gender. We have two versions of the newsletter now. It's actually one template. It's dynamic. It was a bit like that, right, it was like that can't be true, right.

Speaker 3 4:11

So back in the time at Rocket, basically, I understood that, first of all, crm is totally undervalued in most companies. It's actually not in the old, very old fashioned companies the catalog retailers and stuff. They've been doing this for 20, 30 years, right, because they wanted to optimize their catalog sending and stuff. But for the new companies, it was just crazy how undervalued CRM is. It's not that case anymore. It's so cool what you are presenting every month of how companies, even startups, are investing in CRM, and I think it's really cool to give them a good head start. Back in the days, it really wasn't that way, and this brought me to the idea of building a customer data platform, so a platform where you have all kinds of data in it's behavioral data, it's attributes around this and you don't need SQL to get the data. But you can actually do this with a user interface. I don't know.

Speaker 2 5:06

Back in the days it was like you had to talk to BI to get a segment and you would.

Speaker 3 5:08

But back in the day it was still it's still yeah, okay, yeah, you have some data and then you go to BI and do this. But that was the idea of building a CDP right, bringing the data to the marketer and actually make sure that we have meaningful segments in there. And another law that I have is for customer lifetime value. I believe in that metric. It can be quite challenging to implement that, but, on a basic note, I believe this is the one metric we really need in CRM to understand our efforts and effects on the value of a customer, not only the single transaction. And maybe why I'm so passionate about that is we once ran a test at a company that there was a CRM manager calling us at CrossFit Engage and he was saying like, hey, management wants to send another newsletter. We already have four a week, but they want the additional revenues. And they said, hey, additional newsletter will give you additional revenues. So, look, let's do this. And the CRM manager was really worried. It was there was already a high subscription rate. Complaints were high. They're just classical sales was dominating CRM. I don't know if you know this fact, like the sales department has a lot of power and they come to you. So that's another one, because they have these short-term goals, right, we can understand that, right. But what we did actually, we went and applied a CLV metric and what we did was we were predicting who actually should receive less emails, right, and what we did. We then run a test and we had this one group who only got one newsletter that week and the other ones got four. And we used that with machine learning, like we try to understand machine learning, and we found out these two groups didn't differ any at all, neither in the conversion rate nor the money over a certain period of time. For the simple newsletter. Yes, we got some downturns, but if you look we looked at it for eight weeks we didn't see any difference between four newsletters and one newsletter between these two. It's just crazy, right. So it's basically what is the name of? Tia Monica, I don't know. You just spread the revenues right Across more newsletters. That's what you basically do.

Speaker 3 7:06

And we unrevealed that using like a long-term metric, and that was lifetime value. Basically, lifetime value is a little bit misunderstood. It's not always the lifetime. You can also say what's the lifetime of the next 30 days, 30, whatever. Six months, three months, whatever you need that to be. It doesn't need to be that sometimes a misunderstanding in CLV that it's like this one metric what is Jessica going to be worth to whatever Zalando in the next 20 years? That's bullshit, right. You can also look at what's she going to be, what's her worth in the category shirts in the next 30 days, and that might also relate to your question what you had early on in terms of understanding who will receive, who's likely to convert on a coupon or should be receiving one.

Speaker 3 7:43

In the CLV metric you have two things you have conversion likelihood and you have the amount, the basket value. So it's basically like how probable, what's the probability of me buying next month and how much is that basket going to be? So if there's a probability it says for 99%, marcus is buying, but only for one Euro, I probably wouldn't give a coupon. I would probably do a bundle to increase the value, increase the basket. But if I see there's only like a 10% chance that I'm converting but my basket will then be very likely to be very big, then I'd probably be giving a coupon because I need to push the customer with a cliff and this is what I find so fascinating about the customer lifetime value.

Speaker 3 8:19

It's all in there. It's that one metric. You can just bend it and stretch it to certain timeframes in there. It's not one metric. You can just bend it and stretch it to certain time frames, to different categories, to sub-segments, all this stuff, and then it gets very flexible. But it also can get very complex. And that's what I've, yeah, that's what I've loved doing for for a long while. And that's a little bit about me, what I'm thinking about, what I do and I talk.

Speaker 2 8:41

But as you were talking about customer lifetime value also what I noticed. We always talk about it, but I also asked, like attendees in one round of the meetups, like, what kind of KPIs are you using? And customer lifetime value was mentioned 11% of the time. So what I see is a lot of companies struggle with implementing it and using it. What is your experience there? Why it's difficult for some companies to do that.

Speaker 3 9:16

It's basically two things. So the first thing is how immediate you can measure changes in CLV. So if you have a good system, yes, you can do this quite fast, right, you can see the increase in customer lifetime value if you send out a campaign. But if you don't compute that or you don't have the data to do this, basically the CLV thing is a bit of a long-term effort. You have to wait three months to see whether stuff has an effect, and that's a problem, right. You tell management, right, wait three months and then we'll see what our million dollars of investment did. That's a problem, right. They want to see immediate results and that's why these conversion metrics are so popular, because you see, I put in 100 and I get out 200. Revenue Doesn't matter to the customer, right, but you put in 100, you get out 200. It's so easy to compute the ROI, return on ad spend, whatever. So the CLV metric is very hard for that, because if you don't do this right, it's a long-term process. Looking at what really changes, you can actually go into very deep into CLV and immediately see the changes. But you have to be predictive. You have to use AI and it does a prediction, and a prediction is inherently wrong because it's a prediction, but you and a prediction is inherently wrong because it's a prediction, but you can still. You can do this. Right, you could get more immediate feedback, but it's tough.

Speaker 3 10:27

And the second thing is CoV is not this one concept where you say this is this one thing and it works for every company? I just was on a panel with of the marketing browser and I was talking to Elza Dietz and she was at eBay, kleinanzeigen, and she said this is so tough for us to model even B2B customer lifetime value because the processes there are so complex that I don't really understand what is my cost? What is actually the revenue? What is revenue? What is actually retention in this thing? It's so hard to do so because customer lifetime value what is it? It's the lifetime of the customer to a brand and that what's the value? The value is basically the margin or the revenues minus the cost, divided by some discount factor. Right, that's always future, it's always future. So there are a couple of problems in there, right?

Speaker 3 11:13

Who's the customer in the B2B? Is it a buying center? And I think you had this question earlier on as well right, do we know what the definition of a customer is. A lot of companies don't really know that. So in a B2B setting it's like it's Klaus, no, it's Jessica. Jessica makes the decision, yeah, but Klaus has the money. So who's the customer? This is very difficult, right? Or think about something like MediaMarkt. They have, they sell I don't know, bosch washing machines. So if I go into the store and then I buy this Bosch washing machine, am I a customer of Media Markt? Am I a customer of Bosch? Am I a customer of both? Who am I the customer to? So it's not that trivial to understand in many settings who the customer is, as I said, in the B2B setting. So that's very difficult.

Speaker 3 11:56

The other thing is can I actually look into the future? Because a lot of people look into the past in terms of customer lifetime value and that really doesn't reflect what customer lifetime value is all about. It's all into the future. So what you very often see is you see formulas that go into the past. Which is average customer lifetime, so how long people gonna stay? What's the average margin, and that's about it, right? And you just multiply that and that's basically your number. So you have one number for every customer or maybe for a customer segment. Does it help you anything? Not really right. So it needs to be very individual. So you need to be a CLV for your view. That increases complexity quite a bit. So it's these things, right.

Speaker 3 12:35

So who is a customer? How can I look into the future? Do I actually have data? Do I understand how to model the future? What am I data to understand how to model the future? What are my? Do I? Do you know the?

Speaker 3 12:46

If you do a, if you do a sale, who's in e-commerce or sales goods? Do you know for every customer what the product margin is? Basically? There it starts, right. Then now we get into the problem and where the? Where? There's the problem with CLV? Because we're talking about margin and not about revenue, right, because that's inherently in the customer lifetime value. It's the problem with CLV because we're talking about margin and not about revenue. Right, because that's inherently in the customer lifetime value. It's the profit. If we don't even understand what the margin is you're getting from one single product you're sending out. How can you actually compute a customer lifetime? It's not possible, right? If there's a margin of 10% or 90%, it completely changes the lifetime value and then you're back to okay, I look at revenue and then you're back to okay, I look at the revenue of the newsletter. This is like kind of the chain that happens in this thing. So there's even more complications. So it's about understanding what margins are, understanding actually how long customers are going to be with you. For now it's not that. So if you are Netflix, it's simple to compute whether somebody is still a customer. I'm just writing them and say Netflix, I'm out.

Speaker 3 13:50

The pricing piece didn't like it. In the best case, if you're in a hotel or e-commerce or whatever is transactional, and you're leaving the hotel, how is the hotel supposed to know that you're going to come back? So how do I model? It's tough right. It Is the hotel supposed to know that you're going to come back. So how do I model? How do it's hard, tough right. It's statistical modeling again. And then we get it complex. And then it gets complex and that's the big problem. Clv, who is the customer? It's different in different business models, b2b versus B2C. Then it's different in different verticals, whether you have e-commerce or whether you have kind of subscription basis. Then you need to be able to look into the future versus just going to the past. And if we add all this stuff up, given that it's a metric that if you don't use it it takes a long while to change. People are saying I'm going back to revenue producer and that's the problem.

Speaker 2 14:39

I would say Is there any company type where you would really recommend to put the effort in to do that calculation?

Speaker 3 14:49

Yeah, a subscription base is super simple. It's because you have pretty steady margins, steady revenue. You can actually compute the churn quite easily, predict the churn quite easily. And then you do sub-segment CLVs. So you say, hey, for this cohort that we won with this campaign or this kind of viewers have this kind of CLV. So it's super simple to then compare cohorts.

Speaker 3 15:12

But the measuring in terms of in a subscription business is significantly simpler than in transactional business. So in a transactional business it gets harder. But you need data points. So if you have a long, that's another complexity. So if you have long purchase cycle I don't know it's who does furniture six months or a year, whatever you don't have enough data points to go there. If you have a business model that has a lot of data points, that certainly makes sense. And you need past data yeah, otherwise it's it's tough and you can't really do anything. So again, subscription business check yeah, it should be super, otherwise it's tough and you can't really do anything. So again, subscription business check yeah, it should be super straightforward. Never simple, but straightforward. If you have a transactional business, it's more than you need the data points. If you don't have the data points, you have a long purchase cycle, super tough If you don't, if you can't map any data and that's another thing you don't have a lot of data to run this.

Speaker 3 16:04

It's also difficult. You can start CLV with just transaction data and gives you a good overview, but if you really want to do CLV you need more behavioral data. I'll give an example Reactivation campaigns. So very often we're sending it only on the transaction date, right? So last transaction was 30 days ago, 60 days ago, but isn't there a difference whether somebody has put something into the basket in the meantime or not? Right? It's two different, completely different groups, right? The one was still engaging, it just didn't buy, and the other one wasn't even engaged, right? So you can't model this into your CLV and your campaigns. Then it gets hard. So being able to track a significant amount of data is another thing.

Speaker 2 16:47

Yeah, there's one question behind you. Do you have any thoughts on retail media and pushing for more or bad placements, because I have the feeling that this often harms CLV.

Speaker 3 17:01

Look a great question, and that's the. I think the biggest challenge that we have in CRM is we need to balance two things that one hand side, we need to balance the salespeople who want to make sales and who need to make sales, and they're usually incentivized on making sales. You need to sell off these many units, right, and that's when you get your bonus, jessica, right, and then I'll just set it on a campaign.

Speaker 3 17:25

So this is what a bonus, jessica, right. And then I'll just set it on a campaign Stop that, right. So this is what the salespeople do, right? And then they push. And on CRM, we have the responsibility of maintaining that relationship, and this is the big problem. And now this is sometimes the problem, why CRM is not always on has the decisive, because sales team says but we need to sell another 50,000 units and otherwise we won't make our business plan. So yes, it does.

Speaker 3 17:50

I think it's a constant struggle to find the balance around it. Was it you? I think this is one of the bigger challenges and I see this with a lot of companies when I do consulting as well. It's the balance between let's the customer experience and, on the other hand, the push to sell something. And I think this is one of the big challenges that we are having in CRM To make sure that we can maintain this customer relationship, because acquiring new customers has become very expensive. A lot of companies have understood that, and I get into consulting crisis. They're like no, we have all these customers, let's make something out of this. Right, let's send another newsletter. That's sometimes the answer, in fact, but this is the balance, and so you're absolutely right.

Speaker 3 18:32

Very often it's not and the problem is the systems are not connected and that's actually a super interesting topic how this is going to unroll for the next couple of years.

Challenges in CRM and AI Integration

Speaker 3 18:39

What? How are we going to use AI in Sierra if we don't have any connected data? Now how are we going to make sure it cuts like a customer journey? Is, let's say, ai optimized? Because we can't like we've done great work in customer journey mapping, but it's also like a lot of work or you have to reduce to certain main journeys, it's not like super individual. With AI we have the opportunity to create a great holistic customer journey, but if we don't have the data and there's some third party provider who sends an email to you that screws you up and you're like I'm done, you're annoyed, then we can't optimize that. But what I did in my last project was the success metric between the CRM departments and sales was a balance between the sales traction that they had, so the sales that we were making, minus a certain cost factor, times the number of unsubscribes, minus the cost of acquiring new customers because they were gone if they were unsubscribed or they were churning so they could push hard in the sales department.

Speaker 3 19:39

But if they had more, like a lot of unsubscriptions and there was like I think, $20 or 20 euros like a lot of unsubscriptions and there was like I think $20 or 20 euros that they said wasn't unsubscribed, so if they pushed too hard, their metric would decrease significantly. So even though they made a million but they just lost 800,000 customer potential, then they get penalties for this right. And then we finally can compare like how good these sales units are taking care of the customer while still making money, because then you have a ratio, because you have this 1 million that you made, but effectively you only generated 200,000, right, because of the lost customers. Then you have an effective rate of 20%. And then you can just compare that and say, hey, sales unit A, why is this 20%? And you have 80%, what are you doing different? And then they start talking because they get the penalty. But if we go to them and say, hey, with this customer relationship, it's important that we are just maintaining that, otherwise they get pissed off. Yeah, let's send it out the newsletter.

Speaker 4 20:40

A lot of companies are talking about synthetic profiles, which is really crazy. So basically you feed the AI data on your customer base and then they create these profiles and then you basically perform your market research and your customer experience queries on the profile.

Speaker 2 20:55

Yeah.

Speaker 3 21:00

I think it's a perfect bridge because I also wanted to go into the AI topic with you. I love it, love it, love it, I love it. Let me. I'm a friend of short answers, so let me show you. So it's a great question. It's a really great question because all the advancement that we are currently seeing in AI is built on top of the amounts of data that we are currently seeing, especially the large language models, right? So the large language models.

Speaker 3 21:23

They're currently saying we've already been trained on the entire internet. We need new data. It's crazy, right. So this is a problem for them to get new data. They learned the entire internet. It's crazy, right. And pictures, right the way the pic. There's so many data around this. Now, look at CRM. Look at what has happened since 2018 with GDPR. We're all concerned about how long we store data, and not only the GDPR legal side, which there is a case for, but also still technically, the data is like spread around and it's a different system. So the last consulting project they had like sales cloud from Salesforce. They had the marketing cloud, they had the service cloud. They didn't really know what customer was actually which and they couldn't really match that.

Speaker 2 22:09

Although that's what they sell, right? Yes, everything is connected.

Speaker 3 22:11

360 degree user view right, yeah, this is connected. 360 degree user view right, that's, yeah, this mystery of 360 degree user view. So so I'm trying to understand. I'm wondering how we're going to benefit from the advances in AI and CRM if we cut off our limbs all the time. And that's data. To me, there's still too big of a problem in terms of the availability of good customer data and having this in a great location, understanding what data is there. And my last consulting project began they had huge amounts of data, but they didn't know about it, so one unit had this and the other one had this.

Speaker 3 22:50

I think this is a huge problem in CRM and I think the discipline of CRM is going to suffer, not on the content optimization part, because there we can use picture generation that's easy. Text generation, like companies like Neuroflash and whatnot, like in product description, blah, blah, blah. That's soft, but on the segment side, it's really tough. And now I think the only way to go forward is actually synthetic data. But the problem in this with these companies is now how are you going to, how are you going to produce synthetic profiles if your base data is not good? You don't have any, let's say, seed data. That's really good. If you had 10,000 customers customers which are like really great and you understand exactly what you did and it has this full profile I'm pretty confident that you can build a lot of synthetic profiles, right. So what is the problem actually in this?

Speaker 3 23:47

So these, the ai models. They usually use these neural neural networks. Looks like little brain. Right before that it was machine learning. It it was logistic regression, like super simple things. We have all done regression in university. Most likely there's assumptions in there. So mathematical models, super simple to use. Ai is using these neural networks which need these huge amounts of data. They need them, Otherwise they just spit out crap. You can actually still see that a little bit on the GBT-3 versions.

Speaker 3 24:19

German was still very bad, so I wrote a poem for my mom she's the best mom. It was still awkward, but in English it was perfect. But she doesn't speak any English, so I had to take the German version and make it up. Why? Because of the availability of the data, right, and the training data. So, long story short for these new models, you need these huge amounts of data, you will need synthetic data, but if we still don't have any base data, that's super correct. I don't understand. I don't know how to extrapolate good profiles from a shitty source, and that's what I'm still struggling with, to be honest.

Speaker 3 24:52

But I think the biggest challenge in CRM for me for the next years, if we don't want to get stuck like all the other disciplines are really making huge progress Robotics, logistics, self-driving cars in the factory, all along the value chain. Why? Because there's a steady stream of technical data that's coming in all the time. It's super standardized, it's super well. Just, us in kind of the customer data field, we're just chopping up our limbs all the time. Less data, and I was working. We had a prediction engine in our software and we needed at least two years of data to reliably make predictions. Two years of data what every customer think about this right? Who do you guys have that? Who has two years of past data? That's not too much, right? That was only two, right? Just think about seasonality If you really want to model seasonality at least, or five at least, not only two years or three years. But so there we go.

Speaker 3 25:50

This is the biggest problem, I think, the biggest challenge that we have in CRM, and we need to work on this not only on the technical side, but also, I would say, on the cultural side, because I think that and I again disclaimer I like data privacy and this is all fine, but the power that I see that some of the data privacy officers have gotten in their companies and they're just we're staying safe, there's no, we can't really do this. And please do this. Four check boxes for every channel. You've got this long list and that's come on, combine this in one checkbox, please, and then it'll have all the consents. No, it's like. This is the small things.

Speaker 3 26:25

I think what we need as CRM, what we need to make sure that we are pushing it then on our site, otherwise we it's like how is CRM going to look like? Because what we actually want in CRM isn't it like that? We want a machine that tells me hey, there's a journey you should actually look at, there's a customer segment that's underserved, there's some certain behavior that you might need to trigger. If somebody buys this or that, then why don't you react this way? Isn't that the way we want to work with software in the next coming years? Would be cool, right, that's fine. But next coming years would be cool, right, that's fine. But why wouldn't you do this in a? You could still do this in an analytical system right now.

Speaker 3 27:01

What's the? What's the? What's the? What would be the benefit of AI in this aspect? Don't get me wrong, I'm interested in it. Okay, it's, I think, the interface, I think you're absolutely on the right track. Like to wait, to like to talk to a system like this and not like doing this technical segmentations of has purchased three times, but not in the last. You know what is kind of cool.

Speaker 3 27:19

And then we come back to the problem. Right, If you need the base data and the base data needs to be very fine, granular, otherwise the model cannot look at the pattern, right, it needs to see a lot of these incidences. Um, and that's the issue, that's exactly what I'm trying to point out. Otherwise, I and that's the issue, that's exactly what I'm trying to point out Otherwise I can see a bright future. Right, you just give it to you.

Speaker 3 27:36

I was always dreaming, even in a crossing age, about this, the system to the market. I log in and it says in the morning Marcus, these are these three things you need to do today, and if you've got this, you can go home. Basically, that was my dream, but it was like it was very hard. It was Usually not, because you don't have the theoretical model, but the data wasn't just there, still not there. Even in our platform, even though we had behavioral data, we had attributes, but still didn't have this across. We didn't have the call center data in, which is important. And then actually the acquisition data would be interesting as well. Which channel did this come from? How much was what was the cost of acquiring this customer? Blah, blah, blah kind of stuff, but it was still not there. The potential is there now with the machine like the AI is there?

Speaker 2 28:21

but the data is the problem. Should we cut it off here? I feel like the data is the problem. I think it could be ending and that we definitely need to work on that. So thank you so much for sitting here and also answering those questions. I think we could talk for hours here. It was really great listening to you.

Speaker 1 28:46

All right. That brings us to the end of today's episode of Inside CLM. I hope you enjoyed our chat and picked up some useful gems to take back to your own CRM toolbox. Thanks so much for tuning in. If you liked what you heard, do me a favor and subscribe to the podcast so you never miss an episode. And hey, if you've got a quick sec, check out our website for more great content and updates. Until next time, I'm Jessica Jansen and you've been listening to Inside CRM. Take care.