What is the fundamental unit of value to a business? It turns out it’s the customer. Artem Mariychin, CEO and Co-Founder of Zodiac Metrics, joins host Allison Hartsoe to explain why companies should care about calculating Customer Lifetime Value (CLV), how to establish an accurate CLV formula model, and the positive business impact organizations can drive. Artem outlines how through having the right information and a data-driven approach to the customer, companies are able to increase their ROI and make better business decisions. See the full transcript. View all episodes.
Allison Hartsoe - 00:06 - This is the Customer Equity Accelerator, a weekly show for marketing executives who need to accelerate customer-centric thinking and digital maturity. I'm your host, Allison Hartsoe of Ambition Data. This show features innovative guests who share quick wins on how to improve your bottom line while creating happier, more valuable customers. Ready to accelerate? Let's go!
Allison Hartsoe - 00:33 - Welcome everyone. Today's show we're going to talk a little bit about a particular tool in the Martech space, and there is an awful lot of noise in this space. I think at last count I saw something like 1700 different tools and the volume just keeps exploding. Now, generally speaking, I don't think there's a whole lot of value in most of the Martech tools out there. I don't think they do you any favors. They silo your data, they don't play well with others, but today we're going to talk about one that is particularly worth your time and attention, and today we're gonna talk about Zodiac metrics. To help me discuss why this tool makes sense for customer-centric companies is Zodiac's founder and CEO Artem Mariychin. Artem is a former hedge fund investor, turned entrepreneur. Artem, welcome to the show.
Artem Mariychin - 01:22 - Hi Allison. Thanks so much for having me today.
Allison Hartsoe - 01:25 - Artem, tell us a little bit more about your background. It's not every day I talk to someone who was active in the hedge fund environment and is now an entrepreneur. Tell us how you got there.
Artem Mariychin - 01:33 - Yeah, happy to be mentioned. I began my career in the hedge fund world and also spent some time on the private equity side, and during that period I was a generalist investor. You're looking really at consumer companies, financial services, energy, and what I really focused on at the core was really understanding the fundamental unit economics of the business. What makes a company sustainable, actually able to generate kind of longer and return them profitability. Very similar to the type of investing that Warren Buffett is known for value investing and what was always really fascinating to me was that when you went down to the core of the business in front of understanding, is this good or not? We really care about it is the fact that even economics. So as an example, if you looked at a restaurant chain or a quick service restaurants like a Chipotle and Burger King really wanted to understand was what I build one location, what does that cost me, how much is it going to return on that individual level, and then going from that individual restaurant, you can understand what is the company worth as a whole.
Artem Mariychin - 02:33 - When you looked at a financial services company, you would try to understand how much does alone generate in profitability, will make that default last beyond it and let me go from that single loan to an overall company in the portfolio
Allison Hartsoe - 02:47 - I see this all the time on the profit. Every time I watch that show, he gets right into the unit economics before he invests.
Artem Mariychin - 02:53 - Yes, absolutely. But then when you look at consumer companies and kind of retail business, in particular, that kind of data sophistication, understanding of economics just wasn't there. It might seem challenging these things through, well if you're Macy's or another retail chain, what is your fundamental unit? And it turns out if the customer and as there's all we really want to care about in all of these businesses is no topic that you speak, so a lot of customer lifetime value. And the more we kind of thought about this, the more it became clear that there was this really this opportunity to take a very data-driven approach and bringing customer lifetime value to all of these organizations. And that's really what helped me transition overall kind of that hedge funds have entrepreneurship
Allison Hartsoe - 03:34 - that must've been compelling to make that transition. It's not every day you make that kind of major transition.
Artem Mariychin - 03:40 - Yep. I think that's right now what made it really easier and it was the cofounder that I had with me, so as opposed to just leaving finance this myself, I went back to professors that I really respected, in particular, Peter Fader about the Wharton School and know he was on your show, I believe in the previous podcast, but his background is, as you pointed out, was really around developing customer lifetime value models for a multitude of decades and it became very well known, but that research just wasn't broadly available commercially, and it made a lot of sense after connecting with him and discussing the opportunity to really take a lot of that core academic work and bring it to market for the first time. And that's really what led to the founding of Zodiac couple of years ago.
Allison Hartsoe - 04:20 - Yeah, that makes sense. Uh, Pete was Episode #9, and in that episode, we talked a lot about the value you can get out of using CLV, but we didn't talk about the formula itself. Would you take a minute and just walk the audience through a little bit of what is in a CLV formula? What's in a correct formula? How does it typically shape up and maybe a couple of things they should watch out for?
Artem Mariychin - 04:45 - Yeah, so customer lifetime value, kind of in the most basic sense you can think of it as what is all of the profit that a customer is going to generate for it from, from that very first transaction until they churn from the brand and choose never to come back and it's pretty simple concept, but there's a lot of challenges to it, and you're the first is that in advance. You don't actually know what is that profit going to be. You don't know it. When you acquire a customer, you don't know at any point in your relationship. And so customer lifetime value-added score is really a prediction problem, and as you know, there's. There are a few typical aspects and so when an organization really wants to care about is how do you figure out what the net present value is and what does this future cashflow stream look like and to do that will you really want to be able to understand is for every customer, how long is their relationship with the firm going to last? Is it someone that's going to sharing very quickly, maybe after one transaction or someone that's going to be a recurring customer for a very long time?
Artem Mariychin - 05:44 - How many purchases or are they going to make over that period, that relationship, and then what's the basket size that the average spend that's going to occur every time they do any good cob formula is going to take all the metrics into account and be able to predict at the individual level each of those aspects. Now, the challenge that often comes up the first is mistaking historical value or what a customer has done to date with lifetime value. An example of values, often you'll hear companies talking about in the first year, we expect the customer to spend this much and the way they arrive at that. If they look at historically, we've acquired a cohort of customers. What did they spend in that first year and that's the number we'll use. The problem with that is for your very best customers; a year isn't very long. They're likely to remain with you for three years, five years, maybe decades, depending on the type of business and if you cut it off arbitrarily at a year, you're actually not capturing the full value that a customer is going to create one
Allison Hartsoe - 05:44 - So the time horizon is too short.
Artem Mariychin - 06:48 - Precisely. The other piece of kind of tied to that is I found out from the first induction until they churn, but if you had a word a customer months ago or years ago, what they spent in the past doesn't really affect your business anymore. It's, it's sunk, and you know at every point our results. When you think about what is the customer worth, do you care about what they're going to do just going forward and that's still yet to fully pull the remaining lifetime value, and it's an important concept to understand as well because if you're an acquisition marketer, you really are capturing the entire value stream from that very first transaction, but if you're in retention, we're retargeting within an organization, truly care about our customers done already that's happened. You've already captured that profit and you really care about what are they going to do going forward and how can you choose the best customers that way.
Allison Hartsoe - 07:38 - Got It, got it. No, that sounds a little bit like you've got your crystal ball and I can picture a gypsy and attend here where you're guessing with some level of sophistication at what a customer will do in the future, how much can people trust this model or trust what will happen?
Artem Mariychin - 07:55 - Yes, it's good to question, and I think one of the things that Zodiac when we talk about a lot that it's important to hold all the models accountable in a very rigorous way and the best way to do that is what's beneficial. Enjoy. Refer to a backtester validation and the way that that's typically done really before you put any model into practice, whether it's old or any other type of data science model the company might be considering using if you want to measure that in this context with. What she'll tend to do is you select. Let's say you have a million customers over a two year period. Let's take some model and let's fit it to the first year for a subset of those customers. So we'll take the first year of data for 500,000 customers and then we're gonna. Look at two pieces for book 500 customers. We're going to predict as at the end of that first year for what do we expect to happen in that second year, and because we actually have that data we can compare versus that second year and to see where we are right or not
Artem Mariychin - 08:54 - and what we care about, not just being right in aggregate. That's great, but that's not really what we care about because we care about customer lifetime value, which are we accurate, the granular level for all the customers that have transacted once? How many times do we expect them to try and back and how many times did they transact for those that are transacted four times same type of question. And then the other piece of that you want to look at is we built the model on the first half of the customers, 500,000, but when you acquire new customers new that remaining sad that we didn't use. Are we accurate there? So from that first transaction, we make a forecast for that new customer and again, what actually happened versus what do we predict? And as you start looking at these types of the stroke of validation, you look at them; you have accuracy over three months, period, six month period periods, 12 months forecast periods. You start to be a comfort in the model, and you can actually start using it to make business decisions at that point.
Allison Hartsoe - 09:46 - I see, I see. So since you do this for so many different organizations, do you see certain trends across the organization? Things that hold true?
Artem Mariychin - 09:54 - Yeah, that's a great question. So there's definitely a few kinds of generalities. I want to be careful saying this because each business is unique and so I wouldn't want anybody listening to the podcast to assume that this is absolutely going to be true for their organization. But a couple of comments that I'd like to make. So the first that's the most common is that there is something pretty similar to an 80/20 rule for most businesses to the Pareto Principle.
Allison Hartsoe - 10:17 - I see that all the time anyways in analytics and it's just amazing that it here it is yet again.
Artem Mariychin - 10:24 - Yeah. And the way that it manifests itself here is that for most organizations if you line up all of the customers best to worst and you take the top 20%, they're generally going to be responsible for 80% of the value to the firm. And what's fascinating about that is that means you're going to be a retail business, something where you might have millions of customers, and you think everyone's about the same as that. There's actually a tremendous amount of revenue and profit concentration. And can you actually think about that if you're running the organization because those other remaining 80% yes, they're certainly helping with six cost absorption and making sure that you have store traffic and all that, but they're not necessarily generating the value to your firm? So that 80/20 pieces often true, and you depend on the business, it might be actually a little bit more or less concentrated. So typically luxury retailers are those that have higher price point products tend to be closer to 90 10. Whereas more mass market companies, somebody like Walmart tends to happen a little bit more homogeneous customer base. So it might be 60/40 for example.
Allison Hartsoe - 11:29 - Wow, that high.
Artem Mariychin - 11:31 - Yeah, that's still pretty, pretty similar. And then on another example that will oftentimes see, and this is almost unfortunate for organizations, but each incremental customer you acquire, the customers you're acquiring this quarter compared to last quarter, tend to be a little bit worse and the customers you acquired last quarter compared to the year before that again, a little bit worse. So over time, you tend to have a slightly lower value customers, and there are a few reasons for this. The first is that you actually acquired a lot of your best customers early on in the life of your firms. Those were the ones that were really attracted to the brand promise, the brand core. They found you, they like the service, and at that point, you're starting to expand a little bit of way into somewhat of these lower value segment. So there's just not that many best customers out there. And then, of course, the other pieces that are that you have more competition and if you're also a successful business, you're likely growing and as you grow you're increasing requisition spend. And so you just naturally have this kind of diminishing return on the back, and so that's something that brands should really be aware of as well.
Allison Hartsoe - 12:38 - That makes sense. I'm surprised that so many of your best customers are your initial customers, but in a way, it kind of gives you a. It kind of echoes the first mover advantage and other the things that we see in startup spaces.
Artem Mariychin - 12:50 - Yeah, it's interesting you bring up scarves here because of examples real. It's true for all organizations. That actually makes a lot of sense for the startup as well. And to that analogy, if you're a seed stage startup and you first just again, you don't have much to spend on paid acquisition, so a lot of your customers are. We're about friends, and family referral and those are, they're certainly so acquired there, but often free and because they're so close to the founders and maybe one degree of separation or they're good customers, they like the service. That's probably why the founder started in the first place. And then we see over and over at these organizations become successful. They'll go and raise series a funding series b funding and the obviously capital there is to put into paid acquisition so they start to expand into digital channels and they assume the customer they require before is with customers always going to look like and that's not at all the case
Artem Mariychin - 13:46 - and as soon as they turn onto paid acquisition channel like a lifetime value often drops and we've seen a drop by 50%, quarter over quarter. As soon as they go into these channels and that's building. Again, it's super important to keep that in mind because if you're a company that assumed lifetime value stays the same and it drops by 50%, all of your operating assumptions are going to be wrong. Your inventory, your financial forecast, your headcount growth plans, and so if you do not measure by time value on this granular level over time by segment, by channel, by the customer, ultimately you can really make a mistake and hurt the business.
Allison Hartsoe - 14:25 - No kidding. Let's say that obviously, I'm a convert of CLV marketing, but let's talk a little bit more about what companies can get from this process. So let's say that they roll out a model, what are some examples or applications that you've seen them use the information for
Artem Mariychin - 14:44 - and I didn't say before going into this specific example, just to kind of highlight why we even care about this in the first place and at the end of the day in marketing departments are oftentimes going to have a very fixed budget that they can allocate and two, they need to determine how do we allocate that. The first it might be to put into the acquisition and customer development or potential and then if you're in one of those departments within acquisition, for example in b, what channels do I put it in the Facebook first page search versus maybe direct mail, and then if you're running Facebook potty target customers. So all of the examples where you care about is how to actually allocate these dollars to where it's going to drive the biggest return.
Allison Hartsoe - 15:24 - And a lot of people would say, oh, that sounds like a media mix model. How is it different than a media mix model or an attribution model?
Artem Mariychin - 15:32 - Yeah, so for food, and I wouldn't say that attribution media mix has no place in marketing far from it, but one piece that attribution tends to really misses that you're getting credit for an individual transaction or for acquiring customers, so one thing you're not really understanding is you get this transaction, but what is the value associated with it? Because for example, if you acquire a customer, and it costs you $100 to acquire that customer and that transaction generated $50 in gross profit for the organization, he actually lost money and the only way you actually can make money in that organization if that customer is going to come back and spend more so you don't want to attribute the transaction. You want to attribute the entire value of the customer that's been created. Kind of one piece. The second is that, again, if you're on the distribution side and you're spending money, let's say, for example, you send people to discount and it causes them to come back, and you're attributing that transaction to the emails that discount associated to it.
Artem Mariychin - 16:35 - You're actually missing the point of whether that transaction would have happened otherwise at full price and so you're not really looking at the incremental value of being created, which is another reason that against CLV is so important because it provides you an expectation of what would happen under the current status quo and then as you take other actions with the incremental lift about that. And the broadest study I've seen on this was actually particularly in McKinsey where he said organizations globally; they're spending a trillion dollars in marketing and from all of the studies and cases that McKinsey had done that on analytics. Every time they've tried to reallocate the budget marketing, whether it was lifetime value based or even kind of attribution based. They saw retreading marketing go up by 10, 20 percent and so they estimated it could be at least $200 million in value. That could be created through better targeting and analytics. And that's at the macro side, and you ask what are we seeing?
Artem Mariychin - 17:33 - It's Zodiac and kind of the specific reliant happened. It's actually precisely that and often inefficiently higher results. And a couple of really quick examples, you know, the first and kind of most common are on the digital channels, but through targeting in Facebook for example, where the way organizations do today's, they'll create the creative in there and then we'll go about shooting. We want to do this, that to demographic based audience or maybe geography based, what we think are really smart, segmentation's adjust and instead you can actually do what's known as a look alike audience where you provide the Facebook a list of who you think your highest value customers are and really just say, help me find more like them to show my advertisements to those and doing ab testing and comparisons of the results companies used to see versus what they're seeing. Doing this look alike audience. We've seen ROI improvement of two, 300%. Exactly. It's amazingly powerful, and it sounds so simple and almost unbelievable, but when you see this over and over, we really have a lot of concepts. That's the case, and Facebook tends to be the best performing channel for of these organization, partially for this reason.
Allison Hartsoe - 18:48 - Amazing. Now I know you can do that kind of look alike modeling on Google as well. Have you seen that yet? Have you seen any similar performance between the two?
Artem Mariychin - 18:56 - We have, so Google kind of equivalent tool, Google customer match and you can use that both for acquisition, for re-targeting. It can. It's a little bit more challenging for organizations to get to do this properly on google. Facebook makes it really, really easy, so that's often the first place to start, but more and more who were also taking this path towards customer-centric marketing and helping organizations better, so we've seen some results, but in the first place that accompanies don't get startled on the Facebook side.
Allison Hartsoe - 19:24 - I see. I see. Okay. Any other cases of ROI that you want to point out that you feel like it's not just in the re-targeting of the online, online world that have you seen it happen offline as well?
Allison Hartsoe - 19:37 - So one example to talk about is that dress barn, which is part of the senior retail group and one of our clients, but there's a really interesting case study that we had with them with catalog mailers, which is really typical for a lot of retail brands, but I think it's become less popular in the past couple of years, but it's a pretty high cost. The tactic, a mailer, my cost fifty cents or a dollar per mailer, which if you have hundreds of thousands or millions of customers is pretty large marketing outweigh. Now companies find it pretty efficient, which is why they still do it, but it is expensive and dress barn with an organization that's been doing this for years had a variety of response models that they were using. Targeting methodologies, whether they were RFM based or based on other historical purchases or segmentation. And what they ended up testing was a Zodiac metric on what is the chance that a customer is actually going to transact in the next six months. Really just kind of probability of activity which is very linked to lifetime value.
Artem Mariychin - 20:35 - And what they found is that customers that had a low probability of transacting, even if you sent them this mailer, they didn't actually come back, and they were losing money on those pillars, but for customers that had this high probability we're loyal, valuable customers actually came up more than expected, spent more and oftentimes they don't even use the coupon from that mailer and but when they set up an ab test and test it, all of it, they found that they're actually saving about 15% of the mailer, which this one kind of example was a few million dollars in savings to the manually that they could then be invested into other parts of the business.
Allison Hartsoe - 21:10 - And you know, what's interesting is they save that on the first mailing, but they save it on all future mailings to, you know, in the same concept of future lifetime value. So how long were they, or how long would they have been doing this imprecise marketing? You could almost stretch that out to say if it was five years worth, maybe it's more like $10 million that they would have spent that they've reallocated.
Artem Mariychin - 21:34 - Yeah, I mean that's, that's my past life in finance, but if you're thinking they're now having a $2,000,000 incremental annuity and Tampax adjust that, that's to the tens of millions of dollars of value to the company. Really from something that sounds as simple as, let me just change one. Do not even the creative or anything else.
Allison Hartsoe - 21:51 - Amazing, amazing, but most marketers don't think in those terms. They think volume, quantity and just get the transaction. They aren't thinking in an annuity or a lifetime model. When the switch happens, when the light goes on, is it one of those aha moments that suddenly somebody goes, oh, or do they just kind of gradually getting it?
Artem Mariychin - 22:13 - It's interesting. We've definitely seen organizations for is this aha moment? You need to get it and oftentimes where that ends up happening is you're pretty senior levels in the organization, so at an AP level are those were in the market or interest responsible for a channel but has brought oversight of the budget or really interacts closely to finance and his understanding of these net present value tech topics. When you are kind of within a channel and your responsibilities, let me have as many customers coming through Facebook, it definitely is a little bit more agile because you really need to show that actually having this CLV driven targeting gets you better results and actually helps you with it. Facebook market or reach your goals better and so for them it's definitely been more of a gradual process where we thought it would work the best are when you start kind of small winds at the ground level where you show the ROI of using the same creative. You're now gonna make 300% higher ROI, and that's when they get excited, and that's when they start heading organic process of, let me convince everybody else on my team, but those large aha moments are often happening more top down.
Allison Hartsoe - 23:23 - Okay, so let's say that I'm convinced I want to get started with CLV marketing. How would I get started? What would be the step side run through?
Artem Mariychin - 23:32 - So you think, of course for any company would love for them to come and talk to us. Zodiac, but more generally, let's step aside, and kind of walk through what we typically see from a change management perspective and the first I think we talked about this already is that you need to actually have a CLV bottle and get comfortable with it. How do you actually trust this and that goes and goes back to evaluating the historic sites and maybe you have a data science team internally that could do as an organization, maybe use an outside vendor, but first and foremost can you have a lot of the comfortable with the second, and I think we were just starting to talk about this, is relating to small wins. Can you actually test this model on a campaign, whether it's Facebook or physical mail or Google, but something where you can actually run an ab test and see what are the incremental results that are striving in the organization? So you start gaining that additional evidence. You'd have that targeting. Once you have that, you can first of all start going to other teams in the company and kind of getting them to sign up at.
Artem Mariychin - 24:29 - The other piece is you don't want it to stay to be siloed within one group or one team or the data science team, so you want to start integrating lifetime value into other tools. So let me take that and put it into a DMP to use within the digital channels. I'm the kind of erratic tools or let me put it within my CRM and within my customer data platform that can use it for email targeting and segmentation and everything else that it might be using. So you want this data to within a broader ecosystem available to the company. And then once you really have all of that and you have those case studies, you can start working in the organization. And what we see is that these cases tend to spread. So I started with the Facebook team and as soon as the paid search team see that they see much better results that they want. Use that data and then from the acquisition team and might shift over to the email team on the retargeting side. And then the customer service team. I get excited, and we do see this kind of organic movement within organizations to start using this because at the end of the day everyone cares about driving better performance in their organization.
Allison Hartsoe - 25:32 - Yeah, full disclosure, we use the Zodiac, and we have found it to be very, very similar process to what you just outlined in that the ability to take that information and integrated across the organization into other places so that people have that fundamental knowledge to use. Whether it's a a label of who is what value or whether it's a score, it doesn't matter. It's just it needs to be surfaced so that everyone can drive by it and it gives a really nice basis for that unit economics that you talked about in the beginning
Artem Mariychin - 26:04 - I think that data hidden away somewhere. Does it create any value on data needs to be actionable and it to be front and center and people need to be seeing it in using it?
Allison Hartsoe - 26:12 - Exactly. Don't we all love that idea since you know, when we all started in this industry decades ago now, that was the fundamental concept of the Internet, right? Information wants to be free and here we are yet again at the same 20, 30 years later or whatever it is, the same idea that the information needs to flow and when you have that basis of what is the right information, it just makes it all the more powerful because now you're not flowing noise. You're flowing signal, right?
Artem Mariychin - 26:41 - Yeah, exactly. Actually act on and really drive results.
Allison Hartsoe - 26:46 - Excellent. Alright. Artem. What's the best way for people to reach you? If they have followup questions you want, maybe they want a demo of Zodiac metrics. They want to understand a little more. How could they get in touch?
Speaker 3 - 26:56 - The easiest way would still be through email so that I can be reached at firstname.lastname@example.org. That's A-R-T-E-M @zodiacmetrics.com, and that can really just be about customer centricity. CLV doesn't even have to be related to Zodiac itself or on LinkedIn. You can find me as well, and if you are interested in putting about zodiac after seeing that demo, the best place would be on our website, which is the zodiacmetrics.com.
Allison Hartsoe - 27:19 - Excellent. Excellent. So let's summarize a little bit when we talked in this episode a little bit about why should I care about CLV. We talked about the precision of the model, and I especially like what you said in the beginning item about the customer is the unit basis for value. All organizations really lined back up to this core valuation and the correct calculation of CLV is really what gives us that sense of lifetime value, and that's because CLV isn't to history problem. It's a prediction problem. So we want to really understand how long will the customers stay and how much will they purchase. It's almost like that future annuity aspect that art in the alluded to earlier on in the conversation, how finance people think about it. So we want to think about not just the past. We want to think about what they do going forward, and to be accurate, you need to have back-tested accurate models to do that. Whether you do that from your own CLV modeling or whether you use a tool that helps you do it like Zodiac.
Allison Hartsoe - 28:22 - Second, we talked about the kind of impact that you can get and the McKinsey model targeted at 20% through better targeting and improvement. That's a hell of a number for most organizations. We talked about Facebook and Google being great places to start, particularly around acquisition that maybe Facebook was a little bit easier to start and when you do this, most organizations are looking at results in terms of millions of dollars reallocated, have better spend, and that can translate all the way into whether it's acquisition or can translate down into winning back model's getting customers to come back and buy again. So the third part of what you should do next. We talked about the ability to get comfortable with the model, and I cannot emphasize that enough. You've got to have people who trust the data, who blessed the model internally. You can't just stand up a tool and be like, okay, here it is. People have to believe in it, just like any other data set, and then look at the small wins that you can pick up as your initial proof cases. That's a classic. We always talk about that and then how can you integrate or how can you share that information across the organization to get the broader spread of adoption. Artem, did I miss anything?
Artem Mariychin - 29:35 - No, I think that's very thorough.
Allison Hartsoe - 29:36 - Okay, good. Thank you so much for joining us today. It's been a pleasure to have you.
Artem Mariychin - 29:42 - Thanks so much for having me.
Allison Hartsoe - 29:44 - Excellent, so remember everyone, when you use your data effectively, you can build customer equity. It's not magic. It's just a very specific journey that you can use to get results. Thanks, everyone.
Allison Hartsoe - 30:06 - Thank you for joining today's show. This is Allison. Just a few things before you head out. Every Friday I put together a short bulleted list of three to five things I've seen that represent customer equity signal, not noise, and believe me, there's a lot of noise out there. I actually call this email The Signal. Things I include could be smart tools. I've run across articles, I've shared cool statistics or people and companies I think are doing amazing work, building customer equity. If you'd like to receive this nugget of goodness each week, you can sign up atambitiondata.com, and you'll get the very next one. I hope you enjoy The Signal. See you next week on the Customer Equity Accelerator.
Key Concepts: Customer Lifetime Value (CLV), Predictive Models, Pareto Principle, Improved ROI, Data Integration, customer centricity, best customer, martech, startup marketing, customer segmentation, A/B testing, win-back models
Who Should Listen: CAOs, Digital Marketers, Business analysts, C-suite professionals, Entrepreneurs, ecommerce, data science
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