Customer Equity Accelerator Podcast

Ep. 38 | Implementing a CLV strategy with Jordan Elkind of Custora Part 2


Part 2 - Who is driving growth through CLV strategy in the retail space? Hear three transformational stories about retailers Bonobos, Eloquii, and Crocs. Learn why CLV is the smartest predictive growth strategy and why this is the golden age for today’s CMO.  Host Allison Hartsoe chats with Jordan Elkind, Head of Product at Custora this week on the Customer Equity Accelerator. 

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Ep. 39 | Investing in your Customers with MIT’s Michael Schrage Ep. 37 | Implementing a CLV strategy with Jordan Elkind of Custora Part 1


Show Transcript

Allison Hartsoe - 00:01 - This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue generating customers, then this is the show for you. I'm your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let's go!


Welcome back to the second part of my interview with Jordan Elkind at Custora and we had a fantastic interview last week, so if you didn't catch that, be sure to catch that part first and then join us for the second part which starts now. Do you have another example along the lines? He's been great, but Bonobos and Eloquii, fantastic examples.


Jordan Elkind - 00:57 - Yeah. I've realized I've gotten wrapped up a little bit in your big changes to the business, big transformation stories around product design and store expansion strategy that I'm able to overlook some low hanging fruit, which is the straightforward marketing applications and there's one retailer in particular that I've had the good fortune of working with over the past few years that I think is just stellar in regards to making lifetime value in organizing principle of its marketing efforts. That is Crocs. Crocs is an incredibly gifted and data-driven team that over the past few years has revitalized the official business. We see them as, you know, we often look to them to see what the next big trends are going to be because they're super savvy around the whole marketing technology landscape. They were actually, you know, back when we first built the store, we had this hypothesis which turned out to be correct, although a little cumbersome, which was if you understand what channels your high lifetime value customers are coming from, you can actually like invest more in those channels.


Jordan Elkind - 02:07 - I think of it as like if your customer acquisition is a portfolio mix, right, like you're turning on the spigot more for the high-value streams and maybe a little less on low-value stream, so if you see paid search is a disproportionate magnet for high CLV customers spending more there and less on perhaps affiliates where the customers can be one and done. I mentioned it's an accurate strategy but cumbersome because it absolutely possible and we've worked with retailers like Back Country that have taken CLV to be to optimization all the way down to the individual keyword level, so bidding more on keywords that high CLV customers tend to search for and less on one side acquire lower value customers. But as you can imagine, that is a tremendously complex engine to manage. We had complicated bade structures and lots of channels.


Jordan Elkind - 02:58 - It's really difficult for a retailer to get beyond very high-level insights around, you know, one macro-channel versus another and one of the great breakthroughs that Crocs had was really the world began to change I would say about four or five years ago where we started seeing this incredible convergence of tools that had typically been like ad technology, so things like bid optimization tools and ad impression, display media tools. Typically those had been managed using, you know if anything like third-party demographic attributes. So think about the process of setting up a display campaign, right? In the old world and even with programmatic bidding, you would typically buy audiences off of third party attributes like, hey, I want to target 18 to 29-year-old men living in New York City. And then based on third-party data, you would get a certain number of impressions in front of those consumers.


Jordan Elkind - 03:56 - On the other hand, you had channels like email and direct mail where for a long time we've known exactly who we're talking to and we know who's high lifetime value and who's not. Well, a couple of years ago we started seeing more audience targeting capabilities being introduced into the ad technology space, so first from my recollection at least was Facebook custom audiences which gave me the ability to upload a list of actual known customers and then reach those customers or customers who look like those customers with advertisements. Next to follow on was Google customer match which allows something similar throughout the Google Network of search and shopping and YouTube ads and inboxing ads and so forth, and then we started seeing the growth of audience tools and onboarding tools like live ramp enabling first-party audiences to be used in display targeting. And what this has meant for retailers is that now it's very easy for them to take a known population for Crocs to take away their high-value customers scored by Custora and feed that list into their demand-side platform and run lookalike prospecting campaigns.


Jordan Elkind - 05:06 - So the ad impressions or getting in front of customers who look exactly like their highest CLV customers. Same thing on Facebook. Same thing on Google, right? When Google sees somebody who looks like one of the crux is highest value customers in the market news to bid more so that that advertisement appears higher in their ad can anyway. All of this by way of saying that is one of the first of our customers to really recognize the convergence of the CRM world of CLV scoring and email and direct mail with the much bigger budget world of Adtech and media, and they've actually been very, very aggressive and I mean that in a good way about using lifetime value insights to improve the efficacy of their performance marketing program so they feed Custora lifetime value scores into all of their acquisition channels and they use lookalike audiences could get in front of customers who resembled or their current best customers and they've seen double-digit increases in math of their acquisition program as a result.


Jordan Elkind - 06:07 - Beyond that, they are doing a lot not just to acquire high-value customers, but to keep them engaged over time. So they have a great turn prevention program in place that recognizes when one of those high-value customers begins to slip off of her a typical purchase pattern. How do we step in at the right moment and gently guide her back, but they've built this incredible machine and when I look at it, our customers, I think they're probably the one that is utilizing lifetime value most seriously across the full range of marketing activities from acquisition to retention to really maximize customer equity.


Allison Hartsoe - 06:43 - I love that. And it, it, it's, it's clear that there is some technology skill inside the company. It's not vendor driven from the outside because of what I see, and in what I call it, vendor-driven strategies is a large platform will come in, and they'll say, oh, you've got the analytics piece, or Oh, you've got the emailing piece, let's now get you a piece, um, you know, x, y, and z to go alongside that. But what the problem with that is, is it's almost inevitably poor at connecting the pieces because those pieces of the platform are almost what they call a frank and stack their acquisitions from different places and they don't always work well together. So not only do you have the resistance in the technology, but you have the resistance to the strategy. And that what might look like a good customer because they're opening your emails on one side, doesn't necessarily look like a good customer on the other side where they are responding to paid search or paid search ads.


Allison Hartsoe - 07:51 - And that's what I think is so interesting in your story about Crocs is they've stepped above that big by using the CLV driven strategy and saying, first let's not, let's not start with the technology. Let's start with what drives our business. In this case, the CLV values, the scores behind customer equity, and then let's layer that into what we need from the technology that's. That is rare to see a company that is driving that kind of innovative strategy internally. And I love it.


Jordan Elkind - 08:25 - Yeah. And I could not be more excited about it myself, and I think you're right. The reason I love talking about these retailers is that they're each exceptional in their own way, but what they share is they're starting from a place of vision and strategy, right? They know what they want their business to look like a year from now, five years from now, they're not allowing them to get caught up in the marketing technology, the arms race, uh, they realized that even average tools extremely well deployed in orchestrating the same strategy. Well, she better results than state of the art tech platform with no underlying vision behind it.


Allison Hartsoe - 09:03 - Very true. Very true. Okay. Well, these are fantastic examples that I'm sure we could hear even more of them at the customer conference. Why don't you talk a little bit about Karma for a minute and then we'll go into what should I do first, second, third?


Jordan Elkind - 09:18 - Yeah, absolutely. I am very excited to be announcing or promoting CARMA. That is Custora's annual conference. Uh, it stands for customer analytics for retail marketers with an extra a the end. Um, it takes place every year in New York City this year. It is on October fourth. It brings together some of the most exceptional brands in retail for spins from last year. Included folks like the Bonobos, Bally, Ascena, Anthropology, Burberry, Dressbarn, Eileen Fisher, many more. It is actually a free conference. So if you were interested in downloading a full day full of best practices, data-driven strategy, CLV and war stories from some of the greatest brands in retail, I would love for you to join us in New York on October the. You can find more at


Allison Hartsoe - 10:10 - I can say I haven't been to this conference before, but I watched all the videos from last year, and they were great. I, I have uh, I have like a size to power ratio with conferences I kind of use as a, as a rule of thumb. The larger the conference, the smaller the value you sometimes get from it. So conferences that are 10, 15, 20,000 people strong. I almost find no signal in those events. But conferences like yours that are very targeted, very narrow, very specific in terms of what are you going to talk about and what's the value you're going to get. Tend to just give such a great return. So I, I'm excited about coming this year and not just watching the videos remotely, but if you can't make it. So you guys are usually pretty good about promoting some snippets, but hey, why not be there in person? Right?


Jordan Elkind - 11:07 - Yeah. Well, um, if you would like to join, I encourage you to hurry up spaces very limited to Allison's point, like to keep it small but one of the real highlights of my time over the past two years. So I hope to get to the face there.


Allison Hartsoe - 11:21 - Good, good. Okay. So let's say that I'm, you know, I like the idea of being a CLV driven strategy. Maybe I'm in retail, maybe I'm not in, you know, as long as you have customers you can use a CLV driven strategy. What should I be thinking about first, second, third, if I want to try to move in this direction and Jordan, feel free to talk about the Custora tool as well? Like I said at the beginning, we don't normally promote tools and less they actually aligned with something that's purposeful that the audience can really grab hold of.


Jordan Elkind - 11:56 - Yeah, for sure. I'll talk first about the methodology and then maybe sprinkle in some customer stuff where it's relevant, but I think we should use our roadmap for an organization becoming CLV centric, or customer-centric would probably start with what may be the heaviest lift of all, which is consolidating or customer data. We have not yet worked with a retail brand where we had to say to them; you know what, like sorry, you don't have enough customer data to begin taking action on lifetime value it in 99.9 of cases they have an abundance of customer data, Plethora of customer data. The problem is an Allison, you referenced this a little bit earlier when you were talking about the siloed view of customer behavior, that some of it are locked away in their email service provider. Some of it is therapy, an Omni channel retailer. Some of it lives in the e-commerce platform. Some of it lives in their point of sale.


Jordan Elkind - 12:51 - Some of a customer engagement behavior lives with their mobile app or with their site, how they're browsing and visiting and so it's really, really difficult to stitch together a complete picture of each customer, which is a prerequisite for understanding customer behavior and so the. I don't mean to be glib because it's. It's a real heavy lift, but the first step in and one that the retailers that we work with have at least started thinking about to some extent is like documenting all of the places that customer data lives and beginning to think of some strategy for consolidating it and organizing it into actual customer profiles. This is something that Custora specializes in. We plug into dozens of systems. We switched data types together. We identify the same user across multiple feeds, and it's something that is also quite possible for retailers, especially ones that have strong data governance and a good database team to begin the process of doing on their own. So step one, a sorry to rain on the house.


Jordan Elkind - 13:54 - Not very glamorous, but get your customer data consolidated and organized. You know, break it out of channel molts and organize it into a customer. Yeah.


Allison Hartsoe - 14:04 - You know, I always say this about channels. Channels don't buy a product, and I see channel alignment all the time. You know who you're going after. I want to know how much email to send. No, you want to know about the people. I hear you there.


Jordan Elkind - 14:20 - Yeah. You know we actually last year ran a funny ad campaign. I saw it, which was basically highlighting the vastly different view of an individual customer that multiple tools in the sac will have right with the Allison looks to an email service provider based on the emails that she's opening and clicking is very different than she would look to a store manager based on what she's buying in storage, the econ major based on what she's buying online in order to really understand who she is and you predict how much, what her pattern of engagement is likely to be with the brand in the future. We need to break that data out of its channel silos and organize it into a holistic profile. So step one is consolidate and organize around the customer. Two is to actually run predictive models. I recognize that within our industry there is a range of legitimate beliefs about how predictive modeling should be done. The importance of predictive modeling versus historical modeling.


Jordan Elkind - 15:20 - I will say, I think I'm a little on the less dogmatic side. I think that anything, even if it's simple, historical reporting is better than nothing, so even if you're doing nothing more than I'm looking at historical cohort reports that that gives you a big leg up in understanding the value of customers at least directionally, coming from different channels, engaging with different products. Of course the gold standard in any approach that we advocate and support extra is real predictive lifetime value where we mine through all of the historical touchpoints, purchases, points of engagement that a user has had, and you said to forecast how valuable is she likely to be over the coming year? How many purchases is she likely to make? What is she likely to buy? The nice thing about the predictive CLV orientation, as opposed to the historical CLV orientation, is for businesses that are in any sense dynamics that are testing new channel strategies that have it changing merchandise assortment that is facing a competitive landscape that's in flux, what you learned from looking at the forward-looking lifetime, but it was actually significantly materially different from what you learn looking in a rear view mirror.


Jordan Elkind - 16:29 - And so while I, um, I do believe that we should empower every team, democratic leaders to do cohort reporting and there's a lot to be learned there. The approach that will advocate is typically like start by understanding which customers are likely to be your highest value segment over the next few years. The, um, the question that always comes with that and we consider this almost part of the same exercise. Predictive modeling is so what, like what it is about those customers that makes them unique or different? And so what I would recommend for any brand that's looking to scratch beneath the surface is to really kind of dig in and understand what it is that makes that segment unique and different from the rest of the customer base. We call this enrichment profiling and Custora. It's a common statistical machine learning technique, but it basically helps us. He's out. Okay. What are the acquisition channels, the product assortment, the demographics that segment email engagement behaviors that are high-value customers apart?


Jordan Elkind - 17:27 - In other words, what can we learn about them and how they're different than the average customer off the street. If you think back to some of the stories that I shared earlier about Bonobos or eloquently by looking at their high-value customers and what made them unique and different that they came to a breakthrough realizations about, man, we should be investing in a different product set, or we should be de-emphasizing certain acquisition channels are doubling down on, on other ones. So That's phase two is actually getting in the weeds and doing that predictive modeling and Allison, anything you want to add there. And, uh, you guys had tons and tons of experience in this regard.


Allison Hartsoe - 18:03 - Yeah. You know, the part that sticks out in that section is I always hear people trying to lead with demographics and then throwing in CLV as an element within the model. But I think what's interesting here and what I think is so important is no, you start with the right predictive CLV model, and then you unpack the elements that make them unique or different. The enrichment profiling as you said, uh, and, and that just yields a different answer than starting with the easy demographic stuff, which I think leads you down the wrong path.


Jordan Elkind - 18:44 - Totally agree. As you might imagine, this isn't the easiest conversation to have with executives who have been funding a team over the past year to understand, you know, who are millennial shoppers or some other target demographics, does your, and really double down on them. But the insights that we see are oftentimes in genuinely startling thinking about the work that we did a couple of years ago with a publicly traded Omni channel brand that I'm not allowed to name by name, but participates in vertically integrated specialty retailer that would describe yourself as maybe like a transitional or entry price point, luxury aspirational luxury, so kind of the same competitor set, let's say as a Fossil and Michael Kors and the executive team had a really clear intuition driven hypothesis about who their best customers were, right? That their best customer was like an affluent suburban mom and making a six-figure income who had two kids and it was almost like you.


Jordan Elkind - 19:46 - They wanted to will that best customer into existence. Now, when we actually started with a data first approach and looked into their highest CLV customers, what we discovered almost knocked them off their chair, right? Their best customers were people for whom a low price point item at this retailer was a huge stretch. I was like a big portion of their paycheck, but they wanted a piece of that luxury of kind of the brand equity, and accordingly, this was a much, much less affluent, you know, oftentimes um single mom living in urban areas in totally different areas geographically than they had originally. Ideated, you know, living more in the southwest like Arizona as opposed to the cosmopolitan, tristate area and in the coast. And I think that highlights some of the dangers of leading with almost the aspirational demographic segmentation as opposed to actually listening to what your best customers are telling you who they are and how certain them more effective.


Allison Hartsoe - 20:41 - Exactly, exactly. So, speaking to the choir.


Jordan Elkind - 20:46 - Yeah. Okay, great. Um, and then the third piece, and I'll just use this to run at the checklist here. So we've got consolidate and organize your customer data, run predictive models and dig in to really understand the why the thirties adopted tests and learn mindsets. We've worked with more retailers than I can possibly name who think that CLV provides the answers. You know, if we understand the CLV of our customer base, then we'll know after somebody first purchase that we should send them three text messages and then send them a Facebook message and then hit them with an email, and unfortunately, it just doesn't work like that. The patterns that we see our best customers, I'm engaged in the behaviors. Those I think of is helping to provide the questions were the to tee up the experiments that we were wanting to run. So what if we were to adopt a certain post-purchase communication strategy, would that increase the LTV of our customers?


Jordan Elkind - 21:45 - What if we were to double down on a certain acquisition channel? Would we be able to cost-effectively acquire high CLV did caught, you know, customer acquisition cost ratio customers? These are questions that come out of the data. The data helps us get smarter about identifying and prioritizing the right questions, but at the end of the day, the only way to understand what's actually working though to move the needle on the business is through testing and experimentation. And so we're big, big advocates of this. It makes me nervous whenever we talk to a prospect who says that like CLV is going to provide them with the answers. I think the more seasoned retailers that we talked to; you realize that it will help them tee up the right questions instead of the right experiments.


Allison Hartsoe - 22:26 - That's a great way to phrase that. I think it's when you have really good data; it does do exactly that. It doesn't give you the answer, but it queues up the right questions, and then the right questions will take you right into financial impact, and that's that's what the best companies do. They figure out that right focus the right strategy, and then they go after it with the right questions, constantly looking for innovation or cost reduction in order to maximize the longterm customer equity. So I am so pleased that you mentioned that test and learn is the third.


Jordan Elkind - 23:04 - Well, we've had the good fortune of working some really, really extraordinary retailers. We've learned a lot from them, and it's exciting to get to distill down to the exciting innovations that we've seen around customer lifetime value.


Allison Hartsoe - 23:17 - excellent. Now if people want to reach you, how could they get in touch? Jordan?


Jordan Elkind - 23:21 - Uh, they can contact me directly. My name is Jordan Elkind. I'm the head of product of Custora as Allison mentioned, my contact information is Jordan


Allison Hartsoe - 23:36 - Excellent. I'm going to run through a really quick summary. We've been summarizing a lot as we went along today. So I'm just going to hit this very fast and please feel free. Jordan, if I've missed anything or you want to elaborate on something, feel free to call it out. But we started out with why should I care about the clv strategy? And I, I love what you said about the golden age of retail is kind of buried underneath the retail apocalypse. And then, uh, we, we talked about the fanatical focus on who the best customers are and how do you cater to them. How do you, I like to say, how do you be of service to them and what is driving that is what we almost called the age of the CMO. Oh, I think you mentioned it came from an investor and I never thought about it this way, but I think it is indeed true where you see the CFO's focus in the fifties and sixties and then the operational efficiencies up through the eighties, nineties, and then now because of the richness of the customer data, the marketing teams are in the driver's seat and the ability to understand customers and what makes them tick is leading a revolution inside the company to drive broader strategic decisions. Anything I missed there?


Jordan Elkind - 24:52 - No, I think you nailed it.


Allison Hartsoe - 24:56 - Good. And then, uh, then we talked about the impact, and I love the examples that you brought through. Um, one of the side comments was that the leadership team doesn't always know what they're looking for, but when you follow the CLV trail, it gives you the right insights. And I know that is the point of the CLV strategy. I love how the Bonobos was really using CLV to lead around innovation with guide shops and having not just a quantitative expectation, but knowing that they wanted to create an experience for high-value customers that were early in their lifecycle. That type of precision is not always what we see in customer experience, but it is possible with CLV Jordan strategy, and we talked about Eloquii and thank God they're just getting on top of talking to customers. There's always such a gap with large companies, especially where the act of calling customers seems like a huge risk, but yet here you've got a CEO who's in the driver's seat literally on the phone with customers, and maybe that's not possible till the end of time.


Allison Hartsoe - 26:06 - You know when there are hundreds and thousands of customers that they have to deal with. But the fact that there is such tension to it and the fact that they're listening and trying to qualify that feedback much more than just an initial survey is a great way to empower their product development. And that's exactly what marketing did. They heard, they found, and they went and empowered product development changes around fit, and then I think later was the example about the returns and the wedding dresses, what a great story and in that respect then we talked about Crocs and their marketing angle, how strong they were in strategy and the combination of maybe your traditional CRM direct mail kind of marketing, attaching that known information to ad tech in order to form a strategy that helps them understand what they want their business to look like and who they should be acquiring.


Allison Hartsoe - 27:04 - Even if they don't know exactly everything about that customer. They can take the known high-value customers and feed that into Facebook and Google to drive the right kind of look-alike models, and that is. I am seeing more leaders do that where they're getting that combined strategy together. Anything else you want to add there?


Jordan Elkind - 27:26 - No. I was just taking a quick look guide shop, and they said maybe they have like eight to 10 locations nationwide. Well, boy was I wrong because they have almost 60 locations, so this has been a really impactful strategy for that, but just further underscores the sensuality of customer lifetime value in their strategic planning and everything you said about crosses right on another great year.


Allison Hartsoe - 27:53 - The examples are powerful and the returns on the investment in these examples, I mean we talked a little bit about at the beginning, but in other shows, we've done the returns are just. I almost never hear about examples where somebody leads with CLV, and they didn't get at least a seven x return on what they spent and some cases the return values are in the hundreds, and they're always talking multiple millions of dollars. So this is a powerful strategy, and with regard to that with what you do next, you know the consolidation of the customer data. We did a show previously with Joe Megibow, and he talked about how pulling the data together across the different profiles, the customer profiles is challenging and what they did. We talked about modeling, and there's a previous show with Dan McCarthy as well as Pete Fader, where they talked about the detail behind a predictive lifetime value, what makes that work and some of the enrichment profiling that works alongside that.


Allison Hartsoe - 28:52 - And then the test and learn mindset. Uh, we heard that in previous shows, both from Laura Bowden at Bain and Brooks Bell from the testing company. Brooks Bell about how that test and to learn mindset is such a critical piece for the leaders. So Jordan, I, I think we've, we've really nailed it, and I loved everything that we talked about in this show because there's so much value in a CLV strategy. I feel like if you just try, you can hardly go wrong.


Jordan Elkind - 29:22 - Well, that's certainly the way that we see it would be CLV all the way. And I, I will say it can seem like an impossible mountain to scale for companies that are just beginning to think about this. Um, which is why the last thing that I would leave folks with is this crawl, walk, run methodology. When we embrace this and everything that we do at Custora, because the reality is it's quite easy for organizational fatigue to set in and this is why we all need quick wins that I've described, some absolutely incredible applications of customer lifetime value, I mean really transformational stories. What I haven't highlighted are simple, easy steps that we see companies doing to make an impact even if it's not as big as redesigning your supply chain or refitting your products or changing your channel strategy, but really this is why the most important step is getting your customer data together, looking for insights and seeing where it leads you. I guarantee there will be some low hanging fruit that will help make the case clear as day for how CLV will


Allison Hartsoe - 30:32 - completely agree. Completely agree. As always, everyone links to everything we discussed today, we're at, and I'll be sure to put a link to the CARMA conference there. Jordan, thank you for joining us today. It's been an absolute pleasure.


Jordan Elkind - 30:49 - Oh, it's my pleasure and really enjoyed getting a chance to talk. Thank you. Awesome.


Allison Hartsoe - 30:54 - 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 follow to get results.


Thank you for joining today's show. This is your host, Alison and I have two gifts for you. First, I've written a guide for the customer-centric CMO, which contains some of the best ideas from this podcast, and you can receive it right now. Simply text, ambition data, one word to 31996, and after you get that white paper, you'll have the option for the second gift, which is to receive the signal.

Once a month. I put together a 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. Things I include could be smart tools. I've run across articles, I've shared cool statistics, or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and the signal. See you next week on the Customer Equity Accelerator.


In case you missed Part 1 of Jordan's interview - Listen Now!


Key Concepts:  Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research


Who Should Listen:  CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers

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