Customer Equity Accelerator Podcast

Ep. 98 | Operationalizing Customer Lifetime Value (CLV)

 

"In my opinion, CLV can be and should be one of those key top line metrics or currencies that helps measure customer-centricity." - Ahmer Inam

 

This week Ahmer Inam, joins Allison Hartsoe in the Accelerator. Ahmer has held senior executive positions in data and analytics for over 20 years in companies such as Wachovia, Wells Fargo, Nike, and most recently Cambia Health Solutions. Operationalizing Customer Lifetime Value leads to customer satisfaction, engagement, loyalty, and clarity about consumer retention.

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Ep. 99 | Bonding the CLV Community Ep. 97 | 1-800-FLOWERS.COM, Inc. 25 years of Digital Leadership

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 everybody. Today's show is about operationalizing customer lifetime value or CLV. And to help me discuss this topic is Ahmer Inam. Ahmer held senior executive positions in data and analytics for over 20 years in companies such as Wachovia, Wells Fargo, Nike, and most recently Cambia health solutions. So Ahmer, tell us a little bit about your background and how you got connected into operationalizing CLV.

Ahmer Inam: 01:03 Thank you for inviting me Allison. I said the pleasure and honor to be on your show and we can geek out of how CLV, yeah, CLV to me I mean most of my or 20 years of data science analytic experience really happened around understanding consumer behavior and then what drives consumers action and very early in my career we started to look at how do we quantify or measure the outcomes of any of the consumer engagement type strategy for now, in fact, even before that I've worked at a company I call national instruments as an intern and then Bruce, and I think you'd probably know him, he was my boss Ted and we did some great work around like read and they really trying to understand consumer behavior and patterns around their behaviors as far as connectivity and connections to the product is concerned and then how that drives that frequency of visit and eventually purchase of the products.

Ahmer Inam: 02:00 And then from there our continue engagement and driving off that lifetime value because that's essentially how they make money from the continuation of the uses of their profit that they are making. So it's really like early foundations in that. And then I started to study a lot in grad school at that time and some of the work that I was doing for a natural science foundation around understanding how he wouldn't behavior from psychometric evaluation perspective, understanding perceptions and attitudes for truly gleaning to the psyche of the human mind. Right. And then game theory and stuff like that. But for game theory, I'm a geek about that. And from there and kind of took it to my first job, which was at Wachovia, my full-time work there, Wachovia was the fourth-largest bank in the US at that time, after the financial crisis, it was bought by or acquired by Wells Fargo and I would get the longer time too. But yeah, Wachovia was considered to be a pioneer in this space. And in fact, consistently year after year in a Michigan study on consumer experience and satisfaction, Wachovia was always way above and beyond the rest of the peer groups of satisfaction. And yes, I mean essentially once I got topped back into and became part of that team that's where, I learned a lot about driving that comes about engagement and satisfaction and loyalty and how to do that. And then essentially the CLV became a measure for that.

Allison Hartsoe: 03:19 That's perfect. Not often that somebody gets that opportunity to find a company that's as forward-thinking and then apply the things that you might have known. Now I have to ask because the game theory is actually has been on this, we have a show coming up, and I'm not sure if it will air before or after this show, but Zach at electronic arts also studied game theory, and he studied it under Shaklee as opposed to John Nash. And you know when you talk about game theory, I think there's always such an interesting connection between what motivates us to take different actions and customer lifetime value. Is there a particular thing you love about game theory?

Ahmer Inam: 03:57 There is, and actually some of my work in the graduate school, and it was more around the National's work, and it's around the behavior of consumers on the internet. So in online media and the idea is it turns the commerce moved to the new commerce online. The aspect of against it became very interesting. And in the beginning, the ambitions of the internet world was to have this free-flowing set of information, right? It's a very almost like an Oslo stick ambition. But what time, what we have seen is that consumers became less and less empowered because you will see a lot of information from like their review, and they said that way. But as far as the consumer's empowerment is concerned, it's in my opinion, actually a lesson or time. Some of my work actually in graduate school from a research perspective was around understanding games when you have synchronicity or en-synchronicity of information.

Ahmer Inam: 04:49 And my early research showed that the movement was to us having less and less synchronicity. So, so therefore, what that means is the entity that accompanies has more information, and they're more empowered. What says the consumers and consumers have less information. So then you have a game that is synchronous that is always going to go in favor of those who have more information. That means consumers are always at the losing end of it. So if you're trying to gain to that massively real or satisfaction where both parties are satisfied in the end, you have to bring that transparency into the equation and trust into the equation. And without this transparency and trust, it's going to be really hard for consumers to feel satisfied. So many of that e-commerce doesn't feel like Amazon has done a good job about, I mean definitely the game and by seeing an of course then bringing in the service aspect of it, this prime delivery in everything.

Ahmer Inam: 05:42 So there are ways companies have done to gain that consumer trust, but there's still enough consumers still kind of feeling like, you know, we don't know what they know about. I said how they used it against us. So it comes into that, and it's actually can very pertinent when I was at Sonic automotive, which is one of the top eight automotive retail companies in the country, like 10 to $12 billion revenue. A very nice company. But the thing is for them, the challenge has always been that stigma of the automotive industry from the retail perspective, consumers do not trust. I mean, when I interviewed for the job there, and the COO said, Ahmer, like I feel bad. I'm like, why? What's going on? He said, well, I learned a company, then let's do that is one of the top reasons for driving anxiety among the medical consumers, and we want to change. So it was like the sense of acknowledgment was amazing, right? And then they wanted to do and what was great to hear was they wanted to bring a sense of trust and transparency with the consumers. And it leads towards that engagement and loyalty by bringing data and analytics as the driving force behind all of the decisioning and then break all of those barriers. So it was fascinating to kind of hear and have that dialogue.

Allison Hartsoe: 06:52 Yeah, that really is. And that reminds me a little bit of what we see when it comes to AI. And so let me give you this question about operationalizing CLV and because I sometimes see the person in the works as creating friction when it comes to how analytics and data can be driving a business to more optimal results. Ideally, making both customers and companies very satisfied. So should I actually care about how to operationalize CLV when everything is moving to AI? And maybe that'll just tell me what I need to do and I don't have to worry about all these little details.

Ahmer Inam: 07:30 No, and I think that's a good question because AI is such a hot topic these days, and you look at darkness. Hype cycle is kind of like geeking at the Hype slightly curve there. It kind of depends on what you mean by AI. I mean, if the AI and I had done, I'm in my background is in AI research back in my undergraduate days. And so I'm going to keep it more purest when it comes to AI because I go back to those and ensuring and then all of those aspects of the AI and recently since the AI as an augmentation of human functions, right? Be it cognitive or physical as a way to help augment humans. And I see that more and more recently predictive analytics. And I see predictive analytics in a broad term, like even like machine learning and into an extent, a lot of the deep learning that I see.

Ahmer Inam: 08:18 I do consider that to be predictive analytics in a way you are using to cycle data and information to get gain more and more accuracy in predicting something like an outcome. So as far as the method and methodologies that are now available to us in predicting consumer lifetime value retention, likelihood of attrition, let the factors that go into calculating and getting a better sense for CLV, even better dimensionalization based on segmentation scheme in a much better and powerful segmentation algorithms and in the day the compute power that is available to us now on cloud. I mean it's not, for me, if we truly talk about broad term AI and we talk about the aspects of machine learning and deep learning as a way to help augment and then get better understanding and awareness of consumer behavior and then how they utilize that to calculate and get better sense for the drivers of consumer behaviors.

Ahmer Inam: 09:13 And then eventually a better calculation of CLV. So to me, AI is not going to be a replacement. I think we have to embrace that, and I think some companies might already be doing that. Then it is actually really going to make it better. One point is that I would also like to make in that is that the marketplace that we are in right now, the consumers are demanding to be engaging, engaged with the companies that they do business, right? They want this to be treated as an individual of one, right. We are in a society where everyone is a curator of their own life in a way, right? And so when we are going towards the consumers themselves are moving to their individuality and expressing that individuality. They're also demanding companies to do that. So when you're aiming for that level of accuracy that is demanded by consumers, then you have to embrace these more advanced methods to gain a better understanding and awareness of that.

Ahmer Inam: 10:04 Then consumer centricity, if they think about it, and there are many, some of the best brand of these companies in the world that are moving in that direction for them and then from my perspective, CLV essentially is a currency of that consumer centricity because they think impassive and measures everything that brings it all to life in a way. So as I mentioned, all of the upselling, cross-selling loyalty, and brand advocacy and engagement, in the end, all of this is coming to drive acquisition to the us. It's really driving that, right? And if you're trying to measure how good is the company you are at driving that consumer centricity and then how do you measure that consumer centricity? In my opinion, CLV can be, and it should be one of that key top-line metrics or currencies in the way that I mentioned that helps measured that.

Allison Hartsoe: 10:51 obviously we love CLV but even if you take it apart into its components such as retention and churn, you're still basically getting to the bottom line of how much do customers love you and are they willing to come back again and again, which is to just say that Hey, we have kind of a dry metric that actually measures love, measures how much people want your product and keep coming back for it. And I like to think about that as customer love. But you know, maybe from a banking perspective it's less about love and more about lock-in. So, you tell us more about Wachovia, where they seem to do so well in that customer satisfaction side where half the time when I look at my bank it's like ah, can't stand it but I can't get away from it.

Ahmer Inam: 11:32 Yeah. And I feel very fortunate to have in part of that experience. And then working at a company back then if was at that was the bank doesn't exist right now coming out of financial crisis and Wells Fargo acquired it. But back then, we weren't an amazing bank. I mean if you were the pioneers in the banking on driving that comes in with centricity, our key Mexico, the KPI topline KPI at the top level, company level C suite level that was tracked and measured and in hond in and was consumer satisfaction and everything else all of our strategy. What basically stands from that we even sales goals, how we did for service modeling, how we did staffing muggings even at the branch level where we opened branches. The branches were combined. How we even structured our data and we get a lot of market research too you know analytics group and it was, that actually was in a way, such a were ahead of its time cause the department, because we had everything from a descriptive and analytics to predictive analytics to market research to database engineering all under one group.

Ahmer Inam: 12:38 So we had this full end to end aspect, and our goal was really to understand and hone into the consumers and the way even we structure the data was based on insights you were gaining from dirty talking to consumers feed. Consumers drive decisions, financial decisions as a household, and they wanted to be treated as household first. They were second nuances off different products that they consumers like to utilize or engage as an individual and then to help support our corporate like a product strategy, we have to go down to that level but so we actually even structured our database and mastered occupancy where 360 data and this is back in 2004 2005 so again way ahead of its time. Yeah, we had our own in house MDM methodologies that we utilize to create a consumer 360 data of this augmentation from external data sources at household level, individual consumer level, and then at accounts and product levels.

Ahmer Inam: 13:34 And this allowed us to essentially go up and down on all of those two levels to help drive consumer satisfaction and lifetime value because in the end that it enables us to do that. They were things like retention, like what does retention mean for a product or an account? What does retention mean for the relationship on an individual consumer level? Then what does relationship or retention means or also the household and then based on the dimensionalization of a grid, they were from a household, they were from an investible assets perspective, the financial strength perspective, their financial needs perspective. And the idea was to try to create a sense of life stage, to be like the data life stage segmentation to understand as a household where different households, wherein their financial life stage so that we can give them design our outreach to them with the product offering. That was essentially tied to where they were in that life stage and what they needed most instead of essentially just bombarding consumers being all kinds of direct mail and in an email. So yeah, it essentially goes up for us everything, all of our strategy and you know.

Allison Hartsoe: 14:39 So there's a couple of questions I have for you in there. I mean, I love the way that you're talking about the structure, but I have to ask you about the completeness of the data, especially back in 2004. Did you have to get the organization to accept that the data might not be every single household, every single consumer in order for them to accept the results on the other side, or was it complete?

Ahmer Inam: 15:02 So, because as a bank, we owned our own consumer data. So we had the accounting information, product information, and those who were behavior as far as their interactions with our different mediums, like everything from their interactions with the tellers to ATM machines to online banking. So back data from the consumer engagement as they engage with us as a company. It was complete 100% complete. When we did the householding that's the thing we are, I wouldn't look at seven different criteria, I believe. I still remember it. Right. And after running through all of those seven different criteria, we were able to household Venmo and 90% of our consumers.

Allison Hartsoe: 15:41 Wow.

Ahmer Inam: 15:41 Yeah. It was fascinating. Yeah, it was great completion and then the rest, uh, so that we might be ready. We would want very openly and transparently share what was synthase we were able to household, what was not householded, what we were excluded from all of the analytics because the data quality was not great yet. And then that data quality was hacking the board time. And in most cases, when we were not able to household where those who were very new for the commute relationship and we didn't have enough information to be able to add them or combine them into an existing household. Partly, I mean we weren't even getting data around things like divorces and marriages are kids growing out and then going to colleges and then probably would have to split the household and kids move out or even small businesses as NSC though getting formed or getting split.

Ahmer Inam: 16:26 So we were really on top of all those dynamics. And then the key driver for that was that most financial decisions are really done as a unit, be it an LLC or a small business or a household. And we wanted to make sure that we capture that in our data to be able to drive up better decisioning and continue to buy that satisfaction and one of the key ways, some of the things that you did in terms of the operationalization is that our segmentation lifetime value necklace product, like a lot of these metrics around driving because customer satisfaction we were the governing entity in a way in terms of what gets pushed out to the consumer. So we had to go and a new function in terms of 20 campaigns, let's say their household or customer is qualified for. We would run that through a actually a lead that we had calculated in terms of NPV and then likelihood of response as to for amateurs and then we had an optimization algorithm behind the scene utilizing CLV in terms of NPV and a couple of other things that then would make that one campaign that should kind of been out and then that's the one campaign that would get loaded to our army channel.

Ahmer Inam: 17:35 Outbound outreach to the consumer to not just like bring all kinds of direct mail or email or you go to the branch, and then you get hammered by the tellers to buy the new product. So we would actually very conscious about all of those aspects because you are to make sure that consumer satisfaction does remain at the top of the DOD is a driving factor. So even in the CRM systems, all that that I've seen, things were loaded with the governance behind it at that we manage.

Allison Hartsoe: 18:01 That's a really brilliant approach on her, and I look at it, and I almost think about it as the Netflix recommendation contest where it's like models recommending models. Is that indeed what was happening is like you had this kind of game of Thrones with campaigns to see who was gonna win?

Ahmer Inam: 18:17 Yeah. We, at the, we measured that they had a really robust system of gathering all of the campaign performance data that we would then bring it back in, and it was then modeled continuously evaluated in terms of like the consumer is likely to respond by what channel sequencing of the outbound messaging to which consumers to start with a letter and then follow by an email. So we did a lot of that, and then they went the continuous champion challenger approach that was built into it.

Allison Hartsoe: 18:44 And talk a little bit about the champion challenger approach, which people may not understand.

Ahmer Inam: 18:49 Yeah, so the idea really is that once you build a model initially and it's screen on your, some of the initial data, any you discuss, you operationalize it and you use it, but over time model e great and to ensure that our models always remain at the top in terms of the power of prediction plus all of the data that coming back in and the people back through in terms of the actions that it drove, less additional data we were getting to augment. So we were continuously building multiple models to ensure that the model that is in production was constantly being challenged by another model and an or time. What you would see is that the model that is challenging the existing model starts to win out, and then we would replace that. So that way, your model for not degrading and actually getting better over time. And if you do that continuously, then your models will continue to improve and get better all the time.

Allison Hartsoe: 19:40 Nice. So there's obviously a lot of richness in the Wachovia example I want to hit on the retention definition briefly before we go onto a different example. But you know, when you talk about what that means at each level, I think that's a question that's oftentimes not really dealt with until you're in the weeds until you're in the thick of it. So, for example, we had one project where somebody said, Hey, can you tell us who the high value running customer is? And in the process, you had to define, well, what is running? How many miles per hour does it mean to run? And what is high value at what thresholds? So is that similar to what you were running into with retention and how did you, did you just kind of develop, here's how we're going to define it and then have people reflect on that and say, yes, we agree.

Ahmer Inam: 20:24 Yeah. So we, if we did, and then, so retention, let me explain a little bit. So how we use retention in the and the the value, right? So we calculated the value as far as each household lifetime value. And then we calculated it and in terms of NPV, which was always correct, right? So it's essentially a dollar revenue to the company that that particular household was driving. And we would calculate the same thing as individual consumers or relationship level. So let's say the household, I have a wife and a teenage kid and a grandmother, let's just say, right? So four people in the house, they all have accounts. So we would then calculate the lifetime value of each individual and then go down to then the product themselves. So let's say the teenager has a checking account, three checking accounts, so we would have a lifetime value associated with a typical, like a checking account at that.

Ahmer Inam: 21:13 Like what is the value that to the company or let's say the the father and mother owned the, had the mortgage together. The grandma has an annuity product, right? So from a retirement perspective, so as a household, we would say to, let's say the father and the mother also had their own individual credit cards and debit cards and online banking. Cause I taught imagine for a number of products that you have in a household, but then you can sound to the primary accountable secondary account holder, and then you come down to who is the owner of an account, right? Who is the primary driver of the decision on an account? So that determines the relationship drivers for the households for that type of product. And then you go down to the accounts themselves, right? So having their dimensionality health in the sense that when you look at total households, very robust relationship that has the product in across all categories of deposits, load investments and insurance.

Ahmer Inam: 22:02 So in retirement, right? And then you go around to individual, then you'll see that the cross sale at the individual level is it optimized or not optimized? Right. And then it then, then you went onto the account level to see do they have the right products and services for the household for their needs based on the life stage they are in for the financial services perspective and then having a CLV metric associated with the retention at each level. What it helps us feel is that they will start at the top line at the household and if you would map measured and continuously monitor segments, shifts from high, medium, medium, high, low, low, high and medium value. Like let's just for the sake of simplicity and keep it at those three levels. So we were continuously measuring and monitor the movement segments, migrations or movements across those.

Ahmer Inam: 22:43 And then we had our team that would do deep-dive due diligence analysis in terms of if we see a movement of 55% of households went down from a high value to less than high value and we would immediately go down and see what grows that, right? So go down to relationship levels like Oh they, they closed their prime equity line of credit, right? And then moved to somebody else, our Vegas, something happened there. Right? So they would go down to that level. So it would help us keep track of all the movement and learn about what was the driving factors behind the migration of a household from a low to medium to high versus the other way, like degradation perspective. And then that fed into all of our predictive models in terms of understanding what drives retention and then based on the complexity of the products and services and the type of products to have and in that predictive analytics then fed into a lot of our outbound marketing. Everything from acquisition, crossbill, upsells, retention to all of the strategies within kind of fed by that. But it is really that dimensionalization in terms of the relationships and then dimensionalization matching of the value itself matching to each level.

Allison Hartsoe: 23:51 Did you have a carve-out for some percentage that you just knew that you couldn't move? Like, let's say the households went down and no amount of marketing is going to move them back up because they've had a major life event of some sort.

Ahmer Inam: 24:05 Yeah. And we did that. And then we also looked at the households. So with our segmentation scheme that we had then brought in factor four factors, and I signed there. It's still behavior from a banking perspective. So things about us like wallet share type of stuff, and then internal behavior as far as what type of products or services they had with the bank and in how they interacted with the bank. The online banking was, is what they primarily a branch, they would go to the branch or the check writers versus debit card just need that type of nuances. So it was a very rich multilevel hierarchical segmentation schema that we had built at each of those levels. And what that allowed us to do is understand all of those nuances to be able to feed into that. Yeah.

Allison Hartsoe: 24:48 when you have something that is so complex and rich, is it hard to get internal adoption of that as you operationalize it? Do you have to simplify it in such a way to get people to take it up and use it? Or was your part of the organization really empowered to? It's like what this part of the organization says goes.

Ahmer Inam: 25:10 Yeah. And then that was the beauty of how, or Coby has a bank, but it's structured. So the analytics was set up at a fury, and analytics was set up, but the COE to support senior management. And the idea was that the lines of businesses or the silos will continue to do their product level reporting. But we were the only entities that would look at everything holistically. And we would also be the only entity that centralized the three 60 view of the consumer. And we're driving that aspect. So while the different products function responsible for driving PNL at the product level, we were responsible for that consumer centricity, and our stakeholders directly were senior leaders in the company. So that gave us that be at the table with the strategies, and many of them actually came from a top consulting firms like McKinsey and all.

Ahmer Inam: 25:58 So there was a maturity among the leadership itself as far as awareness of followers of analytics and what it can do and then how it should drive. The decision is ending. In fact, strategy and analytics are mostly, and almost always went hand in hand. So we want to see a really well-embedded function there. And I've seen that all the time. Many companies excluded that, but the very good structure very well set up well. They were times where they would have pushback on both sides. And then we were having a really highly engaging dialogue about what data is telling and not telling and get into the nuances. But those were very rich dialogues coming from the right sort of is the right approach to it. Right. It was not that dry, you know, it was not a turf war. Yeah.

Allison Hartsoe: 26:42 Yeah. I think a challenge for many companies. I can appreciate the magic that must've been going on here for the leadership to accept the analytics and stay open-minded to the things that the data was saying but also challenge it.

Ahmer Inam: 26:55 Yeah. Yeah. And it's like I've gone to any of the companies from there on, and I've seen the other side of it when analytics is not empowered to drive their options, it's not set up the right way. It's not allowed to have that seat at the table. And if you run into the organizational alignment and in the cultural and political issues, data readiness issues, lack of data strategy, I mean one of the things about the recovery with it, we had a very robust that data strategy that fed everything. So yeah, I know where I am in it. If I feel lucky to have impart of phenomenal the best of the best. And then what the best of the best can be kind of still kind of thrived to again that in a way in most of my old since then.

Allison Hartsoe: 27:34 I could see that. I think some of us have had similar experiences where you're part of a company that you don't realize is actually the best of the best and what I find is the signature in those companies is lack of politics and incredibly smart people that are just kicking ass and taking names and because the organization works so well together, that is definitely exciting. So you mentioned two things that were signatures of an analytics organization that perhaps if we flip it upside down that is empowered. They have a seat at the senior table at the executive table, and there they have a data strategy, and they have data readiness. I guess there's either so far. Anything else you would add to that?

Ahmer Inam: 28:14 And I would say that, and I've been I said in a couple of other conversations that I've had lately is that the role of these data officer or chief analytics officer is one of the hardest jobs in the company. Then the reason for that is that it is truly a change agent role. And most organizations, it's just human nature. We are not as humans are not receptive to change. And when you look at the roles like CMOs, CFO, CIO, CTO, those are functionally deep and then well-defined goals and in those goals, the authoritative figure that sits at the top can drive change or and then drive their function essentially a deep power, the distance of a target that they bring to the table for their function. But the challenge with a CTO or the CEO is that they are in the business of enabling change across all of those functions.

Ahmer Inam: 29:08 And if the CTOs and the CEOs are not allowed to have their equity on the table with those other functional leaders and heads, then they tend to lose out. And what I typically see is that many companies don't structure or set up the organizations for sectors, to begin with when they would out up the organization at the analytics function and a sub-function within other smaller functions. So if you automatically don't have the voice of the data in the C suite to the right with a big deck in front of the stud, the CEO anymore or at hog, it is going through another entity. And then you have these folks who the companies typically hire a highly quantitative, very technical folks to kind of lead those functions. And then they're good at what they do. But they have not been trained in change management and understanding the people and the process and the politics and the organizational currency, which essentially is a reflection of the power centers, right?

Ahmer Inam: 30:00 And then the, these leaders are then getting a tone into fighting or trying to persuade middle management. And in most larger organizations like middle management are aware, those ideas go to die. And so when you see consistently like calm down and boards annual survey from new vantage partners around impediments to driving the adoption of analytics consistently year after year is always lack of organizational alignment is number one. But it's still politics complete politics is number two, lack of data strategy or readiness and step three and it's really bad. And unless the CEOs and the board objectives realize that if they want to bring that strong solution in the organization, the analytics and data has to have that seat at the table at that level to be able to not only drive that change, using that its essentially for both professionally and being a change agent as well as having some sort of authority to be able to do that.

Allison Hartsoe: 30:57 So are we actually saying that to operationalize not just CLV but the transformation of a business based on data. We need the C levels to have a leap of faith that this transformation will actually pay off. And by putting analytics in the senior levels and supporting the change agent that the CDO or the CAO that that function that they need to be, they will get economic benefits on the other side. That's a big leap of faith, I think.

Ahmer Inam: 31:26 Yeah, and I was, you have to have, right? Because if you think about it, when the internet came, how many companies were just not willing to adopt, talk adapts to that, and how many of that still exist today? That's because the CEO or the CTO at that time or the CIO at the time, didn't truly understand the sockets and NTCP IP protocols. Like you don't have to, right? But you don't have to be technical experts in those things, but you have to embrace them. There is something going on that is leapfrogging your competitors, try it. Plus, this thing I've seen is that a lot of the companies, and I've been in both conversations, they're members of the C suite was like, well, do something with AI. You have the data, do something with AI like, but what? What problems have you find the software? So there's this lack of understanding and awareness because they're like, well, everybody's doing AI or data science.

Ahmer Inam: 32:15 Wish, we should be doing it do. That's the wrong way to approach it. And when that happens, what I see is that a lot of the companies, when they start thinking that way, they bring a director of analytics or a senior director of analytics, have them get embedded into deep into some function. And then ask them, let's go find use cases, write things, they'll POC. And when you start down that path, and then couple of folks come in that are highly accomplished in their domains. And then to see that the data is not ready, the company's not ready. There's just such a lack of maturity around understanding of these things, then they fail, right? I mean 87% of data science projects fail and or they don't come out of the POCC is just because of that. And then the other thing is that a lot of the companies struggle with and a lot of CTOs struggle with, just defined the spend that is needed to lead the comprehensive data strategy.

Ahmer Inam: 33:02 But it's so critical to everything and because it doesn't monetize in the short term. So if you, and then this is there, the CFO, the dialogue within CTO and the CFO, but then to become a bit contentious in the sense that the CFO or they're going to say like how are you monetizing it? And then the thing is its tables takes. This is a Cappex if you have to. This is an investment, don't, if you're trying to measure the value of the data strategy in the short term, that's the wrong approach to it because the other company, the other competitor that is not thinking back, right, you thoroughly flog you and you will be the next year is out there. Right? So there's an effect, and when you come to CLV, I mean CLV is one of the best and most easily quantifiable, measurable KPI that as a company or the CTO, what is the CEO you can bring to the table? Like, look at this. This is the value we are driving big data and the strategy. So I'm in kind of bringing it back to it. It's, in my opinion, CLV is a much, has much more power as a KPI then how it's typically used, which is to, right.

Allison Hartsoe: 34:05 I certainly agree with that because it is by nature a longterm measure. Although I do see people occasionally saying, Oh, we're gonna look at CLV and Hey, we ran a campaign, and the CLV changed, and yeah, you can do that. But it's kind of like driving by looking at one square inch of the map. In this tiny, tiny piece when you, what you really need to see is the overall perspective.

Ahmer Inam: 34:28 Yeah. So the way I see it is CLV to doing an analogy between weather and climate. So to me, CLV is climate, which changes over time slowly rather is something that changes more rapidly and in more in the moment, right? But having those two kind of well-integrated and in using the CLV as the barometer and as a way to evaluate and then test in and understand the drivers of it, and then how do you influence weather in a way or mitigate the risks caused by business scenarios and stuff like that to make sure that for time your CLV not only doesn't be great but it actually continues to grow. Right? So that kind of a way I think of it.

Allison Hartsoe: 35:08 Yeah, I like that too. So let's say that I love the idea of CLV, and I want to operationalize it within my company. If I were to take specific steps, would my first step be to say, is this even possible based on the way my organization is structured?

Ahmer Inam: 35:25 I think the first question should be, and again it depends on organizations to organizations, their top line, what is the company's strategy, right? So if the com strategy really is driving consumer centricity, consumer satisfaction, the moment is, are they in the business with that, right? So the other aspect of that is how much of that is real versus just talk. So you could have as fat house that you're buried at the top level, but if that doesn't connect to your day to day tactics, then the point is moose, right? Then you are not setting enough to success. So having that as of like awareness and readiness around the readiness for that is critical or the maturity around it, right? So how are things materialize and operationalize as far as they do or engagement in the cutter structure is set up, right?

Ahmer Inam: 36:11 What are the gaps, what is working and not working? And if you look at the oral consumer engagement model on the journey mapping office, at what point could CLV help drive a better decision to worth driving that longterm than your relationships like loyalty or retention? So I think mapping of that is the next step, in my opinion. It is an important aspect of it. So understand the oral that the strategic map, right? And then figuring out from there on the intubation points where CLV as a metric can be a driver to period working strategic or tactical decisions. So if it's an acquisition for it from looking from the acquisition perspective, like what type of acquisitions are coming in from what channels, is it possible to even calculate a pseudo CLV based on some additional, in some barely kind of a data that came in with as product acquisition, connecting it to those that are similar rights and stuff like that.

Ahmer Inam: 37:05 And then figuring out from there on what type of consumer did we just acquire. Then the likelihood of that on the disloyalty map right to journey. And then how do you treat them as part of onboarding to ensure that you mitigate the risk of acquisition within the 90 days? Right. So going back to Wachovia data, that CV had created a strategy call a two by two by two slowly as a way to ensure that the onboarding was done right. So it was the onboarding component was after the opening of an account, again based on many data points and CLV and all that stuff. Uh, the odd one was based on that. And at two days after the account opening, there was an outreach, and then after another, the next one was after two weeks. And then a second part outreach was after two months of account openings.

Ahmer Inam: 37:51 We essentially reduce that potential for attrition within the first 90 days. Right. So, and then that was also based on like which type of consumers to have this outbound sort of checkpoints, right? To ensure that we mitigate that. So that's was one example of operationalizing it even as your retention and onboarding component to ensure that you are reducing the risk of attrition for that potential high-value customers that were just acquired and then continue to kind of move them up the ladder from a cost sell and upsells and retention and loyalty strategy. So as it starts to kind of embed into all of those different actions, but at least to dig again, I would say I think that's where it could become more and more important. The systems and technology, of course, are the digital components to having understanding and awareness of what technology is, what systems, what products are being used by marketing and a sales associate's functions, And where the challenges may be.

Ahmer Inam: 38:43 And I think especially if you get into that part of the business, then you are truly getting into the people in the process perspective. So one of the things that I've seen is that having lean folks who can truly do a bumper into landscaping, all the integration points is very important for even making some of the processes lean and then video thing inefficiencies, right? And then figuring out from there on which integrate could integrate that as a metric into different processes. But it may or may not be useful unless you design a new process or augment an existing process and train people to use this as part of the decisioning. Make it part of your playbook, stuff like that. So operationalization let you goes well beyond just having the metric data written and integrated into a system so that the change management component has to permit that. Otherwise, people just not get used.

Allison Hartsoe: 39:34 That is a great piece to end on. The change management component has to be there. Otherwise, it doesn't get used. I think that's a good insight Ahmerr. Thank you. So if people want to get in touch with you, what is the best way to reach you?

Ahmer Inam: 39:47 My LinkedIn is really the best way I have and Twitter account too. But the LinkedIn is where I'm generally most active, and I'm happy to continue to engage in this conversation if anybody wants to reach out and just geek out about it, and I always love talking to and engaging with beers in the space.

Allison Hartsoe: 40:04 Wonderful. We will link to that as well in our usual page. So as always, everything we link to is that ambition data.com/podcast, and if you'd like to find Ahmer on LinkedIn, it's www.linkedin.com/in/ahmer, but it's spelled A H M E R, or you can just search Ahmer Inam, A H M E R, I N A M and that will get you to in. So Ahmer, thank you for joining us today. It's been a pleasure to hear about your experience with operationalizing CLV and just the sheer vastness, the different dimensions that go into making a system fly, but the fact that it can.

Ahmer Inam: 40:43 it can. So thank you for having me on your show. I enjoyed our dialogue, and I look forward to continuing our conversation and thank you.

Allison Hartsoe: 40:51 Remember everyone, when you use your data effectively, you can build customer equity. It is 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 Allison Hartsoe, 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, ambitiondata, one word, to three, one, nine, nine, six, (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.


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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