Customer Equity Accelerator Podcast

Ep. 58 | Budgeting for Data Science Projects


"If you want to do something transformative and new, there’s no other way. You have to put skin in the game." Jose Murillo

This week in the Accelerator we discuss how to get budget for your data science projects with Jose Murillo, the Chief Analytics Officer of Banorte Bank. Jose has produced massive value with his analytics projects, collectively totally over a billion dollars to date. He shares his insights on what worked and what did not then offers step-by-step advice on how you can gain budget too.

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Ep. 59 | Managing Up with Michelle Rovner Ep. 57 | Measure What Matters by John Doerr Summary

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 everyone. Today's show is about a topic I know that you have been thinking about, but you maybe have never thought about how to solve for. It is how to secure a budget for your data science projects. And to help me discuss this topic is Jose Marillo. Jose is the CAO at Banorte bank, which is the second largest financial group in Mexico, an incredibly important position because under Jose's leadership this bank has moved forward, and has recently leapfrogged Citibank and Santander down in the Mexican market. So Jose, thank you for joining us on the show today.


Jose Murillo: [01:14] Hi Allison, it is great to be back on your show. Thank you for having me.


Allison Hartsoe: [01:18] Can you tell us a little bit about your background, your role as the chief analytics officer and then maybe how you discovered this topic?


Jose Murillo: [01:30] Sure. Let me tell you a little bit about me. The analytics team, which I lead at Banorte since its inception has been very successful. We have yielded $1.8 billion since CLV during our four-year tenure from a data science project, and a notice that I'm talking about CLV, the metric that your audience is well acquainted with. I guess in tandem with thethe value that we have created, the size of the team increased, and the reason that I'm drawn to this particular project on how to gain the budget that you need to carry forward your projects, your data science projects, simply because I think that's the foundational layer on which you can build a successful analytics slash data science practice.


Allison Hartsoe: [02:17] So That's interesting that you say that budget or the ability to get budget is the foundational layer not putting in certain technologies or hiring people with a really interesting mix of skills.


Jose Murillo: [02:33] Yes. Can I give back the first piece that you have to get this to have the budget? I need flight economics 101. Uh, the audience recalled the first course on economics. There's production possibilities, frontier, it determines how much output you can produce. Can you pencil the inputs that you have, and the inputs depends largely on the budget that you're getting, and even further importance for every person is that their own productivity, which heats the basis of their having a successful career. It hinges on how much capital, additional labor there is and the technology they have access to. So when I'm thinking about budget, that's, that reminds how much people you can recruit in your team, the quality of the people that you're getting, that technology that you have access to. And that's largely the first determinant of how far you're getting the gain of analytics, data science or for any project that you want to think about.


Allison Hartsoe: [03:38] The organization either wants to do the program or they don't. I come up with a great idea. I bring my great idea to management. They should bless it. And let me run with this idea. What's wrong with that picture?


Jose Murillo: [03:54] Yeah, it gives, that's an excellent point. It's usually you would think that it should be self-evident, but it's not, and it happens that there are a lot of, uh, competing projects that require resources. And I think that's something that we've found when we talked to some of our colleagues at conferences that they are really struggling to get the necessary budget. They are really smart guys who are having these hard timing, securing the funds to have their ideas flourish, and that's something that it's not that uncommon. At the end of the day probably because within our industry people are a little bit shyer or even very difficult for the c suite to envision or the budget holder condition. What is the capacity of data science you need to fielding, and that's something that it's very hard and also something that it's not that common is that the way that data science units or analytics units are built are largely made us costs centrist instead of profit centers? And in some sense, it is not expected for the data science to put some skin in the game to advance our projects. So it's something that it's expected to be a largely subsidized by the corporation and that billing non-sustainable in the long term.


Allison Hartsoe: [05:13] So you've said a bunch of things there that are really interesting. So first, I want to pick up on what you just said, which is the data science centers are seen as cost centers, not profit centers. What does it mean to be a profit center instead of a cost center? How does that change the internal thinking?


Jose Murillo: [05:35] I guess the way that it changes is that you are going to grant budget but you are expecting that the budget recipient is going to produce x times the budget that he's receiving, the way that most of the businesses are operated and the way it changes it's also that you are expecting that these data signs unit contributes to the bottom line, that it has a measurable ROI, and that it's Accountable for yielding those results.


Allison Hartsoe: [06:04] You know, and I remember at the chief analytics officer conference in Miami and I'll link to this in show notes, they exactly talked about this issue about creating measurable ROI, and the folks were struggling because what they had basically done was installed technology, and they were just scratching the surface of what it meant, and they were being asked to justify the technology expenditure. Is the process of asking for a data science budget, is that not about justifying the technology, but as you're alluding to getting to the, what can it contribute to the bottom line? Are we saying that that concept has to be sold in so that the tech isn't seen as a panacea? You know, suddenly I'm going to get everything I need from the tech, and instead, you're preselling your management on, I need this to get to the bigger picture.


Jose Murillo: [07:03] Yeah, you're right. At the end of the day, you have most of the people that are going to grant to the budget are not necessarily well versed on the technologies or the experts that your unit wants to hire. Probably there are profiles of people or technologies that haven't been purchased before at the company. And suddenly you are going to ask to hire a guy who is an artificial intelligence expert who can build random forest models, and it's very difficult to understand for those people, if it makes sense and it's subtle, it might seem very expensive, and perhaps they are used to, well, if I hire these many more, uh, call center executives or salesman, they're not can convert these much into sales and profit. And it's something that I've, I have experience in how to process that. But it's very difficult to understand how much profitable is going to be the company if I buy this technology or if I hire these type of people within the company. So it's very much within the responsibility of the budget seekerto, to be clear and explain what's the potential. And sometimes the budget seeker, it's not even really aware of how much he can make. So it's, uh, also it can be difficult in that sense.


Allison Hartsoe: [08:23] Obviously you've been successful at communicating this internally. Do you have some examples or ways that you were successful at trying to get across these early data science ideas? And this was a couple of years back when you started the programs at your bank, right? So this was even before people might have had the understanding that they do today.


Jose Murillo: [08:48] Yeah. I can show some successes and some blunders also. Initially I uh, I guess it came like a blessing in disguise. It was something that I really, to tell you the truth, it was not something that I planned initially when we built our analytics team in the sense that, uh, we were going to be held accountable in the yielding 10 times our cost after the first year of operation.


Allison Hartsoe: [09:15] And, this is what you mean by skin in the game. You know, they put a number on you.


Jose Murillo: [09:19] Yeah, that's what I mean skin in the game, and its, its literal in the sense that it's your personal wealth that it's at risk over your job security in the worst case. At, at the end of the day, if not, it's going to be very, yes, symmetric. The company's going to put at risk, a certain amount of resources. And typically what it happens is that, well if your thought as a cost center, it's something that it's just, uh, thrown into the well, uh, money without any expectation of what is going to be produced. But when you built as a profit center, your thought that uh, yeah, that money is going to yield a certain return that it's expected, and you are going to be held accountable by Eaton. And so you are willing to, uh, put your neck in the line. So yeah, you should be a pretty sure on uh, that if you're asking for a certain amount of resources that you are going to put them at good use.


Allison Hartsoe: [10:12] and be willing to risk failure which is even harder.


Jose Murillo: [10:17] Yeah, it's going to be harder. So going back to the examples that you asked me, data science analytics with a novel concept within the company, and its not the dates, Banorte, its a really large financial group. You have 27,000 employees, and it's not like everybody's left waiting like to say, well, we're now the analytics team is coming, and that's what we've just been waiting for. And so you have to see who are going to be the early adopters, the guys who are going to be your champions, whom you have, you can work with, the ones that you are going to face less headwinds in transforming the business. In our case it was the credit card business unit, which uh, it's actually now the most profitable projects that we conduct our, we've still with a credit card unit since those are the guys we started with. And the first project that we undertook with brought us a lot of credibility with cost to Bateman project, which the first year he contributed to the credit card unit to increase their profits from the previous year on by 25%, and when you pick the project, you have to be very careful on which project you pick because you know, cost projects are not really that difficult. You don't have to build a CLV. It's something perfectly easy to measure and to prove you don't have to meet the CLV. It's something that can be quickly executed. In some sense, they can be like the low hanging fruit.


Allison Hartsoe: [11:48] When you say a cost abatement project, are you really talking about optimization the way we might think about it and the digital space.


Jose Murillo: [11:56] Yes, that's it. It's optimization, it's a cost optimization project, and it still has a lot of challenges because basically you are going to tell there are different stakeholders within the organization that have conducted the way they were measuring cost before for a bunch of years and suddenly there's a new novel way of doing it. So there's still a lot of convincing to do, and you have the idea, and then you have to sell the project. But when it's done, you are not going to have many discussions if it's, you know, if it's really worth what you were saying it's worth because it's in the bookkeeping records. So yeah, you can prove that very cleanly. In contrast, when you move to do like income revenue-generating projects, and you are acquiring a customer, and that's something that you've discussed profusely in your podcast, you have to estimate the value of how much does that customer it's worth, how much it's contributing to the customer equity of the firm. And let me tell you a blunder because the success might seem easy, but I think it's also very easy to mess it up. And, uh, for example, lately these years I've kept the exponential doodle a contribution of data's times largely by doing experimentation projects. But the first experiments that we've run, they took a lot of time to get them done, and probably it has to do that I really didn't pick the right projects, and in that sense, I'd like to stress the point that picks the projects where you face, uh, less headwind.


Allison Hartsoe: [13:33] So, even though you might have a great idea if you don't have the, let's call it political wingman, if you don't have that helpful person on the other side that really wants the information that's planning on using it, that's pulling as much as you're pushing that, that can make for even the best project that can set it up for failure.


Jose Murillo: [13:58] You are right. And uh, I think that you need to build your reputation first before you tackled more challenging issues, and that will create a very good dynamic, and I guess the most difficult parties to get seed capital. After you get the seed capital, and then you start delivering an ROI, then, then swing budget increases are much easier to get, and it's much easier also to have a say on budgets that are complementary to your own success like IT investments or human capital investment.


Allison Hartsoe: [14:34] Are you seeing that as your projects are more successful the way, your organization works together and getting  aproject for a complimentary area that it starts to be less about my division in your division and it starts to more purpose driven. You know, here's something big that we're all going after and here's what I need and what you need.


Jose Murillo: [14:58] I think you've put it correctly. If there's much more cohesion, within the different stakeholders, and at the end, the people have the same common goal. The differences in the way that we've think about it is to how you are going to get there. And if you've proven to be successful before, and the people are used to the way that the projects are explained with data, and they believe in the data that you are showing, and the way that it's processed, you don't have that, that you were colleagues in different departments to understand what's a neural network. The only need to believe that you are doing it correctly and soundly in the deed has seen the past proven to be successful, to be willing to risk their time in going into  new projects. And that's right. Initially, it was more difficult, and now the projects are carried out with less friction and the productivity of the company benefit from that tremendously.


Allison Hartsoe: [15:55] So this sounds like a skill that people need in order to get a budget. It's not just targeting the right places where you might not have as much of a headwind to get your project off the ground, and it's not just targeting something that might be a little easier like a cost of date net project versus an income generating project, but it's also the ability to have people. Would you say maybe see the big pictureor or bind to a bigger idea?


Jose Murillo: [16:27] Yes, and I think people will need to hone their own skill in how they are selling the project, and how they are getting the capital, and it might seem that in a large corporation probably people are not that used to be able or need to have this entrepreneurial skill in getting people on board and your projects because it's supposed to be given, but at the end it's your own career that is on the line. And if you secure the budget, you're more likely to be successful, to be much more productive and to have a more rewarding career through your time.


Allison Hartsoe: [17:02] I like what you said about the fact that in a large corporation you might be more expecting someone to bless your idea and hand you things on a silver platter, but what you're saying is you need to bring an entrepreneurial mindset in order to secure data budget, you need to paint a bigger picture, a bigger purpose, and then not focus on what does AI do and the mechanics of machine learning, but focus instead on, isn't this a great way to please our customers, or what if we solve this process or this problem and get people thinking about the bigger picture.


Jose Murillo: [17:39] Yeah, that's right, and especially for people who want to advance their careers and to have a larger impact within their organization.


Allison Hartsoe: [17:47]  Do you look for entrepreneurs on your team to help do this?


Jose Murillo: [17:52] I guess the team in some sense, you don't need that. Everybody's an entrepreneur, it's  the story about the Unicorn, but that you need at least to have someone with these salesmen skills within the group. In our case, it happens that I actually found that I had that particular skill that came to be useful.


Allison Hartsoe: [18:14] Do you personally had the skill?


Jose Murillo: [18:17] Yeah, and let me tell you a little bit more, I guess something that I didn't tell at the beginning when you ask my background. I funded my way through college by selling houses, being a realtor, and I was pretty successful at it. When I was at college, I used to sell like about, uh, at my peak about four houses per weekend.


Allison Hartsoe: [18:34] Wow. That's a lot.


Jose Murillo: [18:36] So yes, after doing a Ph.D. I thought that I would never use that skill, but it turned out to be pretty handy.


Allison Hartsoe: [18:43] No kidding. Do you find that some of your same sales strategies, you know, perhaps, you know, getting people to agree or getting them to nod heads? Are there certain things that you look for in the process?


Jose Murillo: [18:56] Going back to the time that I used to sell houses, costumers really like in really explaining very well what was the product that I was telling, and I mean something similar when you were doing a data science project, you have to be able to explainclear in a clear fashion what you're aiming for. Any mill language that your colleague can relate to, and that they can understand, and I'm going back to what you said earlier, they don't really need to understand all the dynamics of internal methodological issues on the models that you're building, but it has to make sense to them, and it has to be something that they can believe in that it's credible, the story that you were telling me.


Allison Hartsoe: [19:40] Something they can absolutely believe in, and I wanted to mention, I should have mentioned this earlier, you've been on our show before, and I will be linking to the earlier episode where we talked about how you got tremendous value within the organization and the different stories that were related to that. But in this case, when you're looking for ways to communicate how to get people to believe in a certain idea, is there another example where you want to call out how you were able to not just get the seed capital but get everybody get their hearts on board in a way?


Jose Murillo: [20:19] Yeah. I think the way it has worked out is to have something that we've done with almost all our projects. It's identify all the stakeholders that are necessary within the early on to discussion, because if you forget one of those, it's going to be instead of a supporter or an ally, it might end up being a detractor in a later stage if he was not considered, so what do we do is that we are very careful on who are the different stakeholders that need to be in a discussion.


Allison Hartsoe: [20:50] Do you over plan like if there's a person who might be on the line between yeah, there kind of a stakeholder or they're kind of not, do you include them anyway?


Jose Murillo: [20:59] We include them, its, the more, the merrier. Initially, it seems that it's going to be a very large investment, but then things turn out to be much smoother. For example, now we are one of the very recent projects that we're working on. It's Eh, based on the results that we've gotten from our experimentation from studies that we've done in behavioral analytics and artificial intelligence on the communication on with our customers. We are refining the way that we're communicating to our customers through different channels, different products. And as you can imagine for every product, there are a bunch of stakeholders, its product segment channels, uh, customer experience marketing. And the way that it has worked is that we are holding every day we were seeing one product at a time that we're talking about on the communication, and initially took a lot of investment in understanding how we want it to communicate, how we want to standardize on how we were going to build this champions upon which later on we would put challengers to perfect the communication. And initially, the meetings were a little bit longer, but later on everybody agrees on different things, everybody helps and owning the project. So that's I guess an important thing which I'm having all the different stakeholders. It's that everybody's involved and so everybody has ownership of the project. And now that an exercise that we've been doing for the past two, three weeks, and now it's a very expedite process and we've reached to different agreements, and everybody knows it's going to be even harder to reverse to our old communication ways.


Allison Hartsoe: [22:44] I love that example. It is clear, but I also wonder, especially in the data science world, there are people who are not natural extroverts. And I wonder how you bring those people along too when they're not natural communicators. They're sitting on the edge of the table, they're not contributing to the process. How do you bring those people in?


Jose Murillo: [23:08] That's an excellent question. And let me get back to this example. The way that we started the discussion, June 1st week, we had some uh, internal mockup on which we tried, and the different people ask because thethe large meeting needs to know a bunch of stakeholders. There are a lot of  people, but within a smaller group and had even asking them to bring up the issue when the real meeting was going to be held up. And we did that for about a week. And the fact that when we were conducting  even more extra for people are, are not necessarily that comfortable, lean, willing to risk and opinion when you are facing, you know, the Ph.D. and uh, an expert on behavioral economics and an expert on such and such and an expert on the product. And people might be shy, and they might have really good ideas.


Jose Murillo: [24:00] And what I found is that having like five people agreed upon what they would raise up an issue and asking them a more confined group, each posture discussion. And I've thought people from not only my division but from other divisions pitching up really great ideas on what was missing in that communication and people who had experience on the front line and a what was really missing. And it really fostered a very good collaborative exercise. It, it was not something that happened as spontaneously, but it was something that happened more like, no, it was practiced in some sense.


Allison Hartsoe: [24:40] Am I hearing you right in that you use the smaller meetings both before and after the larger meetings to get that collaboration?


Jose Murillo: [24:48] Just before, it's before, it is smaller group and set up a group of 15 people, a group of five people, and which was alternating the people that were attending them and having, you know, in the more reserved group that they were willing to risk for ideas and the people really have great ideas and ask them, so who would you be willing to put it forward in the, in the big meeting. And they did. And for other people saying like, well it's not only Jose who is telling us the word, it's everybody who's pitching any they, it's more free, and people were more willing to venture their ideas, and certainly we ended up with a much more robust product at the end.


Allison Hartsoe: [25:26] Oh that's, that's fantastic. I have seen this in other entrepreneurial companies that were very successful. This ability to get everyone on board and the phrase to get everyone on board doesn't really capture what this is about. This is really about listening or  leveraging the social intelligence within your company to make the best ask for a budget that you can make.


Jose Murillo: [25:53] Yeah. Now that I'm listening to you, something that also that I'm thinking is if, within the larger group with Donny to foster these people to express their ideas, it's really to keep it positive. We are very agnostic on what's a good idea or not, and what you want is to bend hippo's, the highest paid person opinion. We really don't like that, because at the end any idea can be a good idea, and in some sense what we don't do is to say, well that's a very bad idea. We're not going to do anything about it. Even if the group thinks that it's something that we might test, well we don't know. What we will do is we'll build a champion, and then we can test with a challenger if your idea makes sense and it leads to higher conversions.


Allison Hartsoe: [26:43] I love it.


Jose Murillo: [26:43] So we tried to keep it like a very positive exercise that nobody's really, uh, shun from the exercise.


Allison Hartsoe: [26:44] I think that's difficult to do and congratulations for being able to do that. And I'm sure that does contribute to your success. Are there any other examples that you want to share?


Jose Murillo: [27:01] I guess those two come to my mind as I said, well let me tell you one thing which I didn't get the budget that I thought, and probably made sense to not get the budget. At some point, I thought that it would be useful to have some analytics seminars for my colleagues in different departments to be more well versed on, on analytics and data science and that probably that would help to accelerate the process. The people from HR, Tony, well we are not really sure that people are going to get really excited about this. And when I talked to the people, they said, well, you know, we feel pretty comfortable with you doing the analytics, and why do we need to  learn these? I mean, sometimes I should have scouted before for the people within other departments if they were interested in such a thing instead of me planning it from you know, my office, what would be helpful for building their human capital.


Allison Hartsoe: [27:54] I love that example, and it makes me laugh because I was presenting analytics one day and the person in the audience said, wow, Alison, I always think of that analytics like accounting. And I was just horrified because if you love the subject of analytics, you're like, how could that possibly be seen as accounting so boring dry? And yet it, you know, if it's not your area of interest, anything outside of that is boring and dry. So, um, yeah, so I hear you on this one. It's a, it's definitely the, uh, what I've heard in the, in the past. So let's say that I want to get budget for my company, and can you give us a sense of first, second, third, what should I do? Where should I start? We've, we've talked about a lot of different pieces in this conversation.


Jose Murillo: [28:48] I think there are five issues that you would like to have reminded. The first one is, I'm sure that you will have, you know, a set of prints, formative ideas, select the one that the lease to headwind. Pick the idea that you'd be more likely to be successful and that it's not going to be that surprising for your colleagues or the different stakeholders that you need to push forward. So that'd be the first one, pick the right project. The second one is building a business case that it's a very solid with the data, that it's credible, that it's believable. After you've done that, the third thing that you need is convinced yourselfthat that you're really willing to put your neck on the line for it. We're going to ask that your company to spend money on that project, so you have to be willing to risk something personally, so you have to be really convinced yourself. You're the one that has to be convinced first. The fourth step that I would do is to understand very well who the budget gatekeeper is, and just state your case, and you don't get hurt the first time hitting to a loop in there and then probably you are not communicating correctly hone your sales pitch, but to address it to the right person,


Allison Hartsoe: [30:01] is it usually one person? Are there multiple budget gatekeepers or is there really just one person you're getting to?


Jose Murillo: [30:08] That's an excellent question. In my case, there were multiple, uh, you know, there was my, uh, boss, he was sponsoring the project, but at the end of the day, we needed to go to a committee and get the budget approved. So yes, I needed to have Mr. Orno who is the chief operating officer on board. He was very excited with the idea, but his peers within the firm needed to say yes, the board of directors needed to approve the position of a, of a new chief analytics officer. So yes, there are usually many stakeholders, especially at large corporations. And that's an excellent point because the way that you were communicating to a person that its well versed and advanced team in analytics and data science, you can be much more succinct, but the pitch probably needs to be adjusted for the audience that you are already talking to. And I guess the last part is that you need to measure there, what you've done, you need to measure the ROI and be willing to be held accountable for it and for good or for worse, its uh, hopefully for good.


Allison Hartsoe: [31:17] Uh, and if anyone wants to hear the fantastic ROI results again, our first podcast episode talks about those ROI examples, and I think everybody will love that. So I will link to that again in the show notes. Now, Jose, these are fantastic tips, and I know people will get a lot of value out of understanding how to secure budget for the things that they can envision for their companies. If they have questions or they want to reach you, what's the best way to get in touch?


Jose Murillo: [31:47] I guess through LinkedIn they can contact me.


Allison Hartsoe: [31:50] Great. And is your LinkedIn profile under your first and last name? Just Jose Murillo. Will that gets them to you?


Jose Murillo: [31:58] Yeah, its Jose Murillo @Banorte, that would be very easy to find me. To my a surprise, there are plenty of Jose Murillo around the world.


Allison Hartsoe: [32:08] You know, I'm always amazed, I have a fairly unique name, but there is actually one other Alison Hartsoe out there. Wow. I know, I know the ways we're connected today, at least one that I know of. Maybe there's more. So Jose, thank you so much for all of your insights and, and the discussion as always, we're going to include the links that I've mentioned on ambition and I always enjoy our conversations because they lead me in directions that I don't often think about kind of getting out with a box of analytics into more the human side or what does it take to actually make these, these dreams come true. So thank you for leading the charge and figuring this out for us.


Jose Murillo: [32:58] Thank you for having me. I really admire your work and uh, I think the podcast, it's becoming a really good encyclopedia on analytics, uh, best practices.


Allison Hartsoe: [33:09] Oh, thank you so much. I appreciate that. Remember everyone, when you use your data effectively, just like Jose, you can build customer equity. This is not magic. It is a very specific journey that you can follow to get results. Thank you for joining today's show. This is your host, Alison 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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