Your Customer Data Has No Owner

Most personalization efforts fail long before AI becomes the problem.

It fails in quieter ways. A missing identity. A disconnected signal. A decision no one owns.

In this conversation, Ralph Jovine and Chris Douglas unpack why most personalization efforts stall long before AI becomes the problem. The issue is structural. Data ownership is unclear. Governance is inconsistent. Signals don’t connect across the journey.

They get into what actually needs to be in place for personalization to work at scale, and why the companies that get this right are the ones that treat it as an operating discipline, not a feature.

Support for this week's episode provided by: Netskope

More about this week's guests:

Ralph Jovine brings over 20 years of experience in digital marketing and e-commerce. As a former executive at global agencies like Accenture, Merkle, and CEO of Nervewire, he spearheaded large-scale marketing and digital initiatives for renowned brands such as Nike, Cole Haan, Ralph Lauren, L'Oréal, Toyota, Bridgestone, and Hilton Hotels. Ralph's expertise spans various domains, including Business Strategy and Transformation, CX Product Strategy and Technology, Omni-Channel Marketing, Commerce, CRM, Loyalty, Martech/Adtech, and AI/ML Strategy and Application.

Ralph's top pick: The C-suite's Blueprint to Personalization in Retail and Beyond

Chris Douglas is Senior Director of Product for Unified Commerce at World Wide Technology. He leads the strategy and growth of WWT’s Unified Commerce practice, helping organizations modernize how they connect customer data, experiences, and operations. His work spans commerce strategy, retail transformation, loyalty and personalization, and marketing technology, with a focus on turning fragmented systems into coordinated, measurable outcomes that drive growth.

Chris's top pick: Customer Experience Priorities for 2026

The AI Proving Ground Podcast leverages the deep AI technical and business expertise from within World Wide Technology's one-of-a-kind AI Proving Ground, which provides unrivaled access to the world's leading AI technologies. This unique lab environment accelerates your ability to learn about, test, train and implement AI solutions. 

Learn more about WWT's AI Proving Ground.

The AI Proving Ground is a composable lab environment that features the latest high-performance infrastructure and reference architectures from the world's leading AI companies, such as NVIDIA, Cisco, Dell, F5, AMD, Intel and others.

Developed within our Advanced Technology Center (ATC), this one-of-a-kind lab environment empowers IT teams to evaluate and test AI infrastructure, software and solutions for efficacy, scalability and flexibility — all under one roof. The AI Proving Ground provides visibility into data flows across the entire development pipeline, enabling more informed decision-making while safeguarding production environments. 

Transcript

Where Personalization Starts to Break

SPEAKER_02 0:00

A lot of companies think they're deploying game-changing AI-powered customer experiences. But what they're often building is something much less impressive, a smart moment sitting on top of a disconnected business. And customers can feel that gap immediately. So on today's episode of the AI Proving Ground Podcast, we pose a practical question for any leader trying to make AI real inside the enterprise. How do you create personalization that actually works when the data is fragmented, ownership is unclear, and the operating model underneath it is not ready? Because the job to be done here is not launching just another AI feature. And in today's discussion with these two AI experts here at WWT, Chief Digital Advisor Ralph Jovi and Senior Director of Product for Unified Commerce Chris Douglas will talk about how to build the data foundation, identity resolution, governance, and decisioning required to make every interaction feel coherent, useful, and trustworthy. So let's jump in. Okay, Ralph, welcome to the AI Proven Ground Podcast. How are you today?

SPEAKER_03 1:07

I'm doing great. Thanks for asking. Real excited to be here. Thank you for the invite.

SPEAKER_02 1:11

Awesome. And Chris, in person here in the studio, thanks for joining.

SPEAKER_01 1:14

Yeah, thanks for having me.

SPEAKER_02 1:15

Yeah. Ralph, I want to start with you. I mean, over the last 18, 24 months, AI has just, you know, completely kind of reset customer expectations as it relates to, you know, their experience across a broad range of industries, retail, hospitality. I mean, almost, you know, pick your industry. It's it's making a big impact. You know, experience are supposed to supposed to be faster, more personal, more intuitive. I'm curious from the conversations you're having in reality with real customers, you know, where are organizations keeping up with that pace and where is the gap between what AI can deliver right now versus maybe some of the you know expectations or hype that we read about every day?

You’re Optimizing Moments, Not Journeys

SPEAKER_03 1:53

Yeah, no, great question. I think the expectations are absolutely real, right? But they didn't come from competitors, they came from the consumers, right? Consumers are experiencing incredible, you know, customer experiences with using tools like Netflix, right? Where they anticipate your need and they want that type of experience with all the brands they deal with, right? That's what the expectation, the bar is so high at this point in time. And it started with Amazon, right? Everybody wanted the Amazon look and feel make it real, make the product recommendations relevant to where I am in the buy-in cycle. And I think where there is a gap right now, where organizations are doing very well with AI, and they're doing it within functions or you know, what I like to call it moments, right? Search is you know so much more smarter than today. You know, recommendations working real well. And service has incredibly fast today. I think where the gap is, is you know, in between those moments, right? Outside of those functions. You know, you go to uh JP Morgan Chase, and you know, you're online and you know, you're you're talking about something very, very specific, and that experience doesn't follow me when I go to services at a context when I get service and on a loan or something. Who does it well? You know, you look at Delta Airlines, it's it's more than just the experience in the plane or in my loyalty program. It's how I'm being treated in the lane, I can board faster, I get free upgrades. The experience follows me through my journey. Consumers don't you know experience functions, they experience journeys. And I think that's the you know the big gap I'm seeing right now with uh a lot of major brands.

SPEAKER_02 3:35

Yeah. Chris, build on that. I mean, in my in my experience, I'm I'm experiencing, you know, that transformation more as loyalty and like digital points, but it seems like it's broadening up to a much bigger ecosystem of how AI is personalizing experiences from the moment you walk into the door of a given store all the way to how you buy and experience the brand after the fact.

SPEAKER_01 3:58

Yeah, I think, you know, from the keeping up perspective, you know, brands are doing a good job of, I'd almost say it's like at the feature level, right? Like they're deploying chatbots, maybe recommendation engines, using AI for generating content and things like that. So they're, you know, they're basically enhancing that customer journey that Ralph was talking about. I think that the gap is probably underneath all of that, like the ability to advance some of those AI type features into something that's more decision-oriented, right? And that gap is, I think a lot of brands are struggling with the what's underneath all of that, right? Like the data foundation, the the operationalizing data to actually create a decisioning layer that makes those features, you know, essentially intelligent in real time versus how they operate today with without that decisioning layer, right? Yeah.

SPEAKER_02 5:02

I mean, just real quick. Yeah. Well, what else is under that? You know, you talk about the the layer below. You mentioned data foundations. What else to consider just to make an experience that feels personal and loyal and high-tech, but at the same time kind of low touch?

unknown 5:18

Yeah.

SPEAKER_01 5:18

I mean, it comes down to like being able to operationalize all the data that you know they're they're ingesting from those customer interactions, right? At the feature level, being able to operationalize that data across their data foundations. So, you know, do they have a data governance model in place? How are they continuously updating for consent and maturing their kind of data pipelines and then you know incorporating a decisioning layer within the context of all of that to continuously feed those like feature-level AI components? Yeah.

SPEAKER_03 5:53

I think the gap is not technology, right? It's readiness. It's all the things that Chris talked about, but it's also understanding your existing processes and workflows and where AI fits, right? It's it's gonna change, but it's interesting. Every time you meet with a client, you know, first question I'll ask them is, well, how do you do that today? Like I always start off with, it depends. When I hear it depends, I'm gonna go. Long discussion, right? So it's hard to apply or apply AI to the processes that make sense when you don't really understand how things are done today. It's kind of like strapping a rock in a broken bisous, right?

SPEAKER_02 6:34

Well, Ralph, if we strip away that hype, you know, leaders ultimately care about outcomes. So are you seeing right now some tangible real benefits as it relates to AI-driven customer experience, you know, that's driving value for the customers or for the business?

SPEAKER_03 6:50

Yeah, absolutely. We're seeing a lot of organizations see a lot of ROI or retention, basket size, lower cost to serve, right? I mean, if you look at marketing agencies as an example of embarking on AI impressionalization, they're all aligned by channel, right? Your outbound channels, your digital channels, your offline agents-assisted channels. When AI can operationalize that whole thing, we're seeing that there's a lower cost to serve, right? Think about media spend, right? And you know, to Chris's earlier point about decisioning capability. If I can learn things from all these channels and apply them to my media spend, and AI can help me say, use preparing models to say it's not worth spending money on Ralph. He's already a loyal customer. He's gonna buy anything we serve up at him, or the opposite. You know, he's never bought anything for us forever. So why are we spending all this money on media? But you know, keep in mind these organizations, regardless or the industry, are spending, you know, tens of millions of dollars on media. You can cut that by you know 30%, that's pretty significant sayings.

SPEAKER_02 7:58

No, absolutely. Yeah, absolutely. You know, Chris, a moment ago, you mentioned operationalizing some of this technology to deliver these experiences. And then Ralph is mentioning it all comes down to readiness. I'm curious from the conversations you're having with organizations, where is that readiness barometer? Are they mostly ready? Are they mostly not ready? Are they somewhere in between? And how do you start to advise them to get to that ready point?

No Ownership, No Personalization

SPEAKER_01 8:23

Yeah, that's a great question. I'd say, you know, brands are all over the place. You know, from a readiness perspective, I think, you know, one of the first questions we have is is there is there a single owner for customer data, right? Like, is there an organization around customer data? Then is there, you know, a data governance office that stood up, right? Like so kind of sequentially, are there these layers of maturity that exist around the data itself? And that gives us a good idea of how ready a customer is to, you know, embark on maturing their integration of AI into their brand experience. Yeah. And why is that ownership important? Why is that an important signal in terms of the readiness? Because if they don't have anybody that owns that, then it's you know, probably the Wild West in there, right? Like it's there's there's not, there's, there's no organization around it, right? And and we we do see that a lot of times. It's probably a varying degree. You know, sometimes there is an owner of customer data, sometimes there isn't, sometimes there's, you know, some level of data governance that's been implemented, but there's not really like a data governance office. So it hasn't been, you know, it hasn't been integrated across like all the business units across the org, et cetera. So those types of signs are early signals of how ready is someone going to be to get to the next step.

SPEAKER_02 9:47

Yeah. Always comes back to data. I could ask why, you know, four or five times, and data is always the answer at the end of the trail. It's always gonna be the answer at the end of that trail. I mean, Ralph, we're talking about readiness, speed. Uh, you know, executive teams are wanting to move very rapidly here. So how are we balancing, or how should we advise organizations to balance that speed with making sure that you're ready?

SPEAKER_03 10:09

Yeah, I I think, you know, to add to the readiness piece, you know, I always love it when to Chris's earlier point, when you start thinking about here are the decisions that we're struggling to make in real time. Right. You know, when to promote, when to turn off loyalty, when to suppress loyalty. You know, that shows me that they really thought about what they want AI to handle once they get past the date the data piece and that they're using AI in judgment and not just plumbing, you know. And you know, from a speed perspective, I don't know if speed is really their the issue, other than them feeling confident that, you know, their data is in place and this is Ralph that I'm talking to across lines, right? And you know, when you go fast, you got to have guardrails. And you know, guardrails without speed is just irrelevant. And I think the organizations that do the best are doing both, right?

SPEAKER_02 10:59

Yeah. Ralph, I'm gonna stick with you here for a second. You know, Chris mentions data ownership in terms of one of a you know a key signal that an organization might be ready. Anything else from your end in terms of, you know, you walk into you know a customer environment that that shows you that they're ready and kind of serious about taking on AI and and driving those those better experiences for their customers. Like, what do you see first that we're like, oh yeah, this is gonna work?

SPEAKER_03 11:24

Yeah, I I I think see Chris's earlier point. He's we see a lot of organizations like nobody really owns the data, or everybody has a version of the data or a slice of the data. So your email team is working off a data lake, everything else is running off of a CDP, you know, which is a customer data platform. And, you know, even on the creative side, right, we see partial data and a DMP and then the other parts and something else, right? Or CMS. And so I think a struggle with like who owns the data. And when I see data ownership and a clear view of how data is all being stitched together, even if it's not in one place, like I have my behavioral data here, you know, that tells me that you know they're ready or or own a trick, right? They want they want someone to come in and take a look and assure them that you know they have the right data in place to make the right decision and you know, leave it to AI to kind of work off that data. And it's also identity data that we forget about, right? Customer data is very, very important. But you know, is when I sign up for a loyalty program, I use my telephone number and then I go online. You know, they don't know that that's my telephone number. They treat me like a new user, right? So the stitchy is not really there.

SPEAKER_02 12:39

Yeah, Chris, keeping with the data theme here, are you thinking that organizations are underestimating the value or the need to have that data readiness in place, or are they just not able to get to the point where it's all, you know, kind of in the same spot or cleanly organized and things like that?

SPEAKER_01 12:57

It's probably a little bit of both. I think organizations underestimate the complexity of, you know, actually creating some standards and establishing that foundation. I think building on what Ralph was saying around those early signals, I think the other thing that we look at as well is you know, is is the organization aligned around the ROI they're expecting to get out of deploying AI? Are they aligned around the use cases that they want, you know, enable AI to solve? So you know that I think yeah, there's a lot of alignment issues essentially on top of just how ready are they? So there's kind of you know, compounding complexities there, right? That that can certainly slow down, you know, taking a solution to market, obviously.

SPEAKER_02 13:50

Yeah. I'm gonna I'm gonna pick on ROI there for a second because I felt like 2025 was a year where everybody was talking about what is the ROI? How can I articulate the ROI? And I don't want to say that those conversations have quelled, but I haven't heard them necessarily as much. The question is gonna be how has the kind of concept of ROI on AI shifted over the last 12 months? Has it changed at all in your in your experience?

SPEAKER_01 14:16

You know, that's a great question. I don't know that it's necessarily changed. I mean, I think companies are still looking to solve a lot of the same things, you know, increasing right, increasing revenue, increasing margin, increasing frequency, recency, et cetera, you know, using AI to cut costs. I think maybe what's maybe changed is how they're thinking about the amortization of the investment to actually get to that ROI. Like how early are they going to get signals that this is working, right? Yeah. Considering, you know, to to really deploy this at scale or AI at scale within the context of, you know, either operationally or or on the customer experience side of things, there's there could be a significant investment if the company is not, you know, ready, right? If they're if they're on the like lower end of maturity from the readiness perspective. Yeah.

Wingstop Got This Right First

SPEAKER_02 15:14

Well, Ralph, I do want to, you know, as long as we're talking about ROI, let's talk about an organization that that we work with a lot here at WWT that we're seeing a lot of good traction with. Um, as I understand it, Wing Stop, you know, everybody's favorite neighborhood wing restaurant, they're seeing real progress with these data foundations and starting with those data foundations. I'm wondering if can you just walk us through some of the journey that we've seen Wingstop go through and what makes them such a leader as it relates to driving these experiences?

SPEAKER_03 15:44

Yeah, and Wing Stop's a perfect example, right? It's a perfect use case of how to do things right. You know, they didn't start out saying, hey, you know, let's personalize the app, right? Let's redesign the app, let's wow our customers with AI. They started off with, you know, you know, demand forecasting, store level optimization, employee optimizations, and it didn't start repersonalization, it laid that foundation in place for our personalization to be successful. And you know, experience is downstream from the operation achieved, right? Get your operations in in place. I can't offer you know offers to customers when I can't fulfill those offers, or if a store is stressed, or employees are stressed and have an employment issue in one of the stores. So they optimized all that stuff first. And as a result, I think the number was like a 70% increase in digital sales as a result of part of all these things in place. It was pretty significant. Chris, I know you worked with them closer than I did. So it'll be interesting to hear your thoughts on that as well.

SPEAKER_01 16:53

Yeah, no, I think they, you know, started in the right place. I think that's where things can break down quickly. You have a lot of brands that do maybe try and start with that customer interface, and that's just gonna expose, you know, weaknesses down the line to Ralph's point. And I think the other, you know, thing about starting with the operations side of the business is it allows, you know, you're you're basically working from the inside. So you're you're shielding that customer experience from it. And it allows teams to develop kind of that that shared vision for for what they want to, you know, get out of the data and AI use cases. It starts to establish those standards around ROI as well, you know, inside the company. And they they're building that foundation and they're learning inside the company, right? Before they even take that to the customer experience. And I think that's that's you know, a huge learning lesson for a lot of companies because if you do it the other way around, you're, you know, you're just gonna start to see cracks in the operations. You know, the customer is gonna see track cracks in the operations.

SPEAKER_00 18:07

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When “Smart” Tactics Backfire

SPEAKER_02 18:21

Yeah. No, I was gonna ask you, and you kind of have already alluded to it. Is this a pretty standard playbook that a lot of organizations, you know, even outside of QSRs or retail should be following, or is there nuance when you start to get into other industries?

SPEAKER_01 18:35

No, I think I mean I it definitely translates to other industries as well, right? I mean, I think that's the that's definitely the learning lesson here. And and I think a lot of brands are seeing that. I think where some brands, though, make mistakes is they see like a like a tactic almost as a as a solution, if you will. And they kind of ignore that underlying work and the work that needs to go into shoring up the operational foundations, right?

SPEAKER_02 19:05

Yeah. And well, you give me an example of what, you know, like a tactic as a solution.

SPEAKER_01 19:10

So, you know, the concept of say, you know, maybe deploying maybe a cross-sell, upsell engine, right? But maybe maybe the product data is out of whack, or there's identity resolution problems. So ultimately, you know, they the the shiny object is, oh, we'll increase our sales opportunities, get some revenue lift, increase some margin or whatever, right? With whatever products are trying to upsell, cross-sell. But on the back end of things, there's you know, identity resolution problems or inconsistent product data. So ultimately what they're putting out there from a upsell-cross-sell perspective is, you know, influenced by the wrong data or or the you know broken data, right? And ultimately they're not then seeing the return. So they kind of see it as like a tactic versus a strategy.

SPEAKER_02 20:00

And the downfall of that is the strategy starts to fall apart apart later, or what's the what's the risk?

SPEAKER_01 20:06

Right. Starts to fall apart. They're spending money on this solution, they're not seeing the return. Maybe they're even you know making the customer experience worse by you know constantly showing you offers that you're not even interested in, right? Or additional products, right? So I think that is where things start to break down and and where we're seeing, you know, sometimes companies come to us and say, Oh, we've deployed this thing, it's not working. Okay, well, here's why, right? Yeah.

SPEAKER_02 20:32

Ralph, you see that out there in the market as well?

SPEAKER_03 20:36

Yeah, I've seen it from a positive perspective. Okay. I mean, if you look at other industries, right, where you have regulatory compliance, right? Financial services, healthcare industries, HIPAA, you know, they had to get it right, but they couldn't afford to, you know, you know, break down personalization and break those regulatories that are you know important to keep the business alive. And in financial services, you know, in my opinion, they were the leaders in automation and AI, but they were you know using what came within marketing platforms like you know, Adobe's AEP platform that had built-in decision and capability, had built-in AI and ML and propensity models. And you know, it was a little bit easier for them because they don't have as many SKUs as maybe a retail organization has. Retail has thousands of SKUs. So the next best offer has to process all these SKUs. So back in the day, we didn't have the horsepower to do that in real time. So we did it on a segment well, right? So here's a discount on a pattern, but not a specific pattern, right? So as you know, banking has maybe 20, 24 SKUs. Maybe they have a boys, they have a checking account, they have a HELOF, they have a long credit. And but they had to get the data right because of the reparatory, government reparatory things that they have to adhere to, especially around customer data that wasn't allowed to be shared.

SPEAKER_02 22:00

Chris, I mean, uh that that delta between SKUs, is that a lesson to be learned then from you know taking taking a nod from financial services, which maybe doesn't have quite as complicated of a of an offering portfolio, or is that just going to be the nature of the game when it comes to to retail that you're just gonna have this amount of complexity?

Compliance Gets Messy Fast

SPEAKER_01 22:19

I mean, I think it's I mean, certainly there's aspects of, you know, I'm I'm a bank and I have, you know, maybe 50 to 100 products versus, say, a retailer that has, you know, 100,000. Sure, there's definitely some complexities there. But I think it's relational, right? Like, I mean, I think it relates still the same way. There's, you know, what are the you know, attributes essentially that you know your consideration set has to have to deploy something that is, you know, maybe specifically targeted or or whatever that layer of personalization is. I think it's it's it's definitely relational.

SPEAKER_02 22:59

Yeah. Is there such a thing as too much personalization here? I mean, AI can learn a ton about, or a ton about, you know, any brand's customers for that matter, as personalization gets more personalized, I guess. Where does the line get drawn, if anywhere?

SPEAKER_01 23:14

I think the line gets drawn, you know, if it's one, either not being helpful or two is potentially creepy, right? Yeah. So, you know, AI should be able to observe behaviors, detect preferences, and offer, you know, a frictionless experience based on that, right? Take take friction out of the experience. I think if you know it starts to make assumptions on your behalf based on, you know, data you either didn't knowingly give it or otherwise, then, then yeah, then it's you know probably not helpful.

SPEAKER_02 24:00

Yeah. Do you get the sense or have you seen anything out in the wild right now that would give you the the inkling that maybe we are leaning too much in that direction?

SPEAKER_01 24:10

That's a great question. I don't I don't try to think of experiences I've had. I don't know that I can come up with one off the top of my head. Yeah. Well, then that probably means that it's not out there yet. That's but I mean, I'm sure it's happening. I mean, you know. Yeah. I'm sure it's happening.

SPEAKER_02 24:27

Yeah. Well, Ralph, I mean, this gets this reminds me of a conversation I just recently had on another episode of the AI Provement Ground podcast with IHG CTO Jolie Fleming. And she said that, you know, she's obviously in the hospitality space, and she said she's being tasked by her board to be equal parts high tech and high touch, if you know, high personalization touch. How do you balance that? You know, it's kind of speaking to you don't want to be creepy, but at the same time, you want to be as kind of tech forward as you can. So, what's how do organizations balance those ideas?

Helpful Until It’s Creepy

SPEAKER_03 24:59

Well, friction's not bad. Bad friction is bad, but sometimes friction causes memories that you'll never forget, right? So an example of that is you know, you go to your airport, your fight's being delayed, and Delta's sending out messages to you. Can I help you in a hotel since you're to spend the night? I already got you booked up in the night next flight out, but it's tomorrow morning. You know, that's like, wow, that was cool. I did that with one click. I just booked a hotel. Like, so fridgeing is sometimes is is is not bad. And as far as creepy is concerned, you know, what I used to tell customers is you know, think about would you say that to a human yourself? Because if you do and it sounds weird, it's gonna sound real creepy coming from a machine. Right. And so yeah, it you gotta you gotta be very careful about messages that are being sent. There is such a thing as overpersonalization to the point where it starts feeling like I'm being a surveillance, right? Like hi gig knew I'm looking at this stuff, right? You know, I'm looking at very sensitive products to help with a health product, for instance. And here you are trying to upsell me something that's relevant. That that's probably not cool. What is cool is when I go to Disney with my kids and you're reminding me of the moments that I've been there before, to me that's magical, right? So there's a there's a fine line, but I think the easy thing to do is would you have a human employee say that to your customer?

SPEAKER_02 26:36

Well, certainly, I mean, the more we learn, or the more organizations and brands learn about their customers, you start to understand there's a lot of nuance in how you can drive personalization down to the to the individual. And that brings about how brands think about developing personas and how those personas map into different, different routes. Chris, how how should organizations think about persona mapping, what goes into it, and how does it relate to driving personalization, you know, with AI tools?

SPEAKER_01 27:05

Yeah, I per you know, persona mapping, I mean, being able to establish, you know, audience segmentation is key, right? And obviously there's a you know, use a use AI to essentially, you know, interrogate like all the inputs, right, that that you get from particular customer, which then, you know, obviously puts them in some sort of audience bucket, right? And you know, that's key to making sure that you get the experience right. For example, you could have, you know, one customer say to your website, may have all the intent signals of of you know, knowing which product they want and they just want to buy it with one click, right? Another customer may have some intent signals that they're doing some shopping, they're reading reviews, they're comparing things. So maybe you'd want to surface customer support, you know, chat or something right then to answer questions for them. So, you know, being able to use all these different intent signals and behaviors to kind of understand how someone fits into, you know, a particular audience bucket certainly helps create that more personalized experience, maybe expedite a transaction, you know, understand and anticipate like what a customer's needs is. I think that's kind of the big piece of it today, is that brands are starting to use AI to try and predict, you know, what the next engagement that that a customer is going to have or or going to need from the brand, right?

SPEAKER_02 28:40

Yeah. Ralph, build on that. I'm gonna ask you. So beyond kind of the prediction there, you know, these pers these personas and persona mapping, that was always kind of based on data, but still kind of seen through the lens of a of a team of humans. How is AI accelerating the development of these personas or deepening the you know, the depth of personas?

Personas Don’t Cut It Anymore

SPEAKER_03 29:01

My belief is that segments and audiences will go away. And we'll be doing truly one-to-one people-based Marco. JP Morgan Chase is really on the verge of that right now. They're getting behavioral signals on an individual level and you know, stating that to give them insights into what should be the next best experience, next best offer, next best action. And you know, they're pretty far along with a lot of that stuff. You know, and that what they've learned is if we can do this on a sediment level, an audience level, why can't I do it on an individual level, right? And personas, if you think about it, early in the early days, they really didn't make sense, right? And I experienced that, you know, a lot, right? Because I had a bunch of older sisters and I buy shoes for them for Christmas and stuff, because well, my Christmas was a shoe company, and the shoe company thought I was a female, you know, because I'm buying all these shoes, you know. But as they learned more and more about me, and they have more data, intent data and you know, propensity, you know, they they're getting it right and they're starting to reach out to me and create these experiences for Ralph Javina as opposed to this demographic or persona that they thought that I lived in. And you know, you take a bank, and you know, a bank can't do things on a segment level and audience level because they have eligibility rules, right? I can't offer Chris the next best offer based on the portfolio, the financial portfolio that he has. I'm not gonna do that unless he's eligible to do that. Well, eligibility can be done by an agent. It can be done very, very fast. So I think with AI and the horsepower that we have now and to compete, that we'll see a lot of one-to-one based, people-based small thing. Which was the holy grail for many, many years.

SPEAKER_02 30:52

Yeah, well, I was gonna say holy grail for sure. I mean, that's it's such a coveted space to be in to have that one-on-one. You I mean, you mentioned how JPMC is perhaps a leader right now in that, you know, in that perspective. But you know, financial services is is typically ahead of the curve of where many organizations sit today. So in order to get to that individual personalization, what is the playbook to get there, broadly speaking? Is it mostly what we've been talking about here today, or is it opening up a whole nother can of worms?

SPEAKER_03 31:24

No, it's what we talked about today, but on an individual level, it's just having a lot of data about your consumer and knowing we consume it very, very well, right? Understanding their propensity, where they are in life stage and financial services. If you think about the amount of data they keep about you, it's it's easy to get there, right? Based on where you are in stage of life, where you are in in your journey. And I think that it is we we've seen it in the past that people-based marketing can work, right? And it's the same thing with you know, good healthcare companies, but they kind of have to be, right? They can't just send a broad message out about something that's HIPAA compliant. It might require that I opt in for these types of messages, but I think people would be okay with that as long as we don't get really tweaky with them to our previous conversation.

SPEAKER_02 32:21

No, that makes sense. Chris, I mean, knowing that that's kind of the holy grail where many brands want to go, get back to some of the data foundations part that you were talking about earlier. What are some of the first couple steps or what are some of the first few priorities that brands should consider to start to position themselves to get to that more individual outcome?

What Actually Matters Next

SPEAKER_01 32:41

Yeah, I think we, I mean, we talked about it a little bit at the beginning earlier in the conversation, but definitely being able to, like I said, have clear ownership of customer data, like a strategy around customer data, an actual, you know, ownership model, uh, data governance office, right? Having a data governance office that's stood up and you know integrated across the organization, right? Across all the departments, you know, those are all like key pieces to just establishing that data foundation. And then it just kind of you know builds from there. So it's it's certainly, you know, it's it's a it's a complex initiative, right? But it has to, it has to start, you know, in the in the most basic sense, like around the data itself, because we've seen just so many times customers who, you know, think they have data somewhat short up, but it's it's not, right? And that is going to affect the downstream outcomes that you're that you're looking to achieve with any sort of AI solution. Yeah. Yeah.

SPEAKER_02 33:50

Well, Ralph, just to close this out here, because we're coming up on the bottom of the episode. That's kind of uh the go forward uh marching orders from Chris. Anything to add in terms of what leaders should be prioritizing right now as it relates to driving more personalization as it relates to AI?

SPEAKER_03 34:07

Operationalizing the entire system, right? And how it behaves, not the isolated moments, right? Personalization is is great, but you can't fulfill on the promise of the challenge there. We lose trust. And it's not going to be the person that wins that has the most AI. It's the person that, you know, has the most discipline, orchestration, and trust.

SPEAKER_02 34:34

Well said. Excellent note to end on there. Uh Ralph, Chris, thanks so much for joining. Uh, Chris here in studio, and Ralph uh out in virtual land, appreciate the time. We'll have you on again soon to talk about this uh fun concept. Okay, thanks to Ralph and Chris for taking the time. For all the attention on AI features, the companies that make personalization work are usually solving a more basic problem first, like who owns the data, how AI is governed, and tackling real pain points that exist inside the business. This episode of the AI Proven Crown Podcast was co-produced by Nas Baker, Kara Kuhn, Ginny Van Burkham, and Megan Wood. Our audio and video engineer is John Novlock. My name is Brian Phelps. Thanks for listening. See you next time.