Personalization at Scale: An Interview with Mark Abraham, Global Leader, Boston Consulting Group

MA:: Ten years ago when we started there was a lot of work convincing companies on the importance of that. It's been incredible in the last five years though how that has shifted. I took over the leadership of our marketing and sales practice right at the cusp of COVID. So literally March 2020, and overnight, I mean one month we were worried nobody was going to have any work and then the next month the phones were ringing off the hook. Literally everyone recognized how critical building a digital AI driven capability was. Even our clients in for example, take retail, a grocery where the vast part of the interactions were offline, brick and mortar, suddenly you saw E-commerce and grocery jump to high teens, 20% penetration and you could no longer ignore it as a critical channel. So, in every industry you can go through examples of that. Our own business and team doubled in the span of 18 months when I first started looking after the practice. And since then we've had to keep thinking in new ways and innovating. And I think the solutions change and the ideas we bring to clients have to be forward looking. So there is convincing on that front. But I think the imperative to do it is no longer a question.

SS:: Yeah, you don't have to convince companies that it's become a competitive necessity. Let's start at the basics before we dive into some detail. Just the idea of personalization at scale. What, what in your mind does that really mean?

MA:: There's been a lot of talk about personalization frankly for as long as I've been at BCG and before actually at BCG. We wrote a perspective about this in 1989 at the advent of the Internet, about arguing that there should be a segment of one approach to talking to customers. And it was a dream that was pursued for many decades until about 10 years ago. But it often meant, you know, putting someone's name in an email or if you bought this, then buy that. You know, the kinds of recommendations that Amazon started with that were very product first and to me that's not personalization, that is maybe customization or tailoring. Personalization at scale really means it's this idea that you learn something about a customer in every single interaction you have with them. And are you using that insight and knowledge and data to make their next interaction better, faster, cheaper, more convenient and doing that not just for one customer, but actually across millions of customers and billions of interactions if you're a large brand. So if you play what that means out, it's not just about pushing a product and you know, putting the customer's name in an email, it is about, thinking about how you're empowering the customer, what are you trying to get the customer to do? Is it about going back to your bank when you've had a mortgage and are trying to refinance and not have to find and dig through, you know, 10 boxes for all the papers and fill out the same damn form again? And having that at their fingertips and making it seamless for you to get that refinancing done? Or is it as simple as if you're in apparel, inspiring customers with the right seasonal recommendations for them? That may be more about gift buying during the holidays and more about refreshing your wardrobe in the spring. That's really tailored to your style. There's going to be a variety of use cases across industries, but it has to start with the customer. So I love that we're on the Customer First Thinking podcast, because that's what this is all about at the end of the day.

SS:: Absolutely. I mean, that is absolutely so true. I like to use the phrase, you need to build an experience where the next interaction picks up from where the last one left off. And that's so hard for organizations, even that simple concept to pull off. Which brings me to your equation, because you say in the book that the main idea can be distilled into this one equation: P equals N times V to the power of two, volume of interactions multiplied by speed to the power of two. I think I had that right. How did you arrive at that particular formula? And why is speed so important in the equation?

MA:: Yes, so let's unpack that a bit. You know, it's a simple distillation of a ton of research that we've done, benchmarking hundreds of companies on what we call the personalization index, which we'll talk about later. But it's a quantitative way to actually see where a brand stands and delivering these personalized experiences, as well as serving thousands of customers on their attitudes towards personalization across the globe. And what we found was, number one, if you want to do personalization at scale, you do need a number of interactions that you're capturing digitally with data. And so that's where the N in the formula comes from. How many interactions am I having? And actually, do I capture the data from that in a seamless way? Now, obviously, this is easier for brands that have lots of customers, but it also underscores the nature of, even if you're, let's say, a smaller insurance company where you only buy insurance once every decade, and you only have a smaller number of customers - how are you engaging with the customer through that life cycle so that you actually have more interactions than you have transactions? That may be as simple as reminders, education about your benefits. When you do make a claim, really capturing those issues and pre-empting and anticipating those issues and addressing them, all of those are interactions and data points to leverage in your personalization. But the speed component is the most important and that's why we underscore it in the equation. Most companies struggle with a long campaign based mindset. They're essentially pushing out communications to customers and that oftentimes takes three, four months to stand up a campaign and let alone measure it and let alone try to improve on it. So the personalization leaders, and this is the top 10% of companies, only qualify in this personalization leader category. They're maniacally focused on shrinking the time of learning. So they will think about and lay out their campaign process - that 16 weeks - and they'll map out maybe 80 people are touching that campaign across my agency, and my internal marketing folks, and the data folks, and the tech folks. And there might be 15 handoffs. We did this map. Actually these numbers are real from an actual bank we worked with. And then you think through, well, what if I organize that differently? What if I thought about a certain type of interaction, let's say churn management at a bank and laid out, how could I get this done in a week or less? Where I launch this campaign and measure it and actually can turn around and do it differently the next week. It means some automation. It means measurement that's done looking at the KPIs around engagement, transactions, but also customer feedback on a regular basis. And it means looking at content creation and some of the supporting processes in a different way with things like Gen AI and tools so that you can now learn every week instead of every four months. That's why the digital natives, if you look at companies like, Spotify, or Netflix, or Uber, they operate in a mindset of running thousands of campaigns and experiments, at the same time learning from each of them and optimizing the experience as a result.

SS:: Yeah, so it's, it's compression of time in the interest of accelerating learning to improve the experience. Does that…

MA:: Yes.

SS:: …capture that thought.

MA:: Yes. And I think what's also unique in this is before it was more about predicting what the customer might want here. So, you know, there was tremendous emphasis on the data science and the AI used to predict that. Actually some of it is test and learn, you know, even if, you know these sets of customers are maybe high propensity for this product, you know, what kinds of marketing messages are they going to react to and not react to, and which channels - all of that can be very quickly tested into rather than trying to derive it from some data a priori.

SS:: Well, it's making the experience contextual to some extent because you're obviously aware of what just happened and anticipatory of what might happen. But it's more in the interest of the customer. Whereas in the old days - I can remember because I was merchandising the concept of predictive modeling, back in the early 90s - it was all about cost efficiencies and today it's really about how can you have a more efficient customer journey for the customer versus from the perspective of the company or the marketer. So that's an interesting perspective. We're going to explore that theme because you come to it time and time again in the book. You mentioned “personalization index” that you can use to score companies on their level of personalization maturity. Can you just describe, I know you do this in the book. Can you just describe the components of that score, and they have specific weightings?

MA:: Yes. So I tried to boil down the decade of learnings we've had across companies in the book and how I did that is that ultimately the personalization leaders deliver on five promises to the customer that they make. Anytime you personalize, you're implicitly delivering on these five promises. Empower me. So this is, how are you trying to use personalization to help me? If I'm Spotify, I'm trying to help you get access to the music that you love. That's where it starts. And we actually measure this by looking at that experience. Mystery shopping with customers, surveying customers, looking at emails, and digital interactions, and in person interactions, if you're a retailer, let's say. And scoring what is the outcome for the customer? Are you making it better, faster, cheaper or more convenient? So that's where it starts. And that's actually half the whole equation when it comes to the personalization index, because ultimately, is the customer getting a better experience, is what it's all about. But in order to do that at scale and sustainably, you also need to have all the enablers for that. And that's where the other four promises come into play that you've got to deliver on. So, that's, the second one is Know Me. You need to actually have the data about the customer. And again, back to the Spotify example, they have not just my profile as a customer when I log into the app, but all the songs I've listened to in the past, what genres they were, all the metadata about those songs, how long I listened to them, which ones I skipped over, who my friends are that I'm connected to on the platform, et cetera. That is the key foundation on which personalization is built and which one of those data assets needs to be real time versus lagged and batched. There's a lot to think through from a data architecture standpoint. The third piece then is Reach Me. So once you have all that data, you've got to know the time and the place and the channel when you want to reach out to me and where I can actually access that experience. This is where AI really comes into play. So understanding, if I have a set of content, in the case of Spotify, I've got this amazing music library. How do I use AI to curate playlists? For example, Spotify creates these daily lists, they call them, that are incredibly personalized to you as a listener, and that's done through AI tools. Next comes Show Me, you actually, in order to use all this AI and have the data make sense, you've got to have the content. And so obviously in the Spotify example, that's not just your catalog, but also what new songs and content you put on there and where else you could expand into in terms of content with podcasts and so on. Or even, when you might want to reach out to customers and notify them like, my favourite artist is coming to town. Hey, you might want to listen to them, get tickets and shop their merch. And lastly, and this is the most important enabler actually, because it comes back to this point around speed, Delight Me. Are you making the experience better every time I come back to you and every time I provided my data to you? And to do this again, you've got to set up experimentation, learning, and the whole way of working in your team to be able to make the experience better each time. In all of our consumer research work that I alluded to, we found that 90% of customers actually are willing to share their data when they have an explicit, better experience back. And that promise is delivered on versus only 20% when you ask them to co-fill out some sort of preference center off to the side. And it's not clearly indicated where that data is going and how that's going to benefit them. So there's tremendous appetite for personalization and even with data privacy and all the concerns around it, interest in personalization as long as companies deliver on these five promises. So that's how the index works. And we score companies from a simple 0 to 100 along all these components and we find that indeed only 10% of them score above a 75 or so, classifying them as a personalization leader. The average company only scores a 49. So there's a lot more work to do on personalization, which is one reason I wrote this book.

SS:: Well, and the book does an excellent job of systematically walking through those various points that you just described. You also say that only about 10% of companies and you just referred to them are hitting the mark. Obviously the leaders, as you would call them, above the 75 threshold. So a lot of those companies scoring in the 40’s are struggling. What are the, and you do talk about this in the book as well, but what are the main reasons that companies are struggling with the transition to personalization at scale.

MA:: So it really comes down to not just the AI, and the data, and the content which are, are not easy to get right. And we've actually done some numerical work on this at BCG looking at all our transformations. About 30% of transformations fail because of all that stuff. 70% of them fail because of the people. And so I would start the answer to that question with the operating model. Especially for personalization. It is the most cross functional initiative any company can undertake. It requires customer first thinking. So, the marketing team, the insights team have to be at the table. But in order to actually deliver at scale, you need analytics, and data, and engineering, and tech, and digital experience to come together as well. And you need to really pinpoint that against key use cases that deliver value to the business. Because there are real investments that have to happen against all that and are really going to drive a meaningful difference for the customer. So that's really the art of getting it right. But also what many companies struggle to get right and as a result, invest money and for example, big data lakes and customer data platforms that then don't drive value and are considered failed investments.

SS:: So we're going to cycle back to some of those things a little later in this conversation. You do mention that personalization at scale comes down to content. You referenced that specifically in the book - is that, I mean there's the people, and organizational, and structure, and processes, and technology investments a lot. But is content the biggest choke point right now? Is it the challenge of creating sufficiently granular, modular, reusable, content components at that, for use at that individual level? Is that where organizations really are struggling?

MA:: So it's been a fascinating evolution over the decade I've run the personalization practice. Initially the big bottleneck was the data and what I would call predictive AI. So getting the algorithms right and fine tuned and making sure the data is set up and clean and still I would say very, very few or almost no organization has that fully right. It's a constant evolution. But once you get that to an okay place for a given use case, content becomes the bottleneck. And I've seen clients go through this evolution time and again. What's interesting now in the last couple of years though, is it is way easier to create content with Gen AI than it has ever been and we're in the midst of a content explosion. So you can create not just static images or copy, which is now a pretty scaled and well established use case, but even video and human characters all with Gen AI tools, in a matter of minutes and hours. What used to take really expensive photo shoots and many months of planning. Now there's a lot of considerations around this from a brand perspective, from an ethical perspective, but it can't be argued that this is not shortening the timelines massively and removing a lot of the rework that happened in creative brief writing, and reshooting, and rework of these creative assets. And it also enables you to create one campaign, let's say an ad for a product, and create 100 versions of it. So you can do things like a global launch across 50 markets and aimed at five different personas, across demographic groups or interest groups, that you would have had to do a ton of manual rework from a design and creative standpoint. So, what's exciting is you can set up these content libraries that are geared towards your target customers with a Gen AI approach and pair the predictive AI with that to serve up the right content at the right time.

SS:: I'm thinking a tool like - is it Jesper - that is one of those go-to tools right now that does that?

MA:: Yeah, what's interesting in this is the tools are evolving massively. So Jesper, Dall-e, et cetera are great tools for images. When it comes to video, you know, Google's VO2 just launched, but then you have other elements of that like Flare.AI, Flickr, etc. that solve different elements like motion or taking a character and consistency of that character across a video image. You know, Adobe's Firefly allows you to take copyright approved content for things like images of humans and characters. So there's a plethora of tools and different ones are good for different specific tasks in the content creation space. So what I'm discussing with a lot of my clients now is that we are seeing the evolution of a marketing technologist. There need to be teams internally that are on top of the evolution of those tools, the knowledge of how to mix and match those tools. The role of the agency is still critical and they're also constantly innovating their internal processes. But it's a partnership across clients and agencies to work together to leverage these tools fully, to know how to align the incentives around managing them, thinking about where they can take cost out and invest back in further content creation. But one that's aimed to provide a better customer experience.

SS:: I'm going to swing back to that conversation in just a moment. I do want to touch on one other pain point that you call out in the book with respect to the struggles that companies are having with personalization at scale. And that, and you alluded to it earlier, and that is really process change which is finding more agile ways of working. That's the speed component you were alluding to earlier. You say that's the hardest thing to get right. And is that to go back to your campaign mindset reference, is that because companies just aren't used to thinking that way?

MA:: It often comes down to incentives and operating model. So a great example of this is, travel and airline client that had 50 different products. You can think of it that way, that they were trying to sell to the customer everything from, flights and seats, to ancillary flight products, like an upgrade to a credit card, and travel insurance, and other products. And each of those products was set up as a P and L owner who was responsible for making quarterly targets. So of course they were creating great content and pushing that out to customers through email and on the website and the app in order to hit their targets. And they were even using sophisticated predictive AI to figure out which customers would be interested in which products so that it was somewhat targeted. The problem was that the highest value customers, when you laid it all out from a customer first perspective, were actually getting hit with most products - they were high value and high propensity for a lot of things. And then the least engaged customers were not getting anything. So some customers were getting 10 emails a week and a lot of outreach through the app and others were getting nothing. When you flip that on its head and actually ask, what's the best for the customer you get to a very different approach. But in order to make that change we had to in that case set up a personalization lab, carve out a team, carve out a few hundred thousand customers and show the organization this is what can happen when you take a customer first approach. And we can reduce the number of emails we send by 30%. We can take chunks of the website and personalize it that we hadn't used and thought of that way before and we can drive 10% more attach and cross sell and revenues. But it will impact different categories differently and we need to set up and forecast and plan differently against that. And perhaps we need to adjust our quarterly targets for different businesses in different ways. And we need a team that's actually going to think customer first and act as the air traffic control across all these teams and build the customer data platform in a way and the wiretech in a way where it can operate that seamlessly. So that was a huge change. It required rewiring their organization, their incentives as well as real data work and tech work on the back end, where they were dealing with siloed data and tools.

SS:: So we'll pick up a little bit on this a little later in this conversation because I do want to talk about organizational structures, marketing operating models and that sort of thing. But before I do that, I just want to dive back into the technology question because the other challenge that organizations have, you alluded to it earlier, is foundational infrastructure. Go back to Covid and the urgency to suddenly digitize the business. And to some extent personalization is a subset of that initiative. Almost like a big use case I suppose you could call it. But data is obviously a challenge in a lot of organizations or at least access to consolidated data, CDPs have come along of course, to help in part solve that problem. But let's talk about what you believe and I know you outline it in the book, but what are the main elements of that infrastructure that absolutely have to be in place to pull this off correctly - to do personalization right.

MA:: So I would emphasize a couple of things for your listeners. Number one on the data, that's why it's so critical to start with the use cases and trying to enable for the customer. If I come back to that fashion example, I want to get recommendations right, for each customer. That means I have to have accurate data on things like gender and preferences and when they might have key moments like gifting occasions in their lives. And that's the critical data to get right. I may not care about other types of data. So I can go into my data platform and really understand where is the data in a great place, and what am I missing. Back to the Starbucks example. Like many retailers they had when we started out, many different product hierarchies. And so in order to get product recommendations right we had to really understand what is the right product hierarchy to think about from a customer standpoint and make sure you're getting a great beverage that's the right one for you. Thinking about all the customization that people like to do. So really start with the customer use case and line up the data accordingly. Next though, there is a whole set of components that have to come together from a Martech standpoint. And so that is, the tools to be able to set up lots of experiments. We talked about the importance of that learning. I've got to be able to set up one group of customers to get one experience and another to hold out as a control and a third one to get another experiment. How do I do that? Instead of just manually pulling lists, which is a lot of the work my clients still do. And there's automation for that. There's also the actual delivery of that experience into given channels where oftentimes different vendors and suppliers will be responsible for that. This is where the concept of smart integration really comes into play. It's very rare that one software provider will be able to address all that across all your channels, and all your markets, and all your businesses. So, it is about making sure you integrate the right components. Select off the shelf. There's a lot more tools that are available now that you had to build in the past. But then there is customization typically required on top to make sure you're measuring things in the right way and quickly and you're experimenting in a way that's tailored to your business, versus just relying on out of the box models and the like. So I would say that's the place to overinvest when it comes to the tech stack.

SS:: Right. What does that, I'm going to use the term ideal personalization stack really look like? What are the must have components that doesn't work if you don't have it?

MA:: Yeah, I mean look, the underlying data platform, I call it Customer 360 is critical. And having universal ID by customer that stitched with the data, not just the types of things I described around the customer profile transaction data, but getting back the engagement data from the various channels. Too often I see clients trapping that data around engagement and the tools that they use to communicate in that channel and not stitching that back to the customer. So Customer 360 is key. Experimentation platform is number two, the measurement coming out of it so that you can power your feedback loop with real time measurement on many of the engagement metrics and incremental revenue or incremental profit measurement as well because the CFO is not going to fund the effort if you don't show the returns. So making sure you're capturing that in a way that the finance team is buying into. Measurement is another key. And then all the channel delivery and orchestration elements of the stack, so that you're delivering the experience and channel. And finally the content management platform and system as well, which houses the content where too often it is assets that are created one time and used one time versus the modern approach of building a content library with the meta tagging so that you can either reuse the assets or at least take them as a first draft that then can be personalized, let's say for a different season or a new campaign context.

SS:: Right. So a CMS plus a digital asset management system, natively integrated. And BCG has a personalization platform. I think it's called Fabrique, is that correct?

MA:: So what we've done in our approach is really think about it from a customer first perspective for a client. So there's a starting point for everyone. No one's starting from scratch. Many of our clients are on old legacy systems that they're in the process of upgrading. And we think that there's a lot of great software platforms out there, the Adobes, the Salesforces of the world that are solving many parts of this equation, especially when it comes to things like delivery, orchestration and so on. The customization we've seen time and again comes into play for large brands especially. You want to build experimentation and measurement and the notion of pairing content with customers, that whole predictive AI logic that is really fine tuned for your exact use case and industry and set of customers. And that's where we've through all of our work built a code base where instead of starting that customization from scratch, we can build on top of the big platforms that our clients have to do that in a really specific way and pinpointed way and then arm our clients with that as part of their cloud environment and something that they can continue to own and maintain. So it's a unique approach. We think that again the name of the game is smart integration. So no one provider has all the answers for all industries. But this pairing of a deep Martech team and AI team that knows what's available off the shelf, how to make your tech stack work harder for you and then where it's needed, build customization on top, hopefully leveraging things so that you don't have to start from scratch, given the benefit of us working across many companies and many, over many years.

SS:: So let's move on back to organizational structure because one of the thoughts I have is that personalization first and foremost has in the past at least been a marketing tool. And I guess mainly because it drives incremental revenue. Business case is easy, demonstrable payback if you can pull it off. But it's as you talk about in the book and have in this conversation, it's also importantly a way of enhancing the customer experience so they feel better about the company and loyalty and satisfaction, all those things go up. Given that who should take charge of owning that transformational change required to pull it off? There's a lot involved. We've been talking about it through this conversation. Who has point on it?

MA:: It's one of the biggest issues in organizations. So I have a whole chapter dedicated to navigating the C-suite as I call it in my book Personalized. I think ultimately personalization rises to the level of a CEO agenda. It should be part of corporate strategy for a company. And the whole point of my book is that AI with personalization can be used to drive growth. In fact, personalization leaders grow 10 points faster than laggards. And so, at the end of the day, the CEO should have it on their agenda. Now what that means is still operationally you've got to think about aligning a set of cross functional leaders around a roadmap and a vision. Where are we going over the next three to five years? And what are the use cases we're going to tackle to get there with what payback and investment? Someone needs to own that roadmap and hold the organization accountable even though they're not going to control all the resources because some of it will sit in tech, some of it will sit in marketing, some of it will sit in analytics. That leader typically is at least a VP, but often SVP-level leader, who has the buy in from the organization, respect from the organization so that they can really understand where the various teams are with the progress. They can raise issues and roadblocks and really problem solve against it and hold the organization accountable back to the CFO that those investments are going to generate the return and that, you know, there are clear stage gates every six months or less to unlock the next set of use cases. That SVP or VP can sit under a number of leaders. I've seen it under the chief marketing officer, under a CEO or chief customer officer type role, under a chief digital officer, even seen it sit in the technology organization if it's not just an IT organization but it's really owning digital as well. But the key is that cross functional senior alignment as well and that mandate for that leader to drive change.

SS:: … be a change agent. And you use the term personalization officer, is that …

MA:: Personalization leader, head of personalization. Many organizations call it different things, or it can be just part of a customer growth officer type role as well. Chief digital and analytics officer might have that as part of the mandate as well. So again what it looks like will be very organization specific. But that is the idea and you know that that role in itself can't solve everything. So it's also about how you set up the tiger teams that bring together, you know it's kind of like what Amazon really perfected early on. Two pizza teams that are not 60 people in a room trying to legislate things and not even align their calendars and not be able to make decisions. It's boiling down the use case so that a two pizza team can go attack it, make progress in three to six months against a set of KPIs, show the value and then line up the next set of investments after that.

SS:: Right. So prioritized use cases that can map tie back to your roadmap and the company can incrementally invest as opposed to throwing a whole bunch of money and having a big bang. So, but let's talk about that a little bit, because the major challenge from my perspective anyway, is that most companies, and I think you even talk about it in the book, don't really, aren't really customer centric. Right? It's a buzzword or it's you know, they're mouthing the words rather than really meaning them. They're product centric. So products lead and that's, and then there's also just the organizational functional silos that exist, obviously, hence the 15 handoffs you talked about earlier in this conversation around campaigns. So that's a big challenge and then you say in the book that you think the future is going to look more like what, you use the term jazz ensembles instead of a symphony orchestra. Can you explain what you mean by that?

MA:: Absolutely. So, you know, I think instead of command and control trying to set up the direction from the top, this is about in those tiger teams, who is the quarterback that has the ability to change direction as you're getting feedback from the customer? If you've set up the process where every Monday morning that tiger team is looking at the customer dashboard and looking at, did I drive engagement for this sub-segment or did I not? Is the creative working or does it need to be tweaked? Then that quarterback with the team around them can say, well, actually the content guru needs to go and make these tweaks. The data and analytics person needs to make these targeting tweaks. Let's go launch that campaign in two days and see what happens next week. So it is much more akin to a jazz group figuring it out and improvising than it is, I've got my set music sheet and I can just perform against that.

SS:: I want to move on to discussion about agentic AI because that seems to me one of the big game changers now - because a lot of what we've been talking about is pretty intensive from a human resource standpoint, notwithstanding GenAI and analytical AI and all those things. And I talk about this idea of taming the complexity - because that to me is one of the biggest challenge marketers have today to pull this off. Is GenAI really going to be the answer? Basically push button customer interactions where that, where the AI tool itself decides what that experience is going to look like.

MA:: We are on the cusp of a massive revolution here again. I always like to say that what we've witnessed so far is push personalization. Everything I described as, you know, content I'm creating and then how do I push it out to the customer? We are now entering this phase of pull personalization. Customers are going to be in the driver's seat, and they get to decide when they want a personalized experience and how they want that. One of my favorite examples, I actually laid out in the last chapter of the book, you know, what I think travel will look like in a few years where you're going on your family vacation and it's anticipating when you need to get in your Uber to the airport. And when you arrive to the airport your bags go there seamlessly and when you arrive at your destination you get a recommendation from a restaurant to go eat and recommendations for what to eat for your family. And you can walk into your hotel and just breeze to your room and it's awesome to see. You know, if you look at Delta and what they've announced with Delta concierge at CES just three months after my book was published, that is exactly the vision they have for it. It's the Delta app essentially being your concierge virtual travel assistant, anticipating when you need to go to the airport, surfacing exclusive YouTube TV content for you on your phone while you're in the plane or on the back panel of the seat and then predicting when to have that Uber show up at the gate when you land so that you can just sit right in. All from one virtual assistant that's your buddy throughout the trip. I think this is the direction we're going. And the old world of email or even apps where you're clicking through an experience that will be transformed into these virtual assistants that through voice and chat and video are navigating your experience. Whether it's travel or things like gifting and finding the perfect holiday gift for your family and loved ones, to cooking and food, all sorts of experiences can be transformed through these agents and the agentic AI experiences.

SS:: Yeah, I think you, you talk about this idea of gateways that a brand can be a gateway to a bunch of value added partners so that it's much as you're describing and much more seamless experience.

MA:: Yeah, I think that's incredibly important. It is not just one company trying to provide all these services because if you think about it that way, you're going to be stuck in the product mindset. That's what I love about the Delta example. You know, tying together YouTube and Uber and Joby and all their partners to try to solve a seamless travel experience. And other companies will need to think about this too. For example, if I want to launch a beauty assistant to help you, you know, look great every time, there's an aspect of that where I might want to incorporate product recommendations from my portfolio as a beauty company. But then what other needs around that, services, et cetera, do you need to provide to deliver on that promise of looking great. And that one example. So I think that's where this is headed, is, who will orchestrate those ecosystems? Will it be startups that solve this from scratch? Will it be the big tech companies that already have the data and technology and the operating mindset to standing up things like this, or will it be category leaders like Delta, in the example of travel, that orchestrate these ecosystems? It's a really exciting question.

SS:: Well, it's a provocative one as well, because you certainly question the role of marketing in a world where customers are hiring basically agentic AI tools to guide their shopping decisions.

MA:: Yeah, yeah. I mean one of my CPG clients is revamping their entire marketing process and organization because they've told the board, you know, in the next three years a third of their multi-billion dollar marketing budget will be marketing to agents and AI, they believe. And so if you believe that, and this is not even, you know, a company that you would expect, it's, you know, they're just selling grocery items, food and beverage. So in a world like that, how would you need to show up so that the assistant actually recommends your products and they show up in the right way to customers in that shopping experience? So big, big change coming, I think for many.

SS:: Wow. That's your next book by the way.

MA:: Yes, I was already talking about that with my publisher and coauthor on, um, we should be starting to write as soon as we got this published. The world is changing so fast…

SS:: Oh my God.

MA:: …our projections for three years from now have already come true.

SS:: I have windburn. I mean I go back to 1993 and One to One marketing on my bookshelf back there, which actually was quite prescient. If you go back and look at some of what they said. I had the opportunity to interview Don, as one of my first podcast interviews actually.

MA:: Oh, fantastic.

SS:: Which was fun.

MA:: They're amazing. Yeah. Inspiration for me as well. That's awesome.

SS:: Oh yeah, he was totally, he and Martha. So I got a couple of questions for me. I have a little bit of time remaining here, a couple of questions that I want to make sure I get your answers to. And one is we've talked about a lot of game changing ideas today. We've also talked about, you know, the challenges and, and, and they can seem insurmountable, they can seem so formidable that I think you asked the question, you know, is it even worth it in the book, you, you ask the question and I, I would imagine that's what CFO’s are challenging proponents of personalization. Really you're asking me to do all this investment and is the payoff going to be there? Now I know you talked about control groups and experimentation and all that, but it seems, that seems very incremental. What's your answer today to the CFO who says, yeah, I don't think that's worth investing in? What's your response to that?

MA:: Yeah, unfortunately there are many jaded leaders out there who've, you know, been told, we'll just make this $10 million investment and this Martech tool will solve it all. And this customer data platform will have all the data cleaned up and then the investment is made. And there's no difference to the actual customer experience because all the five promises have to come together, and one leg of the stool is not enough. So I basically always say get your cross functional team in a room. No company I work with has a shortage of ideas on how to improve the customer experience. In an hour you can fill the whiteboard with use case ideas for how you can improve the customer experience. Start with a specific channel or start with a specific step in the customer journey, or start with a specific market or set of products and map that out and size the value, you know, what KPI are you going to move over what time and challenge yourself that it has to be over three to six months because the CFO will not be patient and show that first with a pilot approach - it can be really helpful to make it easier for the organization to swallow the change. But too often people think about the pilot as the end versus it has to be planned for scale from the beginning and that scaling has to come in year as well. So how do you start with use cases that are going to build back confidence from the CFO and the organization? And the truth is, today there are many, many proven use cases that do pay back in year with personalization in every single industry. That is the biggest change actually. When I first did the personalization index benchmarking 5-10 years ago, it was really retail and digital natives that were far ahead of everyone else. Today it's banks and healthcare companies and B2B companies as well, showing the value in specific use cases. And they are different by sector, but some common ones include personalizing your offers if you're in retail or have any investment in promotions. They're getting recommendations right, for industries like fashion or even B2B and there are things like call centers and personalization of the physical interactions, human interactions as well. If you have a lot of those. So pick those use cases and win back the trust of their organization that way versus, you know, spending too much time on the grand vision.

SS:: Right. So a couple of minutes left. I just have one question and that is, you referenced Starbucks earlier. You have your personalization index. You scored companies and you talk about that 75% threshold, 10% of companies really being graded at the high end of that, who would you rank top 3, if you want to just limit it to the one, personalization leaders that you can point to and say these guys really get it and they're really doing something good for their customers and this is working for them.

MA:: I mean the digital natives, not surprisingly still score the highest on the index. So if I had to pick a couple. Uber and Spotify are doing this really, really well. You know, when you pull up your Uber app, it knows where you are, it knows where you're likely going and it tailors that to you. It's even doing things like the ads and recommendations they have in there, which has become a billion dollar business for them. Those are actually, you know, tailored to your context and don't seem like they're hawking a bunch of products that are not relevant to you. And the app has a lot of the flow itself is personalized that way. I love what Spotify has done with, you know, day lists have gone viral because the little name and tagging that's personalized to you is so iconic that it's become a way for Gen Z and Gen Alpha folks to interact with each other and get to know each other quickly or shorthand or even what DJ Xavier is a Gen AI virtual assistant essentially built into the Spotify app that has just come out of beta mode a few months ago and basically takes that element of navigating you through and explaining why it's making the choices that it's making. I think that's one piece of the traditional approach to personalization is if you're relying too much on the black box algorithm to do it, without also taking the customer on the journey of what you're recommending and why they're trying to solve that problem with DJ Xavier. And so, those are just a couple of examples. But you know, I think on the brick and mortar side, there's a lot of interesting companies. I talk in my book whether it's Fidelity or Voya on the financial services side, or the likes of Woolworths and Grocery or Tesco or Home Depot that have also figured a lot of elements.

SS:: Well, and that wine company that you reference in the book is, if you start right near the start of the book, is just fascinating. I was really intrigued by that example.

MA:: Yeah, I love it because it's, in that case, actually, they're not doing promotions, they're really just getting recommendations right. And actually there are a couple of these wine companies, one in the US, one in Australia that's doing this. They've basically taken Spotify as the inspiration. What is my content library of wines and spirits and other products I can educate people about? And then what are the customer personas I'm talking to and how do I build content around that, that's really immersive. It's talking to the history of the vitals and the origin and the way the wine is made and exposing you to inspiration for your next, differently for your next dinner party versus if you're stocking up your pantry, versus if you're gifting to a friend. And whether you're doing that via email or in store, it's going to look very different. So they've done a nice job with tailoring that.

SS:: Yeah. Nice. Very inspirational. And your book's inspirational, I have to say. Again, lifelong database marketer. I'm seeing, you know, the world start to swing more in the direction we've been talking about for so many years. And so the book's really, really good and well timed and just serves an immediate need for a lot of companies, I think, to recognize the power of this, under the mantle of customer first thinking. I think you've got that exactly right, so.

MA:: Thank you. “Personalized Customer Strategy In The Age Of A.I” - check it out. It's in the airport. You can't miss it. I love the yellow, bright yellow jacket.

SS:: Yeah. And it's, and your branding is great on the website for the book too. So congrats for that too.

MA:: Thank you.

SS:: You're a very good writer as well, I have to say. I just. It's very accessible, easy style to absorb. It's not overly technical. And so that was refreshing as well.

MA:: Thank you. It's been a labour of love over the last couple years along with David, my co-author and I. It takes a long time to bring a book to market. We wrote it pretty fast, but, you know, that process is, is what it is. And if you do go to personalizethebook.com, there's a Gen AI “Ask the authors anything” tool in the bottom left. Have fun with that. You can chat with my GenAI version - we had a lot of fun programming that. STEPHEN SHAW (SS):: I have to do that.

MA:: Absolutely.

SS:: Well, thank you so much, Mark, for the time. And I have to say, I just loved this session today. It was a lot of fun.

MA:: Learned a lot. Thanks so much, and looking forward to listening to your next guest.

SS:: Yeah, no, I'm going to have this labour of love on this one, man.

MA:: It's great.

SS:: So thank you so much.

MA:: Thank you.

That concludes my interview with Mark Abraham. As we learned, doing personalization right means taking everything you’ve learned about individual customers – what they like, don’t like, what they buy, don’t buy, what they do, don’t do, what they say, don’t say – and use it to make their lives easier, more convenient, more effortless. In other words, personalization is not just about getting customers to buy something by serving up hyper-targeted ads, offers and messaging – it’s about building a relationship with them on a foundation of cumulative knowledge gathered with each interaction. As that knowledge grows richer, the organization will naturally get more adept at recognizing and responding to customer needs, in real-time. That hopeful scenario, first imagined and popularized over three decades ago in Pepper and Rogers prescient book “The One-to-One Future”, is today close to becoming a reality, in large part due to the rapid adoption of AI-driven platforms and tools. But it takes more than technology to do personalization at scale: it takes a corporate commitment to putting customers first. Because the time and effort and cost required to deliver on the promise of personalization – to get it right – is all-consuming. New systems, processes, skill sets, organizational structures and agile methods of working are required to pull it off. Even more important than the infrastructure is a change in the mindset of marketers - from pushing out campaigns, to putting customers first.

Stephen Shaw is the Chief Strategy Officer of Kenna, a marketing solutions provider specializing in delivering a more unified customer experience. He is also the host of the Customer First Thinking podcast. Stephen can be reached vi