With all the data at our fingertips, why do so many companies still struggle to deliver truly personalized experiences at scale?
Agility requires being able to cut through the noise of data and culture shifts to deliver experiences that truly resonate with each individual.
Today we’re going to talk about personalization maturity, the role of machine learning, deep learning, and generative AI in driving relevance, and how to future-proof your martech stack with open, flexible architectures that enable best-in-class personalization.
To help me discuss this topic, I’d like to welcome Yaniv Navot, Former Dynamic Yield by Mastercard CMO and current SVP of Commercialization for Customer Acquisition & Engagement at Mastercard.
About Yaniv Navot
Former Dynamic Yield by Mastercard CMO and current SVP of Commercialization for Customer Acquisition & Engagement. He is marketing leader and personalization expert with nearly two decades of experience driving performance-driven marketing at scale. He shaped the personalization market and led the company’s rise as an industry leader. Joining as the first marketing hire, Yaniv built a world-class team and partnered with top B2C brands to deliver impactful, cutting-edge personalization strategies. Follow him on LinkedIn: @yanivnavot.
Yaniv Navot on LinkedIn: https://www.linkedin.com/in/yanivnavot/
Resources
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Transcript
Greg Kihlstrom (00:00)
With all the data at our fingertips, why do so many companies still struggle to deliver truly personalized experiences at scale? Agility requires being able to cut through the noise of data and culture shifts to deliver experiences that truly resonate with each individual. Today, we’re going to talk about personalization maturity, the role of machine learning, deep learning, and generative AI in driving relevance, and how to future-proof your martech stack with open, flexible architectures that enable best-in-class personalization.
To help me discuss this topic, I’d like to welcome Yaniv Navot, former Dynamic Yield by MasterCard CMO and current SVP of commercialization for customer acquisition and engagement at MasterCard. Yaniv, welcome to the show.
Yaniv Navot (00:41)
All right, thank you, Greg. Happy to be here.
Greg Kihlstrom (00:43)
Yeah, looking forward to talking about this with you. Before we dive in, if you could share a little bit about your background and your role at Mastercard, that would be great.
Yaniv Navot (00:52)
Sure. So hi, I’m Yaniv. I’ve spent most of my career in the intersection of marketing and growth. Started out in performance marketing on the agency side, then found my way into the startup world and now I’m in corporate. It’s been quite a journey and I’ve always been drawn to the challenges of connecting products to people in meaningful ways. About 11 years ago, I joined a small team in Tel Aviv working on what became later on Dynamic Hill.
a personalization technology provider. I was the first marketing hire and what really pulled me in was the vision. It solves real problems. I experienced firsthand in my, you know, my previous work in the agency side. And, you know, we grew the company into a global leader in personalization and A &B testing, competing with giants like Adobe and Salesforce, and eventually went through two acquisitions, first by McDonald’s and then MasterCard.
Today I lead global commercialization at Mastercard across loyalty, personalization and marketing services, essentially helping shape the way we take our most complex marketing solutions to market at global scale.
Greg Kihlstrom (01:58)
Great, great. So yeah, let’s dive in here. And the first thing I want to talk about is personalization maturity. And I mentioned at the top of the show. And so I want to start at the beginning, despite greater access to data, seemingly common knowledge across the C-suite that personalization brings with it better returns, as well as increasingly better MarTech platforms, customer engagement tools.
Can you first define, you what do we mean by personalization maturity and maybe explain a little bit why there is still such a big personalization maturity gap?
Yaniv Navot (02:30)
Yeah, sure. So I’ll take a step back. We wrote the marketing in the AI era book because we saw that there is so much noise in the market and not enough clarity. After more than a decade of leading the field of personalization, working with the top brands, know, across industries and building some of these proven strategies and methodologies for success, it felt like it’s time to share what we’ve learned. And so
You know, the way I look at it, the world is moving faster than ever. Companies are flooded with data and now AI is disrupting everything. I spoke about the noise. AI made the noise even more meaningful and it’s easy. It’s easy for marketers and companies to feel overwhelmed. And that’s why we believe that personalization is not just a tactic. It’s a strategy and it’s how brands can cut through the chaos and connect with people. So to your question.
I think of personalization maturity as a company’s ability to consistently deliver relevant, timely, and value-driven experiences across the journey. So not just in isolated campaigns, not just in isolated channels, but as a coordinated ongoing practice. And although I represent the vendor side here, I’m also a marketer and a personalization practitioner myself. So the truth is that it’s not just about the technology.
Having the right tools or data is important, but what also is important is having the right mindset, processes, structure, you know, all of these things that are needed to actually activate on the technology. And if you just have the technology, it’s not going to be enough. So what we see in the market is that personalization often gets framed as a marketing tactic when in reality it’s an organizational strategy. It touches product data technology content operations. And if all of these teams are not aligned, you end up with pockets of efforts instead of, you know, doing the real strategy, the real thing. Yeah. Secondly, a lot of these companies have invested in the tech, but haven’t done the harder work of supporting it, you know, shifting how teams work, how success is measured, how decisions are being made and personalization by nature forces you to move from one size fits all to new ones. And that takes experimentation, iteration, and a lot of cross-functional coordination, which many organizations aren’t set up to support. And so there is a change that’s needed. And we see that typically when there is a center of excellence around personalization and there is a clear leader, a clear leadership to it with single person ideally dedicated to the personalization program.
This is a big factor on the success or maturity of the program. Without the personalization leader, it’s not going to be enough to push it through holistically throughout the company. just to summarize, while the ambition is often there and the value is well understood, the execution still has real operational and cultural walls. And that’s the gap that we see.
Greg Kihlstrom (05:36)
Yeah, yeah, because I mean, to your point, I think a lot of organizations still think of, know, they’re going to buy a platform, you know, as good as the platform is, they’re going to buy a platform and it’s going to kind of solve all of their all of their issues without thinking through the people process, even the data components of that in addition to platform. And I know you touched on some of this already. And I like that idea of the champion within, you the leader within that’s really leading the charge with personalization. But when you’re looking at a company’s personalization maturity, you know, what are the key indicators that you look for in, you know, from a resourcing and operating model? And, you know, how important are these aspects in overall success?
Yaniv Navot (06:18)
Yeah, that’s a great question. I think there are three or four of these indicators. When I assess a company’s personalization maturity, I look less at whether they have a specific tool or data set and more at how they’re set up to deliver personalization consistently. And so some of these indicators can be first ownership. Is there a clear team or leader responsible for personalization success across the journey? Or is it scattered across different functions? You know, think about
The classic setup at like most companies is that you have the email team and you have the mobile app team. Like there is only one customer and it’s the same customer across all of these channels. So it, it does it make sense to keep it that way? I’m not sure. The second thing is resourcing. Do they have the right mix of people? Not just analysts or campaign managers, but people who understand content strategy, testing, experience design.
Personalization is a multi-disciplinary field and mature companies recognize that and they need to structure their teams accordingly. And by the way, a lot of agencies and a lot of vendors can augment these skills. You know, if you don’t have the right expertise, the right experience inside, then agencies and other companies can augment that and support that. The third thing is how decisions are made.
You know, other test and learn driven is personalization embedded in planning cycles and KPIs, or is it still reactive in a way? Something that they turn on for a campaign and then turning off for the next one, which is very common. And then maybe the last point is we look at how integrated personalization is into the operating model of that company. Think about, you know, product marketing, data engineering.
Are they all aligned around shared goals and timelines? Or are they siloed by design? The more mature companies treat personalization not as a feature, like I mentioned, but as a way of working. And so the operating model and resourcing are absolutely critical. And you have the best tech in the world, but without the structure and accountability to support it, personalization would just stay stuck at pilot stages.
Greg Kihlstrom (08:33)
Yeah, yeah. So I want to get back to the team structure, the aspect that you mentioned. And certainly this is something I run into a lot in my work as well is, again, great strategy, great platform, overcoming the data silos and all of those other things that often go into some of these efforts. And yet the team structure, it could be just legacy you know, channel marketing channel silos. It could it could be other stuff going on. How do you recommend that organizations structure their teams and processes to move beyond some of what you were mentioning? You know, those one off pilots that are great, but not sustainable and, you know, structure things towards something that is more sustainable and particularly as more companies are moving towards that omni channel goal as well.
Yaniv Navot (09:23)
This is one of the biggest challenges that we see organizations stuck in short term mode. run isolated tests, isolated campaigns, and some of them they show promise, but then they struggle to scale it because the structure isn’t there to support it. And so to move beyond that, the first step is to treat personalization as a strategic capability holistically across the company, not just as a one-off project or even multiple one of products. means that building durable cross-functional teams, you can call it pods, can call it squads, call it whatever you want. But these teams can bring together different functions like marketing, product, data, engineering, content, experience design.
These teams should have shared goals and the autonomy also to experiment and iterate. And the more autonomy they have, the more control they get over the different components of the experience, the more impact they can bring. So that would be the first thing. The second thing, there needs to be a clear operating model. Like I mentioned, who owns personalization, who owns the strategy, who sets the priorities, how success is measured. Without that personalization gets fragmented and disconnected across channels.
Everything I would say is that, and especially with omnichannel in mind, organizations need to build for reusability in a way and consistency. Things like centralized audience definitions, modular content, scalable decision logic. You can’t reinvent the wheel every time you need to do something. We need to be agile and efficient. so having these centralized resources and definitions is really important. And finally, having the leadership buying is also key. Personalization at scale often requires changes to planning,
text data integration and how teams collaborate. And so for that, you really need leadership buying because you have the potential to transform the way the company operates. So it’s not just about having more tools or data. It’s about building the connective tissue that, know, terms experimentation and personalized experiences into ongoing value delivery.
Greg Kihlstrom (11:34)
So I wanna switch gears a little bit here and now talk a little bit more about the artificial intelligence aspect of this. personalization, I mean, I feel like we’ve been talking about personalization for years and years, but with some of the latest developments, whether that’s Gen.ai or some other things, it’s really starting to be realized in some new and meaningful ways. Traditional machine learning and deep learning, they’ve been around for years of, so where does generative AI fit into all of this? How do these different approaches, you know, all kind of under that umbrella of AI, but how do they complement each other in bringing faster, more accurate relevance to consumers?
Yaniv Navot (12:14)
Yeah, traditional machine learning and deep learning have powered personalization since the beginning, really, are excellent at prediction, you know, understanding what a user is likely to click on, buy or engage with next. These models are trained on holistic behavior and are great at optimizing things like product recommendations, content recommendations, rankings, targeting, know, the mechanics of personalization, essentially. Gen.ai brings something new to the table creation. In a way, it doesn’t just predict, it produces. So instead of choosing the best subject lines or images from a predefined set, it can generate a brand new one in real time, tailored to the content or context of the visitor segment. It opens up a layer of flexibility that traditional models just weren’t built for. Gen.ai can also create recommendation strategies in real time segments and can even stitch together coordinated journeys. Used together, these approaches are really powerful. But the key is orchestration. Gen.ai is not just a magic button like we spoke about. It needs to be guided by the same behavioral signals, testing frameworks and governance that makes personalization effective in the first place. And Gen.ai is becoming a commodity or maybe already is. We see that there is a clear split between companies using it to drive revenue and companies using it to drive efficiencies and cost savings. Obviously, the best companies are the ones that are using it for both. Definitely interesting times.
Greg Kihlstrom (13:47)
Yeah, yeah, definitely. Can you maybe share an example where Gen.ai unlocked an insider customer experience that wasn’t possible with these previous and predictive models alone?
Yaniv Navot (14:01)
Yeah, sure. I think that one of the most exciting areas where Gen.ai has made a real leap is in site search, guided shopping experiences, especially through LLM powered search and conversational commerce solutions. Traditionally, site search solutions really dependent on keyword matching and rule based logic. Even with machine learning, Lerudin, they’re mostly just optimizing rankings or autocomplete but they don’t really understand intent and complex queries. So if you type in, I don’t know, gifts for a picky 12 year old who likes science, you really get most likely a no result page or a random of products that are unrelated to that. With Gen.ai, we’ve moved beyond keyword matching to actual natural language understanding.
And in the case of search, an LLM can now translate the full context of query. It doesn’t just look at product tags. If your product feed is not tagged correctly or in a comprehensive way enough, then it’s not going to be a problem because the LLM can understand the meaning and return relevant results that might not even include those exact words that appear in your product attributes or product data feed. So side search is one example, but
Relatedly, conversational commerce solutions are also taking it further. It acts as, you know, like a conversational assistant on the side guiding customers with real store, sort of like a real store representative. You can describe your needs in plain language and response with curated, intelligent suggestions. And I think that’s the key here. The most exciting thing is that Gen.ai can curate these product recommendations for you.
You don’t have to be dependent on, you know, predefined sets of curations or bundles of products. The AI can do it for you. If you take it even one step further and think about the fact that every website today, every app experience is built on use of templates. You have a homepage template, the PDP template, PLP template. And then within these pages, you have basically a merchandiser, a human or a marketer or product manager making these decisions on what to show and where in the autonomous future, Gen.ai will be able to create these recommendations and page structures on the fly, tailor them specifically for every user. And you won’t have to be dependent on templates, which is like the foundation today of every website experience, every landing page, every app screen.
Greg Kihlstrom (16:39)
Yeah, yeah. And, you know, even thinking more broadly than, you know, just specific AI use cases. mean, as far as the Martek stack goes, you know, certainly there’s there’s some trends moving away from that monolithic all in one stack towards a more, you know, composable solutions. I’ve had plenty of people on the show talking about various composable solutions and, you know, there’s various hybrid scenarios and modular scenarios, all those kinds of things. What are you seeing as far as, know, does some of the things that you’re talking about as well as others point us in this increasingly modular architecture to kind of stay one step ahead?
Yaniv Navot (17:18)
Yeah.
My view is that in reality, personalization is messy. Personalization, its foundation touches data, content, experimentation, know, journey orchestration, and each of these areas involve at a different pace and requires different functions. And so having a closed all-in-one system might check the boxes on paper, but in practice, it will limit your ability to move fast or
Plug in the right tools for your business. And that’s where having an open model or architecture comes in. From a personalization standpoint, composability can be quite meaningful because it lets brands design and orchestrate experiences across channels, leverage consistent data models and content elements, and then essentially be more agile, test faster without the need to re-platform every time they want to level up.
And so what we see in the market is that brands want choice, speed, control, and composable stacks when done right. And that’s the key. And it’s not easy. They can deliver on that promise. But again, it depends on having the available resources and unique needs of each brand and making it work for you.
Greg Kihlstrom (18:28)
Yeah, and along those lines, then, you what are some of the criteria, but as you know, and even considerations that leaders should be taking into account when evaluating new martech solutions? I mean, certainly extreme flexibility is great, but then there’s also, you know, you’re you’re doing all the integrations. And yet on the other side, as you mentioned, the monolithic system may check some boxes, but give very little flexibility. So, you know, what criteria should leaders use to you know, ensure things like seamless integration, avoiding things like vendor lock-in and still maintain flexibility for what may come tomorrow.
Yaniv Navot (19:05)
Yeah, it’s a great question. And it’s becoming more important as the market landscape gets more and more crowded and fast moving. And it’s crazy to follow what’s happening. So when evaluating new solutions, think that leaders need to look beyond features and ask, how well will this play with the rest of my ecosystem? A few key criteria come to mind. First, openness. You know, does the platform offer robust APIs integrate easily with your existing data sources, channels, decision engines. If it lives in a silo, it requires heavy custom work to connect, and that’s a red flag. The second thing is modularity. You want the ability to adopt what you need now and expand or swap pieces later. That avoids being locked in and gives you the leverage as your business or tech stack evolves over time, and I’m sure that it will.
Right. The third thing is look for data portability and governance. Can you move your data in and out freely? Can you control where decisions are made in the platform, in your CDP or somewhere else? That flexibility is key for long-term agility. And maybe the last one is time to value. The best platforms don’t just integrate well, they make it easy for teams to activate personalization and gain insights quickly without long implementation cycles, without heavy dependence on one vendor services. And obviously in this climate, if you’re investing so much in bringing in a personalization solution, you want to see value quickly.
Greg Kihlstrom (20:40)
Yeah, yeah, definitely. Yeah, the time to value definitely a key key metric there. Even before that, though, to get, know, I feel like most leaders even outside of the market, you know, the CMOs and all are understanding more and more the value of personalization. As you said, it’s not just a marketing thing. It’s you know, it’s a cross customer experience, service, sales, all of those things. And yet there still are competing priorities and things like that. What’s been the most successful way that you’ve seen to get that executive stakeholder, like not just to say we believe in personalization, but to actually get on board with investing more in personalized customer experiences.
Yaniv Navot (21:19)
Yeah, and you can guess what I will say. The most successful way of saying is just showing value. So tying personalized customer experiences directly to revenue and efficiency outcomes. Simple as that. Executives respond well when personalization is positioned as a growth lever and not just as a tech feature. Personalization should be seen as a way to drive incremental value from existing traffic, existing customers, existing channels, which makes it more cost efficient than acquisition heavy tactics. know, typically people talk about what are the best examples in the market for successful personalization? And people say Spotify and Amazon and Netflix. And these companies, when they publish materials on personalization, you see that they view personalization also as a long-term a strategic investment and not just a short term solution for, you know, tactically improving whatever viewing times or sales or listening times. Personalization should be seen as a long term strategy, but when you work with executives, you need to show that it’s really moving the needle with short term wins, you know, uplifting conversions or retention or specific segments.
So it’s important to align the long-term vision to short-term strategic priorities and show that to the executives.
Greg Kihlstrom (22:41)
Yeah, I love it. Well, you need thanks so much for sharing your ideas and insights today. One last question for you before we wrap up. What do do to stay agile in your role and how do you find a way to do it consistently?
Yaniv Navot (22:52)
For me, staying agile means constantly adjusting to change without losing focus. It starts with three habits, curiosity, passion, and embracing a critical mindset. For me, curiosity keeps me scanning for what’s next across customers, competitors, culture. AI is definitely, there are a lot of interesting things that are happening. But like I said in the beginning, there is a lot of noise.
So be curious to experiment, try it yourself and don’t just follow hype. Passion keeps me energized, you know, through all of the things that I’m doing and through ambiguity. It’s what fuels the momentum, even when the path isn’t clear. And the last and most important piece for me has always been embracing a critical mindset, which means that I’m willing to challenge my own assumptions as uncomfortable questions and challenge the status quo. And I think if you ask me, that’s the number one most important skill that a marketer should have, especially now in the AI era.
