#704: Delivering personalization at scale with Yaniv Navot, Mastercard

Summary

Podcast Summary: The Agile Brand with Greg Kihlström® - Episode #704: Delivering Personalization at Scale with Yaniv Navot, Mastercard

Release Date: July 14, 2025

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Introduction

In Episode #704 of The Agile Brand with Greg Kihlström®, host Greg Kihlström delves deep into the intricacies of delivering personalized customer experiences at scale. Joining him is Yaniv Navot, the Senior Vice President of Commercialization for Customer Acquisition and Engagement at Mastercard and former CMO of Dynamic Yield. The discussion explores the current landscape of personalization, the integration of artificial intelligence (AI), and strategies to future-proof Martech stacks for enhanced customer engagement.

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1. Understanding Personalization Maturity

Yaniv Navot introduces the concept of personalization maturity, emphasizing that it's more than just employing the right tools—it's about fostering the right mindset and organizational structures to deliver consistent, relevant, and timely customer experiences across all touchpoints.

“Personalization is not just a tactic, it's a strategy and it's how brands can cut through the chaos and connect with people.”
— Yaniv Navot [04:55]

Navot highlights the prevalent maturity gap, attributing it to companies viewing personalization merely as a marketing tactic rather than a holistic organizational strategy. Successful personalization requires cross-functional alignment among product, data, technology, content, and operations teams.

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2. Indicators of Personalization Maturity

When assessing a company's personalization maturity, Navot identifies several key indicators:

• Ownership: Clear leadership and dedicated teams for personalization ensure cohesive strategy implementation.

  “Without the personalization leader, it's not going to be enough to push it through holistically throughout the company.”

— Yaniv Navot [07:03]

• Resourcing: A multidisciplinary team that includes analysts, campaign managers, content strategists, and designers is essential.

• Decision-Making Processes: Adoption of test-and-learn methodologies embedded in regular planning cycles fosters sustained personalization efforts.

• Operating Model Integration: Seamless integration of personalization into the company's broader operational framework prevents fragmented efforts.

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3. Structuring Teams for Sustainable Personalization

Navot emphasizes the importance of moving beyond isolated tests and campaigns. He advocates for building cross-functional teams—or "pods"—that integrate marketing, product, data engineering, content, and design. These teams should possess shared goals and the autonomy to experiment and iterate.

“The more autonomy they have, the more control they get over the different components of the experience, the more impact they can bring.”
— Yaniv Navot [12:24]

Additionally, establishing a clear operating model and ensuring leadership buy-in are crucial for scaling personalization initiatives effectively.

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4. The Role of AI and Generative AI in Personalization

The conversation shifts to the evolving role of AI in personalization. While traditional machine learning and deep learning have historically powered personalization through predictive analytics, Generative AI (GenAI) introduces a transformative dimension by enabling real-time content creation and adaptive customer journeys.

“Gen AI can curate these product recommendations for you. You don't have to be dependent on predefined sets of curations or bundles of products.”
— Yaniv Navot [18:19]

Navot provides concrete examples, such as enhanced site search capabilities and conversational commerce solutions powered by large language models (LLMs). These advancements allow for a deeper understanding of user intent and more nuanced, contextually relevant interactions.

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5. Embracing a Composable Martech Stack

Navot advocates for a composable rather than monolithic Martech stack. He explains that personalization is inherently complex, touching various aspects like data, content, experimentation, and orchestration. A modular architecture allows brands to integrate best-of-breed solutions tailored to their specific needs, enhancing agility and scalability.

“Compability allows brands to design and orchestrate experiences across channels, leverage consistent data models and content elements, and then essentially be more agile.”
— Yaniv Navot [23:13]

This approach mitigates the limitations of all-in-one systems, providing the flexibility to adapt and evolve with changing business requirements and technological advancements.

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6. Evaluating Martech Solutions: Key Criteria

When selecting new Martech tools, leaders should prioritize:

• Openness: Robust APIs and seamless integration with existing ecosystems are vital to prevent silos.

  “Does the platform offer robust APIs? Can it integrate easily with your existing data sources, channels, decision engines?”

— Yaniv Navot [24:52]

• Modularity: The ability to adopt and expand components without being locked into a single vendor ensures long-term flexibility.

• Data Portability and Governance: Free movement and control of data across platforms are essential for maintaining agility and compliance.

• Time to Value: Solutions should enable quick activation of personalization initiatives without lengthy implementation cycles.

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7. Securing Executive Buy-In for Personalization

Navot underscores the importance of demonstrating tangible value to secure executive support for personalization initiatives. By linking personalized experiences directly to revenue growth and operational efficiencies, leaders can position personalization as a strategic growth lever rather than a mere technical 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.”
— Yaniv Navot [26:27]

Highlighting both short-term wins and aligning them with long-term strategic goals can effectively convey the significance of sustained personalization efforts to stakeholders.

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8. Maintaining Agility in Personalization Efforts

In closing, Navot shares his personal strategies for staying agile within his role:

• Curiosity: Continuously seeking out new trends and experimental approaches.

• Passion: Sustaining energy and motivation through challenges and uncertainties.

• Critical Mindset: Regularly challenging assumptions and embracing a culture of continuous improvement.

“Curiosity keeps me scanning for what's next across customers, competitors, culture... Passion keeps me energized... Embracing a critical mindset is the number one most important skill.”
— Yaniv Navot [28:02]

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Conclusion

Episode #704 offers a comprehensive exploration of delivering personalization at scale, highlighting the critical interplay between strategy, technology, and organizational culture. Yaniv Navot's insights provide actionable guidance for marketing leaders aiming to enhance customer engagement and drive sustainable business growth through advanced personalization techniques and agile Martech architectures.

For more information on Yaniv Navot and his work, listeners are encouraged to visit the show notes.

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Notable Quotes:

• “Personalization is not just a tactic, it's a strategy and it's how brands can cut through the chaos and connect with people.” — Yaniv Navot [04:55]

• “Without the personalization leader, it's not going to be enough to push it through holistically throughout the company.” — Yaniv Navot [07:03]

• “Gen AI can curate these product recommendations for you. You don't have to be dependent on predefined sets of curations or bundles of products.” — Yaniv Navot [18:19]

• “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.” — Yaniv Navot [26:27]

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Additional Resources:

• Book Mentioned: Marketing in the AI Era by Yaniv Navot
• Mastercard and Yaniv Navot: [Links provided in show notes]

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This summary is intended to provide a comprehensive overview of Episode #704 for those who have not listened to the podcast. For the full conversation and additional insights, please subscribe to The Agile Brand with Greg Kihlström®.

Transcript

[Greg Kilstrom]

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[Unknown Host]

To deliver truly personalized experiences at scale?

[Greg Kilstrom]

Agility requires being able to cut through.

[Unknown Host]

The noise of data and culture shifts to deliver experiences that truly resonate with each individual.

[Greg Kilstrom]

Today we're going to talk about personalization.

[Unknown Host]

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 the Yaniv Navat, former dynamic yield by MasterCard CMO and current SVP of Commercialization for Customer Acquisition and Engagement at MasterCard. Yaniv, welcome to the show.

[Yaniv Navat]

Hi. Thank you, Greg. Happy to be here.

[Unknown Host]

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'd be great.

[Yaniv Navat]

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 small team in Tel Aviv working on what became later on Dynamikil, a personalization technology provider. I was the first marketing hire and what really pulled me in was the vision, and 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.

[Unknown Host]

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 and as well as increasingly better martech platforms, customer engagement tools. Can you first define what do we mean by personalization maturity and maybe explain a little bit why there is still such a big personalization maturity gap?

[Yaniv Navat]

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, and not enough clarity. After more than a decade of leading the field of personalization, working with the top brands, you 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 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, 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.

[Unknown Host]

Yeah.

[Yaniv Navat]

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 nuance. And, and that takes experimentation, iteration and a lot of cross functional coordination which many organizations aren't set up to support. And so there's 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.

[Unknown Host]

Yeah, yeah. Because I mean, to your point, I think a lot of organizations still think of they're going to buy a platform. As good as the platform is, they're going to buy a platform and it's going to kind of solve 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 the Leader within, that's, that's really leading the charge with personalization. But when you're looking at a company's personalization maturity, you know, what, 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 and overall success?

[Yaniv Navat]

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, 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 mobileck team. Like there is only one customer and it's the same customer across all of these channels. So 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. 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, you know, stay stuck at pilot stages.

[Unknown Host]

Yeah, yeah. So I want to get back to the, the team structure, the you know, aspect that you mentioned. And certainly this is something I run into a lot in my work as well is again great, great strategy, great platform, you know, overcoming the data silos and all the, all of those other things that, that often go into, into some of these efforts. And yet the team structure, you know, 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 the, 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 omnichannel goal as well.

[Yaniv Navat]

This is one of the biggest challenges that we see. Organizations stuck in short term mode. They 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 off projects. That means that building durable cross functional teams, you can call it pods, you 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, and you know, 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. Yeah. Third thing 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. And 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, tech, static 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 turns experimentation and personalized experiences into ongoing value delivery.

[Greg Kilstrom]

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[Unknown Host]

So I want to switch gears a little bit here and now talk a little bit more about the artificial intelligence aspect of this. I mean, you know, personalization. I mean, I feel like we've been talking about personalization for years and years, but with some of the latest developments, you know, whether that's Genai 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 course. So where does generative AI fit into all of this? How do these different approaches all kind of under that umbrella of AI, but how do they complement each other in bringing faster, more accurate relevance to consumers?

[Yaniv Navat]

Yeah, traditional machine learning and deep learning have powered personalization since the beginning. Really. They're excellent at prediction, you know, understanding what a user is likely to click on, buy or engage with next. These models are trained on holistical behavior and are great at optimizing things like product recommendations, content recommendations, rankings, targeting, you know, the mechanics of personalization. Essentially, Genai 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 sense, 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 it 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.

[Unknown Host]

Yeah, yeah, definitely. Can you maybe share an example where Genai unlocked an insider customer experience that wasn't possible with these previous and predictive models alone?

[Yaniv Navat]

Yeah, sure. I think that one of the most exciting areas where Genai 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 layered in. 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 Genai 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, you know, 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, you know, relevant results that might not even include those exact words that appear in your product attributes or product know data feed. So site search is one example. But relatedly, conversational commerce solutions are also taking it further. It acts as, you know, 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 responds with curated intelligent suggestions. And I think that's the key here. The most exciting thing is that Genai can curate these product recommendations for you. You don't have to be dependent on 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 P2P template, the PLP template, and then within these pages you have basically a merchandiser, a human or a marketer or a 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. And which is like the foundation today of every website experience, every landing page, every app screen.

[Unknown Host]

Yeah, yeah. And you know, even thinking more broadly than, you know, just specific AI use cases, I mean, as far as the Martech stack goes, you know, certainly there's, there's some trends moving Away from that monolithic all in one stack, towards a more composable solutions. I've had plenty of people on the show talking about various composable solutions, and there's various hybrid scenarios and modular scenarios, all those kinds of things. What are you seeing as far as does some of the things that you're talking about as well as others point us in this increasingly modular architecture to stay one step ahead?

[Yaniv Navat]

Yeah. My view is that in reality, personalization is messy. Personalization at its foundation touches data, content, experimentation, journey, orchestration, and each of these areas involved 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 modular 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 replatform every time they want to level up. Yeah, 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, you know, having the available resources and unique needs of each brand and in making it work for you.

[Unknown Host]

Yeah, and along those lines, then you know, 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 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 ensure things like seamless integration, avoiding things like vendor lock in, and still maintain flexibility for what may come tomorrow?

[Yaniv Navat]

Yeah, it's a great, great question. And it's becoming more important as the marktech landscape gets more and more crowded and fast moving, and it's crazy to follow what's happening. So when evaluating new solutions, I 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. Does the platform offer robust APIs? Can it 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.

[Unknown Host]

Right.

[Yaniv Navat]

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.

[Unknown Host]

Yeah, definitely, yeah. The time to value, definitely a key, key metric there. Even before that though, to get, you know, I feel like most leaders, even outside of the, 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 across 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 Navat]

Yeah, yeah. And you can guess what I will say the most successful way I've seen is just showing value.

[Unknown Host]

Yeah.

[Yaniv Navat]

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. You know, typically people talk about what are the best examples in the market for successful personalization. And people say Spotify and Amazon and you know, Netflix. And you know, these companies, when they publish materials on personalization, you see that they view personalization also as a long term 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. Yeah, I love it.

[Unknown Host]

Well Yaniv, thanks so much for sharing your ideas and insights today. One last question for you before we wrap up. What do you do to stay agile in your role and how do you find a way to do it consistently?

[Yaniv Navat]

For me, staying agile means constantly adjusting to change without losing focus. It starts with three 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 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, ask 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. So yeah, I hope that answers your question.

[Unknown Host]

Yeah, yeah. Love it. Well, again, I'd like to thank Yaniv Navat, SVP of Commercialization for Customer Acquisition and Engagement at mastercard for joining the show. You can learn more about Yaniv and MasterCard and get a copy of the book Marketing in the AI Era by following the links in the show notes.

[Greg Kilstrom]

Thanks again for listening to the Agile brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find.

[Unknown Host]

The show as well.

[Greg Kilstrom]

You can access more episodes of the [e-mail] that's theagile brand.com and contact me. If you're interested in consulting or advisory services or are looking for a speaker for your next event, go to www.greggkilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile. The Agile Brand before we continue, I wanted to share a key strategic resource that a majority of the Fortune 500.

[Unknown Host]

Are already aware of.

[Greg Kilstrom]

Finding the best technology, business and talent solutions is not easy. With business demands and competitive pressures mounting, you need to be able to design, deploy, and optimize your technology to provide leading customer experiences while driving business growth. Those of you that have been listening to this show for a while know that this podcast is brought to you by Texas, a global provider of technology, business and talent solutions for more than 80% of the Fortune 500. TechSystems accelerates business transformation for their customers. Whether you're looking to maximize your technology roi, drive business growth, or elevate customer experiences, Tech Systems enables enterprises to capitalize on change. Learn [e-mail] that's teksystems.com. now let's get back to the show.