PegaWorld iNspire 2024: Connected Marketing - Bringing Data, Content & Intelligence together for Marketeers

Okay. Good morning everyone. Uh, my name is Anil Mishra. Uh, I am the global partner manager, uh, within Pega. And I'm extremely excited for introducing this session. But before I do that, I just have, uh, two requests. Let's be attentive, focused so that we can definitely grasp what is getting shared here, which is extremely powerful. And we are able to, you know, take back and implement at our respective organization. And second one, let's just put our phones in silent or switched off mode so that it's not disturbing anyone.

And we are all focused here. Right. So, uh, I have the privilege to introduce this session, which is titled, you know, Connected marketing, uh, bringing data, content and intelligence together for marketers. And I think it's extremely powerful, as we heard in today's keynote as well, how it is so powerful to, you know, have this in place so that we can serve our customers as they are single, you know, customer for us. So, uh, with that, uh, I have I would like to introduce our speakers here. Uh, we'll have Jeff Vince Jeffs. He's the senior director of product management at Pegasystems. Who? We'll have Phil Lockhart, chief digital officer at Credera.

And then we have with us Alex Burton, global head of customer decisioning at Elevance. Sorry. So, as they say, there, there is power in three. Uh, and if we see and recall, you know, we'll see so many examples of that. And we have three powerful organizations here, uh, gathered with us, uh, to essentially talk about this connected marketing platform. So over to you, Alex, and let's get it going. Thanks, O'Neill. I think everyone can hear me. I probably speak without a microphone.

Um, firstly, thank you to you guys for coming along and sitting in this session. Secondly, I'd like to thank the two gentlemen on my left. It's actually my privilege to be sitting next to them. You've got two industry experts, uh, who, you know, if they don't know something about the tech industry, there's really nothing to know about that particular industry. So very excited to be with them. Very excited to be with you, to have a conversation today about connected intelligence and connected marketing. Um, I want to just to touch on one thing really quickly. Even though technology is important to overall success of a customer engagement strategy, it's not the most important thing. But what is important from a technology perspective is that the right technology gets chosen so it's fit for purpose.

And that's a critical thing to always look at. Um, before we go into a bit more detail in regard to connected intelligence and connected marketing, um, a few words from our sponsors. So origins are a company, um, 100% pure play, um elite Pega implementation partner, uh, specializing everything decisioning. Um Credera, on the other hand, is a boutique management consultancy specializing in martech. Credera is part of a large organization, and I'm sure a lot of you have heard of Omnicom. And Omnicom is a, um, is a holding company for some of the largest agencies globally from a media perspective, from marketing, um, from creative. Um, and as you can see by the numbers, I think they speak for themselves so well. Over 75,000 employees globally, 1000 plus agencies. So you've got a lot of agencies working together.

And that's an important point that I'll just touch on in a second. Um, various different disciplines. And I think as you as we go further in this presentation, you'll see one of the disciplines that we play in is really supporting those agencies from that data driven strategy and execution perspective, um, right across the globe. And finally, probably the most important one 5000 global clients. So large, large organization. Um, now, I'm not going to put up a huge number of slides in front of you, but really, I wanted to put this one up. And the diagram is really just showing where Credera sits in the broad spectrum of Omnicom. So amongst a lot of these media advertising, digital, um, companies, Credera really is the arm that supports all these agencies around driving that data driven strategy and the execution. So we bring the expertise and we bring the technology to help our clients to deliver on on the outcomes and their objectives.

Okay. That's a mouthful. All right. I'll, uh, I'll move a bit further. Well, actually, what I'll do is my first question, Phil, and it is a question for you. I wanted to stay on the on the Omnicom, um, on the Omnicom topic. So let's speak a little bit about it. So most organizations, whether they realize it or not, probably have an Omnicom agency already working with them. So whether it be the media advertising.

So from your experience being in this space and really your personal perspective, um, what's the benefit that the brands have working with Omnicom and the Omnicom Precision Marketing Group when it comes to designing and delivering their their strategies, their customer engagement strategies? I think one of the big pieces of of this is that, uh, being part of Omnicom has allowed us to kind of see the full cycle of this, not only just the build, but since they operate doing the media purchase and the actual activation of the content and then the optimization of that, you get a chance to kind of see the full feedback loop, which informs the processes that are there. So actually engaging with an agency, it's one of the most important things is because they're going to be responsible for two key elements that is key for, uh, decisioning. One is content. They're going to be creating the content, the creative aspects of that, creating the processes, the tracking of it and the data not only of like what campaigns are doing, but the performance overall. So having those elements in play and part of your strategy in the way that you deliver decisioning is going to make sure that the performance is is the highest. And then also the last piece is if you're not engaging with them, they're going to come in and they're going to be bringing their perspectives and their points of view on technology and the way that things should be done. So get in front of it at the very beginning. Get And get alignment within it to make sure that you're comfortable with the way it's going.

Versus just something that an agency is bringing in for something as important as decisioning. No, no, that's great. And I guess the other aspect is you get that end to end perspective as well. As opposed to having siloed groups coming in with their own opinions and sometimes getting in the way of, of progress within a particular delivery of a, of a solution. For sure. Excellent. Alrighty. Um, so excuse me. So the topic and the theme of this session is, is connected intelligence and connected marketing.

And really I think as, um, as was said earlier, it's about it's about bringing data or connected data. And Phil will talk in a bit more detail about the various levels of data that will help organizations to actually get to their final objective. It's bringing in connected content, and content is a we're not going to go into too much detail. I think I was going to say this is not an Adobe conference for us to go into that sort of level of detail, but we will talk about the importance that content plays in the whole cycle. And finally, it's connected decisioning. And really it's about connected decisioning is about bringing all the pieces together. But that's that's technology that we talk about. Now one of the things that always gets missed in delivering this customer engagement is the people aspect of it. So one of the things that Credera provides is, is the ability to actually set up an organizational structure that is actually going to be able to adapt to new ways of working.

So it does change. And I think we've seen a lot of organizations struggle to really move to a new way of working and put new roles in place that can actually contribute to success. And that leads to the next one, which is really looking at the change management, because, you know, we are trying to bring in a new way of engaging with customers. And I think a lot of you have, you know, the power of decisioning. Um, but you really need new processes and new ways of working. And the final one, and I guess this is where we'll go into a lot more detail is, is the Platform. And this is really where we've looked at. How do you bring the best of breed technologies that are out there to actually, you know, firstly justify the investment and secondly, be comfortable that technology is going to contribute in the right way to achieving that, that final outcome. Um, Phil, it's a quick question for you, um, looking at all this and I'll probably jump to the next to the next slide.

Looking at this and looking at the platform as it sits right now. And really we call it an accelerator. We're trying to help organizations to, to achieve, uh, time to market and time to value much, much quicker. Um, let's go into a bit more detail in terms of what what business problem, what business challenge are we are we really solving with this platform? Yeah, the primary business challenge that we're looking at is personalization or actually creating meaningful engagement with individualized outcomes. So it's got to be something that that makes sense to the individual. It's not going to be an interruption. You know, the the video at the beginning talked about the 10,000 messages that that happen every single day to someone. How to break through that noise.

Don't break through the noise with your voice, but actually figure out a way to come alongside someone. An example of that, which I think was was great is that athletic company shoe. What they did is they partnered with a company that built their built a running app, and what they did is that they were using that data to understand, hey, I purchased the shoe. So they knew when the shoe was purchased, they knew how much miles were being run. You know, within that the wear and tear. So they knew when to advertise and recommend new shoes. And they also knew what type of shoe from that. So with that it's like, hey, you need some new shoes. And here are the things that's going to fit best to you.

So it made sense, you know, with it. And it's actually something that happened to me just a few weeks ago. And it was a great outcome because I needed some new shoes. My shoes were garbage and it made a made a big impact. So that's the big thing from a business standpoint, are you making an impact on the individual versus just adding to that noise? And it's time to value, isn't it, as well? Because, you know, I don't know if you call it business or technical, but a lot of organizations go through a pretty staggering RFP process for each individual technology. Each process takes 3 to 4 months before commercials acquisition. So you're actually on the back foot before you even start.

So what, you know, we'll go into a bit more detail, but what are the technical challenges that we are solving through this platform? And just kind of to break this down a little bit, we use the word connected because oftentimes with technology, you think about that in a silo on itself. You don't think about all the other aspects of the people and process with it. So we're trying to to hammer that home with the way that we represent this. This is a reference architecture on how we like to think about the aspects of those three components that we mentioned brought together. It's agnostic, with the exception of having Pega in Pagan in the middle there for the decision engine. But our goal is to make sure we have a way to bring these things together. It seems like a lot there, so we'll unpack it a little bit so that you can can think through all the pieces. But these marketing capabilities span across multiple systems.

So that's one of the technical challenges. And then also, you know, um, the, the, the aspects of bringing data, making sure it's available in the right systems are important. So we'll jump into it to kind of break this down into the different pieces. So connected content itself, when you're thinking about this, a decision is going to pop out a couple of different things and offer, and it needs to know what content that needs to be associated with it. So when you think about this and all the conversations around GenAI, all this, this new data that's being created, um, there's a couple challenges that are brought forth on there. It's overload of content. How are you going to manage the fact that you're going to have all this content to help serve and personalize to an individual? It's an incredible amount of content. So you have to look through the processes, but also the technology that's there.

The MRM solutions within it. So we want to make sure these are all part of the conversations. Besides just let's spin up a decision engine and not think about how it's going to be going to be activated within it. The second piece on content is if we go back is the measurement on it. So the effectiveness that needs to be there, we'll touch on each one of that, each one of these. And the last thing is the assembly you have. All this content needs to go in a different different channel. So those are the three things that we can touch on within it. Want to make sure we're not just explaining the the diagrams, but providing some information that will be helpful and some challenges we see when we deliver this.

We can jump into connected data as well within within this. So there's three aspects of of this which is data is often siloed and fragmented and usually not available. Or if you do have it, it's not available in the systems when you need it. So the reference here is is first party, second party, third party data, even data sources. You wouldn't think about that. Maybe the agencies own that. You need to get into your environment. And how do you feed that so that you have that available for for the decisioning with within the. The second piece is how do you structure that and make sure that it is in in a good, good format.

And the last piece is do you have capabilities within this data environment to do data science as as well as advanced analytics. So those are the three core themes. When we help companies with this that they need to drill into. And we'll talk about each one of them. And the last one on the the connected decisioning is all this content that we're talking about. How do you how do you achieve it. Secondarily, do you have the longitudinal data that's available, not just what's available right now in the instant, but the fact that you know all the history that can help bring in the the next best action. And the last piece is just how do you set up the arbitration of those rules to ensure that you're delivering and not overloading the customer? We've had situations where clients will hammer their customers with 64 different messages because they have all these different audiences, but they don't understand that they're hitting them with an individual response and then multiple audiences.

There's audience overlap with that individual. They're not tracking it. So those are the challenges. All right. Cool. All right. We're going to jump into each individual pillar and go into a bit more detail. So let's let's start with the connected data. And really this is the foundation.

You know this is this is probably the key component. As much as we all love the the bit in the end which executes everything without data you can't make anything happen. Um, Vince, it's 2024, right? Last time I checked. Yeah. So so some some organizations, some industries do this better than others. But as Phil mentioned, you're taking a lot of siloed sets of data trying to bring them into a consolidated single view. Why in 2024, are we still not great at being able to create a unified view of our customers? Yeah.

First of all, thanks for having me to support the conversation here. Um, and fun fact, I actually worked for Omnicom Rapp Collins, years ago, but I have a good sense for the the power of getting all this. You know, these different parts, right? As Phil was explaining, um, and I do have an affinity for the third one, Decisioning Lead. So I'm sure we'll get to that. But let me let me talk a little bit about the data and the importance and why organizations have struggled. So I think that primarily, um, I would say one of the main reasons is because data is not a static thing. It's ever changing. Its ever growing.

The amount of data that's available now and the devices and channels is just rapidly evolving. And so organizations of course love that and want to collect that data. Um, and that's good and fine. But you've got another force on the other side, which is the consumer. And the consumer is concerned about that data, the security associated with that data, the control of that data, the proper use of that data. So these things together mean that there's just this ever evolving set of requirements and challenges associated with data. I would say another big reason is the the technology has changed associated with data management, so that technology has not stood still. Right. There's these old legacy platforms.

Customers are trying to get off of those and onto more modern cloud data management platforms and data lakes. The technology used in those more advanced data management platforms is evolving to where they're using AI and machine learning to effectively and efficiently deliver that data. Uh, and then the fourth, which is really not a so much of an excuse as it is a old habit that I think can easily be broken by organizations, is they look at data the wrong way. They, they, they look at it through the lens of collecting the data and building it as an asset and getting it to be sort of this perfect asset. And that's a problem because you're never going to get there. And so then the whole project of, of of getting data perfect never ends. And if you take it from another lens, if you take it from the lens of an outcome and what you're trying to accomplish with that data and the applications that are actually going to be the consumers of that data, which I think this platform is, is doing, then you're going to have the right perspective on just finding the right data for the right applications, getting that into those applications at the right time and effectively, you know, getting better results out of what you're trying to accomplish from those use cases. No, that's that's that's exactly right. I mean, maybe this is a naive way of looking at it or a simplistic way of looking at it, but it still takes significant responsibility of building the data set.

Um, and a lot of times they are removed from what the objective or the end objective is, because as far as they're concerned, it's really let's let's build a data lake and they will come. Whereas instead of working in a, you know, I guess from, from the back sort of way. Well, this is what we'd like to achieve. These are the data points that are going to support the first 12 months, the next 12 months and so on. So but you know, we keep on running into these into these situations. Phil. So we we do understand that to actually get great execution, um, deliver great customer engagement and customer experience, there's a massive reliance on the underlying data set that's there. So can you just talk us through in a little bit more detail what a returns and credera are actually doing with the connected with the connected data platform? Um, I know we're going to talk about more, you know, how we bring first and third party data together, but it's also how does it contribute to great advanced analytics and insights that are going to come out of it?

And then how does it support decisioning? Um, at the back end? Sure. I think the biggest thing is better data, better decisions. So making sure you have that, the quality of the data, not just having data, but making sure it's structured and then enriched as well. So we'll touch on, on on how we do that. But our biggest thing that we're encountering with clients was the fact that getting these projects started was was very difficult because it took time. It was like, well, what is the data? And as you mentioned, sometimes it's not as familiar with some of the marketing data or the behavioral data.

I don't even know what tables to get. I don't even know how long I should get all those different pieces. So we built an accelerator to to help with that. So basically it's a Blueprint and that the that light blue diagram that you're looking at right there is a representation of what we call map. And it's just basically an accelerator for us to help clients use leading cloud providers from Azure, GCP or AWS, regardless of what it is, we want to make sure that we can help them build an environment that's going to be helpful for them your environment, your data, your insights is the perspective with it. We talked about agencies. Oftentimes the agencies have all of the media performance data, the cost data, and they're not bringing it in. They would love to and make that available. So we've made a way for us to very quickly onboard the data, transform the data, and structure it in a way that makes it useful.

A representation of that to get that started from kind of start to finish with that would be like three months and a pretty, pretty decent investment within it. And we can now do in like ten minutes with it for the automation, because a lot of this data is very consistent. So we want to make sure that we had a way of doing that very quickly. But then since we're using leading cloud providers and all the technologies and solutions that that company is is familiar with, they can build solutions within that to ensure that the content is performing as well. Because if you have access to all the different things that AWS and Google will provide to make sure that you're continuously adding and advancing, and it's that stack that you like and are familiar with. No. That's great. Um, I want to just to go sideways a little bit. So, um, there's various components in the data architecture.

One of those things is is a CDP. So there's been a lot of conversation in the market in regard to the benefits of CDP. Um, and for those of you who don't, CDP is a customer data platform. So it's really the ability to capture digital data about what your customers are doing on your website or on your mobile, and then stitch it together to create a unique identity. So you're basically bringing together your online and offline customers. Um, I've seen scenarios where an organization has acquired or purchased CDP because they think it's going to give them a way to find acquire new customers. But I think one of the things that they haven't grasped is that it's actually going to benefit them even more if they apply it across the whole decisioning funnel. So, Vince, so question for you as my voice, as my voice goes up. So, um, you've written and this is not a plug for you, but, uh, it should be a plug.

But you've written quite a few articles on CDP's and those cases where they should be used, or where potentially they should not be used. Talk to us a little bit about how you feel that CDP's help, the decision engine and Pega Customer Decision Hub make more insightful decisions and recommendations. Sure. Yeah. I mean, I think the concept of a CDP and the, you know, the role of it that people envision is that it provides this curated customer profile, and marketers have chased that for years. That's, you know, this goes all the way back to the days we've all been, you know, around this landscape long enough to know that, you know, the data warehouses. Marketers didn't like those. They wanted to build data marts that were specific for marketing, marketing, data warehouses. There was all these names of this technology that sort of evolved to this idea of CDP.

The difference was the CDP now is a SaaS solution, but the idea that you can curate a customer profile is good, and that you can figure out a way to syndicate that to the constituents that want to use that data is good. What is not great is when your, uh, your, your approach to that builds some latency into the, into the equation so you can't get the data to where it needs to be fast enough and you don't have the right curation of that profile. Again, you're you get the wrong people figuring out what that profile needs to be and for what purpose. Um, so I think that the again, the concept is good, but what we've seen is we've seen this kind of bifurcate into two main areas. One is what I would call the package CDP, which is a lot of the big providers are now offering this, and it's kind of like, hey, we've got everything in one box. That's all you need. Um, trust us, we'll build that curated profile, we'll be able to syndicate it. And of course, they can syndicate it fairly well to their own applications, but maybe not so well to other applications. And then there's the composable CDP.

And I think what, you know, what you see here is kind of more in that camp, and that is the idea that you pick the right providers of technology to the right parts of CDP when you break it down. And then you build that back up into a platform. I think there's a name map for this platform that then is able to effectively produce that curated profile in real time to the applications like Customer Decision Hub that needed in real time, and can syndicate it so that it can use it and then turn around and make decisions and, and, and send those to the right channels at the right time. So to me, that's the big difference. And and when you look at different platforms, at different vendors are, are, you know, pitching, I think you need to look closely at what they're actually pitching and whether it's fit for purpose. I think you're spot on, because I think when that syndication of data, when you start pushing data back to the channel, you're sort of taking the industry back a good decade because then you're confusing the channels and that that customer experience that's supposed to be consistent across all channels just disappears again from that. Phil, you've you've done lots of deployments across CDPs. Again, your perspective. What is the value of a CDP really.

Yeah. So I think one of the things that we have the benefit of doing is coming into a company and kind of seeing where they're at. So with that, and that's why we use reference architectures that don't have the technology listed, first thing we do is we drop down the technology that have we come across situations where it wants to build that functionality internally. We come into situations where they they have the Tealium, they have the Adobes and the Salesforce in place. So how do we make sure that we we make that work? Because, um, the main thing is that there's going to be a data governance structure that's in place on where that data is and, and how it's being utilized. The benefits of a CDP are going to be that identity resolution. Who is it and how do we connect that to the single view that the customer making that that data available. So you have the longitudinal data, but then the connectivity out into all the other systems.

That's one thing that's very valuable when you think about all the connectors out into all the different, different martech solutions. You know, there's there's thousands of them. So when you look at that, you often have to come up with different plays with with that, maybe use different features and functionality of that CDP in unison with, with this data. But it needs to fit to your organization's governance strategy and just long term view of how they want to handle the different different pieces of the of the of the technology. Cool. All right. We're going to keep moving. So let's move on to the next the next pillar. And it's really connected connected content.

So um, again as I said, this is not supposed to be an Adobe conference, but we're only going to allocate a couple of minutes to anyone. But content still plays a very important part in it also. For me, marketers have struggled over decades to actually provide that stringent process and guidelines in regard to controlling the content supply chain. So it's from, you know, generating content from cataloging contract content into their dams. The approval process and the review process has been arduous for a lot of organizations. So, Vince, a lot of agencies have made a lot of money providing this capability for their clients. Talk to us a little bit about the path that content plays, not only in this connected ecosystem, but also within the Customer Decision Hub. So how does it interact with the decision making process? And again, what's the importance of content when you do make those critical nbas or decisions?

Yeah, I mean when you when you think about content, at least when I think about it, I think about its role in just attracting your attention initially to a an offer, a promotion, any kind of message that you're sending to me as a consumer, like it's going to determine whether I'm even interested in reading it or looking at it to begin with. So that's to me like a primary role of content. And the other is you've got to have enough variation there. So you've got to, you know, we call it hyper personalized, but you've got to have hyper content so that you can really put content in front of me that I that resonates with me and may not resonate with you, Phil or you, Alex. And so that like that role of content and content variation is critical in certain use cases, particularly in acquisition, when you've only got a few moments to attract somebody's attention on a website or mobile app or in a paid media ad. Um, and then I think that the, the variation of the content that really is the Holy Grail now, because when you think about where we've come with data and analytics, it's helped us a lot with hyper targeting. It's helped us a lot with getting price points right and getting different kind of variation of promotions, right. But it hasn't necessarily, as of yet, helped us with that breaking that content bottleneck. So what does that mean?

That means being able to generate huge variations in things like the language you use in the content, the tone, you know, the snippets, the imagery, all these things like just think about a call to action button on a website, like how that is actually, um, you know, structured what words are on it, what colors used in it, all that is content. So that now hyper personalization that's starting to become available with gen AI and other ways of generating this content like that now is great. We're breaking that bottleneck. But now you've got to have a platform that can effectively handle all that, test it and learn which variations are really going to matter because GenAI is great. It'll come up with interesting and creative ways to use language and to put that into your content. But it's not necessarily right all the time. You've got to figure out, like with a platform like this, test and learn what is right, and you've got to do that in combination with these other things that are part of what I would call the anatomy of an offer or promotion, which is the content, the targeting, the timing. And actually, you know, like the, the, the promotion that you're making the offer. And I guess I was just going to add to that.

I mean, we've learned that you need to trust AI that's been around for 15, 20 years to sometimes make the right decision and maybe arbitrating with other things. But we're now getting to a point where we're going to have to start trusting AI around selecting the right content as well and the right combinations of content, because otherwise that testing process is just going to take forever, which means you're going to take much, much longer to get something to market. Yeah. And the and the, you know, the adaptive technology we have inside of Pega is very good at that. So it can go down to, you know, what we call the treatment level. And actually adaptive models automatically spin up on these variations of treatment and be able to quickly learn which variations of treatment are working. Great. Phil, um, talk to us a little bit a little bit about the the content supply chain. So that process that marketers are battling with.

So and how are they going to control and govern the massive volumes of content that GenAI GenAI is supposedly going to create for us? So we're just going to be in this overabundance of content. It's got to be controlled. Yeah, that's the overload one that we were touching on. And, you know, companies are having to to go, uh, revisit their MRM views and perspectives. So often we'll come into very large companies doing a ton of campaigns, and they're managing it with just an Excel spreadsheet, which sounds pretty crazy, but when you start getting into the situation now with the GenAI actually creating it, there's more governance that needs to be there. So that process is important. The skill sets, the people, the reviews need to be put in place. So that's that's an element that's going to be incredibly important because you know, everyone looks at well did the decision work.

You know poor content. It's going to be poor performance with it. So you have to make sure that you have that that review process with within it, because it might be selecting the right content that it's suggesting, but that content itself might resonate with this audience but not with with another. We had a company that that we worked with pretty heavily that says, you know, we can target anyone we want, anytime we want. But what I don't know is with the content that we're working with, what elements of that really resonate with that specific audience? Because it varies. And we were able to determine quite a few things on the the atomic level, on things that work with this audience, that don't work with others. It was within the pharmaceutical industry. And and some of those things are very important from an empathy and tone perspective.

So there's a lot that can be done there. So there's a sophistication and a kind of a crawl walk run that you need to to walk through. But that governance is is huge. Cool. Thank you for that. All right. We move into the last pillar of the Platform and the solution. And that's really putting all the pieces together and being able to really execute at a 1 to 1 level. Um, so and I think a lot of you have seen over the last couple of days that there's no doubting that the technology that we are proposing as part of our solution, you know, Pega is definitely or Pega Customer Decision Hub is, is really light years ahead of anyone else in, uh, in the market.

But I feel and Vince, I will ask you a question in a second, but I feel true. And I'm going to ask you what true decision in years. But real decisioning separates the boys from men in terms of being able to achieve that. True. Um, customer centricity. Yeah. So, um, and again, it's being able to execute at a, at a 1 to 1 level and it's being able to determine what is the right thing to say, what is the right thing to offer to each individual customer. But so the question for you is, um, decisioning as a, let's call it a word has become ubiquitous. So it means different things to different organizations.

So as an authority on this subject, um, so talk to us a little bit about what, what true decisioning and true and decision management actually is. And you know, like what level of sophistication does it bring to an organization? I know we've got a lot of sort of sessions that are going on concurrently that may talk a bit about this, but as an authority, just, you know, what does differentiate sophisticated decisioning that Pega has and what organizations want to do from other things, and other solutions in the market that have also called themselves Decisioning Lead. Well, first of all, I agree with you that we are light years ahead. So I think that's that's spot on. And there's there's some there's some reasons for that. I'm of course I'm a little biased there, but I like that we have Pega in the middle of here. The Decisioning Lead there's a reason for that because it's not just that I work for Pega, but it's that because I really do believe, you know, one of the people that's been trying to guide the the product that we do have a technology that is very differentiated. The reasons why I think that is, is that, first of all, we built this technology from the ground up to be 1 to 1.

And I know that sounds a little bit trite, but what that really means is important is that every time that we are asked as our decision engine to make a decision, we are never pre-determining that decision. We are looking at that customer, the profile that we talked about give us the best data associated with that customer. Let us understand the context of that customer. So that's part of the data. But that's the most recent data you're getting often in a payload that's coming, you know, from a website or from a mobile app. And then we're making that decision. That's the 1 to 1 part. We're making that decision just on that customer, just in that moment. The other thing that we're doing is in that, by the way, uh, is inbound or outbound.

So if we're asked to make a proactive decision because the organization wants to reach out and engage, we're going to do the same thing. We're not going to put customers in segments, and we're going to look at that profile at that moment. Um, the other thing is the we talked about a little the adaptive learning that we use. That's a huge differentiator because there are many technologies that we'll talk about data science and being able to do predictions and and get scores. And that's all well and good. But very often those are latent. They're they're static. They're old. And when we say that we mean they, you know, 24 hours a week old that's old.

In this day and age in 2024, you need to be able to update those predictions nearly instantaneously. So the adaptive learning is key. And then the third sort of aspect is the real time arbitration. So when you are asked to make a decision, are you really doing that immediately and thinking about all the different possibilities of the content, the you know, everything we talked about the anatomy of a next best action, all those pieces you're taking into account the eligibility rules and then you're arbitrating and you're doing all that in a 10th of a second at scale for millions. So some of the brands you guys are working with, these are huge brands with millions and millions of customers, you know, literally billions of interactions a year or month. So that's the things that differentiate this engine from other engines. Now that's that's that's exactly right. That's very perfect. Um, so we're almost ready to end this session.

But Phil, I do have one more question. I guess it does encompass Decisioning Lead. So someone's been in the tech industry for for a long time. Um, you look great, by the way. So it hasn't aged you at all? Uh. Not me though. Sorry. You look even better.

So I wanted to get your personal perspective on the importance and the role that a decision engine and an orchestration engine plays in an organizational martech stack. Yeah, and I kind of go back to I might use a different analogy than what you referenced, but more of the maturity or sophistication of like where people are at with with this, I think it's important to note, but um, the that the importance of decision and orchestration. I think the biggest thing is that, um, a lot of companies start with one channel. And but when you're looking at this from a level of sophistication, you need to manage all the different channels. That's why we bring it up front. The intelligence has been sitting in all the channels and they're not channel aware, which means that something's happening over here and I don't know what's going on. So like So there's conflict of messages or just just overload with it. So making sure that not only do you have the best recommendation or decision that needs to to go out there, but how do you actually bring that into the right channel, which is the assembly, you know, of that, that information? I mean, there's a lot of challenges from that content that need to be brought in in order to, to execute this.

But the difference is, is incredible with within it. So like it, you know, a lot of companies have started with, you know, some functionality that might be in a CDP which we touched on, which is have got some information available that's like real time right now and a little bit of historical information, kind of an if then type of a deal, go do this. It's trigger based, event based. When we're starting to get into like this longitudinal always running, you know hundreds of models and you got to figure out when do you actually engage with them. There might be a bunch of things that that you could do, but which one actually takes the, you know, the the precedence on it, like it matters quite a bit. We've done some real time decisioning Lead of just engagement on websites where we're changing the content on the fly based on the audience and the persona. And that the lift is, is incredible. It's like 2 to 3 times the number of repeat, repeat visits and like a 30% increase in the engagement with within that session. So you're going to get stickier sessions, you're going to get people that are going to be involved more.

But there's a lot of things that have to get involved in order to to bring that bring that to life. That's great. That's great. All right, guys, thank you very much for listening. I just wanted to sum it up. So I guess what you've what you've heard today is a conversation about a a connected intelligence strategy, but also connected intelligence platform, which, which drives the ability to deliver the objective. And I know a lot of organizations probably go and acquire these things in a siloed fashion, but this is an opportunity that we're bringing to market, where we look at the whole process of connected data, content and decisioning as a single end to end process, which I think is extremely important. So I think we're going to we've got a bit of time for a couple of questions. Thanks.

And I think it was a wonderful discussion for sure. Um, I would request if anybody has any question, can you please come to the mic and just ask the question? Hey guys. Thanks for for this is so great. Um, as it relates to third party data, that's a data source that's being deprecated right now. And it's also critical in stitching together IDs. So what do you see as a potential replacement for that to fill those gaps? What do you see out in the marketplace specifically? Phil you probably see a lot of brands.

Um, try to figure this out. And how are they doing it? What a great question. Um, we're seeing that quite a bit. Uh, so I think one of the things that being part of Omnicom, it was up on on the slide, I mentioned enrichment. So it is like what you what can you do on the the first party data for the enrichment? People are going to try to, to figure out how to get people to raise their hands more within it, which means that there's there's more that you have to make sure you create compelling content and compelling reasons for them to share that the solution that Omnicom has was was mentioned is Omni. It's one of the largest, um, you know, identity graphs. So there's enrichment of data from demographics to geo location to purchase.

It's an incredible amount of information. So the aspect of it is let's focus on trying to create the right experiences to get people to raise their hand. So that's where that progressive profiling of getting managing that anonymous user within your system, getting them to raise their hand in some capacity, um, to to have that and then enriching that data even more with, with those type of solutions. Um, it varies a little bit by industry. You know, retail is pretty easy. People sign up because you got to check out and do that. Pharmaceuticals really challenging. Um, you know, um, energy is a little bit easier in some capacities because you have to log in and check your things. So based on the industry, there's some, some differences.

Yeah. Yeah I'll just add that, um, you know, from, from the Customer Decision Hub standpoint, that's the perfect again, um, source of what I would call a conveyor belt of features. Uh, and so it's usually what we find is that those, those first party data supported features tend to be more, you know, predictive. And so if we can find some of those third party features that are predictive also, and they've been well curated, we can quickly figure out, you know, which ones of those matter and which ones work through our adaptive learning. So I think that fills the gaps over time is that you just get the right data, you know, um, hand handpicked really for for the decisioning. Yes, please. Hi. Super interesting. Um, can you talk to me a little bit about, um.

I just forgot my question. Oh, uh, when we're testing and optimizing and customers are, like, in multiple pull segments, different parts of the journey across different, say, product categories right there at different stages. Can you talk to me a little bit about that? Yeah. We can I mean, that's actually big um, with, with within a lot of the challenges. So let me see if I can put it into context of, of um, an example with, with it. So maybe within a banking where or telecommunications where they might have different product lines or lines of business. Is that, is that a good way of representing it or just traditional retail? Yeah.

So I think the first thing is, is like one we we always deep dive into the, the audiences in particular. So we do reviews to make sure we can actually see the overlap with within it. So then um, there is a, a process we have to go through to constantly update that and be aware of the, the touchpoints that are, that are happening. So that's starting with, with the awareness of, of how big is the problem, um, within it. And then it goes back into, um, the challenge that if within that company, if they're using different technology, it's a little bit harder. So you have to bring that data up into like a marketing analytics platform. So you have a little bit more visibility with within the, um, the challenge you have like maybe a, a touch point that I'm not hitting on. Sorry. So yeah, my my question is more specifically like say like traditional retail, like a department store.

Right. So I have different categories men's wear, women's wear kids okay. Right. So I could be in a certain journey in women's wear different journey with menswear. Different journey than than kids. Right. Can you tell me a little bit about when we think about journey orchestration and next best action, right. Can you tell me a little bit about how we're serving that up? Because I'm in different stages depending on what I'm looking at on your website.

Say. Yeah. So, um, like taking one at a time or okay. Like, say I come one time and I'm looking at women's wear, I come another time or I'm looking at cross category, right? How is the Decisioning Lead and next best action? How is that going to get served up to me? Okay Vince do you wanna grab that one? Yeah, I can because it's a great question. And I think again, one of the differentiators in this, this, you know, CDH engine is that we can we can understand all those journeys so we can have representations of all those different journeys.

But what's most critical is what is the right next best action at that moment. So the context of what's happening, the latest behavior on the website, the mobile and the store. If that's available, then we can wait. The importance of one journey and one journey stage over another, because ultimately you only have sort of one thing that you can really say to that customer that's going to be the most relevant. Hopefully that's the latest journey that they're actually on. That's a shopping journey in. You said men's you know, uh, department then and that's what's on their mind. They're really looking to purchase something for themselves or for their husband. Whatever that is the ability for the engine to quickly pivot and put emphasis on that journey where other tools will just want to sequentially go through certain journeys and always sort of hammer you on that journey until it's finished.

Yeah, sorry. That was a great clarification on it. And you know, as was mentioned, it is it is having that that arbitration level of understanding of the context of all the different pieces and knowing and you're keeping track of each one of those journeys and each one of those opportunities to engage with them, but making sure that you are constantly weighting that and oftentimes, like the most recent information, is going to help boost that. And that's where you can actually put in some of the rules to to go through and change it. You know, there's always challenges of do I have enough clicks in engagements in order to get something like that? So I think those are some of the the pieces that you have to work through and actually maybe, um, change up a little bit, but it's adaptive can actually actually help you escalate that and get through that that faster. Okay, so I hate to interrupt this conversation. I think it has been, you know, wonderful. But I would like to thank all the speakers for coming here.

What I got from the session, if you want to be precision in your 1 to 1, uh, you know, personalization and engagement, I believe you know, the power of three. That's the marketing platform. Uh Pega. Decisioning Lead and expertise. I think that will definitely help you out. Uh, do that. We have our speakers here, so if there are any more questions, they can definitely, you know, talk about it. Thanks once again for, uh, all the audience who have attended here and have a great rest of the PegaWorld.