DigitalTransformationTalk: Closing the engagement divide in the age of AI driven customer expectations

Speaker A: Foreign.

Speaker B: Good morning, good afternoon, and good evening wherever you are, whatever your time zone. Welcome. This is Digital Transformation Talk and I am your host, Kevin Crane. Welcome to the show. Today we will be discussing the topic of closing the engagement divide in the age of AI driven customer expectations. Research suggests that while many organizations report improved engagement metrics across digital channels, customer satisfaction and perceived experience quality are not improving at the same rate. This growing disconnect is often referred to as the engagement gap, where activity is up but meaningful impact is not keeping pace. As AI, uh, real time data and personalization technologies continue to mature, organizations are under increasing pressure to move beyond surface level engagement metrics and deliver truly connected, responsive and consistent customer experiences across channels. So today's we will be exploring ways to identify this confidence gap. Why brands are failing to meet customer expectations despite improved engagement metrics. We'll look at the operational barriers holding businesses back from disconnected data to siloed teams, fragmented customer journeys, and so forth. We'll also look at how AI unified customer data and real time insights can help our organizations deliver more meaningful and scalable customer experience. It should be a great session today and we're going to dive in. But first, I want to say thank you to everyone attending today, and that includes the folks that are joining us live on LinkedIn. Hello, everyone. And hello everyone joining us today on Zoom. Thank you too. As always during today's discussion, we would like to hear from you too. So I'd like to encourage everyone attending today to participate. So join in with your comments in the chat section. If you have a question. Also along the way, just jump on in. I will attempt to get some of your questions and comments into the flow of the show. All right, we have a great panel of guests starting today with Charlotte Nichols, Regional Vice President of Revenue for SAP. Charlotte, are you with us?

Speaker C: I am. Good afternoon. Hi, Kevin.

Speaker B: Hello, Charlotte. Where are you calling in from today?

Speaker C: I'm in London. I'm down towards Heathrow and Feltham in our SAP office.

Speaker B: Wonderful to have you today, Charlotte. Thank you for being with us.

Speaker A: Thank you.

Speaker B: Also joining us today is Diana Petrova, digital growth and product head at August Collection. Diana, welcome back to the program. Where are you calling in from today?

Speaker A: Thank you. Also London, but, uh, in Holborn.

Speaker B: Okay, wonderful. Fantastic. Great to have you with us. Joining us on our panel today is Andres Dela Haut. Also with us, uh, again for another time, expert in personalization data and analytics. Andres, welcome aboard. Where are you calling in from today?

Speaker A: Hey.

Speaker D: From Mexico City.

Speaker B: Kevin, Mexico City. Wonderful to have you with us again, Andres. Thank you so much. And completing our panel today is Renko Jelika, marketing director at Lactalis Group. Renko, welcome aboard. Are you with us?

Speaker E: I'm with you. Hi, Kevin.

Speaker D: Hi, everyone.

Speaker E: Um, I'm coming.

Speaker B: Where are you calling in from, Renko?

Speaker E: Calling and coming from Ljubljana, Slovenia.

Speaker B: Wonderful. Excellent to have you with us. Well, thank you everyone for joining us today. Look, I'd like to get us going right away by pointing our attention to a recent article published by the Swiss business magazine Organizator. How do I say that? Organizader. Am I saying that right? That's a weird name for a magazine, in my opinion. Anyway, the article argues that customer centricity today requires more than digital engagement. It says that organizations must combine AI, unified customer data and data cross functional collaboration deliver really personalized and consistent experiences across the board. I'm wondering what everyone thinks about this article. Is it missing anything important that we should consider? Charlotte, what are your thoughts?

Speaker C: Yeah, I think I like the article. I think it definitely calls out those four areas that you mentioned where we need to have consistency. So it doesn't position AI as this sort of silver bullet that's going to fix everything it needs to have that orchestration. But I do think maybe where we could take it a little bit further is the operational reality behind that. So a lot of organizations already know what good customer engagement should look like and how they want to execute their strategy, but they're struggling to deliver that across all of the different channels in the customer journey, across all of the different teams. So if the data is disconnected, teams are measuring things differently, then actually layering AI into that can sometimes exacerbate some of those gaps rather than solve them. So there's an operational reality that maybe we need to consider as well.

Speaker B: And Deanna, what did you think of the article as you were reviewing it?

Speaker A: Yeah, I think I would have to agree with Charlotte, because where each and every kind of research into AI and data kind of ends up now is the vision as to where we can go versus the actual. Yes, operational reality of all the companies that, um, are trying to patch together all these monolithic old school systems that they have been building on for many, many years before any of us started talking about AI, which actually holds that data, holds that experience and that connectivity at the moment is probably the biggest challenge of developing and deploying AI, um, layers at scale to actually deliver a good outcome for the customer experience.

Speaker B: Indeed. And we hope to take, uh, a look at identifying that confidence gap here today. Andres, the article points to, uh, consistent experiences across every touch point. Of course, that's what we've been shooting for, for years with customer experience. What are your thoughts on the article?

Speaker D: I think that they are, um, just pointing the. An issue that we as an expert have been talking about for many, many years. And that's it. That we as a company have a very fragmented idea of what a customer journey is. It depends on the department and it depends on the people who is in Charlotte that moment. So I think that the article is going to just help us to start a conversation about that. But, yeah,

Speaker B: and Renko, really, we're positioning AI, uh, as an enabler of stronger customer relationships rather than a replacement for human insight. Where do you fall down, uh, on that subject?

Speaker E: So I think, as an fmcg, regarding the article, I think the touch points are, uh, quite different maybe from the reality. So the digital touch points usually are measured in a digital world. They are quite isolated. But, uh, once you come to a business like I work currently and this is fmcg, um, you can feel that simple things that are not so simple, like distribution, like, like, uh, shop experience, like, like product taste, like everything that a physical product and a physical experience can bring you, uh, it's still valuable. So, uh, just by doing, uh, a good campaign or by crafting a great brand through, uh, digital touch points does not mean that the whole experience is good. So I think we are still quite far from the whole blend of, of all the touch points.

Speaker B: All right, very good. Well, the article is from Organizator, and you can see the link for that article here in the webinar chat feature. Everyone attending today, please just take a look and let us know what you think about this article. Does it resonate with your place of business or does it miss something important that we should consider? Anyway, let us know. All right, well, everyone, thank you so much. I'd like to get us moving into our main discussion points now. Uh, we have three today, and the first one is identifying this confidence gap and why brands are failing to meet customer expectations despite improved engagement metrics. As I mentioned, research indicates that many organizations are seeing improvements in digital engagement metrics, yet customer trust and satisfaction are not improving at the same pace. This sort of suggests to me a growing disconnect between what businesses measure and what customers actually experience. Uh, Charlotte, how are organizations addressing the disconnect between data metrics and real customer outcomes?

Speaker C: Yeah, I think one of the key ways that customers are doing this in the right way is that they're moving beyond some of those channel Metrics that you, that you mentioned. So whilst opens, clicks, campaign performance, those things still matter, they don't necessarily tell the full story. So the better question that we should be asking ourselves rather than did the customer engage with the message is did we move the customer relationship forward there? So we're trying to link it with more outcome based points. So retention, repeat purchase, customer lifetime value, customer loyalty and changing how we're framing those measures, it can help to drive unity across the business. So we're all then trying to drive towards the same goal of the customer outcome rather than clicks in a campaign. I think that helps to reframe it across an organization.

Speaker B: Now Charlotte, are organizations still relying too heavily on outdated KPIs?

Speaker C: Think you'd be surprised a lot of meetings that I'm still in as we're looking at click to open rates and click rates and which links were pushed forward rather than really trying to understand as a whole from M, our customer M database, things like customer lifetime value, we're looking at uh, individual campaign performance rather than initiatives across an organization that uh, are being driven by multiple touch points from multiple different parts of the organization.

Speaker B: So indeed maybe it's not the technology that's the problem, it's maybe our approach to how we use metrics and the technology. Andres, how do you define the confidence gap between engagement metrics and actual customer experience?

Speaker D: Yeah, I agree with Charlotte. Many people are still measuring opens, clicks, etc. So I think the biggest gap is that as a company we do not understand that the user, the customer is going to compare you with the best experience they ever have and probably is going to be Disney. So if you are selling shoes, you are never going to get the same level of engagement and um, love that they are going to have with Disney. And the problem is that you also as a company you are not thinking the experience, the full experience for this customer as Disney is planning everything, right? So the, the gap I see is that when marketing is planning the customer experience, not overwhelm the, the customers with messages, the best communication, personalization, target to the that specific customer, etc. Etc. And then the customer gets to the store and they do not have context of this customer, the, the products that the customer searched before, purchased before. So that disconnect is between the physical uh, digital departments, uh, is the gap that I see is not reaching the full potential of what your company is going to get from customers or uh, engagement, loyalty, etc.

Speaker B: Now Andres, what do you feel are the most common blind spots in how brands measure personalization? Success.

Speaker D: The problem is that there are two ways to um. I often see measure personalization. One is, am, um, I providing the customer with enough variety of products for them to explore and help them to grow as a customer? For me, that's exploration. And the other one is am I going to send a specific product and they are going to immediately purchase that product because so precise that they are going to present in that moment. And these two ways of measure, of thinking of the stakeholders, of thinking about personal vision is in conflict of, between one another. So from one point of view, they want to the customer to explore our products and increase their basket. And the other point, they want to be super specific. So it's difficult to deal with this position, uh, with the stakeholders. So I think that that's one of the biggest problems I see when we are talking about personalization, the objective that we are reaching for now.

Speaker B: Diana, when it comes to this confidence gap we're exploring, have things just changed? How has customer behavior changed in terms of expectations for personalization and speed?

Speaker A: Yeah, I think it's really interesting to look at because, um, I think 85% of companies believe that they personalize well, but actually only 60% of customers agree with that. And I would say I think the 60% is probably higher than what the reality is. This is probably again, somebody pushing the numbers up. And I think the reality is we, we have moved into this world where there is an expectation that everything is going to be personalized to you when you interact with a company. And especially if you're a loyal customer or you have a profile or you've created some form of like an engagement with, with this company. So the expectation is pretty high. But it's also interesting that um, very low percentage of people actually trust companies with their personal data, yet they do expect a personalized experience. I think it's in the low 30% where people actually say like, yeah, I'm happy to share my data so I can get that experience. So it's a little bit of a catch 22 as well where we do expect proactive personalization. And I think it's even more beyond personalization. Right. Like we expect the AI to just be on it to know, kind of predict what we're going to be asking for. And I think some companies are really trialing it. But I think aside from you being in the service sector where you're like maybe a bank or this type of a provider, which is kind of, you kind of give your data by default and they already know it's very difficult as a consumer brand to get to that stage where you can truly service the customer if they're not willing to A, give you the data, and B, for the organization itself to kind of go through those silos and actually connect them. So I think we are right now in a time of a bit of a paradox between expectations and, you know, where we are in terms of technology that has been deployed. But I, uh, guess we're closing it. That's what everybody's working towards.

Speaker B: Should reorganizations be rethinking their measurement frameworks to better reflect this customer reality?

Speaker A: Yeah, I definitely think so. To what Charlotte and Andres said. I, I do think that we are a little bit stuck maybe as marketeers to put one hat on into some of the legacy metrics, right, like the click rates, the open rates, the engagement rates that are some of the older metrics that we would have grown up with as we've moved through, through our careers. But now I think it's so much easier through all the data that is connected to actually see the action that certain metrics have driven. And I do feel that we don't do a good enough job to, to map these two aspects. It's like, okay, great, you've sent a great email. There was a lot of engagement, lots of clicks, but actually the outcome was zero, for example. So I think it's redefining what is an important outcome. And companies, I think, need to stop going for, um, legacy meas measures. And I think maybe, um, I don't know how, like what to call it, like some flashy numbers that just look good on paper. But actually at the end of the day, in most commercial functions, you look at the end result. And I still find that there are quite a few companies that shy away from being very much correlating activity to output or outcome. Um, so I think that's probably a big shift and a big change that's coming because everything can be measured now. I think we've moved past the point where we struggle to connect data. I think now it's just working towards getting it perfect now.

Speaker B: Renko, as I mentioned earlier, especially for consumer brands, engagement can be high while brand loyalty remains unchanged. Why does this happen?

Speaker E: This is quite often, I think this happens. Um, it happened even in the past, frankly, because even, uh, during just a period of TV campaigns or even before tv, uh, the public image was not really reflected always in sales. And also there are many brands that are still valuable and they are not advertising so much. So I think first thing, it's. It's a Relatively normal thing. What we need to understand is that um, there is a different definition from a company perspective and from a customer and consumer perspective. So what the consumer sees is not necessarily what the company sees. So let's say a good point of a shop, let's say in a public space that covers 100,000 passengers from buses and trams etc. During the day is still more valuable than just a click journey through a nice email campaign or whatever. So I think uh, those disconnections are not today maybe easier to understand again not so much in FMCG because we don't own um, all the customer data. So it's somehow split in half between us and between retailers or what the channel is. It can be also Hodika or other channels. But uh, still there are still blind spots where we don't operate well and they can and they do define what we basically sell to consumers. So there are also a lot of sales, uh, that is automatic, kind of an autopilot, uh, cultural that also maybe the digital journey does not cover so well because uh, not everything is a push. I think that the pool is still very strong. Uh, and there are cultural barriers that prevent you to, to maybe scale in some, some segments. So uh, expect very good things from campaign. Yes, but miracles not necessarily.

Speaker B: And Renko, we talked about the blind spots, but what signals actually tell me that my customer engagement is actually translating into business value?

Speaker E: Very, very good, very good. Uh, question. So if I touch uh, a brand like, like uh, fmcg, um, it's very, very difficult to, to real time assess this value because in fscg, as the journey is broken, uh, our data are usually one month, three months or even one year behind. So the measurements that we do usually if we don't count only the campaign measurements, pain measurement, you can get uh, quite reliable data of future sales but not necessarily about uh, brand growth, brand strength and other other things that, that would somehow define all the journey at the same time. At the same time. Uh, sales and, and interactions are still one of the key metrics that uh, that we feel it's the uh, we are doing a good job. Uh, we need to detract um, promo campaigns and other push activities that can influence it. So it's, it's rather complex but still it's a blend between physical, physical touch points and physical data, um, and pure campaign data that are available. Let's meet with some of the delay. While I worked in the telecom industry and other digital industries, it was much easier I want to say because um, you get, you get immediate feedback for what you are doing.

Speaker B: All right, well, Charlotte, Deanna, Andres and Renko, we have some questions and comments coming in from our attendees today. I'd like to go ahead and get to a couple of those. Uh, and I will put these out to the entire group. So please jump in if you have some feedback. Uh, this one coming in from Sherene. Thank you Sherene for your contribution today. Shereen says this. I am looking at our dashboards and everything looks amazing. Our open rates are soaring, click throughs are up and our campaign management metrics are all hitting all time highs. Yet our overall customer retention and lifetime value are completely flat. How can we bridge this disconnect where marketing is winning on paper but the business isn't seeing the loyalty. What metrics should I actually be looking at to see the true picture? Good question. Does anyone have some feedback for Sherene? Charlotte, what are your thoughts?

Speaker C: I can jump in? Uh, please. Yeah, I think it's really interesting and I think one of the key parts that you said there of marketing is being successful but the business isn't seeing the results. And I think that's what we were talking about of a cross department view of a customer. So understanding different touch points that that individual might be having outside of your business. And something that Ranko was saying that kind of prompted a thought in my mind as well is how are you measuring something like customer lifetime value? So if I am engaging with your content, I'm engaging with everything but I'm not necessarily converting to make a purchase. Am I referring a friend to you instead? Am I visiting in store? But you're not getting that data across from in store to online. So maybe you don't think that I've converted, but I've gone to an event or I've told you a friend or a relative about you and that's converted to a purchase. So I think when we're looking at the value of a customer then intent is really interesting and understanding. What do you. Are you just looking at conversion from a campaign or are you looking at. I am somebody who buys every three months and then if I've missed one month that's a concern. But if I haven't just bought this month but I bought last month, then I'm not a risk for you because that's my individual customer behavior. So there's some different areas that we can start to build in of how do you have a full view of that customer and how they're behaving with your brand.

Speaker B: This comment coming in From Julian. And Julian, thank you. Julian says this, uh, personalization. What does true meaningful personalization look like in 2026 that goes beyond the basic right message, right time formula and actually makes a customer feel understood? How do we bridge that gap? Loyalty. Does anyone have any feedback for Julian?

Speaker A: The one million pound question, right?

Speaker B: Exactly.

Speaker A: Maybe I'll just give it a stab. I don't think I have the answer. Um, but I think we're all probably exploring that journey because going back to what I said originally, I think if we are able to gather some of the data from the start or we already have a good base of data that we can make sense of, I think from here onwards is your understanding as a company and as a marketer or whatever your, your role is, right, as to what are the true needs of that individual, of this specific user that you want to personalize for. And, and this is where I guess there is a little bit of magic of creativity and understanding. So there is the data aspect which is going to give you the quantitative data, but the qualitative aspect as to what you deliver as a company back to the customer is kind of really dependent on your marketing, on your strategy, on kind of how are you working with the company and your, your users. I don't think that there is like a silver bullet to this because it's going to be very, very different for each type of organization and for each type of customer. For example, I work in an industry where the customer life cycle is super, super long. So we are looking sometimes of even two years long. So what my customer would need versus someone who is, you know, buying something on um, you know, a Shopify website, which is a click and collect type of experience is very, very different in terms like right message, right time.

Speaker D: Right.

Speaker A: Like, I mean that's maybe a 10 years old concept now. But for example, the way that I need to deliver that personalized customer journey to, to my customers, I need to tell the story for them to get on board over these maybe one to two years consideration period to then come and convert or refer their friend or you know, say, wow, this is amazing, let me do something with this company and so on. So I do think it really depends on industry, on company strategy, on your base in terms of data. Uh, but um, this is where I would even AI or not AI this is your own creativity that's going to come into play to kind of marry all of these up.

Speaker B: Um, all right, one more before we must move on this one from Ruslan. Thank you, Ruslan, for your comment today. Ruslan says this and customer data in real time is incredibly hard. Getting our engineering and operations teams on board to help unlock that data is harder. But anyone have some feedback for Ruslan about how they might approach this?

Speaker D: Uh, I think that in my experience you will surprise how the engineering team is really creative and they really enjoy solving problems. So of course if the engineering team is connected from the marketing or the sales team, it's going to be really hard to make them to share with you the data provide you information because they are going to see as another task. What helps or what I see that helps in company like Abi is that they are um, really involved in the problems that they are going to solve on the marketing side. So now sharing the data is not a problem because they are part of that. So the only thing that I will say is that also the expectation that you have with real time it's really difficult. I haven't seen really ah a lot of companies with real time data. So it's often the most is uh, the next day very few companies on real, real real time minutes. But I haven't seen companies like leaving the real time data uh in all the marketing activities and sales. So I would say that expectations on real time also we should be a little bit less enthusiastic probably right now.

Speaker E: I think I had some experience with real time data uh in my previous lives I would say. And um, you're completely right uh, Andres. So I think uh real time is valuable of course but it's not really the most relevant thing for decision making day by day. So um, again in a business time now uh, real time data uh would probably even confuse us in, in terms of you know if a yogurt is sold well there and the milk is here and there, who would catch like 500 or 300 SKUs that you operate in one market or thousands of SKUs you operate in many markets. I think it would a ca. Uh even AI probably would. Would quit the job that to do that thing at the same time. At the same time uh, I had a positive experience with it. Uh, I'll tell it a plastic way but uh, again it, it has holes and gaps. So we split um the journey into regions and when we had campaigns or tariffs in the telecom industry we had real time data how was sold here and there. We had other data uh that were mapped across and we were comparing so we knew that the campaign and the product was successful if it was sold well here and there. And usually people were complaining from our shops or from digital or, or, or Phone, uh, offices if the data uh, were inconsistent for their region and we knew what was the trigger of sales in one region and we somehow quickly transmitted it same day to other, to other parts of the country. So it was quite positive, but it was not a miracle solving and it's not for every business. It costs quite some money and uh, you need to train people, really physically train people to uh, to manage that. It was around 2018, I think it was done very well. The colleagues were super skilled. But at the same time it's uh, today it's probably slightly easier, but it requires a lot of people management, not only digital management.

Speaker B: Well this is fantastic. Thank you Ruselin. Because it brings us to our next discussion point and it's some of the operational barriers that are holding us back. Disconnected data, siloed teams, fragmented customer journey. In fact, despite significant investment, many organizations still struggle with disconnected data flows that prevent a unified view of the customer journey. Charlotte, from an enterprise perspective, what are the biggest barriers to a, uh, unified customer data?

Speaker C: Yeah, I think it's a lot of the things that you've just mentioned, right? It's a lot of fragmented systems, it's uh, inconsistent data structures. There's often also an unclear ownership across functions. So who owns that data? Who's responsible for creating that full view of the customer? So it really leads to that inability to act on those customer signals in real time like we were just talking about. Because how are we supposed to be surfacing and understanding that view? Um, and many brands as we talked about, are trying to run AI on top of that disconnected data and we're only seeing fragments of the journey. So then it's impossible to try and create a full consistent view for that client.

Speaker B: And Andres, from your point of view, how can organizations better connect front end engagement with back end operations?

Speaker D: I want to tell a story about the AVI experience. So we have at AVI, we are selling beers, right? So we have two types of sales, the B2B stores and also to B2C to consumers. So the problem we have with consumers is that this is disconnected from the B2B consumers. At the beginning of this project, B2B is pushing a lot of specific brands, skus to the stores, but consumers are not buying that type of products. So how are we going to connect this engagement, these needs from marketing team with the operational is that we understand that the value we have with consumers is the signals that we have that they are uh, the brand loyalty, the specific schedules that you are asking on social media, on our platforms. So this is an input for B2B to plan the operation that we are going to run in specific areas. Understanding this, we also get value of this consumer data to provide the B2B team. So we now put a money value to these signals and we launch an experiment where we are going to provide signals, push SKUs in the specific area and this is going to improve the volume of this SKUs in. In that place. I think that when we have people in both areas that is often disconnected that are willing to provide uh, at the end the company goal to increase the beer volume that we are going to sell is when you are going to reduce the gap we have between the operational teams. But this only when you have two people willing to work at the same goal.

Speaker B: And Diana, is it really about our organizational structure? How should teams be structured differently perhaps to reduce friction across this journey?

Speaker A: Yeah, I mean going back to what both Andreas and Charlotte said, I, I do think that having different owners at different um, kind of parts of the customer journey makes it quite difficult to create a unified customer journey. And probably having a person, probably a C level role which we kind of keep on seeing now appearing like a customer experience officer, sorry, Chief Experience Officer, customer Chief Customer Officer and so on. It's probably pretty fundamental for organizations to start going into, you know, one direction with all of their data because the reality is if you're not a new company, right, that was established in the last few years, you are working off of legacy data most of the time. Even a company of like five years already has legacy data systems by now that are disconnected, fragmented and so on as you build and scale up. So I definitely think that that like unified view needs to come from another organizational C level position that leads across and that's like how is the customer actually experiencing the company? Because the customer doesn't see, oh, right now I'm um, talking to team A and then I'm talking to team B and whatever they are just experiencing the product or the service as a customer on the other side as a whole. So they don't have the concept of the org chart of the company which is something that I think a lot of companies forget because internally I think a lot of conversations we have is like, oh, but this is this team or this is my team or this is whatever team. But the customer doesn't care. Right. Like they're looking at us as a brand and being like, you know, I want better overall. So I definitely think that's important to have this one person that leads the customer experience part.

Speaker B: Now Renko Deanna was looking at the organization but mentioned also legacy systems. And I do have a question about legacy systems. Are legacy systems still the main blocker or is it organizational structure or is it the data? What are your perspectives on where we sit with legacy systems and are they holding us back?

Speaker E: I think legacy systems are holding us back. Especially when you are facing let's say uh, strong MNAs where you have different legacy systems. When you have different profiles and channels of the business, their legacy system can be especially, especially I would say hard to uh, understand at the same time. At the same time I think it's more about people than about only legacy systems because uh, especially with A.I. i think we can overcome many barriers of legacy systems. The data can flow much quicker and much easier. The decisions are quite here one click, two clicks from us. So um, skilled manager can today be much more effective than any time I would think in the past. So I would not hide behind uh, legacy systems or behind wrong processes because at the end processes are done, are done by men. And uh, solutions like AI I think can uh, also led by men, not led by AI I think still led by men can enable us to improve all that decision making process and also the journey.

Speaker B: This is Digital Transformation talk and I am Kevin Crane. I am here with Charlotte Nichols, Diana Petrova, Andres Dela O and Renko Jelika. We are today exploring how organizations are increasingly turning to AI unified data systems and real time intelligence to bridge the gap between engagement and meaningful customer experience. All right folks, I'd like to move us on in the time that we have remaining to our third discussion point today. And that is how AI unified uh, customer data and real time insights can help us deliver more meaningful and scalable customer experience. Look Charlotte, how does unified customer data enable AI, uh driven customer experience at scale? Who's doing it well and what are they doing and what can we learn from it?

Speaker C: Yeah, I think and Ty's really nice on top ranka was just saying the unified data when we have that uh, it gives AI the context that it needs to then be able to scale. So it can then act intelligently and consistently at ah, scale across the entire journey. Without that unified customer data we can only personalize in fragments like I said earlier. So we cannot orchestrate that entire journey. It allows us to then be able to identify the customer signals, suppress irrelevant messages which is often a big bugbear of consumers. Recommend the best next action for a brand. Prioritize your at risk customers when it comes to maybe service and delivery and help you to coordinate that across all channels so you can then move away from what we see, a lot of which is that isolated personalization, a highly personalised campaign, but an isolated campaign to then a connected experience across all of that entire customer journey and deliver that at scale. I think brands that are doing it really well, we've got a, a couple, I think one which I find particular, particularly fun is Gibson, the guitar brand. Uh, those who, who know that well, um, and they knew that in order to be able to get to that level they had to start with unifying that data. They had to try and surface uh, some of those insights that were previously inaccessible to the business. So they can execute that strategy effectively across all of their different regions, across all of the different departments. Um, and the whole business shared that view of the customer and their ideal customer profile as well. So that really allowed them to push themselves forward.

Speaker B: Now Andres, how can AI improve personalization without losing the authenticity that customers expect and can really bring a better customer experience? AI and authenticity, are they two opposite sides of the coin? Do they work together? Tell us more.

Speaker D: Yeah, so I think I'm going to take a little bit of Charlotte to follow up with this. I think that there are two points about AI and how we are going to provide a meaningful experience to consumers or authentic. The first one is what does a meaningful conversation means with AI? So I, I think the first thing that we are struggling with AI is that we are very good at creating a text, a fixed text that is going to live in on Facebook. But we don't know how we are going to redact a uh, conversation that we have no idea how it's going to develop. It's not under control to, to follow the conversation, to say to the consumer, you need to answer this to, for the, to answer that. So we have no control over that. So what does that mean for conversation? Means how is the conversation going to flow? And once that we as a company can define this, the outcome that we are going to get from this conversation, the fall box that we are going to have if the, the human find out that is talking with an AI or they are angry because it's an AI, so what are we going to do with those outcomes? And the second one is we can personalize or implement AI in a lot of experiments, AI and uh, specific parts of the journey. The problem I'm seeing is that AI is not uh, a technology that we are going to implement over the process that we already have and companies are struggling with this. We need to redesign the complete process, it's not over the top. It's really a complete process. And once we are going to scale these MVPs, these experiments to a whole process is where we are going to understand the value that we are going to have as a company. And also we are going to begin to define those new KPIs, new definitions, new interactions. What does meaningful interaction with consumers mean?

Speaker B: And M. Andres, where can I get my biggest bang for my buck? Where do you see AI delivering the most value in customer experience today?

Speaker D: Definitely conversations, conversational. And I think that once you can uh, classify the conversations and the outcomes that you are going to have from this conversation is really helpful to understand the intention the consumer has, the content that they are within and also the outcomes that, that it's going to be easy to channel them to the outcomes you want.

Speaker B: And Diana, what are some of the biggest risks? Uh, can I over automate my customer experience?

Speaker A: Oh, I definitely think you can. And I also think there are quite a lot of people nowadays that do disengage if personalization feels off. So I think there is this kind of golden equilibrium that you need to land your campaigns in because over personalized, over kind of saturated communications do put customers off as well. It also feels a little bit creepy. Um, and then also going back to the data being correct at the start, I think this is also super important because you can also run the risk of over personalizing the wrong things which can then escalate quite a lot. So I do think that where we are in the world at the moment, there is a risk, risk that if you are relying too heavily on AI with systems that are not necessarily clean and organized and orchestrated in the right way, plus trying to be a bit too AI driven, you can put off quite a lot of your customers.

Speaker B: And Diena, I'll ask you the similar question. Where can I find the most success? How do you see brands using AI to create more meaningful customer experiences? What are they doing and how can we do it too?

Speaker A: Um, I think for me, um, a lot of where AI has helped me at least in my role has been pattern recognition and just early warning signs of lead and lag measures of data which I think at least to me seems on an internal level to be the biggest win because large kind of data sets that before would have needed a human or let's say a software, um, to be analyzed and still the output would be quite heavy for the human to read and comprehend. AI can now simplify in quite a meaningful way where it pinpoints you exactly where you need to look again, I'm caveating this with the fact that the data behind it needs to be correct to start with. But I think for me this has been a game changer as to how fast. For example, I can look at forecasts and just model certain things that um, you know, change the outcome for, for the business. So I'm quicker to understand where my threats are, if you want to call it that. So at least from, from my point of view this has been the most AI application.

Speaker B: Enrico, I'm circling this question about where I can find my most value. What signals tell you that customer engagement is actually translating into business value?

Speaker E: What signals, uh, again for the FSCG business, it's a very, very difficult, uh, very difficult question. So I've had uh, businesses and brands that had super interaction in a digital world, but not necessarily, not necessarily the action on the, on the. And vice versa. Uh there are many, many brands that are, that are not uh, uh, capable to do any interaction outside the the shop but still having quite, quite some sales that are generating revenue and also profit. So again there's no magic, magic button for, for even for a business like fncg, uh and especially not for all the world. Probably in a digital world would be some kind of um, interaction and touch points that, that are measurable in a positive way that you can expect, let's say some growth from uh, KPIs you expected. In our world I would say we are still uh, hard combination between physical uh and real uh feedbacks from customers and also campaign management and campaign results from consumers. So it should be a blend with us because we are still. I said before, not to repeat too much uh, too much myself, quite disconnected. What is maybe changing now even for fncg and quite strongly change, um, I see AI quite strong in defining the psychological profile of a shopper, uh, of a consumer. And uh, also with some regional differences it's quite easier to track it. So you could easily know what guy in London expects from uh, street food and what the guy in Ljubljana expects from street food. And what does a coffee experience look like in the center of Ljubljana? And what, what is the difference from a coffee experience? Since Chelsea this was not possible to be done two, three years ago. Now we use it. It's here, uh, you can tailor your design, your product, your message and even the price. So I think there are things that are maybe not so directly monitored uh by a campaign but the uh, utilization of all those disconnected touch points that were before quite independent without any relation. Now I think Brings huge value and we are already there.

Speaker B: Enrico, is that what separates organizations that succeed with AI, uh, driven customer experience and those that do not?

Speaker C: Yeah.

Speaker E: So I don't know the answer, frankly, but I think yes. So, uh, not many companies publish what, what they do, frankly. Uh, at the same time. At the same time I feel this is the difference. I feel this is a difference because, uh, uh, to quote someone, I think S. Mandela or somebody like this, he said a person together with AI now already can join a 300, uh, IQ measure. And uh, I think we are somewhere near that when we do these decisions. I'm not saying 300, but I can certainly say that a professional working in marketing today or product development can elevate its own capabilities through using those experiences from data driven, but also from decision making, pure decision making and profiling a customer. So, uh, I think the organizations are now better if they use it well.

Speaker B: Renko, Andres, Deanna, Charlotte, it has been great speaking with you today. We have reached the action item round of the program. I'm wondering if each of you could provide with us with a quick action item that our viewers can use to take advantage of your ideas and advice. Andre, do you have an action item for us today?

Speaker D: Yes. So take AI Personalization is not going to change the customer experience. It's just to make it faster to deliver. So I will say better define what you actually want, define what customer means for you personalization, and um, just apply tech or process or redesign your whole experience around that.

Speaker B: That is Andres Dela O. Andres, thank you so much for being with us today. Renko, do you have an action item for us?

Speaker E: So I think yes. So first thing is, uh, I would still rely on brand and brand quality in terms of how you define your brand wheel or brand tree, however you want to call it, and what are all the touch points around that brand. And uh, it should be somehow human, led and maybe amplified by an AI at the same time. We are able now to somehow, uh, do a better projection of price of consumer insights. And I would really use that data, uh, combined with human knowledge to, to amplify the result of, of a brand. I would not rely, and this is maybe the message, I would not rely completely on AI driven, uh, results. Uh, every day I read something that comes on my desk, uh, in that term and it's, you don't need a AI detector to see that it, it crafts the results in a very beautiful, uh, way but somehow meaningless in many terms. So I, I still think that combination of a, uh, skilled professional with uh, amplification of AI. It's something that currently we need future. I don't know.

Speaker B: That is Ranko. Jelica. Thank you, Ranko, for being with us today. Deanna, do you have an action item for us?

Speaker A: Yeah, I think it's, it's really important to sometimes step away from like your role and your team and what you're doing in the company and put yourself in the shoes of the consumer. I, I do think that AI would always, again, it would be great to give you some of the data, but it will struggle to necessarily connect on the emotional, uh, part of what the consumer actually wants from your brand. And I think that is very, very important in, in actually bridging that gap between the metrics and the data and the actual customer experience is like, be the customer, experience it, like step away from being the brand and just think, if I was to buy this, if I was the customer, am I happy with what I'm experiencing right now or not? And I think that can also answer a lot of questions that potentially some of the metrics will always struggle because they don't have that qualitative aspect of, you know, the human

Speaker B: that is Diana Petrova. Diana, thank you so much for being with us today. And Charlotte, do you have an action item for us?

Speaker C: Yeah, I think from my side, the discussions that we had a little bit earlier around authenticity, I, uh, think that does come from trying to keep the customer at the center of what you're doing. So to echo some of the peers on the panel as well, AI should help you to remove some of that friction rather than being the chance to get more messages is out of the door. So my m action that I would suggest is try and pick one high value customer journey that you are already operating today. And, but that's onboarding, repeat purchase, renewal, service recovery and try and map what data, what teams, what systems is creating friction for you right now. And then try and look at where you could implement AI to help solve that. Rather than starting with let's just try and apply AI to what we're doing and see where we can, how can we use it more? Try and use it on a specific, solving a challenge that you already see and that will then help you to understand where has it driven value for your business to help with future business cases, trying to connect some of that data across some of your other teams. You've then got a, uh, use case to evidence how it's helped you to improve.

Speaker B: That is Charlotte Nichols with SAP. Charlotte Deanna Andres Renko. It has been great speaking with you today. Thank you so much. What a great panel. Your perspectives and advice are spot on. So thank you so much and I hope that we get a chance to talk again soon. And to everyone joining us today, thank you too. We really appreciate you being with us. Join us again next time for our sister show, uh AI talk. That will be on July 7th with another great panel of guests. We'll be discussing the topic of re engineering bank workflows with AI enabled decision making. That should be another great discussion. In the meantime, if you'd like to connect with me, you can find me on LinkedIn. I'm happy to connect there. Check me out. I'm Kevin Crane and you can check out my weekly audio podcast, the Digital Transformation Podcast. But for now that'll do it for this episode of Digital Transformation Talk. And until next time, I am Kevin Crane saying thanks for watching.

Speaker D: Sam.

Speaker A: Mhm.