How TheyDo And PwC Are Rethinking Customer Experience At Scale

[00:00:03] Every business says it wants to be customer-centric, but the harder part is turning those ambitions into something real when data is scattered, fragmented, teams are working in silos, and insights often arrive longer after the moment to act as already passed. Well, in today's conversation, I want to look at how organisations can finally start to move beyond collecting customer data and start using it to make better decisions.

[00:00:33] Reduce waste and get back to creating experiences that actually improve business outcomes. Make a measurable difference. And I think this is somewhat of a timely discussion, much needed too, especially as AI promises speed and clarity. But only if the foundations and the data underneath are strong enough. Joining me today is the co-founder of a company called TheyDo.

[00:01:01] And they've been setting off my tech spidey sensors for all the right reasons. But enough scene setting for me. Let me introduce you to him now. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do? Yes, my name is Jochem van der Veer. I'm one of the co-founders at TheyDo. I'm currently the CEO. I have a background in design.

[00:01:24] And we have been trying to solve one of the fundamental problems in many enterprise organisations, which is everyone says they want to be customer centric. And customer experience is the strategy. But in terms of execution, it all falls down. Data is all over the place. And it's really hard to get a view on experience.

[00:01:45] So what we are doing is we're building a context layer for all the teams that need the experience intelligence for their decision making, but are currently relying on customer experience teams to provide them with that data, which is incredibly slow and time consuming. And we're helping experienced teams, conversely, to actually be able to share what they have learned about the experience to make impact and influence decisions. And data is so important.

[00:02:11] Every tech conference I go to now seem to say the phrase, no data, no AI. Those days of fragmented data, siloed data is just no good anymore. And many organisations invest heavily in customer experience, but they still struggle to translate that investment into measurable business outcomes. And by that, I mean things like revenue, retention or brand impact. All things that seem to have been failing in many circles in those expensive AI projects.

[00:02:39] So I've got to ask, why do you think that gap still exists? Yeah, what are you investing in is the right question, right? And if you think like CX is a department, then that is obviously maybe something to reconsider. Because what you're investing in is your for-profit business and even the non-profit or the government is investing in like the end users, the citizen, the people that are consuming their services.

[00:03:02] So when you think about the focus on the customer who paid the bills to ultimately generate the revenue, that's what every business does. So therefore it's a non-starter. But when you're investing in customer experience, what people typically mean is that their internal teams are siloed, their data is siloed. And therefore the decisions they're making, the items they put on the roadmap, the policies they implement make so much sense from their perspective.

[00:03:29] But not necessarily from the customer's perspective. And sometimes it's like two or three teams even working against each other, making the customer experience fragmented, not on purpose, but by accident. And I think like when we're saying invest in customer experience, it means often like untangling that mess in large companies to really rally the teams together and say, let's together move the needle for our customer.

[00:03:56] Let's align on what the customer experience actually is and take coherent action together. And that's what they mean with investing in it. And one of the things that put you guys on my radar was They Do research, which suggested that fragmented systems and disconnected insights still remain a major barrier. But I suppose in many circles, people will think, well, that's no big surprise. But what does that fragmentation actually look like inside a large enterprise?

[00:04:25] And how does it hold back decision making? Because we hear this story a lot, but maybe people think it's something that happens to someone else or another enterprise or their competitors. But how do you know when you've got the problem? What does it look like? Well, it's like, are we actually using that data to inform our decisions or did we invest heavily in teams that are actually like reporting on that? Like you would be surprised, but about 80% of customer experience teams are backward looking.

[00:04:54] So their job is to collect the data, analyze it and report on it. That's it. That's about it. And then executives, product leaders, people in the business operations, P&L owners, they will get those insights. I mean, they travel sometimes even up to the board and it's like, okay, MPS is up or down. Here's what the data says. And then they're like, yeah, but we have a different strategy or like we're working on it.

[00:05:18] And there's no connection between what the data is telling the company where our problem is versus like what is actually acted on. So that's what happens in reality. Like in many organizations, that is a blind spot because it sounds like we're doing a great job. We're doing our job. But if the job is to just collect the data, analyze it and report on it. So there are business insights, but they're not acted on. Then what is the function about? It's just a cost. And I think that's the reality of it.

[00:05:48] And it's more like a hidden tax in a business than that. It's actually like very clear that the cost is real because the insights are there. And in some organizations, they've been democratized even. But if they're not acted on, they're worthless. And we do hear about the bad side of this and not enough about what happens when you get things right. And I've got to shine a light on they do here because PwC has incorporated your platform into its CX practice. So huge congrats there.

[00:06:16] But from your perspective, what does combine strategic CX frameworks like PwC's growth through experience model with journey management technology? What does all this enable organizations to do differently? What's the ROI there? So the ROI is expressed almost like as a capability. What I really like how PwC has framed it is like their growth through experience framework is basically decoding experience. And that's what we're all about, right?

[00:06:45] And experience is subjective. But if you have a structured approach to understanding it, relating to it, you can actually be forward looking to it and actually rally your strategy and your execution against it. Now, in practice, that means they have four dimensions, how they think about specific journeys. And they have 26 attributes that they have decoded experience on.

[00:07:11] And together, they can understand like, okay, if there's a crisis journey happening and the attribute that we are like serving as a company is X, is like confidence. How do those two relate to how we then build the actual experience for people? Whether that's like, what did they do with our technology? How do our people engage with you at this certain moment in that crisis journey? And that gives the business a lens to say, okay, we want to be customer centric. We have our growth goals.

[00:07:41] We have our cost saving targets for the year. But then the mandate for the product teams, for the experience teams, for the service teams, usually is fuzzy. It's like, okay, just build whatever you want. But if you put that lens on it, it operationalizes the data you already have. It operationalizes the strategy. And it gives you a clear window on like, okay, what should we do next and where? And that starts with, and that's what they are using data for in their practice, discovering what we already know.

[00:08:06] What the data we already have says about how we as a business show up to our customers, what type of journeys we have, what type of attributes that we're actually seeing in the experiences we're delivering. And are they in line with what we say we need to do to move the needle for the business, to generate more revenue or to keep our customers longer and more engaged, right? And they have built a beautiful way. And then the capability building internally is where the rubber meets the road. It's not a one-off thing. It's like, okay, and here's the inside. No.

[00:08:35] It is for the teams to realize to be experience-led as an organization means that we're using a framework like that to help all the different teams unify their decision-making and take coherent action. And that's the difference between, okay, let's do a project here and let's do a project there and this team, yeah, we'll leave them. They have a technical roadmap anyway. Versus like, no, no, no. Experience is the greater good. How do we align our teams?

[00:09:04] What is the language we use to align execution with strategy? And that's a big gap that we see in the market in general, and they're filling it. And I think if we look back over the last three years, we've all seen AI accelerate the volume of CX data being generated. But now the focus seems to be shifting from just collecting insights to acting on them. So what practical steps can enterprise take to move from dashboards?

[00:09:31] Nobody wants another dashboard and another report to real operational decisions. So what used to be very hard is that, I'll give you an example. So any contact center executive will notice, right? And every enterprise has a contact center. So this will be a very easy problem to relate to. We know there's problems that are reoccurring. And what happens is I'm calling because I have this problem.

[00:09:56] And the agent or the AI today, like whether it's a human agent or an AI agent, they resolve it. Okay, great. But the root cause of the problem still exists. Before AI, we weren't able to quantify this. We would maybe know how much volume we got on a certain problem, but that was about it. Now that we can layer it on with the broader feedback we're getting from the different customers that are not calling us,

[00:10:22] with the data that we're seeing in our systems, with the CRM notes we're having, with the surveys we're sending. Before that, it was too much, too many for even the biggest research or insights teams to collect all of that and analyze it. Now we can. So for the first time, we can say, okay, so this problem that we know is a problem in the call center because people call a lot about this particular issue. But now we can quantify it and enrich it with the other data we have and say, okay, if the contact center is getting this problem,

[00:10:52] considering all the other opportunities that we're seeing, this one might actually help us to reduce our costs with like thousands of calls a month, which equates to millions of dollars saved a year. That's worth more than focusing on this other opportunity, which is a generative new value prop that we wanted to deliver. And that balance used to be very murky. But now with AI, it becomes easy to say, considering all the data, what is actually the most important to focus on?

[00:11:19] And that gives a new lens to data analysis that we didn't have before, especially when it comes to the unstructured qualitative inputs that we are gathering from our customers. And you've spoken in the past around experience intelligence as the almost new frontier for the enterprise. But how does that differ from traditional customer analytics or journey mapping approaches?

[00:11:45] So for anyone listening that are familiar with that legacy model and interested in what experience intelligence looks like, where that's taking us to tell me more about that and how you see this evolving. So it boils down back to the original question, like a business doesn't exist if it doesn't have a customer. Okay, so the customer is having experience, whether you agree with it or not, they are having that experience. It's their experience. So you need to quantify it, you need to decode it, you need to understand it. Experience intelligence is the manifestation of it.

[00:12:14] So if you are able to understand the experiential, what the customer is having frustrated, they have like a massive gain, they're like very positive, that it doesn't matter. That's their experience. But then you need to see that materialize in your metrics. Like, is that customer staying longer? Are they churning? Are they leaving? Are they easier to upsell? Are they ready to buy the second product of you, right? So there's all these nuance that experience ultimately brings action into the business, either positive or negative.

[00:12:45] So experience is the whole. It's not just the customer experience. It's also how that manifests in the business metrics. So experience intelligence is understanding if I'm responsible for this KPI, that I can relate to like why this KPI is up or down. What is underneath that? And the answer to are we doing something about it? Or what should we do something about it, right?

[00:13:07] So experience intelligence is nothing more than having that full picture based on the business goal you're having, the project you're running, the KPI you're responsible for, the OKR you're driving. To really have the wisdom to understand like given all the inputs, here's the next course of action. And in our fragmented tool landscape, it used to be like, okay, we analyze our surveys and MPSSX. We analyze our click pods and we have like journey analytics from our digital services. And they say why?

[00:13:35] Our product teams are looking at engagement and they say, so it's all like everyone has a different picture. But experience intelligence is saying, I mean, if you achieve it, that basically means like we're structuring all that data in the context of the customer experience, which gives us an intelligent layer on top of that that says, okay.

[00:13:55] And therefore, now we can explain the root cause or the actual impact on the business and decide what action to take or take no action because there isn't a problem because now we're seeing the whole. And you're in quite a unique position here because you're working with global enterprises across multiple industries.

[00:14:14] And I'm curious from what you're seeing there, where do you see the biggest gap today between executive ambition around all things CX and combined with the promises of AI and big tech companies and what's actually happening operationally inside organizations? What are you seeing and hearing here? So the story is easy, right? We're customer-centric. We're customer-centric. We're customer-obsessed. We're experience-led. And then the strategy is then trickled down.

[00:14:43] So that is nothing new. Then we go into understand like one layer below the surface, like what is actually happening on the ground. And then you can't expect the organization to magically change with a new strategy. So that requires a different way of working, a different way of aligning, a different way of goal setting and taking action. So that's an organization design problem. And that's nothing that is accelerated, decelerated by AI.

[00:15:11] But what is actually happening is that the assumption is, oh, AI can do everything, right? It's like it's very easy to just prompt your favorite LLM with a question, give it some context, and out comes the answer. In an enterprise organization, it's much more complex and nuanced. There's always like 20 different sides of a specific problem to take into account. And that brings it back to the data has to be brought together.

[00:15:39] And what I think is the misconception that I'm seeing between leadership thinking like, okay, we can operationalize this strategy and AI will solve a lot of problems to like the reality on the ground. It's incredibly difficult. Like I haven't seen any large enterprise even collect their feedback and structure their data warehouse in a similar way. Like all of them do it slightly differently. It's a similar direction, but everyone has nuances.

[00:16:06] So when you're building technology to solve that problem, like almost every customer has a slight different approach to doing it and bring that together specific to them. And then the question is, if your problem sits in the KYC process or if your problem sits in the onboarding stage of the customer experience, that also comes with all this nuance. And that's why even though the technology is here, that if you would have perfect data structures, the answers would come out and the priorities would be straightened out.

[00:16:35] In an organization that has 50,000, 60,000 employees, it's so much more nuanced. And therefore adoption is much slower than people would like. And it's not a result of technology not being able to. It's just the nature of change. It takes some time to materialize. The larger the company and the more human beings are in that company.

[00:16:56] And I think there's also an increasing focus on the return on investment of every tech project now, in particular AI, after a few missteps in recent years. So if we look ahead, though, how do you think organizations will know that their CX strategy is truly delivering sustainable ROI rather than just simply adding another layer of tools, another dashboard of reporting to an already complex environment because we kind of need less noise, more insights. But how do you see all this evolving?

[00:17:26] Yeah, the simplest answer is to actually start with something that you can control yourself, right? On the customer experience side, it's so hard to wrap your head around it in this omni-channel, AI-centric customer experience world that we are in today. But what you can control is, let's say, take your cost base, right?

[00:17:48] So if you know what it costs to identify a root cause, analyze a problem, get the teams to build capabilities and use that time on the roadmap, work with your partners, you can say for a given quarter, innovation costs us X. The question is, what is the problem you're solving? Are you crystal clear on what it actually is?

[00:18:12] And in many organizations, like I can give an example of one of our customers, Lufthansa, they saw a problem in baggage booking where baggage booking was down for the ancillary team who's responsible for any upsell after like a person has booked their flight. That was a big problem and they wanted to solve it. Until someone said, hey, let's look at the broader booking experience and that they could see upstream in that experience that another team earlier in the customer journey had launched a promotion.

[00:18:42] That also happened to include an additional bag. So the baggage booking was not a problem. It just moved in another team and the KPI was in that team down, but in the other team it was up. So overall for Lufthansa, no problem. For the team a problem. And this is manifesting in many organizations in many different ways, but the R&D waste is huge if those teams are not able to connect on the same level around the customer experience. So that's something you can control and that's real.

[00:19:09] So if you're able to use AI to bring all that data together much faster than humans would actually do, that's where a real ROI can be made because deciding against the course of action in their case can save millions of wasted dollars on a problem that wasn't necessary to solve in the first place. So if you truly want to see ROI for your technology on AI, focus on your cost base.

[00:19:34] Where are your teams taxed by all this misalignment and R&D waste because they simply aren't connecting the dots so easily? And you're someone that's working right in the heart of this space, speaking with businesses all around the world. And I suspect like everybody else, if you've got an area of expertise, when you find yourself doom scrolling on the Internet, you'll come across myths, misconceptions, even untruths.

[00:20:01] Are there any of those things that you see out there repeatedly that we can lay to rest today that frustrate you? Any big myths or misconceptions around this technology? Yeah, look, you see the headlines. CX is dead, right? That one. I mean, it's a very good question, Neil. I mean, I always cringe a bit when I see that. Everything is dead. Everything is changing. So everything is alive at the same time. So it's just like, ah, just another pundit trying to make a point.

[00:20:30] But the underlying thing is often true, right? There is something there that we should be talking about. But it's more like if I walk into a room of like customer experience executives, you can clearly see a shift. And those group of people are forward thinking. And those ones belong to like the backward looking report experience professionals, right? And I think like for those listening, you know in which camp you are or where your organization focuses on.

[00:21:00] I wouldn't say CX is dead. But I would definitely say CX is more like a capability today than it is a department. So when we think about what is actually happening is that customer experience can now finally become part of the way of working of an organization instead of like, oh, we'll outsource customer experience, which is basically like why we exist in the first place, to a department. And I think that's a realization.

[00:21:28] And the teams that are in customer experience that are modern, forward looking, trying to do this, they will find a new home in the organization. They will be much more close to where the business action is. They will find themselves embedded in the teams. They'll be leading strategy rather than reporting on it. And I think like that's a very valuable thing to acknowledge. But it's messy for people that are in there. And if you read the headline CX is dead and you think, oh, I am dead. Yeah, then you're probably in the wrong organization.

[00:21:56] Yeah, I would say any article that finishes with is dead. Avoid it. It's just there for clicks alone and to trigger us all. So avoid those. But no, I completely agree. So many great points there. And I will add links to They Do's research that we referenced earlier about fragmented systems and disconnected insights remaining a major barrier. I'll also include a link to the work you're doing with PwC. But for anything else there, if people want to connect with you, your team, find out more about They Do. Anywhere else you'd like me to point, everyone?

[00:22:26] Yeah, you can find me on LinkedIn. I'm very active there sharing my thoughts and trying to be engaged with the community. So that's a great place to find me. Awesome. And you don't post any stories about CX being dead on LinkedIn, right? There's nothing like that there. It's tempting, but I won't. Well, I will have links to everything. But more than anything, just thank you for taking the time to shine a light on this and bust in a few myths and misconceptions along the way. Thanks for joining me today. Yeah, pleasure to be here. Thank you for having me.

[00:22:57] What I really enjoyed about this conversation is that it cut through a lot of the noise that we see in our timelines and news feeds. And there is a big difference between knowing what customers are experiencing and actually doing something useful with that knowledge. So I hope today's interview offered a clearer view of how experience, data and decision making are all becoming far more connected.

[00:23:22] And maybe most importantly, why customer experience should be seen less as a department and more as a business capability. One that touches everything. And no matter what those articles tell you, customer experience is far from dead. But over to you, techtalksnetwork.com. Let me know your thoughts, any takeaways from today's conversation or anything you would like to add to it. I want to hear from you.

[00:23:49] Lots of ways you can get hold of me there at techtalksnetwork.com. Other than that, I'll be back again tomorrow with another guest. Speak to you then. Bye for now.