Roundtable Discussion: Your Data Is Lying to You – And Your AI Is Making It Worse

 Four CX leaders get brutally honest about why organizations are drowning in customer data but still can't act on it 

In this CX Today Roundtable, Associate Editor Rhys Fisher is joined by Blair Pleasant (COMMfusion), Nick Lygo-Baker (Paradigm CX), Tabitha Dunn (CX Transformation Leader), and Simon Leyland (Cloud Interact) to tackle one of CX's most persistent problems: the insight-to-action gap. 

If you suspect your analytics stack is working harder than your organization is actually using it, this conversation is essential viewing. 

Most CX teams aren't short of data; they're short of the ability to do anything meaningful with it. 

Four industry leaders dissect exactly where the breakdown happens and why AI often accelerates the problem rather than solving it. 

The wallpaper dashboard problem: Simon Leyland describes supervisors cycling through 12+ tabs, making decisions on six-week-old data, and analytics too unusable to trust. 

Measuring the wrong things: Nick Lygo-Baker argues VoC programmes are built around business journeys, not customer goals – collecting feedback that was never going to answer the right questions. 

AI without trust is just faster noise: Blair Pleasant and Tabitha Dunn explore why agents fear AI tools and why skipping root cause analysis means optimizing for the wrong outcomes. 

The metrics that need to go: Tabitha makes the case for retiring NPS and CSAT in favor of measuring tangible, year-over-year experience improvements. 

Next Steps 

Audit your dashboards: are they driving decisions, or have they become wallpaper? 

Ask your frontline team whether they trust the tools they're given. 

Subscribe to CX Today for more roundtable discussions from across the CX space. 

Transcript

SPEAKER_04 0:08

Hello and welcome to CX Today. I'm Reese Fisher, Associate Editor, and today I'm delighted to say that I have been joined by quite a few guests actually. I'm joined by Blair Pleasant, President and Principal Analyst at Confusion, Nick Lygo Baker, founding director at Paradigm CX, Tabitha Den, CX executive and transformation leader, and Simon Leyland, the CEO of Cloud Interact. Thanks so much for joining me today, guys.

SPEAKER_00 0:36

Thanks for having us. You're very welcome. Pleasure to be here.

SPEAKER_04 0:40

Happy to be here. Absolutely. So the reason I've brought together this kind of group of customer service and experience aficionados is to discuss what I think is one of the biggest topics in the space right now. And it's this idea of organizations being perhaps more data rich than they ever have been before, but still being fairly intelligent or poor. You know, despite decades of investment in analytics tooling and the explosion of AI powered insight capabilities, the the gap between data availability and meaningful action, I think remains uh one of the most persistent frustrations in CX. So Nick, I I wanted to start with you, coming at things from perhaps a practitioner angle. In your experience, where does the breakdown most commonly happen? Is it at the data collection stage, the analysis stage, or the point where insight is supposed to reach a decision maker?

SPEAKER_02 1:36

I think it's a bit of all of the above. I I think for me, I know it's easy to say that. The um from a business perspective, the directive that's given the KPIs that are followed is a is a big factor in what people measure in the first place. But I also think people don't measure the right things in the right way. So they will look at say voice of customer, they'll write a questionnaire based on a certain journey that the business wants to understand, that's not necessarily driven by what the customer's trying to do, what the um jobs they're trying to achieve are. So from the outset, they're not necessarily collecting data in the right way. So it doesn't matter how good the analytical tools are, and say they've improved massively. If you've not got the right outlook and you're not listening to the customer both behaviourally and from a voice perspective, it becomes very difficult. And yeah, we we know that cognitively people's recall is very different, so quite often we need to look at behaviourally what people are doing to help form really good understandings of what customers do versus what they're trying to achieve, and that's where I think the breakdown between what we know, what we do, and what we understand then from an action perspective can then be used to um yeah create differences for that customer experience. So it's there's a lot of things in at play in that, and I I I know that it's almost like a mixing desk. You've got to fine-tune different things to get the right outputs, but that's not easy and it's not always done very well.

SPEAKER_04 3:00

Yeah, yeah, I really I like that analogy of the mixing desk. Um, I also wanted to ask you, you know, we've heard that vendors have been promising to kind of close this gap for years, you know, better dashboards, more real-time analytics, AI powered summaries. Why do you think the market hasn't been able to solve it yet?

SPEAKER_00 3:19

Well, the vendors are always further ahead than the market, that's for sure. Um but I I I think there's there are different things going on. So one, so I always relate everything to the Beatles. So um, so there's um George Harrison once saying, if you don't know where you're going, any road will take you there. And I think that's kind of the opposite when it comes to CX. You have to know where you're going and you know what outcomes you're trying to achieve and what you're looking for and what you're trying to do with it. And I think a lot of the practitioners just don't know what they're trying to do with the data. You know, they can collect the data, but then they don't know what they want to do with it and then how to act on it. Um, so there's definitely a governance and an ownership issue also. Um, you know, the data lives in one place, and you know, operations live somewhere else. So getting those groups to work together really takes a cultural shift and an organizational shift. And I think it's hard for organizations to do that. Um, and and the data, a lot of companies just have all this fragmented data, which I I I'm pretty sure we're going to be talking about. But you know, getting the right data sources and getting everything to work together is really a challenge. Um, so so there are lots of disconnects going on, you know, between the data, between uh the people, um, and then you know, trying to figure out what you what your goals are and what you're really trying to do is is the big challenge. So, you know, you can collect as much data as you want, but then you know, really knowing how to take action on it is a different story.

SPEAKER_04 4:47

Yeah, it's always uh always good to hear a Beatles reference. I was trying to think of a pun, but my brain wasn't quite wasn't quite up to it. Um just I guess a follow-up almost to that play. Obviously, we're seeing CDP, CCAS, CRM markets all kind of converging on this problem, each sort of claiming to have the answer. Do you think consolidation actually could be the fix for this data fragmentation, or does it just involve maybe just moving the problem around if that's what you're gonna do?

SPEAKER_00 5:17

Yeah, it's interesting because uh last week at Enterprise Connect, um, Salesforce announced that they've now got a CCAS product. Um they're you know, we always talk about tying in the system of record and the system of engagement. Um, so that's kind of what Salesforce is doing now. Um so we're definitely seeing, you know, like Twilio acquired um, you know, segment to have CDP, you know, Salesforce now with their CCAS. Um so we're definitely seeing a lot of this coming together. Um the question is is it a good thing to have everything in one stack, or is it better to have, you know, kind of different buckets? But I I think we are seeing more and more everything is coming together. And it's easier to take action if if you own the data and if you have the data and again the system of action in the same place. Uh so I think we are going to see more and more of all of all of these things coming together. Um, because it it's simpler for customers, um, it's simpler to take action on the data if if you have the data um all in one place. So I think we are going to be seeing it. And you know, a lot of the CCAS vendors they talk a lot about data lakes and CDP. Um so more and more we are going to see all of this coming together. And I think it's just going to simplify things for the customer.

SPEAKER_04 6:35

Yeah, yeah, that's Salesforce and Oxon feels like the kind of the boldest step yet in that kind of CCAS, CRM conversions that's been growing, I think, over the last few years. I think it is, yeah, like you said.

SPEAKER_00 6:48

Yeah.

unknown 6:49

Yeah.

SPEAKER_00 6:49

And it really has, you know, a lot of the CCAS vendors thinking, you know, well, you know, do we need to be working, you know, more tightly with you know, owning um the CRM and the CRM vendors thinking, do we need to own the CCAS? So I think there's going to be some mergers and acquisitions coming in the next few months based on that.

SPEAKER_04 7:08

Yeah, yeah, absolutely. I guess obviously we've talked about the kind of understanding a little bit why why that gap currently exists, but I suppose it's also important to understand what it's actually costing customers and costing businesses. Uh Simon, I want to ask you, you know, when when you go into a contact center operation, what are perhaps the most common signs uh that the analytics capability exists but isn't perhaps being uh appropriately acted upon? You know, what does that look like on the ground, would you say?

SPEAKER_03 7:38

Uh oh yeah, it's very, very consistent. It's the dashboard that nobody looks at. It's the wall board with the screen with all the great data. Uh that I think anyone could put anything on those wallboards and uh, you know, and and you know, they wouldn't look at it. And then you look down and there's a supervisor, and they have 12 tabs open and they're frantically alt-tabbing, outtabbing, looking for the data. So yeah, the fancy stuff is what is is mostly wallpaper at the moment in most places that you can walk into. Um, and it's uh just because it's been built possibly by somebody else uh and not the operations team. So there's a disjuncture there. Uh, and it's not driving decisions, it's just it's just data for data's sake. Um, and then you've got the second trap, which is so easy to find as well, where you walk into maybe a customer a meeting between the customer experience team and the operations team, and they're talking about data from six weeks ago about oh, well, our average handle time really spiked, you know, six weeks ago. It's like, well, that's that's you that's almost useless. Like you need it now. Uh, you want it, you know, your customers, you know, they don't want to know, oh well, six weeks, you know, six weeks forward, my my issue will get spoken about. And um, and it happens too often. Um and then the third and the most telling is when you actually speak to those frontline supervisors, um, you ask them if you had a good day or a bad day. They know it in their gut. They sit, they tell you in their gut, they don't look at the, they don't alt tab, they go, yeah, it it runs on instinct. Um, and that's probably because and that's not because they're bad at their job at all. It actually means they're really, really good at their job, and the data isn't helping them. Um so uh so the gap isn't really data, it's a distance between the data and the decision makers and how long it takes to travel there. So that's my that's I think you could walk into 99 out of 100 operation centers and see exactly the same thing.

SPEAKER_04 9:36

Yeah, that's really interesting stuff. I like what you said there about data for data's sake. And I know I'm going slightly off topic, but it reminds me a lot of what we're hearing at the minute of AI for AI's sake. And I think it's that same point that uh links back to what Blair said at the start. You have to have kind of a goal or an end point in mind before you implement these data tools or these AI tools, otherwise they're not going to be able to deliver on what they have the potential to deliver, I suppose.

SPEAKER_03 10:02

Yep, 100%. And just one very small example we found in in just a healthcare area, and it looked at sentiment analysis. So they were focused very much on sentiment analysis and almost judging the agent on, and you know, is the agent a good agent or a bad agent bearing on the sentiment? Well, depending on the industry you're in, if that agent is doing a really good job but is giving bad news, then the sentiment's always going to be low. So it's like, well, you can't really judge an agent on that, they're actually doing the good job, you know. So that's a classic example of that misalignment between data and and an actual outcome.

SPEAKER_04 10:39

Yeah, yeah, absolutely. I guess um Tampa, I want to come to you for this. Obviously, so far we've we've talked about this problem kind of primarily from a technological uh perspective, but I think there's also a version that's about culture, you know, teams that don't trust the data, leaders who prefer gut instincts, silos that protect information. Which one do you think of the two is perhaps harder to fix?

SPEAKER_01 11:04

That is really dependent on how the business makes decisions, right? So as a CX leader, my focus is really on ensuring that we drive effective transformation that leads to better customer outcomes, you know, more impactful business outcomes that are measurable, but also a better experience for our employees. And I think that often what happens for a CX leader is they go straight from, you know, they listen to the customer to they expect the business to act. And they're skipping a step in the middle, which is learn. Like you, you actually have to dig deep to understand what is root cause. You have to be able to dig through mountains of dirty data and many different systems. You have to interview the people, like Simon said about like this is their gut instinct. What are they, what are they seeing? What are they hearing? And you have to kind of iterate through that to get to root cause because you might be fixing the wrong problem. And I've seen that so many times where they're going maybe one or two Y's, maybe three Y's deep, and then they're going, oh, that's the problem. And then they go, I don't, I don't know why we didn't fix it. Um it's because they were really looking in the wrong direction and expecting the data to just automatically tell you, or the system to automatically tell you, this is the root cause, come fix me, is not really going to work. Um it's why you need the humans in the system to come and look for that and really dig it out and say, okay, if we change this, then that will affect these customers in a positive way. That'll really help the employees take their processes down for maybe 32 steps to 15 steps, and it will help us be able to have measurable improvement. Like we might deflect more calls or we might be able to improve customer satisfaction or customer retention or customer onboarding. Like you can really get down to exactly what you're trying to improve and what the outcomes are. And a lot of CX leaders really struggle with saying, if if we change this, what's the measurable outcome? But if you get to root cause, you'll know what to measure.

SPEAKER_04 13:01

Yeah, yeah, I think it's a really good point. I know uh human in the loop has become a little bit of a buzzword, but I think as you all learned it there, I think when it's when it's applied correctly, it is still vitally important. I guess on that human level or that human area we're discussing play, I just wanted to come back to you. Do you think there is kind of uh an employee experience to mention here that is being underplayed? You know, if agents don't trust the tools, what happens to adoption and what does that mean for the return on the analytics investment, I guess?

SPEAKER_00 13:34

Ah, you hit on my favorite topic, adoption. Uh adoption and trust are two very, very important things that have become even more important in the world of AI because people aren't trusting AI. And if they're not trusting the tools that they're working with, they're not going to use them. It's a little bit harder in the contact center because you know, workers are given specific tools that they have to use, but they can find ways around it. And um, so if they're not trusting the tools, um then yeah, they're going to be less likely to use it. And you know, instead of using AI, they're going to use, you know, as tabs that you know their gut. Um and you know, maybe not getting, you know, into you know the core of the problem. Um, but you know, these tools are absolutely amazing, but people need to be trained in how to use it and they need to be trained not only in how to use it, but why, you know, what's it going to do to help the agent or the supervisor, you know, how's it going to help them do their jobs better? So once you do that, then you can get the trust and the comfort level. Um, because right now, um, a lot of agents and supervisors are afraid that AI is going to take their jobs. So if you really work with them to help them understand, you know, how it's going to help them and work alongside them and augment what they do, then they're more likely to adopt it and use it. Uh so for me, it's, you know, not just how to use it, but why and how it's, you know, and the what's what's in it for me. So that's how you're going to, you know, get the trust and and get that usage.

SPEAKER_04 15:01

And uh, yeah, I'm so glad you you brought that up, Blair, because I think sometimes people forget to ask the why. I think in that scenario, you know, we look at okay, agents don't trust these tools. There isn't that trust there, but why is that? Well, and not just why, but what can we do to instill that trust, like you talked about, which is vital if we want these tools to work in the way that we think they can work and help these agents, which is I guess the end goal, really. Great. So I suppose so far we've kind of examined the the structural and operational dimensions of uh of the insight to action gap. But I also want to look at kind of a little bit more on that human cost. Uh Nick, I was just wondering, you know, agents are increasingly being given this real-time guidance, um sentiment signals, AI-generated prompts. But if those signals are based on bad or incomplete data, are we creating almost a false sense of intelligence at the front line that could potentially be making service interactions worse?

SPEAKER_02 16:02

There's always a danger of that. I think there's a in the contact center environment, quite often there's policy process guardrails, if you're in financial services or regulated sector, for example, there are things you can say, you can't say, you can't lead customers in certain directions. Uh, all of that is in the back of the mind of the agent whilst they're trying to talk to a customer. All of it is distracting from the customer from being able to listen, sorry, from the agent being able to listen to the customer. And back to Blair's point about trust, you you have to listen to be able to respond effectively rather than listen to be able to answer the question. So you you've we've really got to use the tools to help agents in a way that is not going to intercept them and prevent them from actually doing a basic job in the first place, which is providing that really simplistic dyadic service that is just respond to the customer need, allow the customer to answer, explain what they need, and for you to then diagnose that effectively to then create an action that's going to be meaningful for that customer. The tools are great if they can understand how to get onto par, and by that I mean if a customer's full of energy and the agent's a bit relaxed, then sometimes you need a prompt just to say, look, step it up a little bit, let's get into a point where your interaction is on a level where the customer's going to feel more relaxed and more trusting. Because if that's not on par, then that becomes harder to achieve. What you don't want is a parent and child kind of conversation because that doesn't help either you as an agent or the customer as the person's talking to them. So it's it's a really difficult thing to get right. And I think the more tools we introduce, the more challenges we've got. We've already had examples, I think Simon mentioned so many agencies have so many different tools, and they've all got them on different screens, and they're having to flick between them all to try and navigate. And that is really distracting from the core purpose of an agent in order to respond to that customer, who's more often than not, it's the 4% of people who are trying to actually do something proactive about the problem that they've got. Yeah, the 96% of customers that just don't bother and just fade away and don't return are the ones that you lose. So the the evaluation of the impact of that is quite often unknown in terms of scale. So there's a lot, there's a lot going on there, and I think to to leverage AI isn't possibly in the way that I think it's being described now. We talk about the agentic side of things. AI can allow some self-serve. That takes some load away, it takes away some of the traffic into a contact centre where people want to be able to do things for themselves because people aren't necessarily well, they are lazy, but they're lazy in the fact that they don't want to wait on the call for five minutes, they want to just do something. What's my balance? Okay, I'll go and find that out by logging in and getting the information. But what we do need to do, therefore, is release agents to be able to have those conversations for the much more complex things. And that's where AI can perhaps prompt and pull things through. But if that's not accurate and it's not right, you still need to allow the agent to make a decision on that. So there is that balance between you know allowing the AI to be useful and prompting, but allowing the agent to also have that autonomy. You can't allow that to really be taken away from the agent.

SPEAKER_04 19:20

Yeah, yeah, I think balance is a really good way of putting it. I think again we talked about AI for AI seek earlier, but I think the other big thing I'm hearing is AI. Too many organizations see an AI as this magic bullet, which isn't the case, it needs to be used, it needs to be nurtured, I guess, when it is implemented within an organization. Simon, I wanted to come to you next again to look at things perhaps maybe from a contact center perspective. You know, if your analytics are telling agents one thing, but the customer in front of them is communicating something completely different. How do you train people to maybe uh handle that that tension almost?

SPEAKER_03 19:58

Well, I first of all, I think being an agent in a contact center is one of the hardest jobs I on the planet. I in you know, obviously there's there's harder jobs, but this is a really difficult job. And even before AI, I think there was an I think there was a gap between leadership and agents, and and a lack of in a misalignment of incentives of agents want to provide a really good service to their customers. But sometimes that misaligns with with the uh leadership objective of we just want to get, you know, we want to get our average handle times down and we want to answer only certain questions and not other questions. And now when you're adding AI into that, I think it requires real leadership within a contact center organization to bring those agents along, along with them and say, look, we're not they've just got to be honest because AI will impact the contact center and it could impact people's jobs. It could. It's it's as simple, it is it's it's inevitable, it could be inevitable. Now, in my view, I would keep the same number of agents, but as Nick said, I would let the AI take the simple, you know, the simple questions, the balances, or you know, when does the program open, all that kind of stuff. Things that a lot of people actually don't understand that contact centers take a lot of simple questions like on a daily basis. Like 60, 70% of the questions could just be those simple questions. But the really difficult ones are the complicated and the complex. Uh, and that is where I think AI can really help those agents. Because again, if I'm an agent and I'm on the front line and I've got some, I've got a vulnerable customer who's asking me a quite a sensitive, you know, it's not a standard, uh, you know, it the application opens on day X or whatever, but it's a it's a it's a difficult question. I want I I want that I want that help. And the great thing about where technology can help, and and it's only been available in the last couple of years, is it the the all the right answers are within a contact center. And now by transcribing them and and storing them with and grading those answers, we can actually provide those right answers to the agents and provide the right prompts. But of course, as always, you've got to bring the agents along with you. You've got to explain what that is. And the data has to be right. It can't be, well, I've made this up. Like, and when I say make it up, the difference I mean there is I think we've all been in processes where people sit down for like six months and they do this huge process tree and say, Yeah, it's definitely this and definitely that. And it's like, it definitely and the agents are sat there going, it definitely isn't that. That's not what we answer on a daily basis. So you've got it, you've got to get the right data, you've got to bring the agents along with you, clear out the standard calls, get those, get people happy on those, and then give them the trust to answer the difficult questions.

SPEAKER_04 22:52

Yeah, I think that's a really interesting point, Simon, that we haven't touched on yet. Kind of that really listening to the agents. Do you think that is an issue that we have? Are they are they ignoring too often these areas?

SPEAKER_03 23:04

It's a massive issue. I mean, often agents don't even work for the end customer, right? They're they're a they're a you know, they're in a be they're in a business process outsourcer. And as I say, the in the economic incentives are often set up to say, well, you know, you've got to clear time or you've got to do X, Y, and Z. And I also think, and I say, as AI will inevitably come in and it will inevitably clear out the simpler calls. Again, thinking about it from an agent perspective, I quite like getting simple calls and I quite like answering them because it makes me feel good and I know what the answers are. Uh, but if you take those away, it becomes a much more difficult job. So you've got to bring people with you. You've got to get, and I I I will I will wince at listening to senior managers say, yeah, we're gonna take out you know half of our staff and we're gonna replace it with with a bot, and you know, that's it. That's you know, happy days. I please don't do that. You know, don't think far deeper than that. Because I think this is a real opportunity to really, if anyone thinks that everyone's happy with customer experience at the moment, like customers are really happy with contact centers, then you know, they should be taken out and not not work in this industry. Let's get let's get the real people in who can actually use the this technology development as a real as a real chance to build trust, not just with customers, but agents and supervisors as well.

SPEAKER_01 24:31

I just have to say, Simon, I loved hearing you say that. I just and I think that's really important is if you're gonna shift them to really being great at empathy and deep dive into helping customers with really difficult problems, you have to change what you measure them on too. If you leave them with the same sort of like, oh, average handle time and how many calls they complete for the day, you're gonna drive entirely the wrong behavior. And I think that that's where a lot of leadership really struggles. It's like, oh, we're gonna give them, you know, more time to solve difficult problems and the customers will come away happier with a lot more confidence and trust in this company and the solutions I get from them. Then you want to measure them on how did I increase customer confidence and trust? How well did that work for them? Did I genuinely solve their problem? And that is a way different set of metrics.

SPEAKER_04 25:21

Yeah, just picking up on that point, Tabitha, obviously, I just want to speak to your experience a little bit. You know, you've built CX programs at some huge organizations over the years. I was wondering when analytics does fail to kind of drive action, what does the customer actually experience at the other end? And is that damage visible to the business? Or does it tend to perhaps sometimes go unnoticed or doesn't get picked up until it's too late?

SPEAKER_01 25:46

Uh I think the challenge for um if we're still thinking about the AI space, is that, you know, when you layer agentic AI, it's each path, if it's not shown how it actually fits together in the overall customer experience and how each decision that the AI is making, how that leads to other decisions and who really is the one driving what those outcomes are, um, or rather, which system is, depending if you let all the systems just sort of try and mess together to create that outcome, you forget that the purpose of technology is to make people's lives better, not to just replace them with the technology. That doesn't actually work. And your customers are really going to feel that. And it drives people away when it's an experience that they don't want. So the analytics and the tools can really fail you when it changes what the customer expects the experience to be. So many years ago, um, I ran customer advocacy for Xerox, and it was the printer division. And that was all of the really big, like, it had already been escalated to a manager, it'd already gone to like, and then they were like, please try and figure out how we can make this customer happy. And a lot of the job when I inherited that team was, hey, we're great at solving problems. And I said, that's wonderful. And we have all this analytics and data that says we were great at it. Um, how well do you go back and coach the agents and the managers on how did I solve this problem so I can empower you to solve it next time, if that's possible? And that really closing the loop of really understanding like this is how you get better and you help people get better. It goes back to Blair's point. If there's nothing in it for them from an employee or a leader perspective, they're not going to use the tools and they're not going to give you good data and they're not going to trust the AI. So you really do have to make sure that it is a closed loop. Like this whole process comes back together and helps make their jobs easier as well as make the experience better.

SPEAKER_04 27:53

Yeah, yeah, I couldn't agree more. A perfect way to kind of bring that section to an end, I guess. And I suppose finally, I just had, I suppose, a little bit of a quickfire question uh for you all just to finish off. And I'll go to you first, Play, with this. But if you could remove one specific barrier, you know, one thing that organizations keep tripping over that would immediately close that insight to action gap, what would it be and why?

SPEAKER_00 28:20

Probably fragmented data. Um, you know, having data in so many different places and not being able to act on it, um, and having um inaccurate data, you know, old data, um, you know, the whole thing garbage in, garbage out. Um, all of this is based on data. And if you don't have the right data, clean data, structured data in one place that's easy to access, um, it's just not going to work for the AI or for the human agents.

SPEAKER_04 28:52

Yeah, definitely. Uh Nick, I put the same question to you. I know it was a little bit wordy, I can repeat it if you'd like, but well, it's both.

SPEAKER_02 29:01

Yeah, I think data is really important. I also think um the voice of the customer and the voice of the employee have to be listened to in equal balance. Um we talk all the time in CX about the customer leading, but organizations still build process, they still build tools that are very much around the business and not in favour of the customer, and then leave it for staff to try and fix the gap. Whereas if they would listen to the customer to understand what they're trying to achieve, and then empowered the employees to work out the best way of delivering it, you'd then build systems around that that were enabling people to deliver the right service to the right customer at the right time.

SPEAKER_03 29:40

I would merge the customer experience role and the operations role. I wouldn't allow that role, those two roles to exist uh in in my organization or in an organization that I would be advising, and I would merge them. Uh and I would uh Yeah, I think you'll get a lot more greater outcomes out of that. You get an alignment of both what the customer needs, but also the operation, you know. Ultimately at the end of the day, it's easy to say, oh yeah, we'll give the customer what they want. But we know there's an economic uh there's a there's economics and operations behind that. So yeah, I would merge that role. I wouldn't have the those two roles separate.

SPEAKER_04 30:20

Nice, a nice, a nice bold claim as we come to the end here. Um, Tabitha, do you mind uh just finishing things off again? Same question to you. Sure.

SPEAKER_01 30:28

I agree with everyone's points, and I would say, Simon, you're absolutely right. A good CX leader actually does have in-depth knowledge of the operations and improves the customer, the business, and the employee experience at the same time. I would change what we measure as the goal. Too many companies say, I want to increase customer effort score or customer SAT or I want to increase NPS. Um, and many of those functional areas, both the employees and the leaders, struggle with how am I supposed to move that number? And if the number moves at all, they might get credit for it and they didn't do anything. Or if it goes down, they their good improvements don't get noticed. So instead, I would measure people on tangibly, what are you gonna do differently this year that will improve the customer experience, the business, and your employees' lives. And if you can do that and say, this is the measurably the one thing I'm gonna do this year, you have that continuous flywheel of improvement across the business. And they're working on the right things, and the numbers will go up as an outcome, not telling them you have to move the number. You have to move the experience.

SPEAKER_04 31:33

Correct. I think uh yeah, merging two roles and ripping up some uh traditional metrics is a nice place to leave things here, I think. Um thank you so much for your time, guys. I think it's a really, really interesting chat. Like I said at the front, I think this is a topic, certainly I think over the last 18 months, two years, that is kind of evergreen. It keeps coming back, which shows that you know no one's really been able to crack it yet. So I think that's why these uh these conversations are so important. So yeah, thank you for your time.

SPEAKER_01 32:00

Thank you for having us.

SPEAKER_04 32:01

Cheers, guys. I did also just want to quickly thank our audience as well for tuning in today. If you enjoyed this chat, remember to like and subscribe to the channel. And also remember you can head on over to cxtoday.com for more stories like this. Until next time, thanks for watching.