Tom Morgan: 0:35

It's time for another ACP and I'm joined as ever by Alex. And we're also joined this week by Jonathan from Emite. Jonathan, it's great to have you with us. Long term listeners will remember. We did talk to you a bit during the Amazon Connect inaugural. The independent user group we caught up with you then. But we wanted to come back for a bit more of a deep dive. So welcome,

Alex Baker: 1:00

Welcome Jonathan.

Jonathan Boyd: 1:00

Thank you. It's good to be here.

Tom Morgan: 1:02

Before we get into Emite and analytics and all of that stuff. Should we start with you? Have you always been in contact centers? What's your kind of career history in this place?

Jonathan Boyd: 1:11

Many years, I did work in British telecom and Sky TV and their kind of contact centers, but then I kind of diverted more into I T and I T management. So all of my background is actually service management. So ITSM helping organizations get the best kind of practices for managing technology as it's applied into the business. And through doing that, I met. A number of kind of people on that journey. I ended up writing a book called DCMM, which is basically all about digital capabilities and focusing on a lot of the changes, which was back in what, 2018 where we were focusing a lot of the changes that were coming with what's being now termed as the agent age, which is where AI. Is effectively going off and doing all sorts of things kind of unaided so that that next level of automation and how that rising tide was increasing and effectively creating a different way of working, which was shifting away from the kind of traditional manual work into more of the knowledge work. So a lot of traditional roles are now viewed very, very differently. And that kind of brought me into the whole CX space. So employee experience, customer experience and collaborative experience. And effectively, I started off with he might actually looking after their EMEA consulting team. And they asked if I would like to take over product about three years ago, and we set about a very ambitious program, which we're working in partnership with AWS and one of their partners, DNX in order to bring about the kind of next evolution of our solution and help support our customers and what they're going to need to be able to do. With data for the purposes of CX and improving the delivery of service to their customers.

Alex Baker: 3:05

I was going to ask, it seemed like a good segue into a bit more about exactly what it is that Emite does. So we talked about data and Tom and I have chatted quite a bit about data and reporting and how you might get data from Amazon Connect. We know it produces quite a lot of it. But yeah, what does Emite do? How can you make the most of the data from that kind of source?

Jonathan Boyd: 3:31

Yeah, so look, I mean, Emite is a CX analytics platform. So what we do is we get data from all those different sources, whether it be Amazon Connect or any other contact center solution and bring that into a kind of single, location where the business can self serve reporting. So creating dashboards, KPIs, and calculations that are very specific to their requirements, and then share that across the business. So essentially, you know, Emite does three very simple things. We manage the data. So in terms of ingestion, so getting it from those data sources the ETL process, if you like we then store that centrally and we give the visualization and augmentation layer. So. If there are multiple sources or even something's multiple different APIs bringing in different data from the same source, we're able to reduce the amount of time that customers spend trying to cleanse that and manage it into that one single location. And then, of course, the visualization layer where you can create the right kind of messaging and storyline behind that data in order to effectively cover not just the descriptive. You know, understanding of what happens now, but also with the view of what is likely to happen and when you make the decisions around how you want to change behaviors in organizations by that data, so make decisions, it's also important to be able to track and manage that. So he might is basically that one location to pull in all the data, turn it into something that's useful and valuable to the business, but then also track and manage. Is it working? Is it going in the right direction when you've decided where you want to go?

Tom Morgan: 5:13

How, how much of that is on the user? So I know you sort of said self service, you know, ability for users to create their own reports. I get that, like, you want to be able to service that need of users to say, I want to see this information in a report and have them be able to do that themselves without needing to get IT involved. The flip side of that is that often users don't know what's available to them, right? Do you do anything to kind of, like, bubble up some interesting bits of the data and all that kind of stuff? And is that even possible? In a world where you might not know the data, or is it kind of bounded enough that you do know the type of data you're going to get?

Jonathan Boyd: 5:46

Well, this is a really interesting point because, you know, with many conversations with our customers, there's various different makeups, but typically they fall in line with you've got a BI or an MI team, so management information or business intelligence, and they know everything about the technology. They understand data. They're very technical. They know how to sort out ETL processes, et cetera. And they, they typically work with integrations, with app syncing, with database migrations and coded ETL processes. But what they don't know very well is the nuances of CX data. It's very different to I. T. Type data, which they're very, very versed in. Whereas you have the business side. So these are the CX leaders, the contact center leaders. They don't necessarily know all the data platforms. They don't necessarily know the coding and the ETL stuff, but they do understand the nuances of CX. They know what is noise versus what is useful. And the problem that I see in organizations is typically you've got two kind of sides to this. You've got the business who are saying, What I'd like to do is get more information from the different APIs, or there's new functionality and features, or there's a new solution that we're now using, we want to thread that in, which happens, you know, almost on a daily basis. And then at the other side, you've got those technical people who are like, You know, look, I've got, you know, 30 tickets to get through today. You're one of them. We've got all these coded ETL processes. It's like a spaghetti junction in there. The person who coded some of this actually left six months ago. So I've got to unpick all that, get my way through it, understand what's going on, make the change, then we've got to test it, go through the whole Dev QA testing process so that they don't upset the system. The data lake that they're trying to actually create. So where we sit is right in the middle of that, and it's one of our key differentiators. We're one of, I think, the only solution in the CX space in terms of CX Analytics, who actually allow our customers and indeed our partners not only access to be able to easily create integrations through our iPaaS solution. So very simple wizard driven configuration to be able to integrate, but also to update integration. So you want additional fields. There's maybe a different API. You want to bring all them in together and bring that into the Emite solution. So we make that easier for the technical teams. To be able to do it because they need to know how API's work and pagination. So we make that very simple to manage. And on the other side, what we do is we give a nice UI for creating your own calculations. So adding, subtracting, dividing, multiplying, adding business logic and a very simple UI. That allows them to create those KPIs and then select them in different kinds of reports. Everything in Emite is a bit like Lego bricks. So we give you all different kinds of Lego bricks, which helps you surface the right things the right components to basically get the right data and the right insights. With the ability to be able to drill down or drill through into that data live, it means that the business don't have to go back to MI and say, Great report, but it's showing me X, Y, and Z. Why is that happening? They can actually click through into it and go into, okay, well, what is the underlying data? What's changed? You know, is it something in the IVR that's not quite right? Is it something that's coming through the bot transactions? You know, what is it that's happening? Is it agent behavior? They're able to identify that and then plan a way to resolve it with something that's measurable.

Tom Morgan: 9:34

No, that makes sense.

Alex Baker: 9:35

I think you covered probably one of the other questions I was going to ask, which is, so is it just capturing data from Amazon Connect or your contact center platform? Or can you bring in other, you know, a great use cases, maybe CRM data. So we've had customers recently where there is quite siloed sets of data. You've got the contact center data. which doesn't really talk to or know of the CRM data. And it sounds like you can use Emite as that central data repository for, for all those types of data.

Tom Morgan: 10:07

Okay.

Jonathan Boyd: 10:17

platform. So we have a number of customers on AWS. In fact, one of Europe's largest AWS customers is a customer of ours using. Amazon Connect and for them, limitations in the built in reports were unworkable. You know, they have large teams with a maximum only 50 agents per page trying to manage that flicking backwards and forwards while everything's changing in real time, just didn't make any sense and not being able to get everything they need on a single pane without having to go through I'll use the word again, pain of setting up data lakes and, you know, having several different types of technologies in place to try and create an Amazon Connect data lake where they all have different pricing models. They're all working differently. All of that challenge and difficulty we tend to deal with and the customer usually CRM is the next step. So Salesforce, we got Salesforce. We've put in a custom attribute into our IVR that takes the Salesforce record. And we need to bring that data in, so we'll bring that data in for them. And then they'll move on to, okay, there's lots of ticketing systems or work orchestration that we've got. How do we bring that data in? So we'll bring that in as well. And at some point, you kind of hit the question of, okay, so every month or year end or month end or quarter end, whatever it happens to be, we've got a team of people who have to take spreadsheets from all over the place, databases from different places. We've got to get data from the WFM solutions. We've got data from, you know, the CRM solutions and contact center. And, you know, they spend maybe two or three weeks just trying to build these reports for either reporting to the market or further up the food chain within their own organization. Emite helps to automate all of that. So effectively you do it once. And because the data is live behind that configuration and business rules. then it updates itself continuously from that point forward. Basically anything that is available in a database a CSV or a RESTful API we can connect to.

Tom Morgan: 12:30

would you say he might, this is kind of like a, A fairly basic question, maybe, maybe a silly question, but I'm going to ask anyway, would you say he might is more for kind of quantitative reporting on the performance of the contact center? So things I think like core volumes, average handle times, or is it information for agents in like the knowledge of the organization? If you know what I mean?

Jonathan Boyd: 12:55

I think it's actually an interesting paradigm. So if you think about the, the, the evolution of business you know, over the last few decades you can go back to things like Henry Ford's model. So everybody understands the model T it revolutionized that industry. And of course, you're talking about a time when people were still horse and car. And what he did was he perfected the production line. So the idea of operations at that point was how do we get a whole bunch of resources through a repeatable process and at the end you get a car which is delivering value. And Most organizations, and if you look at any MBAs, this is, this is the model that they teach the industrialized thinking. However, we changed away from that probably about just after the internet became mainstream. Where we entered into what people called the information age and, you know, the data age and we're, we're now moving into the agentic age and it's really how things have evolved. So if you fast forward from Henry Ford's production line to Elon Musk's production line, then what you have. Is you still have a production line happening, so the car is still made. It's still got different stages to move through to get there, but a lot of it's automated using machines. So what we're now doing is we're taking data. From these machines, telemetry, data, you know, metrics and information, not just helping to improve how many, you know, nuts or bolts or wing mirrors or whatever you can do within an hour. But you're actually looking at a much wider set of data spacing. How the. The actual production line is set out in order to improve things. I mean, many people will understand when we talk about the kind of lean and agile processes and Toyota and everything that they did to help evolve that. But you're still only getting 50%. And this is where I think talking about knowledge workers and how to understand that is vitally important because if you take the Tesla, now Tesla is open source. So you can go and download the blueprints. You could build one yourself if you wanted to. So therefore the value is not necessarily in having the car. The value is the fact that that car generates about 10 gigs of data every few minutes. That's a huge amount of data that's being tracked and managed. And I remember reading a story about a guy who was analyzing his Wi Fi traffic and realized that his new smart washing machine was generating three gigs of data a week. What on earth is a washing machine doing creating that amount of information and this is where it comes back to Qualitative cannot be forgotten So the time that we take that call from when it originates through the IVR to an agent to successful closure Like the production line is still important. We still need to make sure that that happens effectively. And you know, we need to make sure that the service that the customer delivers is what they expect. The challenge you've got with it is, the whole idea of customer experience is highly individual. So if that customer happens to see another service, which they feel is comparable To the service that you deliver, but they see some new technical gadget, whether it's the had a good experience with a ball or, you know, whatever. Then their perception of the experience from your business. will have changed even though you haven't done anything different.

Tom Morgan: 16:46

Silence.

Jonathan Boyd: 16:50

missing a trick. So it also means that the other side of that coin is more qualitative understanding of the customers, how they're using the service, and in order to be able to Maximize that. Your biggest asset is the contact center. It's the people who are daily discussing things with customers, whether that be to, you know, buy or amend airline tickets, or, you know, talking about service, asking questions about the services and products that you offer. All of that is vitally important. And there's been a change there. As I think when we've spoken before, you guys pointed out very well is, you know, contact center agents are no longer just about transacting. They aren't there sticking wind mirrors on anymore. What they're doing is they're actually conveying information and knowledge to the customer in order to help facilitate that customers. Need or requirement, which means they are knowledge workers. So they have to understand what's going on, not only from the customer side, but also internally to the business, which must be changing regularly because the market is changing. Customer behaviors are changing. Competitive landscape is changing. So all of these things that are happening means that the agents need to be empowered effectively with knowledge. In order to be able to do their job the most effectively and give customers the best experience, which is why I always refer to them now as knowledge workers.

Tom Morgan: 18:23

So they're almost brand ambassadors at this point, and for many people, they are the first, maybe the only human. Interaction you'll have with that organization.

Jonathan Boyd: 18:32

Exactly right. I mean, our customers, you know, they'll spend more time using our solution itself and probably speaking to our support people than they will speaking to anybody else in our business. So those are two massive areas. I mean, I remember speaking to one of our customers in the car rental business, and I said, where do you think, You know, customers spend the most time with you or interacting with your brand. Is it when they're on a website looking for a deal to hire the car? Is it when they're standing in the line waiting to get their keys and, you know, sign the documents? Is it when they're maybe even phoning up to ask questions or to book over the phone or is it when they're in the car driving to where they want to go?

Tom Morgan: 19:15

Yeah, it's in the car, right, I guess. Okay. Okay.

Jonathan Boyd: 19:23

smart cars, all these car hire companies are going to be changing their vehicles to smart cars over the next few years as governments, you know, start to look to effectively reduce or restrict the sale of diesel and petrol vehicles. So if you imagine a fleet of, you know, many thousands of these vehicles, all generating diesel Around 10 gigs of data every few minutes. That's a significant scale of data that organizations are going to have to get used to. And it's not just car hire, it's every walk of life. Whether it's your mobile phone agreeing to Share health data. You know, we all got Fitbits. We all keep tracking our steps, etc. All of this information, especially in the U. S., is very helpful to health insurance companies because it allows them to give a much more accurate quote. and to reduce the cost that they spend, you know, fortunes on actuaries as all insurance companies do to get a very accurate and precise understanding. That's going to help them be more accurate. It's going to help bring down quotes for the majority of people. And, you know, we've seen it actually in the UK as well, when our, teenage sons and daughters first learned to drive, mine's actually going through that process at the moment, they can now take a black box and the insurance company will give them a cheaper insurance because they are sharing data. which helps them provide a more accurate understanding of the risk associated with that individual. And as you start to think about this, more and more IoT that comes out and comes to mind is more and more data points. So something needs to be able to bring that together to make it useful, because no human is going to read that amount of data, make a good decision based on it within the time that it's actually live and

Tom Morgan: 21:10

Yeah. I mean, I, I'd love to think all of this coming just on your automotive example, all of this data that going into electric cars or coming out of electric cars I'd love to think it is going to result in a better experience for us and, and, you know, a more personal experience or whatever. I'm also a bit worried it, it will result in more targeted advertising, but that's just, that's just the rest of the industry, I guess. This is quite, yeah, it's, This is a good segue into AI actually, because none of this, looking at this data would not be possible without some, AI, I don't think. And so I think it'll be interesting to see, you know, how that pans out for three might, what's your experience been like with AI? Like, are you using it? Where are you in that journey?

Jonathan Boyd: 21:52

So I think we, we've been quite cautious with AI. And I always tell the kind of story about a washing machine that I, that I used to have. And it was the first one I ever had with direct drive, no belt. It was brilliant, lasted ages. And it had this great feature where you could kind of set it and it would weigh. The, the close and set the time. And eventually it did die and I had to go buy another one. And they had this great new model. Which had the direct drive that I really liked. And it had this really cool new AI function. And what it did was, it weighed the close and it adjusted the

Tom Morgan: 22:28

Mm hmm.

Jonathan Boyd: 22:29

Exactly the same as the last one, but now it's got a I slapped on front of it. And when you think about, you know, what most organizations actually call the eye, you're talking about decision

Tom Morgan: 22:39

Mm hmm.

Jonathan Boyd: 22:40

Have AI slapped all over it. But the reality is it's very, very different, and I think we're actually starting to realize that all this kind of AI gimmickry is not, it's not really helping. It's great to look at, it's fun to play with but it's not really making any great changes. What is making great changes is providing people access. To that technology to go create ideas to come up with things to use it in new and innovative ways. There was quite an interesting one. I read just recently where the guy had managed to set something up with AI to create music, basically get it onto like Spotify or something. And then use a whole load of bots to effectively stream it. So you'd created almost like a circular economy to create a whole load of money, which was I don't think it was within the terms and conditions we'll see. But you know, it's a genius way that humans will find to use this technology. That's where the innovation is coming from. So, you know, I use a I use GPT like many people because it's it helps me get things done quicker. And that's fine. So where we are with Emite is we're trying to take those best bits and apply that inside our application. So I'm not. Looking to give people a predictive KPI and say, we've got a I in our tool. What I'm looking at is how do I make things like Amazon Bedrock available to customers in a way that is simple and easy for the purposes that we have in CX to basically create a custom. Predictive KPI. How can I help them use their data without having to go a huge process of learning the AI models to be able to provide predictive analytics? And the other area that we're looking at is also about NLP. So, you know, can we converse with a solution that is smarter in terms of coming up with the right calculations, the right mathematics and being able to help us get the best out of our data without being overly prescriptive. Because if I prescribe something, a feature or function, then I'm making the assumption that one size fits all. And it

Alex Baker: 24:58

That seems like a great application of it within, within an MIBI tool. So to give someone that doesn't have a, like a data science, any experience, the ability Dive deeper into the, into the data just by interacting with a bot or something just to tell me the, tell me the key metrics over the last month that I should be interested in as a, as a contact center manager, that kind

Jonathan Boyd: 25:25

Yep. Exactly.

Tom Morgan: 25:27

Okay.

Jonathan Boyd: 25:32

likely to hit production probably around April next year where we'll be able to actually make use of these technologies. But again, in a user friendly way. The whole point exactly as you're saying is how do I open that black box of data science

Tom Morgan: 25:49

Okay.

Jonathan Boyd: 25:49

So that I can get rid of the bottlenecks and allow them, enable them to be able to do what they need to

Tom Morgan: 25:55

It's almost treating it like another source of data, isn't it? It's, it's, it's no different from some of the other data sets that it needs processing. It needs analyzing. It needs presenting, I suppose. Okay.

Jonathan Boyd: 26:17

before, was creating more productivity in the manual worker. Improving that production line. All the statistics were about how to basically do more widgets in shorter time at lower cost. Whereas in this era, what we're starting to see is that management's greatest value is how to actually improve the productivity of the knowledge worker. How do you ensure that they are empowered to be able to do what they need to do better, faster, easier?

Tom Morgan: 26:50

Yeah. Silence.

Alex Baker: 27:01

which you were kind enough to share. Yeah, with us. When we, when we spoke to you before it, can we introduce the concept of, of social capital? And can you tell us what you mean by that? Cause I think that's sort of featured or the, you know, the, the subject of the blog that's that you're going to be releasing soon. Thanks

Jonathan Boyd: 27:21

knowledge workers. So if we accept that people are now knowledge workers, the manual labor aspect is probably moving out or has moved out of contact center work. Social capital is effectively the science of measuring how knowledge flows about your organization. And it was fascinating because as we all remember you know, the whole COVID thing. So while COVID was, was terrible and the impact that it had on business, on people one of the interesting points that I saw, especially coming from an IT background was how reluctant businesses were to implement Things like video calling, like we're using just now for, you know, to record the podcast collaboration tools. It was all very much centered around the office

Tom Morgan: 28:10

Mm. Okay.

Jonathan Boyd: 28:28

and get these tools. And now we need them to function as a business and was huge. And what we're basically making the assumption is that people are using collaboration tools to share knowledge. So. If you take that in a CX context what you're hoping that your, your teams are doing is sharing the right information with the right people at the right time. In order to be able to answer questions, you know, track the latest trends in customer requirements and business process or any changes that have happened there, technology changes in the organization which kind of ties into EX as well. We want to make sure that when we put a new tool in place, it actually makes life easier. better, not just adds another load of check boxes that they need to go through. All of this is knowledge moving around your organization. So if you can't harness that and make sure that that is filtering through to the right people. then you will never be able to truly capitalize on CX. You'll never truly be able to give that personalized experience with, data that is up to date and things are moving in real time. It's all very, very fast. So you've got to be able to ensure that there is a flow of information. So social capital is looking at that and saying, you You know, are these tools working? Are they delivering the right information? The interesting thing is a friend of mine who focuses very much on social capital working with the likes of Edinburgh University, etc. He, he showed some examples of organisations where when you map all this out you actually start to see pockets of information in your organisation. You know, you'll, you'll see things like finance and where it connects to in the other parts of your organization, where it doesn't connect to, where that information doesn't filter through. So we, we kind of make the assumption that if the tools and the databases are talking to each other, then people know. But that's just not the case, because of so much information happening constantly. What you tend to find is that they need to know what to ignore. And that's where things like social capital and actually measuring, you know, where are these pockets of, I like to call it the siloed mentality, not necessarily silos in terms of just data, but the silo mentality, where You know, there's a breakdown in that communication and how can we improve the use of those collaboration tools to be able to deliver better services to our customers and to do it in the most operationally efficient way?

Tom Morgan: 31:03

And I know you've written this up as a blog post, And we'll put a link to that in the description of the show as well. We could talk all day. We're already five minutes over. But this was really, really interesting. And so we should bring this episode to an end. Thank you very much, Jonathan. It's been really, really interesting. Thank you, Alex. And thanks. Everyone for listening be sure to subscribe in your favorite podcast player. That way you won't miss it whilst you're there. We'd love it. If you would rate and review us and as a new podcast, if you have colleagues that you think would benefit from this content, please let them know to find out more about how cloud interact can help you on your contact center journey, visit cloud interact. io we're wrapping this call up now, and we'll connect with you next time.