PegaWorld iNspire 2023: Areteans & Optus - Next Best Action & Value Realization: Driving Customer Engagement & ROI

In today's competitive marketplace, large organisation's need to optimise their customer engagement strategies to maximise their Return On Investment. Next Best Action (NBA) is a decisioning approach that helps organizations determine the most appropriate action to take with a customer or prospect at a particular moment in time. To ensure the success of an NBA strategy, a value realization model is needed to measure its effectiveness and ensure that the organization is achieving its desired outcomes.

Join us for this breakout session hosted by Areteans. We'll explore real-world examples of how NBA and value realization models have been successfully implemented, best practices for designing and implementing an effective NBA strategy, and tips for overcoming common challenges. You'll come away with a better understanding of how NBA and value realization can help your organization drive customer engagement and ROI, with our experience of having done it for our customers.

Transcript:

- Today what I'm going to do is I will be inviting our panelists over here for this session, and this will be going to be an amazing one, right? And the reason I believe is, first of all, congratulations to you, you have decided to be part of this session. That means, you know, CDH is an important, you know, decision for your organization, you want to make it successful. And that's what you are going to hear from our panelists as they'll be sharing about the successes that they have seen at Optus by the NBA as well as value relations strategy itself, right? So let me invite these speakers here. This session is hosted by Areteans, a pure play Pega company, global Elite, one-to-one customer engagement specialist partner, and the recipient of, you know, the Market Maker Award yesterday. So, that's there. So with that, I would like to invite Manjula, Mark, and Ritwik on the stage, please.

- Thank you, Anil, thank you for the introduction. So, good afternoon everyone. I know it's that two o'clock time where lunch is settling in, jet lag is probably kicking in a little bit. There's a couple of us that have flown from pretty far to be here, and we definitely know what jet lag is. So, you know, today we do have, it's a privileged session in terms of, you know, what we're going to talk about with two very esteemed panelists and speakers here today. What I want them to do and share with us in this session is give us some very powerful insights in terms of next best action. So what we hope to share with you today is how you can actually maximize the value at every stage of the customer life cycle. So whether you're looking at it from a marketing perspective, acquisition, retention, cross sell, any of those sort of things, but also service. So in preparation for the session today, I did a little bit of research, but there's one paper that caught my eye, it was a paper by McKinsey. And what it highlighted was AI-driven customer value measurement is really what is the key driver for future growth, particularly for comms and media type of companies. You know, it's a very competitive environment, lots of disruption happening. So this is what allows you to start looking at your strategy going forward. And one of the things that that paper talked about was the organizations that excel at this, and they start to get it right in terms of their one-to-one personalization approach. It actually gives them, it generates 40% more revenue than their peers who are on, from an average scale perspective. So it's very powerful. I think the business case makes sense, it all adds up. So what we're going to do today is give you two different perspectives on it. We're going to have some insights from a Pega perspective, but we're also going to talk about the lived experience at Optus itself. Now Optus, for those of you who don't know, and hopefully a lot of people have heard of Optus, they haven't quite been on the Pega journey for 39 years or 40 years as we heard this morning with some of the other organizations from a Pega perspective. But they've certainly been a front runner in terms of their adoption of next best action. They were one of the first adopters offered in the Australian, New Zealand region. And you know, what we're doing with them now, and this is coming from an Areteans' perspective working with Optus and Pega together, is we're really looking at how Optus can start to adopt some of the new capabilities that is in Pega CDH, and this is part of their next phase of the journey. So my name is Ritwik Singh, I am vice president at Areteans for the ANZ part of the business. And I would like to now introduce our panelists. So our first panelist, Manjula Nathan, she is director of omni-channel personalization at Optus. But interestingly enough, I found out this yesterday, she's got a beginning passion in kickboxing. You heard Anil talk about Areteans is a pure play Pega partner, we certainly tend to see ourselves as punching above our weight in terms of our size and being a global elite. But I think kickboxing, that definitely packs a punch. And a delightful contrast, and Mark loves the word delightful in how I'm using this to describe is second panelist Mark Davis. He's a senior director at Pega, and he finds his adventure spirit in sailing. Now I was trying to come up with a connection between sailing and kickboxing, and yeah, I'm not going to share with you what I came up with because it didn't really come up with much on there. But I can kind of see why things like generative AI and cute little kittens can start to come into play in terms of what things will assist you.

- When you start combining that, all of the interactions that they're having with us and we're capturing all of that data, and that's not just about the action that they're taking and feeding that back in that with the logic and AI logic of Pega, you start gaining some really great insights into who that customer is, what they're doing with us now, but also what's the next best thing for us to talk to them about as well. I think for us what that really means is that you've got that closed loop view of how that customer is actually working and how we can really drive value with those engagements that we're having with them.

- Thank you. And Mark, if I could bring you here, I think it's important to sort of baseline. We heard this morning from the Citibank keynote in terms of moving on from marketing offers towards customer conversation. So from a Pega perspective, what is next best action, things like next conversation have come up in the lexicon. What is the baseline view of next best action at Pega now and what it used to be as well?

- Yeah, sure. So I think it's probably worth highlighting that next best action has always been really core to our philosophy, right? We've never believed in next best offer, and that's because you need to have something to recommend to customers every time you have an interaction with them, right? And customers aren't always in the mood to buy something, the situation may not, you know, suit that. You still want to have a conversation with 'em, you still want to be able to build a relationship with them through constant communication. So it's important to have a really broad range of actions beyond just offers. And from a customer experience, a customer relationship perspective, it's also really important because it allows you to make offers that might, you know, provide information, might provide useful education, nurture the relationship with the customer, even carry on with service processes. So things that show to the customer that they're more than just a revenue generating unit.

- I think that's so critical and I think organizations very easily get caught in this trap that personalization means a discount or an offer, and I think it's really important that you change that mindset. It needs to be about service messages, it needs to be about how you engage with your customer. And sometimes that's just about saying happy birthday to somebody, sometimes it's about telling them that, hey, there is iPhones about to be launched, and getting them prepped for that idea that actually there's an opportunity that you have coming up shortly. But it's not always about going in for a sale. And I think you've gotta look at customer holistically and say, okay, how can I actually support this customer throughout their entire customer lifecycle?

- And I think that makes a lot of sense. So if we take the cue from that and add Optus, and let's get stuck into the topic itself, so how do you go about starting to actually measure the value that this is going to drive for you?

- So look, I've probably got two points of view on this one. One is really around the program implementation. If I start there, I think we've had, we've learned this lesson in the hard way. Whilst we had a really strong business case, we had clear metrics upfront. What we didn't do was properly measure that as we went on that journey through that process. And subsequently what was really difficult for us to do is to be able to articulate the value that was actually being driven from the platform. That creates a lot of problems across the ecosystem. Things like if you're asking for additional capital investment, how do you do that? But also from our people perspective, and this is probably what was really critical in this space, was people didn't know the value that the platform was actually driving for them and subsequently didn't trust the platform to do that for them. And when you start looking at it from that perspective, it's really difficult to get people engaged in that process to want to use the platform the way that it has has to be used. And so you really need to build that trust for them.

- And so if we then extend that out to the second part in terms of value in the conversation itself with your customers.

- Yeah, so look, I think when we start looking at that, I think there's a couple of core things. One is about make sure you know what success looks like. Set that north star for yourself and measure every decision that you make against that North star. It ensures that any capability that you're building is not built for the sake of building capability, but it's actually going to drive value. I think secondly, it's about sitting back and looking at each one of the conversations that you want to have and match them back to what it is that they're actually going to drive. When you've got that, you can actually build that back into your taxonomy, but you also get that opportunity to sit back and say, okay, how is this going to work in amongst the scheme of everything? And the final point on that I'd say is, measures aren't always about, whilst the customer is critical to that, it's not always just about the customer. Think about it from an operational sense as well. What do you want for your people? Is it about you want to create some operational efficiencies in there, you want to make it easier for your people? And if that's the case, let's make sure that you're measuring that as well.

- Oh no, absolutely. And I think some very, really good points there. So Mark, if I could bring you in from an implementation perspective here. You know, we've heard about the Optus experience, can you kind of sort of broaden that for us in terms of the clients that you look after?

- Yeah, sure. So I'm fortunate in the time I've been at Pega that I've been able to work with lots of clients around the world, different geographies and across different industries. So there's a lot of best practice that me and my team have been able to pull together as a result of that. I think one of the key things to highlight, and it kind of touches upon what you're saying, is that it's really important to generate as much value as possible from that first implementation. And that means you need to pick the right place to start. We want to have a focused place to start, but something that really delivers value. And there are two things you need to think about when you're thinking about that starting point. The first is the use case that you want to apply one-to-one customer engagement against. And that varies by industry, right? So if you look at telecoms as an example, then retention is probably the best place to start. It's the single use case, it's probably going to generate the biggest uplift in financial benefit for a telecommunications organization. Whereas in consumer financial services, they don't really have retention problems that work in the same way as telecoms. They're more interested in increasing the portfolio that their customers have. So they're more interested in growth or cross-sell upsell use cases. So that's the first aspect. The second aspect of picking the right starting point is picking the right execution channel to deliver the actions to the end customers. And for that we are looking really for something that has a high volume of interactions, and that's really important for a couple of reasons. One is, the higher the volume of interactions, the higher the volume of opportunities we have to put something in front of a customer and therefore the bigger potential upside from a value perspective. But also, because of the way our solution works, we need responses to train our models. So we want high volumes of interactions or channels with high volumes in so that we can train those models quickly, quickly optimize them and get them to be really accurate. So for telecommunications we might look at contact center or digital channels, and I think Optus started the last iteration with mobile, which is a great choice. And then for consumer face, we typically look at digital, so online banking or mobile applications.

- So if we expand on that, you know, I think that makes a lot of sense from the incremental mindset and doing that, you know, starting small and radiating out. But one of the things that always comes up and having been a few conversations like this, the data question always gets asked. Can you talk to that part of it? Do you need your data sorted out?

- Yeah, no I think that's a great question. Data does come up a lot as a question from clients and prospects. So we've done a couple of things to address that. One is we've built out standard data models for different industries. So we've looked at what our successful clients have used, we've synthesized data models from that for consumer face for comms, for healthcare and for insurance. And most data models describe a typical set of attributes that you would want to have to drive prediction to make sure you've got the right kinds of data in place to drive compliance rules, those kinds of things. So that's I think a good accelerator. The second point to make about data is I think it's important to be really pragmatic, right? You don't need to wait until your data is perfectly organized before we can start using it. It doesn't all have to be collected in one place, we don't have to have a single customer 360 view. We can pull in data from all kinds of different sources, combine it in our tool, into our customer insight cache and use it to drive value really quickly. So the important thing, and we're going to probably touch on this a lot, is that you want to be pragmatic when you pick your starting point. And what you need for that so you can start quickly optimize and then continue to radiate out from there.

- You're so right, Mark. Data can be an absolute minefield, especially when you are working in large legacy organizations that has so much data available to it. You want to bring everything in, but sometimes the data doesn't match from your primary keys, sometimes it's not of the quality or grade that you're after. And being able to take that pragmatic approach will allow you to get to market a lot faster. I think you can get drowned in the amount of data issues that you could face. But I think by taking that MVP style approach, and the way we did it was looking at what do we need to get our first campaigns off the ground and then make sure that we are then building from there.

- So some great points there. Mark, is there anything else you can recommend from that perspective?

- From a best practice perspective?

- Yeah.

- Yeah, I think the next thing we want to think about is, and again it goes back to the point about proving value, it's really about putting in place the right test and control strategy, right? So it's, you know, that first phase has to prove value, and the best way to prove that is through test and control. And typically the way that works is that test will be the Pega solution, so using all the power of our analytics and AI, and then control will be the legacy solution. And what we're looking to do is drive an uplifting conversion rate versus that control solution. We can then use that to calculate the business case, prove the value that we've delivered, and then use that to justify the continued rollout.

- Okay. So I think we've talked about, you know, some very key points there in terms of getting started and having all of this as part of your initial approach. But Manjula, can you kind of talk too from a program perspective about what will actually define success for an NBA strategy?

- Yeah, look from a program perspective, I think there's a couple of critical things that we've taken away, and this is the second time round for us in terms of implementation. I wasn't there for the start of the first one, but was there for the end, an opportunity to do it this around the second time. I think for me there's a couple of critical things. One is about making sure that you understand the role that decisioning plays, but that fact that it's only one cog in amongst the whole ecosystem of all the different things that you're looking at. It's about data, it's about decisioning, it's about all your other MarTech capability that you might have in an organization, it's about your channels as well. And you need to actually make sure that you've engaged with every one of those players and ensure that they've been taken on the journey. Being able to break down what one-to-one personalization actually means. I think if you speak to anyone, they will say to you, one-to-one personalization, of course. My organization is brought in for one-to-one personalization, we definitely want to go down that path. But I think if you scratch under the surface a little bit, often what you'll find is that people will say yes to one-to-one personalization, but they may not know what it truly means that they need to change from a behavioral perspective to actually achieve one-to-one personalization. So things like driving volumes. So if you've got contracts out there that says actually you have to hit, you have to send out 100,000 comms, or if you've got things like that, you need to be able to break those perceptions down and say, actually you know what, I can use different tools and capabilities to simulate how many customers might get it, but it's not necessarily going to hit 100,000. But what you can talk to is the conversion metrics that are coming through that. So actually being able to take people through that journey as to what this is actually going to mean for them, and have that buy-in from all those parties but most importantly from the top down space. I think the only other one that I'd raise in there is around the operating model as well. So get your operating model with all of those areas and ensure that you've got a ways of working that's going to support that and actually ensure that you've got that delivery being able to come through.

- And Mark, I know you're eager to jump in here.

- Yeah, no I am actually, because I think you raised some really good points there. I think that we'll talk about the operating model I think a little bit later on. But the point you made before that around volumes of interactions, and I think you raised something that was really critical, right? The thing that matters is not the number of messages you send out, the thing that matters is the number of conversions you get for whatever you're trying to achieve, right? Whether that's reduction in churn, whether that's an increase in sales of a particular offer or product, that's what really matters. And I think people need to understand that they can separate that from the volume of interactions they send out if they can improve the conversion rate for each of those interactions.

- And are there any tools in Pega that are going to help us with that? I'm kind of leading you towards impact.

- Well, so for that, I mean I think there's, in terms of trying to work out how we uplift the response rate to a specific action?

- Well, sort of more broadly talking about impact analyzer.

- Okay, I mean about impact analyzer I think in terms of test and control more broadly. So that's a capability that's been in the last couple of releases of CDH, it's a pretty cool capability. It goes back to the test and control issue that Manjula raised. And what it does is it allows you to run multiple control groups, or automatically runs multiple control groups so you can see different aspects of what you're doing and how you can optimize the solution, right? The primary control group that it runs. So if we imagine test is the full power or your production implementation of the top level NBA strategy. Probably the most important control group it runs is one that, instead of using AI to target offers will essentially give customers random offers from the set of the offers they're eligible for. And what that does is that shows the uplift that we're driving through our targeting capabilities. So that's kind of like the baseline proof point of how much value we're adding through our solution, right? Some of the other control groups that experiments are kind of interesting, there's one that I think goes to the point you're making which is around looking at whether there are things like eligibility rules that could be relaxed to improve the benefit that you get from the solution. So one of the challenges we sometimes have is that while we want to use arbitration to drive offers, people who are used to traditional ways of marketing will sometimes put in too many eligibility rules to simulate targeting that they've had in previous systems. And one of the ways to identify that is through impact analyzer because what it will allow you to do is look at your production implementation and then relax the eligibility rules in one of the test groups, or one of the experiments rather, and show you the uplift you would achieve if you relax those eligibility rules. So I think it's a pretty clever feature to help you drive up conversion rate.

- Absolutely. I think it helps you then have a pretty rich conversation internally to talk to the things you're talking about from a change perspective, right?

- I might just jump in there real quick.

- Go for it.

- I think one of the things that's really important in that space is these tools actually help you create that glass box. I think historically when we've looked at decisioning, people sometimes think it's a black box. And when you look at it from a black box perspective, you say, I don't understand what's happening inside here, and therefore I don't want to trust what's happening in here and I'm going to bypass all of that logic and do it the way I know will get me the outcome that's been proven in the past for me. So being able to create that glass box and using some of the tools that are actually in Pega to be able to do that gives, allows you to create that confidence with your people to be able to say, guys, you can see what's happening. This is the input I'm putting in and this is the output that I'm expected to get. So helps you really build that confidence with your people.

- Yeah, absolutely, I agree. So in the interest of time, we might skip past a couple of the implementation things we were going to talk about. But Mark I do want to bring you in to talk about the business transformation aspect of it because I think Manjula has talked about it as how important it is. But what is it from a transformation, and you talked about operating model for a little bit, the mindset. Can you sort of elaborate on that as its importance in the whole scheme of things?

- Yeah, definitely. I think, that is one of the challenges we see in our clients is how they transform the way that they do business or change the way they do business, maybe transform is too strong a word. But I think one thing I do want to highlight before we talk about how we do that is that the transformation is really around moving from being product-centric to customer-centric. It's not really a transformation that results from taking Pega's technology. We've got a, you know, there's a lot of organizations in the world that are still very product centric to very traditional marketing. And the conversion rates they achieve really aren't what they need to be. Everybody recognizes, I think, they're taking a more customer-centric approach to marketing drives up higher conversion rate, better business results. So people are keen to make that change, but it does require a little bit of a change in the way that people think. So one of the things my team's done is we've put together a quite a lot of best practice that describes what the operating model, the people and process looks like for organizations when they adopt a one-to-one customer centric approach to marketing, covers governance, process, the roles that you need within your organization, enablement, et cetera. It's pretty comprehensive, and it's been used pretty successfully by a lot of our clients.

- Okay. I think that makes a lot of sense 'cause I think getting that mindset right is key. I think it touches on exactly what you were talking about earlier as well, Manjula.

- So one further point go. And this is just a shameless plug for my panel tomorrow.

- I was going to bring that up

- I've got a panel with three experts from Bank of Ireland, from Navy Federal, from Akima, which is a insurance company in the Netherlands. And they're going to spend about 35 minutes talking about how they've driven that business change in their organizations to focus very much on how they've driven adoption, how they've driven the change in mindset that their organizations have gone through.

- Ah, great. I think everyone should get to that session. Shameless plug, but it's a good one. So I think, you know, we've talked about some of the metrics side of it, we talked about response rates versus conversion rates in terms of what's more important as you're going down this journey. So what we might sort of skip to is, and Mark I'll ask you to sort of come in for this, you know, how do you start to sort of prove that there is uplift, and then how do you kind of translate that proof point to dollars and cents?

- Yeah, so I think we've talked about the way we prove the uplift through test and control, right? The idea of having a control group, which is your baseline, your test group, which is, you know, so you route some of your interactions to control, you read the rest to test and you look at the difference in performance of those two things. In terms of the dollars and cents piece that you're asking about, primarily what we're looking to do with the Pega solution is do a couple of things. First, we're trying to drive up the overall conversion rate and we're usually pretty successful at that. The second thing we're trying to do is skew the mix offers that go or actions that go out to customers and they're accepted towards those that generate more value for the organization. So we're always trying to balance what the customer wants through customer propensity versus what's good for the business from a profitability perspective through the value in our central arbitration. And there are different approaches to measuring the value. We have some customers or clients that are really sophisticated in how they do that. They will look at the difference in offer mix that's accepted in test versus control. They will have a calculation for the value of all of the actions that have been accepted and they will, you know, use that to work out the incremental benefit, or revenue or margin that they generated from test versus control. Other customers are less mature, they might just look at a overall uplifting conversion rate and then look at an average action value, multiply the two together and that will give them their calculation of their uplifting in value. One other question I often get asked on this around value is how do you calculate the value of actions that are not sales actions? So calculating the value of a sales action is pretty straightforward, right? It's either a one-time sale like an iPhone or something, and then it's, yeah, sell an iPhone, it's worth 50 bucks in terms of margin, or it could be an increase in monthly subscription. Pretty straightforward to work out the value from that. Service actions are a little bit more difficult to work out the value. But I think it's usually possible to get an approximate value. So if we use as an example, something like autopay, right? Encouraging customers to take autopay as an action that we would commonly, you know, deliver to customers. And there's a real value associated with that because customers that use autopay to pay their monthly bills are less likely to go into collections so they're cheaper to serve. So there's a cost saving that's associated that can be built into that business case.

- I think the additional add-on I'd put onto that is making sure that you've got a standardized way of actually looking at value. So if you're going to take a service comms and you're going to look at a growth comms, you need a comparative. So whether it's one, whether it's a ratio, whatever the number, you need to just make sure that there's a standardized way that you are approaching it within your organization and that you're continually using that standardized approach to that.

- Oh, absolutely. So in the interest of time, we'll sort of get towards wrapping up. But there's definitely one thing I need to bring up, and I think we, our effectiveness of this session will be rated if we use generative AI or not. But Mark, can you talk to some of the emerging trends that you're seeing?

- Well, you've just given it away. So yeah, I mean I think probably everybody in this room is aware of generative AI and large language models, and I think we're all pretty sure that they're going to have transformational impacts on most of the industries that we work in. It's funny, I had a conversation with some execs from a client I don't know, maybe two, three months ago. And they were talking about how they had to send a communication out to their customer base. And just for a laugh, the execs got ChatGPT to generate the letter, and it was pretty much good enough to go out. And he just had, you know, changed his service in the voice of their company, put it in front of the legal team and it went out. So there's some real positives I think in generating content that are going to transform the way a lot of organizations work. On the flip side of that, you have what I've heard described as hallucinations, seems to be the term that people are using, where tools like ChatGPT will create really compelling content but it's just factually incorrect, right? So there was a new story a couple of weeks ago about a lawyer in North America who got in a lot of trouble submitting a brief to a court that he generated with ChatGPT and he had made up citations and made up case law, right? But it sounded compelling so he put it in anyway. So we've gotta have ways to manage that kind of aspect of generative AI. So I could go into a bit more detail on this, but my boss, Rob Walker is, is doing a keynote tomorrow morning, and I really don't want to start, you know, diving into any of the details that he might be talking about. So I think the recommendation there would be for people to go to the main arena at 9:00 AM tomorrow, there's going to be some really cool content on generative AI and one-to-one customer engagement. But there are just a couple of points that I do want to make. So we talked earlier about the operating model. And I think the operating model that we've defined for how people use one-to-one customer engagement is almost unchanged with generative AI, that still applies the same process, the same governance, same roles. The other thing I think that's really cool about the way we would work with something like ChatGPT or some other kind of generative AI is that we benefit when there are lots of different alternative things to put in front of customers, that could be different actions or it could be what we call different treatments, which are different variations of actions with different content. And if you can scale up the production of those treatments, generate lots of content, then we make it really easy and we provide a really scalable way to get those into market.

- Thanks Mark. I think that's some pretty, pretty interesting stuff there and obviously we heard a bit of that this morning. I'm not keeping count, but that's the second plug you've done now. But I think it's going to be a great keynote in the morning. We've had a little bit of a preview of what Rob's going to be talking about, so definitely get there at 9:00 AM. But Manjula I'm going to give you the last word. If you can kind of talk to this sort of stuff, generative AI or the other trends, how does an organization like Optus prepare? How do you sort of look forward?

- Look, generative AI listening to what they were talking about this morning and all of the things that are coming out in the media, it's an exciting time. Like, how fortunate are all of we to be a part of all of this? I think though, the one thing I would say to any organization is make sure you are embracing these trends but understand what success looks like for you as an organization, and make sure that you keep measuring all of these different, you are evaluating all of these different trends back against what success looks like for you, and only taking it on where it's actually going to be suitable for your organization. I think we're so often organizations start chasing the next big shiny thing. And it's exciting, you want to be at the forefront of technology. And I think you should, where it's appropriate for your organization and making sure it's used in a way that's actually going to drive value for your organization is going to be critical. So yeah.

- Yeah absolutely, I think that's a really good way to summarize it, right? You're talking about knowing your why and your what and then the technology and the capability and some of the tools we've talked about, they're there to then help that conversation. So we do have a few minutes left and I did want to open it up to the audience for any questions. I think we've got probably got time for one, maybe two.

- [Attendee] Thanks a lot for the presentation. Question for Manjula. You mentioned how critical it is to define the right operating model to implement Pega. What would be your main takeaways from that? So what does good look like in terms of operating model?

- I think it's understanding who the players are in your ecosystem. For us, we've got a fairly complex end-to-end process. And sitting back and going, what is the role that we want each individual to play and subsequently, where should they be playing that role? So we've looked at very much a two-speed economy, so how do we keep things, how do we make sure that we're getting our prioritization right but we're also got the capability of delivering at a fast pace as well. So I think that that would probably be my two key takeaways from that space.

- Sorry, if I can just add to that as well. And sorry, which organization do you work for? Do you currently use CDH?

- [Attendee] Very sure.

- Very sure, okay. And we're happy to share this with you, we've got pretty detailed documentation that describes the, we think about the operating model in three ways. We think about governance, which is really how you manage stuff that comes into the front of the funnel. So how you prioritize all of the different bits of demand that you have from your clients and stakeholders within your organization. We think about the process, so how you can efficiently get those actions through the prioritization, through, you know, through configuration, through testing, through simulation into the production environment. And then the third piece we think about is the organization structure. So the people and roles that you would have an essential team to manage the demand that comes in to put it into the live environment. Yeah, I'm happy to share that with you.

- That's probably worth visiting your session tomorrow as well, right? You're still just talking about the overall business change aspects there anyway, so.

- Yeah, so that was, what I was talking about there was really what I think of as the operating model, you know, the kind of the what you do, right?

- The steady state.

- Or how maybe. And then what we're talking about tomorrow is more the, how do you transform the mindset of the broader organization? How do you win over stakeholders who might be in different parts of the business? That's really what we're going to focus on tomorrow.

- Absolutely. And in terms of the lived experience at Optus right now, we're going through this journey at this point. So definitely more takeaways and learnings to come. We've probably got time for another question.

- [Anil] One more question?

- Yeah, I think so.

- Anyone?

- Hello. So firstly, thanks to the panel. You've raised some great topics or points. I'm more interested about how do you ensure that the NBAs that you're putting out there stay relevant in the market where like customers preferences are constantly changing?

- Actually, that's a great question and one of the things we didn't sort of get time to cover off was, what do you also do when things in the market change, and how do you respond?

- I think this is where you start looking at technology as the enabler, but your business strategy is actually what's critical to input into there. So understanding what it is from be it product, be it loyalty, be it service, it's about making sure that you understand what's that customer strategy that you're trying to look at, and we in Optus have actually based this around what our customer life cycle strategy looks like. So what is the conversations we want to have with that customer across any point in their life cycle. And that, Louis, I think that needs to continually keep evolving. We need to continually be able to sit back and go, what is my customer's lifecycle look like today? And subsequently, what is it that we need to be able to talk to them about that's going to resonate with them? If you stay static, that's actually not a great position to be in. But with that, I think being able to then reflect that in your system allows you to then be able to deliver those pieces. But you have to start with your strategy. What is it that you as an organization want to deliver? Have that clearly identified and understood, and then be able to just input that back into your system.

- Yeah, I totally agree with all of that. One other point maybe that's worth making is that we build a capability in our one-to-one customer engagement solution called Value Finder, which is really useful. What it does is it goes through, you run simulations against your customer base and it finds groups of customers that are underserved, right? It comes up with explanations of why they might be underserved. Maybe some of the business rules that have been applied are too restrictive means they don't get offers or actions, or it could just be there's nothing appropriate in the action catalog for those customers. So it helps you identify those customers, identify common characteristics about those customers so you can look at developing new actions or offers for those customers.

- Thank you. Hopefully Louis, that answers your question. Look, I think we're done for time, so thank you everyone for attending. I will throw in a shameless plug for Areteans as well. Do come visit our booth in the Innovation Hub, but there's lots of great stuff there, but the Areteans ones is really good, no bias. Thank you for your time, Manjula, Mark. Manjula, thank you for coming all the way across the ditch as well. It's been very insightful, and there's a lot more talking points we did have. So feel free to sort of find us, we'll be around and happy to talk into more detail. Anil.

- Thank you for sharing all your interesting insights and learnings from your journey. And thanks Mark for bringing out those, that was really helpful. And Ritwik, you have been an amazing moderator, so thank you to you.

- I've gotta go play some games or something out there now.

- Okay, thank you.

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