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PegaWorld iNspire 2023: NatWest: Creating Significant Customer Lifetime Value with Hyper-personalized Messaging

NatWest Group is a long-time user of Pega Customer Decision Hub™. With a highly effective next best action (NBA) decisioning program, NatWest now has connected 30+ channels and pioneered the use of decisioning for outbound and trigger-based decision-making. With its latest innovations, NatWest can now integrate real-time streaming events and use them as fuel to deliver hyper-personalized messages in-channel, just seconds after a customer interacts with the brand. Whether that customer is getting an online quote, applying by phone, banking in the app, etc., when they’re done, Pega instantly re-calculates their next best action and reaches out to do follow-up, streamline the call to action, or complete the transaction. See how NatWest has used this always-on approach to generate significant lift in response, customer satisfaction, and customer value across the entire engagement program. By joining this session you’ll get insight into many of the potential use cases for Pega Customer Decision Hub in banking and the value that real-time NBAs can deliver – both for brands and for customers.


- Hey everyone, I'm Matt Camuso, I'm a product marketer that supports our Customer Decision Hub product and I'm very lucky, fortunate to be joined by Fiona Kirk, the real star of today's session. Now Fiona, if you want to introduce yourself.

- Hi, I'm Fiona Kirk. I work in NatWest and I am the head of customer decisioning for the retail bank in NatWest. I've got 25 years of experience in data and analytics in the UK financial services, and I have always worked in data and analytics and found it a really exciting place to be. I think data's changing all the time, as we all know, but I'm just as excited today with the things that we're doing for our customers as much as I am for the things that we've still got ambition to do tomorrow. So yeah, love what I do. In terms of NatWest, we are a big old bank and we are almost 300 years old. We are formally known as the Royal Bank of Scotland, to which you might know a bit more than NatWest Group. So the Royal Bank of Scotland also has a number of other brands under its wing. Things like Coutts, which is for our wealth customers, or Royal Bank of Scotland International for our offshore customers. And there is Bank of Ireland, Ulster Bank, sorry, if any Bank of Ireland people are in here. I'm not trying to take their bank. So we've got a number of other brands, but that's the main ones. We've got 19 million customers in total, 17 million customers in the retail bank.

- Great. So over the next 30 minutes, Fiona is gonna tell you all about NatWest's journey of using real-time decisioning. So you all saw Promiti from Citi talking about decisioning and their use of it. They're one of our heavyweights for Customer Decision Hub. The other one is right here in NatWest. They're one of our oldest clients, have really led one of the best practices that we built in the product throughout the year, so we were very lucky to be joined by you. So we'll talk about your original investment into real time decisioning. We'll talk about some highlights, some challenges that we've gone through together, the value that this program creates, both for customers, your employees, as well as the business overall. And then we'll talk a little bit about a modernization effort, which has been a little painful, but also very good. We're excited about it. And then best practices that Fiona would like to share with all of you in terms of, you know, people that are just getting started with their decisioning program, maybe that are veterans or even considering such a program yourself. So my first question for you, Fiona, is I know this was before your time at NatWest,

- Yeah.

- but why real time decisioning? Why did NatWest originally invest?

- Yep. So I guess we had quite a progressive setup before we invested in Pega back in 2014, 15. We had a pretty, what seemed at the time, quite progressive. It was quite clunky, it was quite manual, it was very much driven by the business and therefore not very customer centric. And we knew we were on the right tracks with what we were trying to do, but we also knew that we could not scale it to meet our ambitions with the people that we had with the investment that we didn't have. And therefore we needed to find a way that we could scale something that we already thought was going to work and also optimize some of the things that we had as our best practices at the time.

- Awesome. And I think this slide here does a good job of the before and after. I mean, I know we talked to you and Gordon and your team, I mean, it's been years, but moving from those kind of traditional customer experience, customer engagement tactics to modern ones where you're using real time and AI to do that, that's what this is really all about.

- Yeah.

- So based off of that, that original investment, how's the program doing today?

- Today we've got a far reaching capability. We have connected into 33 channels across the bank. So there really, there is only one or two channels left for us to connect to. Things like our chatbot that's on the roadmap for this year. We have three and a half thousand customer MBAs that are live pretty much at any one time. And we're decisioning for 3.6 billion customer interactions every year. So it's quite a far reaching capability. It's across all franchisees. So in the bank we have three main franchisees, a retail bank for personal customers, the wealth bank for our wealthy premier customers and the commercial bank for our non-personal customers. So we're across all of those areas and all the channels that we're operating in.

- Yeah, it really sits as the program sits as the central decisioning behind everything you do, some big numbers that you just shared, the 3,500 next best actions, that is a really big number, really impressive, especially across all the clients we work with. And we will come back to what those actually look like and some, you've got some examples of how that spans use cases. But before we do that, I think we'll take some time to define what next best action is. So we talk about it a lot. Corre mentioned it in his keynote, Alan, it is integral to Fiona's realtime decisioning program and how we, you know, hold position, Customer Decision Hub. So we define it, what you see on screen there, that is Pega's definition. Essentially we think about it as a customer engagement strategy where you're leveraging AI and modeling to engage customers on a one-to-one level. And it's not just for marketing offers, or sales offers, it's across the entire customer life cycle. So that's our little definition. Forrester has their own slice on it, as do other organizations. And Fiona, I love your definition if you wanna tell everyone about yours.

- Yeah, our definition is essentially it's a conversation. So next best action for us is the next conversation to have with the customer. And I guess the way we create those is that we use playbooks to understand all the different journeys that a customer could be in. And then we overlay all the different touch points that a customer has along that journey, with the MBAs that are already in existence. And that helps us understand and visualize where the gaps might be and then where there's gaps. We think about, you know, what are the data signals that we could use to fill those gaps and what are the sorts of conversations and journeys we could design out and build. And if we can and we do, then we then launch that into whichever channels that we feel are the right channels for that conversation. And then obviously the CDH helps us decide on which conversation to surface to which customer and which channel at the right time.

- Cool. Yeah, I love her definition way better than ours. Conversations, that's what it's all about, going from a sort of monologue where you as a business are sending out your actions, or messages to instead, or conversation where you're learning what a customer needs based on the rich amount of data from 33 channels or more, bringing that all together and then feeding that into the AI to make a decision. That's what next best action is all about. But I know that, you know, we've, this has been a journey as we keep saying, and what we think of as comprising anus action has certainly changed. So I'd love to hear you were an early adopter. How has that journey kind of gone?

- Yeah, we've always really had, in terms of the history of the work that we've done in MBAs, the focus has very much been on the acquisition space. So we have great models both external to Pega and within Pega to help with that. And three years ago, I think it was, we had only 7% of our MBAs were engagement focused. There was a real push to try and understand what we were doing with our customer engagement and how we could create more value from talking to customers about things other than products. So that's a journey we've been on for the last three years as well prior to the modernization that we'll come onto.

- Yeah, exactly 7%.

- Yeah.

- Did I get that number right?

- Yeah.

- So that's quite the swing in product focused and other companies say maybe business first to a swing of engagement focused and customer first. So I love that. Now back to that 3,500 next best action number, this is just a representation of kind of what those look like. They're not literal NatWest actions, I mean we're 3,500, it'd be pretty overwhelming to get them all on the slide there. But if you could talk about again, how that's shifted over time in terms of your thinking of next best action and even just what you've learned from interacting with customers and adapting these actions over time.

- Yeah, the, where am I going with this? The customer action library, as I say, has got 3,500. There was 7% that were engagement focused and that's moved to 40% currently. The sorts of things that we are focused on in the deepening customer relationship program at work are things like, so one of the things we've always had is happy birthday, three years ago when I spoke at another conference, we talked about the happy birthday prompt being there and being live, but that would've been part of that 7%. We now have so many other engagement type messages where we are talking to customers just to say, to signal that it's their midway point in their loan, or their first year in their loan, or their last payment in the loan, things like that. And then also things like with the cost of living in issues that we have in the UK, there's many customers facing into a number of challenges and we've got a program of work as well called MIMO, which is Money in Money Out it stands for. And essentially that's spending insights where we are gathering lots of information and using that information in a different way than we've done before to try and surface different things and different topics to talk to customers about. And that work is really helping us talk to customers in an engaging way to just to help them and help them understand their financial health. So things like utility bill payments and people that may be paying more than they need to, people that are subscribing to more than one media outlet. And we could probably save money if you down size there, people that are using paid for cash machines, you know, did you know that you've paid so many pounds this month using these cash machines and if you went to free ones, this is how much you would save. So really just trying to give little nudges to customer to help them save money in the face of the cost of living crisis.

- Yeah, I love that. I mean it's really moving from the acquisition, the cross sell upsell, those are really obvious express actions. I mean, they're directly tied to revenue. They're also really great for early clients to kind of prove value with this sort of program. But it's really that empathetic approach to marketing customer engagement that you and NatWest have done a phenomenal job at where you've expanded the next best actions. And like you mentioned, going from 7% that were focused product focused to way more that are customer focused and building out actions around service, around tent retention, resilience, like you mentioned. I mean that's what deepens customer relationships. So great to hear. Now you've defined realtime decisioning next best action. How kind of the programs have shaped out over time. I think what would be good is actually, and I'm sure people are wondering where does the AI actually come in and select the next best action that gets delivered to a customer?

- Yeah, so over the last three years we've changed that immensely. So before, just when I joined the decisioning team, we had business levers in place where the organization chose to put some weighting against the different product space, product options that customers could take. And obviously those were very commercially led and what that did was as we started to get into the Pega space, I suppose it gave a bit of trust and a bit of control to the managing directors of our organization. But what that turned into was this lever that they used every other week to say, could you pull that lever and put more weight against mortgages next week because we just need a bit more mortgages coming in the top of the funnel. So we did that and then a couple of weeks later it would be, could you pull the lever on credit cards? Credit cards are looking a bit like, could you get a bit more coming in there? So we did that, and of course after a bit of time you think, what is the point of having this great piece of cut over here that can actually make this decision? So we went on a bit of a crusades to build the trust and get the MDs, managing directors to sign off on just completely dropping the waiting, just completely dropping that, letting the tool make the decision, choose the right thing. And what we focused on was, you know, forget about the lever that you thought you had over here, the lever that you do still have is the quality of the message, the targeting of the message, the timeliness of the message. So if you want your messages to come in with greater volume, make it better. So they do have, they do still have a lever, it's just a different lever than they had before. And that's really worked. And the best thing about it is it hasn't impacted on the commercial out outlet, 'cause I guess that's where the nervousness comes from. And obviously I completely understand that, but at the time when we sort of first looked at this, we were about 36% of all retail sales were influenced by a Pega prompt. Just in the mobile app where we're sitting right now is 60%. So we've grown the sort of influence that Pega has on those conversations with, you know, even though we've taken away this lever that everybody thought was the piece of the puzzle they needed to keep.

- Yeah. And that's scary. I mean we talk about it all the time where, you know, transitioning from having total control over your marketing messages, your customer experience and lending the AI take more control, which I mean is inevitable these days. We've all been talking about it. It is, it was really intimidating, especially back then. And I think it's great how over time you've proven out the benefits and it's also led to commercial success as well. So earlier you hinted at a modernization project, which has been, you've been, you know, you were one of our early adopters. How has that modernization project gone? What were the reasons for doing it?

- I'm looking at, my account manager there who's looking like, what's she going to say. Do you know, we have yet, we have modernized. And it was a challenge. I don't think I'm wrong in saying that, Andrew, it was a challenge. It has taken a bit longer, but the reason that it's taken longer Matt, is not it, it was a full scale re-engineering, redesigning, replatforming of the entire suite. So I think we talked bit about as it as an upgrade. No, no, it's way more than an upgrade. So kind of if I take you back about when we first had Pega, or before we first had Pega, I think I said that we had quite a progressive setup. So when we invested in Pega, whilst Pega had some of the capabilities that we absolutely needed to scale, it didn't have some of the things and the flexibilities and the complexities that we had already built into our system. So we, with the help of our friends in Merkel, we built a shed load of customizations. We pretty much customized the hell out of the product to the fact that, to the point that it was almost unrecognizable as a tool. And that worked fantastically for us for a number of years. And obviously while we were busy building our 3,500 MBAs and connecting it to all the channels that exist, Pega were also very busy working on their product and getting it up the curve. And where we're at right now is of where we were at was that the fact that we kept coming to these conferences and seeing all these new tools and capabilities got really excited and then we're told you're too customized, you can't use those. So that was a bit rubbish. And then we took such a long time to upgrade because we had so many customizations that they all had to be redesigned, rebuilt, retested every time. So that took a long time and it took a lot of money and we really needed to move forward. So that was why we had to modernize. And we've got such an ambition to scale whilst was scaled hugely over the last few years. We continue to have quite a big ambition to scale. So we needed something that was going to help us there.

- Awesome. Well sorry, we put you in that position to begin with and glad we've caught up product wise. I don't see any product managers in the room, so I think we can be honest with that. But I mean obviously this is, you did this for a lot of reasons. There were some objectives that were really important for how you wanted to develop your customer engagement strategy, your use of AI, make use of the new features of decision hub, which many of you will see the Rob Walker's keynote tomorrow, or in the invasion hub if you haven't gone already. You talk about those objectives and really how we did against those.

- Yep. So we had four objectives as you can see on the screen. We wanted to bring people in and make it a bit quicker for them to get up the curve and actually start delivering. Usually when we brought someone in, it was taking such a long time to get 'em up the curve, or some that came in with Pega experience. Once they were sat in front of our Pega, they were like, actually I need retrained. So that was one of our big objectives. The second one was being able to deliver a release season of MBAs much more quickly than we currently do. The third one was, let me remind myself now, yeah, the faster to upgrade. So the point I made earlier about taking a long time, being able to upgrade faster and then utilizing the last thing was utilizing those new features. So that was the four objectives. Since modernization, we have really focused on number one and two, we've spent an awful lot of time upskilling our people in our team and in the technology team that support the product, so that we all kind of know how to use the new system, how to get the best out of it. We're still on that journey. We're not there yet. We've got some capability in the team that have just got it really, really quickly. And then others have taken a bit longer to get used to that new framework and just such a different way of working, 'cause obviously we've had another way of working for such a long time, but that has quite a success. We're seeing new people that have come in. We can probably get them delivering in about three months rather than the six, but obviously we'd like to continue and move that forward. We've got some in-house training as well as a Pega certification that gives us what we need, but we still need to work out a way that we can make that even faster. And then on the second one, we had a fortnightly release cycle prior to modernization and we've half that now to a weekly release cycle. And I feel that that should actually get a massive whoop whoop from the audience because the people that were involved in helping us get to that point in time, it was very, very challenging. Very challenging just to re-engineer how we work a release cycle and get it to that weekly rhythm. And the way we worked previously, from a sort of results perspective, it would take us on average six weeks to deliver something fairly simple. And that was because of the way we worked. We had a very set two weeks to plan, two weeks to build, two weeks to design and launch. Whereas now we have a continuous planning phase. So all the specialists can work in their areas, plan out their work, plan out their MBA and then once they're ready they just drop in to the weekly cycle and deliver it. So at best we can deliver in a week, but on average we're delivering about two weeks. So we've gone from six weeks to two weeks on that one. So that's been great. On the last two, as I said, we have been concentrating on one and two, on the last two, third one we are talking right at the minute. It's a live conversation about our upgrading. I've got my technology lady nodding at me, so that's definitely on the cards and we'll see how that goes when we actually do an upgrade. We'll see how quick it is and how simple and easy it is. I'm sure I can just go for a coffee and come back and we'll be upgraded. So looking forward to that.

- Yeah, love that. That's the goal.

- Everybody else likes that idea. And then, yeah, the last one, which has probably been one of the reasons why we wanted to move, we're just not there yet. We just don't have the space yet and the capability and the team to start really looking at those new features. And to be fair, I'm okay with that. I know it's there and I know they'll just continue to improve and if we don't get them before we upgrade, we'll get them when we upgrade to the next one.

- So what I'm hearing is next year we'll do this session again, we'll talk about the new features ops manager, are you using that? All right, so we'll pencil that in. So my last question before we go to questions from all of you, best practices from your real time decisioning program, what were the key best practices you'd like to share with everyone?

- So the first thing that I thought was worth sharing was making sure you're really clear on what your North Star is for your actual implementation program. It's very, very easy to get caught up in other problems as you go through and sometimes lose sight. But I feel that having laser light focus on, and we didn't always have that, so that's why I'm thinking it's a learning, but having laser light focus on that North star really helps guide you in the moments matter when you're making a decision for the outcome of your program. I think that focus would really help. And then secondly, I guess if you're earlier in your stage and thinking about decisioning, I know we've kind of laughed a bit about this, but I think if I asked each, I've done quite a few reference calls about people that are interested in it, in decisioning and bringing in CDH and when we've talked about it here, you know, if I asked a question, could you build really good propensity models to predict purchasers? The answer's yes, I've got loads of great data scientists and could you predict customers that are going to respond? Yes. And could you maintain and build a library of however many hundred or thousand MBAs and get the journeys all working together? Absolutely. And obviously with the advent of data science, there's much more capability in that space and I know that people have the capability to the , and I can take all that and I can do something to orchestrate and make a decision because I know that, because we were doing that a few years ago. But unless you've got a huge purse, if you will never be able to scale without some sort of tech to help you, some sort of customer decisioning engine, whether it be Pega or whether it be others, we know that there are others there. But this is the point you need something like that to help you scale. You just won't get to the ambition that you're probably trying to meet if you don't have something like that.

- Cool. So that is our presentation for today. Thank you very much, Fiona. If you have questions, there's two mics I think I see, or maybe not, mic right there. Thank you. You can use the mic to ask a question, or do you feel confident enough in the volume of your voice and frequency of your voice, you can just shout it out too.

- [Audience member] Can't find the switch. I think I'm just-

- Can you shout Christopher?

- [Audience member] I can shout or I think it's working.

- Yeah, it's working.

- It's nice to see you Fiona.

- Thank you.

- I'm curious, am a big believer in the kind of spectrum you've laid out of the different types of offers to engage the customer and not just engage but keep the customer engaged through their lifetime. And I'm just wondering, it's easy for a lot of us who've been focused on the acquisition side of things and the cross sell to attach value to that. Has NatWest developed value framework, if you will, for the rest of the spectrum for retention, and for service and resilience?

- Not completely. What we have done is we've created a customer lifetime value model, which helps us understand where the customers are today with regards to value and where we think they're going to go, and how we think they're going to get there. Whether it's product acquisition, or using their products in a different way and using features in a different way. So we've got that understanding and that helps us, we use that in targeting to help us, you know, only target sometimes certain segments that we want to see. 'Cause we know they're going to move and we're trying to move them a bit sooner. So that's one way that we're sort of trying to understand that.

- Great, thank you.

- Yeah.

- [Audience member] Thank you very much for sharing all of the details. I'm curious about two engagement policies. For instance, what this means about a customer, how many SMS can per a day, which is means that engagement policies, or do on the other hand think about is then on the mobile application you see the customer's offers and says no and then wait a minute, I don't know the days, how many days later you offer it again and could you give much more details is the other hand, for instance, you have lots of customers, rich customers, normal customers, you know that this is only same engagement policies, or you use the different engagement policies?

- Okay, so for customer contact principles, which is to your point, how many emails do we want to send a customer in a course of so many days? We own that together with our marketing colleagues. So we tend to sit down, have a look at it, look at the numbers, and then decide on whether we want to change it or not. So we do have different rules for the different channels and also for service versus sales. So that's kinda one thing that we do. But then to your other kind of part of your question, which is around sort of eligibility piece, we are using CDH nope, CDP data to bring in some additional signals to say don't talk to this customer. They've literally just gone through an application process. We were doing that previously to having a CDH, but the CDH is allowing us to do that with more timeliness. So, well we had latency of a day or so previously. We've now got that real time view of a customer's actually just gone through an application, therefore when you're doing the next decision, take them out of this type of product, or you know, don't talk to 'em about X because it's contradicting or it's duplicating what they've just done themselves. But there's a whole host of rules around that and how often there's message rules around how often we show certain messages within the mobile app and certain messages within our online banking space as well. I couldn't detail them all out to you, I'm afraid there's far too many.

- [Audience member] Thank you. You're saying you have three brands within CDH. Are you using that within the same application, or are you using a separate application for the three brands?

- Separate applications. They're all working independently across the three franchisees.

- [Audience member] And are you using the, I think the new version as a multi label option?

- Oh, now you're asking, I'll need to take that one away.

- Okay.

- Multi-level option?

- Yeah, I think.

- We're not, no. We're not.

- Not the app. So yeah, the multi app. Okay. You don't use it?

- We're not.

- Okay. Okay.

- Thanks.

- I can't have more than that.

- [Audience member] You had mentioned that to launch it took you six weeks and y'all were able to scale down to two weeks. Is that from the day you created an action from like content building all the way to launching to production, or was that just within launching in Pega?

- That's the, all the work that we've done with the stakeholder, all the design and work with them and getting the word in signed off, that's all pre that two weeks. And then the two weeks is purely the Pega work.

- [Audience member] Like the Pega execution?

- Yes.

- Okay. How did you all accomplish that and how did you identify, like how much time you needed to meet within each SLO?

- How much time, sorry?

- [Audience member] To meet within each SLO. So you first have like your staging, well we use stage two environment to do our pre-work and then we have to do some additional testing to go into production. How did you identify like, how much time to spend within each of those processes?

- We really just broke down the six weeks delivery that on average that we had before. And then said how, if we just looked at different re-engineering options for the two weeks to get to one week and then figured out, could we do it? 'Cause we've got about 60 users that are all coming in to the weekly release and we just figured out that we could do it and we tried it as soon as modernization hit, we went weekly before actually knowing that we were going to stay weekly. It was a bit of a needs must to start with because we had some emergency things that had to happen and then figured actually this is working, could we make it an actual standard framework? And there was some things that were having to be ironed out, but we sort of managed it by accident to start with rather than planned it out.

- Yeah.

- And then did the planning bit, which was harder.

- [Audience member] Awesome.

- [Audience member] Hi Fiona, thanks for taking the time today. Very quick question. How do you deal with changing priorities? So for example, if the CEO comes to you and says, we need to drive deposits because interest rates are rising, how do you change that in the AI or do you change that?

- We don't change it in the AI at all. So that sort of business lever has been completely removed. We don't have any real capability, or even, you know, we probably do have some, but we don't tell anybody we can, so we don't do in the tool to change priority. The only thing I use then to change priority is the workforce. So moving people from looking at loans, looking at deposits right now, which is a massive area for the bank to look at. I just move people and then let the people create new and improved MBAs and conversations across that group.

- [Audience member] Thank you.

- That took a lot of time though. That that was kind of, it was quite a painful move away from having those levers where the business could change the priorities to not having that.

- [Audience member] Got it. Thanks.

- [Audience member] Thanks for sharing the details. I have one question about the customer preferences which keep on changing from time to time based on the stage at which they are in their life. Did you consider that aspect when you're delivering this solution like customer DNA type of things?

- Could I ask you to repeat that? Sorry. I didn't catch the very start.

- [Audience member] Can you hear me now?

- Yeah.

- Customer preferences, they change from time to time based on the stage of life where they are at. When you designed this solution, did you consider a customer DNA type of thing to understand the customer then recommend the next best action, what would be suitable for them at that point of time?

- When you talk about preferences, are you talking about channel preferences?

- [Audience member] No. For example, you mentioned first home buyer as an example. So that is probably today after five years, his expectations would be different based on the stage of life at which they are.

- The customer lifetime model helps us understand what a customer might do next to get into a different customer value bucket. So that would change, like you say, from a customer who's 18 through university and then first job, second job, family, you know, assuming everybody takes that linear journey. But the customer lifetime value helps us understand the preferences and then the modeling that we have outside of Pega helps us target different segments.

- Okay.

- With that in mind.

- [Audience member] Thank you.

- [Audience member] Hi. When you did your modernization transformation, did you have to shut down the old system and kind of rebuild and start over? Or were you able to kind of continue on the current path while doing the modernization?

- That is a great question and I would love to be able to say yes, we closed it down and we gave ourselves six months and then we opened it back up. But given 60% of the retail sales are coming from Pega Messages, we absolutely couldn't do that. So we often talked about, it was like flying a little plane and trying to get into all the users, and all the people, and all the MBAs over into our brand new jumbo whilst you were still flying it. So we literally just had a moment of go switch, which was hairy and it was very challenging for everybody involved and all our partners involved, but it was the only way we could do it. We just could not switch off. Yeah, it would've been great to see, yes.


Herausforderung: Kundeninteraktion Industry: Finanzdienstleistungen Produktbereich: Customer Decision Hub Thema: KI and Entscheidungsfindung Thema: PegaWorld Thema: Personalisierte Kundenerfahrungen

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