34 | 330% Growth with only 33% More Resources - The Magic of AI Business Automation with Kieran Gilmurray, AI business automation expert

0:00Hello and welcome to Leveraging AI. This is Isar Metis your host and in today's show we're going to dive into business automation and how you can enhance it dramatically by combining it with artificial intelligence. At the end of the episode, I'm going to share the news of this week. Like every week, there are some really big, exciting news. This episode is brought to you by multiply.ai. Multiply is spelled M-U-L-T-I-P-L-A-I dot AI. And over there, you will find other kinds of training beyond this podcast that you can take from lectures to boards of directors and leadership teams through consulting and courses that you can take, as well as use cases and tools that you can use for free.

0:49And now let's dive into this week's episode about how to drive amazing business efficiencies using automation and AI. In the next few years, AI technology will change our world dramatically. Whether you are a business executive trying to catapult your business forward or just somebody who refuses to be left behind and want to advance your career, this is the show for you. I'm your host Isar Maitis, a serial entrepreneur and an AI enthusiast. You'll hear invaluable practical tips from innovative business leaders, AI practitioners, and some of the brightest AI minds in our world today on how you can leverage AI in ethical ways to advance your career and grow your business.

1:41Hello and welcome to Leveraging AI, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Maitis, your host. And the topic today is going to be about business automation. And business automation and RPA has been around for a while, way before any of us could spell chat GPT. And a lot of businesses were using it very successfully. But now with the introduction of large language models and AI capabilities for the masses, you can do a lot more, meaning the opportunity to automate stuff in your business in a smart way became significantly bigger.

2:21So the open questions become, how do you find the stuff that you can automate? Meaning how do you identify those low hanging fruits that you should probably focus on first before you try to automate everything in the company? And once you identify those, what would be a good use case for just old school automation without AI? And when does AI actually is worth the extra effort? And how can you put it in place to enjoy the extra value? If you can answer these two questions, you can gain massive efficiencies in your business, which we all want to gain. And hence, I'm really excited to welcome Kieran Gilmoury to the show today, because Kieran has been doing business automation and data analysis for years.

3:08Again, way before ChatGPT, way before it became a craze around the world, he was doing RPA and machine learning for a very long time, implementing this for other businesses across different use cases for over 10 years. So today he's taking that knowledge of 10 years of experience in automation and combining it with AI capabilities in order to allow businesses to enjoy all these amazing benefits. So my suggestion, we are going to dive into actual use cases that he has done for companies. We're going to talk on actual real business examples. So grab a pen. If you're not driving or walking your dog, be ready to take some notes because we're going to dive into real practical ways and really important questions on how you can do this in your business and really have an impact tomorrow if you want to.

4:01And so I'm really excited to have Kieran as a guest of the show today. Kieran, welcome to Leveraging AI. Thank you, Aixler. I'm really delighted to be here. I'm looking forward to giving some of my knowledge to your guests. I love your podcast, by the way. They say never meet your heroes, but why not? Let's do this. Thank you. I appreciate it. And so let's really dive in straight into use cases. And through the use cases, we'll discuss how was the use case selected? And then we'll talk about what tools and what was the process in the actual implementation of that. So let's start with whatever use case you want to start with.

4:41yeah let's we'll work through a range of these tonight and let me try and go back to basics so i'll use the word insure co standing for insurance company not to name or make anyone ashamed at the end of the day perfect you look at insurance and it's a reasonably simple process insurance broking as it were you have got new sales or you've got sales that you need to retain and somewhere in the middle is claims there you go nothing more complicated as the saying goes It is more expensive to get a new piece of business over the line rather than keep a current piece of business. The key bit, though, is selecting the right business.

5:22Not every customer, let's be clear, is a valuable customer. It isn't a numbers game. It is about, in this particular instance, a customer lifetime value game. Let me explain that term. And I'll keep working my way back through terms so that everybody comes with us. You have a customer and you charge them$1 ,000. you may make$200 out of that company that year because you have to pay your money out to your staff, to your insurance providers and everyone else in between. That customer stays two years, you'll make$400. So the customer lifetime value becomes$400. So realistically, what you're trying to do in insurance and in a lot of businesses find high value customers, very cheap for want of a better phrase to serve, but represent very profitable value over a period of time.

6:07And this company had one in every five insurance customers who traveled round and roundabout in this particular country as well. So why did we pick the retention bit? Goes back to what I was saying a moment ago. If you can keep your customers and keep the most profitable ones, give the less profitable to all your competitors. And the problem was your editors, you win on two doors. So we looked at this and went, okay, that's the one we're going to aim for because we've got existing data. And remember when you're building AI models or using robotic process automation or intelligent automation. It's the data that really makes the difference.

6:40Data we didn't have, data we did have, we went after the data we did have. I want to pause you for just one second because you touched on a point that is very dear to my heart. And if you find a way to solve either side of that equation, you're going to win big time. And at the end of the day, everything you do in a business is to grow the profit. And you grow the profit by, there are literally three numbers you're playing with on a very high level. Average cost of acquisition, so how much money you need to invest in order to get an average client. Your cost of operation that in most cases, unless you do something extreme, stays roughly the same as you're moving.

7:20It grows as you grow, but there's no peaks and valleys in that. And then lifetime value. So the two numbers you're really playing with is cost of acquisition and lifetime value. And like I said, if you can solve either side of that equation and increase lifetime value or reduce cost of acquisition, you're growing the margins of the company. And the process that you're describing actually wins on both ends because you're retaining the customers that have a higher lifetime value. You're losing the problematic customers that cost you more money to hold and then support. And so you're really winning on all three numbers that we talked about.

7:54And from a high level approach to selecting any business process you want to select, that's a very healthy approach. So sorry for stopping you, but I think it's a very critical point. So now continue from here. No, it's worth calling that out because as you said, we hit the front and the end and we hit the middle as well, just to be clear. As you start to draw out the riser, you could have 100 ,000 customers and we did. But it doesn't mean 100 ,000 are profitable. Go back to Pareto 80-20, go back to the really profitable ones. if you can get rid, for whatever better phrase, of the 30 % who do not make you money, don't care what anyone says, run your numbers, get your accountant to put the numbers through the book.

8:29They don't contribute to margin or anything if you pick their own customers. Get rid of those. And you don't need as many people servicing your book of business. So you can hit the middle. We went after the first one, we got the other two. And that is the joy of automation and AI. You go after one number and you can benefit in lots and lots of ways. Because what agent wants to deal with problematic customers i could constantly have to bite them argue with them whatever chase them for debt or something else reprune the tree as it were yeah so we looked at retention okay we got this numbers there has to be some insight i call it decision insight or business insight gardner called it this a couple of years later we were doing this 15 years ago and i like gardner but a good company but you were going okay how do we make better decisions compared to our competitors and we're looking at the booking and we ended up and there was an interesting thing that we did and this might be available in different countries the local university had a scheme whereby if we put in an amount of money we got access to a phd student and we got access to an operations professor and a math financial mathematics professor so based on the theory that we had we did a little bit of consulting work with the company going with this work we went look at all this data how could we enrich it what could we do it how could we make the decisions we brought in a student and the professors and we started to run some numbers in the book and very simple stuff to begin because customer lifetime value you can put the most advanced analytical model in place and we did and i'll talk about that in a moment but you also can do it very simply recency frequency value how soon or recent did they buy it how much did they spend and what was that actually work and you don't need perfect science dude you can just work your numbers there the mathematics that we were doing was using advanced ais we're year using machine learning, neural nets, everything that we could get to chuck what I describe as business math to drive better insight on the numbers.

10:21Long story short, what we're able to do is get more out of that process than we thought. Let me explain two parts. Everybody is tempted at Renewal to talk to every single customer. We were able to segment every customer across our book into five or 10 bands. We worked on both, five or 10, it didn't really matter. right the way from the highest propensity to renew. So almost no matter what you did to this customer, they were going to stay. Right the way over to the other end, which it doesn't matter what you do, what you offered them, these customers would go. They were the band that I described as thrill seekers.

10:55In other words, we just want the price somewhere else. And I don't care what you offer, we're just going to go and we're going to leave. And then everyone in between. Started at 10, moved it down to five. It was less complicated for staff to deal with. And that's something that's really key in the lesson we should call out as well. doing the math was not the difficult thing in the end it was an idea we got the mathematicians in we ran the numbers we pulled across using automation as well so instead of staff swapping it but there's a big lesson i know the man i know what i'm trying to do but if i don't explain it to the business and why these numbers matter and what tunes the staff who were dealing with these customers needed to play when they were doing this they would have been back to default behaviors and it took us a while being absolutely transparent to get people convinced that the machine could tell the price better than the individual.

11:40Now, we weren't removing the individual's thinking. And that was another piece. Do I rely entirely on the math? And some didn't loss the customer, some didn't. And then they got their own results and went, the math doesn't work. And you go, actually, you're not doing it right. But you need to just trust it. Subject to, just make that decision yourself because you can offer a price and the customer's going, no, you can't turn around the customer and say, the math told me you're wrong, but it doesn't work. So again, there's a whole lot of change management and really good decision insight is operationalized.

12:08Everybody knows what it is, how to use it and do everything else. Able to segment into those groups of 10. The 10s we never went after. You could have put whatever price you wanted onto that piece of business. And we did. And we added margins on with those who were more likely to stay. We offered more competitive at the lower end and some groups we just didn't touch. Now think about that for the moment. We high margin business, high customer lifetime value. we deliberately priced out the customers who were really problematic high debt never paid argued all the time but the smallest thing to not try and pay they went for the competitors thank goodness and then if we didn't chase that group gone 10 % gone whatever it was and then on the upper end we didn't chase them we didn't make phone calls because every time you made a phone call you negotiate every time you negotiate you give off margin didn't negotiate with any of them unless they were absolutely high value.

13:00We got them at them, wasn't perfect. We were 95, 97 % perfect, but let's just say it wasn't. But we needed to keep them. Then you might make a phone call, but you didn't negotiate all your margin away. You gave a proportion to make them feel good. And the bottom end who were going to go, we never phoned them. We never, you had to write them to tell them the price, but we did nothing with them. So not only were we able to segment the whole book, earn more margin, remove base customers, we're also not chasing customers. and then we started to play tunes with customers. The higher customer lifetime value customers were fed into the database.

13:32They were scored, they were ranked. Then we built all of the numbers into the outbound dialer and into our marketing materials as well. The customers we wanted, we went after. One call, two call, one call plus email, two calls plus email, whatever else. And we automate it and digitize the whole thing. Long story short, it went to 333 % more profitable in just three or four years. And if you'd looked at the number of staff that we would have had to put in, if you had to play those number tunes and get everybody to manually do things, you would have been under 1 ,000 staff. We were able to do all of this work with more customers, making hundreds of percent more margin over the number of years with 33 % of the staff that it would have been were it not for AI, intelligent automation, robotic process automation as well.

14:21I'll stop there because you might have questions. Wow. Yes. First of all, brilliant. I want to say a few things because I want to highlight on a few topics that you touched that are very critical. And then we're going to talk about the how. But first thing is the process that you just described, I want to generalize it for a second because this is, oh, it's an insurance client. It doesn't matter. any business that has enough data. So I'm going back to this. Any business that has enough data, and obviously insurance companies are lucky that way that they track a lot of stuff about their clients because that's the nature of the beast.

14:57But even if you just look back on any SaaS business, even you're in a service company and you have your cleaning offices, it doesn't matter. You have data somewhere, could be in a form of emails back and forth, could be in a customer support ticketing system, could be in Slack channels that you have, like whatever way you communicate with those clients combined with your financial data of we have invoices and when we sent the invoice and then when was it paid and when was it collected? Like all this data can be used in order to review the lifetime value versus the effort that goes into each client in any business.

15:42So then you're like, okay, what's the effort? The effort is sometimes not straightforward. So if I go back to what Kieran shared, it involved high-end research people, like PhDs from university. It involves mathematicians. It involves machine learning people. It wasn't an overnight, oh, let's put this into chat to PT and we get a result. It's not. It's a project. It's a large project. So then the question is, what is the ROI? And in this particular case, you can see that once you deal with the core of the business, reducing cost of acquisition, reducing cost of operation, increasing lifetime value, over three years, you can triple your margin, which is while reducing the staff.

16:29So it's like you win really on every single front. The last thing that we'll say about this, because you touched on this, and it's very important to understand when you start getting into these projects, is why you need somebody who's experienced in this to run a project like this for you. Different than historical software, you buy it to do X, and it does X. And yes, it has bugs every now and then, but that's what it does. Like it does, it gives you a specific outcome. A machine learning model is a statistical model. and you never know when you start where you're going to land with the statistics.

17:08So you can land 97%. You can land 60%, right? Which would still probably give you some benefits, but you can land at 40%. That means you got it wrong more than half percent of the time, meaning you're losing clients you shouldn't be losing, and you're betting on clients you shouldn't be betting on. And it's very important in these projects to do some kind of an initial test case on a much smaller scale, on a much smaller budget, and evaluate as you're moving forward to see, do you want to pour more money into this? Can you fine-tune it better? And so on. So these are the things that comes to mind after everything you said.

17:44But overall, I think it's a brilliant use case because I think it's applicable to most businesses. I want to switch gears and ask you about the how. And the how, the first how I want to ask is, how was that picked? Did the company come to you and say, ah, we want to find our best clients? Did the company come to you and say, we want to understand how we can use AI to maybe improve our business? What was the decision-making process? And then we'll dive on the practical how and how the project was done. Yeah, decision-making process was my own, if that makes sense. I was hired in, if I give you a little bit of background, It was very traditional insurance broker, which is they had a contact center.

18:27They had some staff. You rang up the staff and they put some numbers into an insurer's computer system and they got a number out. And that was it. But this was like 15 years ago. And data analytics, I don't use them before in different enterprises and different businesses. The exciting part was the amount of data that was available. Just to what you said earlier on, you don't need vast vats of data to do so. We're not talking about LLM models built on huge gigs and gigs and terabytes of data. you've got data and you called it out wonderfully a moment ago we have customer data and they're buying behaviors over the last couple of years did they buy house insurance car insurance renter's insurance whatever it is you had the price that they bought it you had the fee before they they fee that you wanted to charge and the fee that you then negotiated the commission in between whether they bought add-on products whether they bought home plus car insurance you'd all these different signals even the geographic area in the country where they were buying stronger than others you had some vehicle data you could see where you're more competitive on insurance for some vehicles and not other vehicles and so on so as soon as you start looking through data the signals from the noise it was around this time i started to read every single book there was on amazon there wasn't many at the time to be absolutely honest years later it was we went we put analytics into everything it was literally a decision inside company so i could tell you which staff were probably going to exit the business, churn analytics, debt analytics, credit analytics.

19:53But we had this data in a database. We had an application that was for the insurance company, box number one, and we had the financial data as well sitting in the finance system. Took both bits of data, moved it in at the time to a SQL database, which was big at the time, on-prem, not cloud, like you would have now. And then as you said, we started to look for trends and patterns in the number, because ultimately when you think about it, there must be similarities between people. There wasn't a hundred thousand entirely different customers. We all make similar decisions. When you read any of the books and looked at any of the numbers, we weren't the first to do this.

20:28Huge banks in America were looking at customer purchase behaviors and patterns and customer lifetime value. So we weren't the first to do it. But I'd done mathematics before. This was a playground and I couldn't do the math. I still can't. If you ask me how to build it, build a high-performing analytics team, absolutely. But I tell you, I couldn't build a neural network for love for money and i love now that to a degree chat gpt can because they chuck my numbers in tell me stuff or tell me how who's going to stay or not put in the sql database got a really great mathematician worked out what do we want to do ultimately customer lifetime value we could do that of a one year three year two year five year three year worked out to be the the most accurate because everything's a degree of accuracy the further away you move numbers of years the less accurate it's going to be because anything could happen inflation war whatever But to your point as well, so we went, okay, let's set up some propositions here.

21:19Let's make some assumptions and then let's run the math or start doing some mathematics in this to see if this actually comes true. So you're going, okay, by age group, are people more or less price sensitive? By vehicle category or risk insurance or by price band, are they more or less insurance? And as you're saying, some of the math was rubbish. It didn't work. We made the wrong assumption. we decided on some things set up the hypothesis and off we went to prove or disprove how we do that put it in the database build a role in analytics using python and matlab at the time why matlab was a very generic university language that the phds were taught on very similar to python in many regards and python's been around for years and it always will be matlab had a lot of mathematical models already built in and capabilities linear regression neural nets all easy enough stuff or that particular mathematical model and didn't cost anything because it was academic versions, not paid for versions and paid for weren't much.

22:15Set our hypothesis. And then we did exactly as you described a moment ago, we took a set of data and we started to actually look at that and say, okay, if our mathematical models are right, does it make sense against this 20, 30 % of data? Okay. We started to notice the patterns that start to come out. And then I set part of the team off going, tell me what I don't know. So rather than me deciding what's in the data, what patterns can you start to look at? And of course, there's a whole pile of patterns and different mathematics you can do in there to start to notice trends between data lines and data points and something else.

22:48And from that, we discovered more things. Age definitely was a barometer. Distance out from renewal was a barometer for lots of people. Economic or demographic information, type a car, age a car. You could start to see some looser trends and some stronger trends. Ultimately, it went around about six different things, price, age, vehicle, location, and everything else. All those things were coming in. Difference between last year's price and this year's price and so on. How long they'd been a customer, were they price sensitive or insensitive? All that sort of model went into it. And then the scary bit was taking part of the book and treating them differently.

23:25You're talking about a lot of money here if we'd have got it wrong, but the math had been done, the analytics had been done, the student had done it, PhD student, the professors had done it. we'd all done it okay now we need to pull the triggers thankfully the very supportive leadership team who went what have you done just explain it and of course at that time you're trying to explain neural nets decision trees whatever machine and of course you've got people in front of you going what my goodness is this that's part of the job as well getting the leadership buy-in to give you the faith to allow you to do it and explain what's in the black box and what could go wrong but we start took aside some of the sales team and started to treat the book of business differently the nervous part was when you're renewing you're looking at about a month out from the data then you practice the model and then it's another month before you can get it so you have a 12th or sixth year where you don't know what's going to happen fortunately fortunately they were sitting at 70 percent retention we went to 85 with that group of customers first model's done in three four months because this all takes a little bit of time folks as you said earlier on you can't just chuck it into chat gpt but bam it's done because even getting the numbers done and the data into the right place and share it out.

24:33And remember, people were renewing, so you had to strip those customers out in real time instead of making an outbound call and linking the outbound call system. So all these things come into play and teaching that group of agents how to treat the customers and to have faith. And we spent a lot of time with them explaining what we were doing, why we were doing it and everything else. And what is this mathematical model that you're now telling me? This black box is telling me what to do. I don't understand. We did all that 85 % retention, which allowed us to do another month. Let's do the same thing for another month and see what we can do.

25:03Because again, if you start playing with your mathematical models all of the time, then what was the item that changed? What's the trend that you noticed? What's the cause and the effect and everything else? You have to have a little bit of faith for a little bit of a period of time. And that can be hard to do if things aren't immediately going the right way. And in some instance, they didn't. Because when we listened back to the agent calls, they ignored what they were told despite telling us perv yep we're going to do it got it understand it they didn't they were just too embarrassed to say or they got nervous because remember we were incentivizing the agents to sell and if they didn't sell they weren't getting commissions and therefore their immediate thing rather than customer lifetime value was give the insurance away as cheaply as possible because i'll get 20 % of the sales price not the margin so even by examining what we were trying to do and listening to agent phone calls and look at how we incentivized agents and look at how that was against the customer lifetime value model where we wanted more margin and therefore we had to get the agents to stick for longer to the price and feel confident about it say that's the best price we can give you they were going back in my mouth this payment or give them money off you discover a lot of things that you can change some things we've been doing processes for years risk and compliance statements and a whole load of steps that nobody really needed to do for four years.

26:19Strip those out. That saved us time. We did the math model, got it working. 85%, 86%. Trained the agents, pushed, adjusted the bonus, said it would give you this bonus here. Just do this. So you're not going to get impacted. Again, off we went 85, 87, 89. Then it gets really sticky because everybody starts to get used to percent and percent. They're going, they're seeing 100. You're not going to get to 100. On that, you have to accept. You have to manage expectations. And the first percentages are tough at the beginning till you build the momentum, till you get everybody aligned, get your commission structure, get your sales structure and everything else.

26:54And then it gets flywheel, it goes quicker and quicker. Then it gets harder again. Because then you're down to the last lots of customers where you really are starting to play tunes and narrower numbers. But got the price, got the agents offering the price. what we were doing in that was as soon as someone phoned in, we popped their data up from this database on a web form sitting beside the application itself. So the two screens, another beautiful lesson that we learned instead of have agents jumping backwards and forwards and screens and getting frustrated, up popped the information. It showed the customer, showed the history, showed everything they wanted, showed the price they'd paid last year, worked out all the maths for them, showed them the difference in price this year, showed them the margin, the three-year lifetime value, all the thing that they needed to do in a very visual format to be convinced that this was the right thing to do, all prepared for them.

27:45So again, it's not just doing the math, like a back to that operationalizing it, bringing the agents through, bringing their managers through, adjusting the bonuses, presenting the data in front of them, giving them the confidence to hold, support them with the phone calls and everything else. And that wasn't the inbound calls and the outbound calls again the same database the same form except this time we used an automatic dialer we had the database we used automation to take the data pop it into the database having checked have we phoned this customer before no maybe we did maybe we didn't someone the odd person ran down their list of hot customers of course ryan because they were getting the commission we had to stop that no follow the script folks follow the numbers that are going to be presented at the time a day that we have worked out when that customer is probably going to be available.

28:30So mothers with kids, you're not going to ring them at nine in the morning, they're bringing them to school. Fathers with kids, you're not going to phone them at three o 'clock and four o 'clock in the afternoon and they're bringing the kids home. It was time a day. All these things were worked out pretty much for them. And that really helped the staff because instead of trying 10 times and they weren't getting them, they were getting the customers quicker. And all of a sudden that made the job feel a little bit easier. They were offering prices with the confidence that if they stuck with that price, they pretty much knew the customer was going to do it.

28:58Instead of hunting around the place for all sorts of information and disparate systems, it was being presented in front of them. And from the agents as well, they were saying, what else can you give me? Can you give me bad debt? Can you give me the last time they were calling? Can you tell me how long they've been in the queue? So again, even if they'd been in the queue for ages, and that did happen at times, were you going to get the renewal? Maybe not, but you were more chance of getting the renewal if went, Izer, I'm really sorry you've had to wait. We've an unusual call volume and I can only apologize.

29:27So before the customer could start shouting or renting or whatever, express their frustration, that data was available to them as well, which helped themselves. The dialers popped the call in, they popped up the screen, everything was pretty much automated and digitized. All the grunt and the hard work taking away, the thinking of working out mathematics and whatever else and margins and commissions all removed to help the agent spend time on the call with the customer and this was the key bit truly listening to what the customer was saying and listening to all their buying signals and whatever else once all that surround noise and pressure and interruption was done they could truly concentrate on everything the person was saying and they could react to the conversation that was being had not the conversation that they probably wanted to have to try and dry sail or try to sail over the line to get the next sail.