[BLANK AUDIO] Ultimately, if we're going to link non-financial metrics to financial performance, we've go to figure out how do we take this analytics results and put them in to a financial model because ultimately, what we're trying to do is predict if these non-financial metrics go up and down, what's going to be the impacts on financial results. So, here's an example of this. We got a computer hardware manufacturer, major supplier of equipment. And again, senior management, they wanted to know whether these initiatives this company was putting in actually paid off or not. And more important than that, it's not just did they pay off in the past. If I need to develop action plans to increase value in the future. What action plan should I take on? And, obviously, what I would like is what's called the biggest bang for the buck. If I put a dollar in something, where am I going to get the biggest return? Which non-financial metrics do I want to improve and by how much? Now, the first thing you need to think about is, when you do these, what are the financial outcomes that you care about? Because I need to predict that, and it doesn't have to necessarily be profits. There could be intermediate financial results you want. So, if you take something like customer satisfaction. Now, there's lots of reasons why you want higher customer satisfaction. You think it's going to keep the current customers you have in retention. You think they're going to recommend your product, and not do bad recommendations. You're going to get a bigger share of wallet, a bigger proportion of the expenditures by your customers. There's various reasons why and you need to layout. When you're going to estimate these models, what are the outcomes that you care about? And ideally, what you'd like to do is what are the intermediate outcomes? If you think about customer satisfaction again, there's no reason that higher customer satisfactions necessarily leads to higher performance. It's only if satisfaction goes up and you can retain customers, and they buy more, and you get bigger share of wallet and they make positive recommendations, that's when it pays off. So you need to setup what are these intermediate outcomes that you want if these things go up. So, this is what the company picked. Now, the idea here is, yes, customer satisfaction is going to impact these things, and we're going to try to estimate what the impact is so we can build a financial model. But if you're a manager, customer satisfaction actually doesn't really mean anything [LAUGH]. Okay, they're satisfied, what does that mean, to say a customer's satisfied? What can you, as a manager, do to actually change satisfaction, and what dimensions do customers really care about? So here's, where you have to put the front in to the model on this customer satisfaction is in the middle. What are the things that customers care about that is going to increase their satisfaction in such a way that they buy more from you that they stay with you that they make positive recommendations. So here, what the company did is let's go out and do some marketing research. Let's do some focus groups with customers. Let's see what they say would cause them to actually buy more from us. And there's various things that could happen, right? Do I think I got a good relationship with my supplier? Do I trust them, do I think the value is high? Something like a personal computer, do I think the thing actually works, right? Can I change over when I put the new software onto this program? One of the big things is can I take my old files and push it over there? So, what you want is manager when you estimate these models. Yes, I want the financial results but I want to know is what are the specific actions I can take of these over on the other side that will impact satisfaction in such a way that my financials go up. And where is the biggest bang for the buck right of these many dimensions I have over on the left hand side. Which one of those if I had a dollar to invest. Which one would I want to invest in such that I get the biggest return? And that's the model we're going to estimate. So, what we have here is basically a regression model, right? First of all we're going to say, if satisfaction goes up, how much does retention go up? How much does word of mouth go up? How much do these ultimate financial outcomes go up? On the other end you've got, which one of these dimensions are drivers actually caused customer satisfaction to move up and down and by how much? So, these are the estimates we have here. Now, if you look at this diagram, we've got two things, we've got things inside circles and we have little numbers next to arrows. So here's what these things are, the numbers in the circles that tells you how is this company doing on a 0 to 100 scale on that dimension. Hundred being the highest right? So, if I get a 70, I'm doing 70 out of a 100. If I get 80 on that dimension I'm doing 80 out of 100 where 80 says I'm doing better. The numbers next to the arrows, those are the coefficients from our regression model. Basically, what it says is if I can increase the score on those drivers to the left by one unit, here is the impact I am going to have on my dependant variable. Either on customer satisfaction, or if I'm estimating economic outcomes, here is how a one unit change in satisfaction impacts these economic outcomes. So, here's an example. If you take those numbers, if you increase the relationship score by 10, what this says is the customer satisfaction score is going to increase by three, right? You take that 0.3 times 10, so it's going to go up by 3. Now, if I can increase my customer satisfaction score by 3, what this predicts is it's going to increase my retention score by 3, or from 73 to 76. Now, based on that, if I can figure out what's the financial benefit from keeping a customer, I can come up with a financial model. Now again, if you go back to the diagram, something I always ask my students is if you had a dollar, where would you invest it given this? Now, a lot of times what people say is, well, invest it in trust. Because that coefficient, that number next to the arrow, is biggest for trust. It's 0.8, which says, if I could increase my trust score by 1 point, my customer satisfaction score is going to go up by 0.8. What seems to make sense, but then the question is do you really believe you can increase your trust score by1 point or 10 points? And how much is it going to cost you? It may be the case. Remember, these are personal computers. They trust you as much as they're going to trust you, okay? They don't trust anybody perfectly. It may be extremely hard to move the trust score above 0.8, where you're already doing fairly well. It also may be really expensive. Alternatively, if you look at your shopping score, you're doing, so badly on that one, right, something in the 60s, that it may actually be easier to go after that. Because if you're doing badly, it might be easier and cheaper to improve that. So, even though it has this coefficient of 0.1 it's easy to move that one by 10 points, and very hard to move trust by even 1 point, you should probably be going after the shopping. So what you need is both, that model we just estimated, which tells you the benefit for moving the shopping or trust score. And, what do you think the cost would be to actually move this, and how easy is it to do that? Now based on that, you can start coming up with a financial model which is ultimately what we want. If I increase trust or if I increase shopping what do I think the ultimate impact is on retention, recommendations and the other financial outcomes that we are about. Now again, this is looking at a straight linear relationship. It just keeps going up in a nice straight line but we found from some of the earlier analyses that a lot of times there's non-linearities. It doesn't keep going up in a straight line. In fact, maybe you want to be really good at customer satisfaction, or maybe you want to stop somewhere like 75. So here's where you want to dig down deeper, so here's what the company did. Let's not stop there. Let's look a little further. Do we have any of these nonlinearities? It turns out this is one of the companies where you really want to increase satisfaction because what they found out is, if you can increase that retention score to 90 or above? The customer bought the same brand again 56% of the time. If it was below 90, they only bought it 30% of the time. That suggest you really want to spend money to get customers above 90. That's when they start buying a lot more of this stuff they stick with you. Same thing with the recommendation score. If it was 90 or above, they recommended about one and a half times. With below 90, it was less than ones. Those are fairly big changes, so here's a company where it would say, if I can push my customers above 90, there's a huge payback, because there's a big difference once you get passed this 90 in terms of whether you're going to keep them or recommend them. So, now the question would be, I've gotta pick action plans because ultimately, that's what I want. Can I use analytics to pick out which action plans are going to result in this higher financial performance? So, based on this what you might say is let's try to pick action plans that are going to move customer scores above 90 because that's where the big pay off is, if I can move them above 90 on here. So, let's make an assumption. And you would have to do some market research to figure this. Let's assume that you could probably move a customer score by a maximum of 10 points. And that's probably true because customer satisfaction scores are really hard to move they're easy to move down. If you've had a disaster, people get less satisfied. They're really fairly hard to move up. So, let's say the most on a 100 point scale you can move them is 10. Well, what that would say is if I want to move people above 90. There's only a small number of customers that I could move 10 points that are going to push me above 90. So,at that point what you want to do is figure out which customers do I want to focus on. Not all customers, what I want are the ones that I could do something that might move them 10 points, and move them over this 90 threshold. That's where you're going to get the biggest bang for the buck. So, here's what you're going to do. If you start looking at all their customers, and here's some diagrams of how many customers you have at each score, the people you want to focus your actions plans on. The one based on the analytics you're going to get the biggest bang for the buck are the people in the red bars. Those are the people where I can move them about 10 points over that 90 threshold. Based on my analytics, I believe that they're going to recommend a lot more, and they're going to stick with me. Roughly, it's about 60% of the customers you want to focus on, not all of them. Okay, now based on that, let's see if we can come up with a financial model. So, what we're going to do is say okay, if I focus on these 16% of the people, how much is it going to cost me to put a customer initiative in that's going to move them up 10 points, if I move them up 10 points, what do I think the financial benefit is from retaining them and from having them recommend people more? Well, the way we can do that now is take the analytics and basically, we're going to do a little Excel spreadsheet. We're going to come up with what's called a net present value. Again, net present value because some of the benefits are going to come out in multiple years, and I need to discount those back because I would rather have a dollar now than a dollar in the future. So, here's the little spreadsheet we're going to have here. So, here's some of the assumptions we're going to make. Okay, first of all, and again, you would have to base this on what your company does, let's assume we got a 5 year time horizon. What happens now is satisfaction's going to have no impact after 5 years, especially with something like technology, right? New players come in, new technology. Let's assume we have a 15% discount rate, right? There's a cost to capital in your organization. How much do I have to go out and borrow the money from? How much do I have to pay to get money from my shareholders? Let's assume 15%, which right now is very high, but we use it for arguments' purposes. Now, the margins on a personal computer are pretty small. Let's say on average, you could sell it $2,000 high-end personal computer. The margin you're going to get out of that's a $145. You're not going to make much on each one of these sales relative to the selling price. So, let's assume what we're thinking about is can we invest $5 million in some kind of customer satisfaction improvement initiative? Where we've got a 5 year horizon and a 15% discount rate. Let's see what the benefit would be there. Now, here's the estimates we did before. We estimated that 25.99% improvement in retention, if I could move people over 90. We estimated a 0.65 change in recommendations if you can move them above 90. Now what you need to think about is of those people, how many do you think you could actually move 10 points? Right, because obviously you're not going to be able to move all of them. So here's where you want to do market research. Based on some more analytics that you do with market research, what's the odds that we can move somebody up at least 10 points? If you think it's 20%, 20%. If it's 30, it's 30%. because that's going to have an impact on whether you think this works or not. So based on those assumptions, which are in the spreadsheet, we're going to compute the net present value. Again, with the net present value, you've got cash going out. Well, that's the $5 million right now. Then, I've got cash coming back in. Well, the cash comes in either because I stick with you and I buy one of your computers later, or I recommend to someone else, and they buy it. Again, based on this analytics and market research, we can estimate that. And based on that we can come back, and just with this simple scenario you get a huge payback. Now, obviously these are all estimates. But the nice thing here is you use the analytics to start the estimates. Based on that you come up with this spreadsheet. Now, you can start doing a lot of what if analyses. What if I changed my discount rate to only 5%? Which is getting closer to what it is right now, where you have almost no interest rates. What happens if my margins go up to $200? What happens if they go down to 100? So, once you set up this spreadsheet that's based on the analytics, you can do a whole bunch of what-if analyses by changing some of these parameters, saying this is an estimate. How comfortable are you if this estimate is off by 10% positive 5% at negative. And come up with a comfort zone based on that, how comfortable are on investing $5 millions to improve this dimension? But it all starts with the analytics because you have to come up with some perimeters to start estimating financial models then you can start doing the what if analyses on these.
