Let's go through some comprehensive examples of how to link these non-financial metrics to financial performance. So here's another example. We've got an advertising firm. What happens is the advertising firm is they have lots of local advertisements. This would be like the yellow pages, the local advertisement book that with the phone numbers. So you can have it in various places in Philadelphia. Could be South Philadelphia, Center City Philadelphia, it could be each one of the little suburbs around Philadelphia has their own advertisements. So a company could decide I'm going to advertise in all the local locations, they could say I'm advertising in only the big ones on this. So, that's the setting here. Now again, their hypothesis, which is what the business model is, is more satisfied customers buy more in the future. Now, this could come through a lot of things. All right. We're going to buy more of the same service. So, instead of me just buying in downtown Philadelphia, I'm also going to buy one for each of the suburbs because I like your service. It could be I'm going to upgrade. Instead of just having a black and white ad, I'm going to add color, I'm going to add a picture here. It could be they like this service so well you're going to be able to cross sell them something else. So, there are some hypotheses about how a more satisfied customer is going to buy more in the future. So based on this, they spent a lot of money putting in these customer satisfaction initiatives, and they like a lot of companies they do market research. They did this market research, and in truth they really didn't know whether these improvements in customer satisfaction ultimately were producing the financial results they wanted. Right? We're spending money on this, let's prove this. So, one of the advantages of predictive analytics I've found in companies is you can use this if you're not working in finance to help convince the chief financial officer that these investments are making sense. Right? Ultimately, they want to know show me the money. So that's what happened here. The CFO was pushing the people in marketing to show me that those customer satisfaction initiatives were spending all the money on, actually are translating through to performance. Because we're using these metrics in our strategy, we're using these metrics for performance evaluation, and like that other company, their bonus plans were based on these performance metrics. Again, it was what's called a top box measure. This is a company where satisfaction ranges from one equals low to 100 equals high. The bonus was based on what percentage of our customers gave us a 90 or above. Let's push everybody to the high side. And again if you think about target setting this is one of those more is better philosophies. But the CFO is going to push us, so let's actually see if this is actually paying off. Now, the nice thing here is this is a company where the customers sign a one year contract. So you can actually link up how satisfied was the customer this year, and how much did they buy next year. That's the hypothesis we're trying to test here. So, here he is the analytics that they did. And this is something called nonlinear regressions. Basically we want to see how these things are linked together here. Now, if you look at this, this is really great for about the first two thirds. Almost a nice perfect line between how satisfied were you last year, and what are you going to buy this year. Then you get these strange little steps, you got to ask what those are. And finally after about 75, people were more satisfied but they didn't buy more. Now remember what the bonus was. Get everybody 90 or above. Now, if I look at this, the first thing you got to ask yourself is does that target make sense. Now, obviously over the whole spectrum here, there is a relationship between customer satisfaction and future purchases. But we also have this target we have to worry about, which is everybody pushing towards the top. Well, if you look at this, I would say the answer is no. It probably doesn't make sense. Right? And it turns out when you talk to these people after about 75, they've bought everything they're going to buy. I don't need to buy any more advertisements. So, you can increase my satisfaction, you can spend money doing that, I'm not going to buy anymore, I don't need anymore. The other thing is you have these weird steps here. And again, here's where some qualitative analysis added to the predictive analytics helps. It turns out what those steps are, is instead of them buying in different cities. So, they bought downtown Philadelphia, and South Philadelphia in each one of the suburbs, they start upgrading the service, they start going to full page ads, they start adding color, they start throwing at a picture of their service. But you had to be a certain amount satisfied before you really upgraded, which you're going to buy as opposed to just buying more of the small ads on this. But again, after a certain point there, it didn't get any more. So again the question is, okay, if I'm not going to set this target up there, you know move everybody above 90, where would you set it? Now remember what you want is the biggest bang for the buck. If I tell my employees to move some people from one box to the other one, where do I want them spending the money. Because they're going to go after whoever I want them to go after, or whoever I tell them to go after. Well, it turns out you probably want to focus right there in the middle. If you believe this model, it says if I can move some people off of that nice straight line at the bottom, and push them from say a 55 to a 65, that's when they're going to buy a lot more. Those are the employees I want to focus on, not everybody. You do not have to focus on everybody. Let's focus on this group that based on this analytics, if I can spend money to increase their satisfaction, those are the ones I should get the biggest bang for the buck. Now again something else you need to think about when you start doing that. Are the things that cause somebody who has given you a score of 90 already, the things that are drivers of their satisfaction the same as the drivers of somebody who's given you a 50? Highly unlikely. Right? The people who already have 90 are satisfied on many dimensions. So, asking about those is not going to help. The thing that's going to get me go from a 50 to 60 can be very different. So, when I ask those satisfaction questions, what I want to know is, what dimensions of satisfaction would cause somebody to move from 50 to 60. That's what you want to measure, not just are you satisfied. Are you satisfied on a dimension that somebody at 50 cares about, not on a dimension that somebody at 90 cares about because you're not focusing on the 90s anymore. Okay. So, based on that let's focus on these people on the middle, on there. So, here's another example. We've got a technology services company. Again, we got this issue of can you demonstrate that this money we're spending on non-financial is actually paying off here. Right? Are these non-financial metrics actually related to future financial performance. Now here's one of the dilemmas you're going to run into if you start doing this. Again, data is not a problem in companies, but data is power. Information is power. What you are going to end up is what we call data fiefdoms. Lots of different parts of the companies own different parts of the data, and they do not like to have you have access to it. My experience the hardest people to get data from are the finance and accounting people. Right. They're going to claim that you won't understand it, it's proprietary, but if you really want to link non-financial to financials, we have to break down these what are called data fiefdoms. Okay. So, we had data fiefdoms here we have to break these down. Now, the financial outcomes they cared about was linking up these marketing metrics, and these quality metrics. So they also had some operational metrics that had never been examined by the company. Now let me give an example of what this company does. Part of what they do is basically do cloud computing. They will do all the kind of back office computing for you, put it in the cloud. They'll also implement systems for you. So, we have two types of metrics. Some our metrics on will ask the customers how satisfied they are in various dimensions. Other ones are operational equality metrics. How much downtime do we have? How fast is our service? So we have two types of non-financial metrics that we have to worry about, summer survey based. Some are really hard core operational measures but they are not financial on this. Now, they had very strong intuition that these customers and quality metrics they had were related to future financial results. And here's kind of what you usually see. We know it's got to be true. I've been in this business long enough that if the customers are satisfied on these dimensions they buy more, if these quality statistics are better, they're going to buy more. Well, that's the intuition. But again, an intuition is a hypothesis. So, here's what they wanted to look at. Let's relate these customer metrics, or operational metrics to financial outcomes. Now, one thing you need to do is figure out what financial outcomes do you care about. They don't have to be the same for every company. Well, here are the two things they really cared about is annual revenue growth. Okay. Because you have lots of costs that stay exactly the same fixed costs, if I can grow the revenue, my profits are going to go up. And what I really want to know is what drives clients to have revenue growth greater than 15 percent. Those are the ones I want to focus on, but I've got to figure out what the drivers are there. They also had these operational or quality metrics. This composite measure of things like downtime, service availability, and things like that they were looking at. The customer metrics, they ask you various things on five point scales. Overall how satisfied are you, again which your willingness to references this word of mouth, would value do you think you'd get out of this? And because we care about contract renewal, what's the likelihood you think you're actually going to buy our service again because it's easy enough to go to another cloud computing provider. So, what you need to do when you're laying out these models is think about what are the dimensions in your strategy you care about, specifically what are the financial outcomes. And here it was really revenue growth not overall profitability. So, here's what you find. Okay. Let's look, and again we want leading indicators. Let's relate what kind of satisfaction scores you give us on these various dimensions, and future revenue growth which is what we want is leading indicators. Well, it turns out a lot of these, the green ones are statistically significant when you run the data analysis. The big ones appear to be overall satisfaction value. Look at service quality though, it's actually negative. It said as service quality goes up, future revenue growth goes down. Now again, we got to worry about this all correlation versus causation here. What is causing this. And here's where you want to dig in. Well, what it turned out this is a non-linear relationship. In cloud computing, you have to have a certain amount of security, you have to have a certain amount of uptime. If you don't meet that the customer is not going to be there. Once you meet it though, they're not willing to pay for to make it higher. If you've got 99.9 percent reliability, and you're going to charge them a lot more to get to 99.9999 percent reliability, they don't want it. It is not worth it to them. So, what you had here is they were pushing that spectrum of service quality too high, it just didn't pay off. Not that service quality didn't matter. In fact, it's what's called a satisfies her. If you don't reach a certain level, you're out of business. But above that it may not make much sense to actually improve too much. You got to worry about whether your customers willing to pay for it. Now again, the other thing they really cared about was not just revenue growth. Tell me the people that have high future revenue growth. So, here you are doing something on a model where the thing you're care about is 0 1. Did you have high futuregrowth or not? So you got to use different statistical methods. But again what you see is this negative on the service quality. When you run it just as a straight model, you'd have to estimate the nonlinearities here. The thing that really mattered here, value. Right. For some reason, if people think this is a great value, that's when they're actually going to have higher revenue growth. Now, always the big question then is, how do I get higher value, what do they mean when they answer this? And that's where you got to go into the peeling the onion back to say what dimensions of value could you as a manager actually do, to actually make this thing pay off.
