How Roofing Companies Can Predict Profits: Data Models & Metrics Explained

So are you still chasing those big revenue numbers, thinking they tell you everything about your roofing business? Hey, everybody looks at the top line, right? But here's the thing. Revenue is just vanity. The reality is if you're not tracking profit and cash flow, you might as well be flying blind. Most companies only measure what happened last month, but what actually matters is predicting what's coming next and having a model that puts cash in your bank, not just numbers on a spreadsheet. Let me break down exactly how to build a data model that shifts you from just hoping for good months to actually engineering predictable, profitable growth. We'll cover what to track, how to define each stage, lead to review, and why it's the difference between surviving and actually dominating your market. Let's jump right into the data that really drives your roofing business.

What we're looking for when we're defining a data model is we have to set the volume metrics and goals of the business and the conversion rates and targets and the time service level objectives. So to kind of put this in to make this make sense, revenue is vanity, profit is reality and cash flow is sanity. Have you ever heard that before? Before? Sometimes they mix up the sanity and reality on the last two. Let's look at that through the lens of a data model. Volume is total volume of revenue. It's a goal, it's vanity. But the conversion of that revenue to profit is reality. It's how much money are you really making? But all that really matters is when do we get that cash in our bank account, the time that it takes, the objectives that we have in terms of our goal to get that money into bank account. That is reality. So, so we have to answer all three of these things with our data. So we have to create this in our data. So we have to use tools to define customer life cycle stages. We have to set an expectation for what the incoming lead quality is, and we have to try and figure out how do we forecast revenue. Then when we build a system that can synthesize or create this data as a byproduct of the work that we do, we can then disseminate that data to the right people. So we can determine what questions must we ask and be able to answer and thus report on to make good decisions decisions. The goal being to move from hindsight, what happened to insight what's happening to foresight predicting what will happen. Everybody here would make more money if they could predict the future. So this is where the funnel part gets broken is most roofing CEOs are reporting on what happened. We got this many of these, then we got this many of these, and we got this many of these. It was worth that much. And then we did it. And we have money. And when we look at the systems, it doesn't look like this. It looks like this. Very unpredictable in how things get to where they're trying to go or how long it takes or the process that we use to get there. This is the challenge of trying to do it. And a data model looks a little bit more like this where we define things such as life cycle stage. So we have lead, mql, SQL, sold build, we get a review, we get a testimonial, we get a referral, and maybe we get repeat. So to illustrate this, I kind of want to say, well, what is a lead? Is a lead someone who clicks on an ad? Is it a phone number? Is it a door that you knock on? Is it an email address? Is it a Facebook comment that tags a friend in one of your posts? Is it someone who says they need a roof? At least lead is someone that you can contact. But would you pay $150 for someone that you can contact? Would you pay the lead cost of that? And so what we've learned today is that not all leads are created equal. A lead is not a lead, is not a lead lead quality matters. And we should put as much control into it as possible when we're trying to qualify leads and what defines a marketing qualified lead, then what comes after this is where we begin to determine the next life cycle stage, which is we have to send them some kind of proposal. So you have to ask them to do business with you. And so that's what this starts to look like is what is the volume, what is the percentage and then the time. But then there's the other side of the transaction that we had. Roofing tend to forget about, which is that how many of those sold jobs do we build versus how many cancel? How many of them leave us a review? Not every build leaves you review. We should know how often it happens and we should know our percentage of penetration on Google reviews. A good standard is about 15%. The best I think I've seen is around 40%. So 40% of their builds results in a Google review. And then what if we could get some percentage of our reviews to also also record us a video testimonial? And then the gold standard of a happy customer is a referral and repeat business because they all feed back. A review could generate more leads. A testimonial could generate more leads, but a referral generates more marketing qualified leads. These aren't just people you can contact. This generates people who want your services. And when they repeat well, they just go right to wanting a proposal. So we need to measure the volume, the V number, the conversion rate cross, and the delta of time that it takes to move through these steps. So this is what I mean by defining the data model is to try and say we need to know what the data is that we're going to collect, and then when do we get that information and how long does it take? Because this is how we can start to define our conversion rates in our business.