After years of searching and waiting for the right time, I finally decided to purchase my dream truck. Since I had prolonged the decision for so long, I had plenty of time to be confident in what I wanted—a 1968 to 1972 Chevy C10. But how to select just the right one? 

Like any good analyst, I went to a spreadsheet. Out came the metrics: year model, blue Chevy bowtie on the front, 4-wheel drive, seller’s distance from my house, wooden bed vs. steel, and finally price. Some quick equations with the requisite weighted averages, and voila, the perfect formula to help me select my truck! After all this, my wife walks into the room, ignores my impressive research, and says, “Get the blue one.” 

After buying Darla, my BLUE 1971 Chevy K10, I started thinking about how my tendency to overanalyze can extend beyond personal buying decisions. It can trickle over into my work and how I think about business. If I’m not careful, this same confusion and delay can happen in my work life and what should be a quick decision gets lost along the journey, buried in too many numbers. Gross margin, leads volume, net profit, invoice lag, and on and on and on… 

In the spirit of full transparency, and by way of example, let me tell you about a time when what I felt was a good business idea fell into this trap. Let me present to you The Effort Quotient (it even sounds fancy)! 

The goal of the Effort Quotient (EQ) was to help me determine the capacity of a project manager (PM) without relying on the circumstantial and subjective opinions of the PM themselves. Here is how I set it up. 

I made a quick table with columns for the dollar amount of the project, the “complexity” of the project (as determined by the number of trades involved), and the number of miles the job site was from our shop. I gave each of these metrics a score, 1 through 5, and then took the average of the three metrics to yield a number I called the EQ for that individual job. I then added up the calculated EQ for all jobs under the supervision of a particular PM and the sum of all the numbers became the total EQ for that individual.  

Here’s an example of the table for my fellow visual learners.

With a little homework and inference, I was able to determine that the average PM at my company could handle about 35 points before feeling completely overwhelmed. This all sounds reasonable; dare I say even logical. 

I confidently rolled out EQ to my team and it worked wonderfully … for about a week. What went wrong? 

What I forgot to consider was that jobs aren’t static. What may start out at a 4 on the EQ scale would change as the job progressed or work was completed and the effort required decreased. I needed columns for “dollars left to produce in the project” and “number of trades left to complete the project.” What started out as a 4 moved to a 3.2 then 2.4 and eventually to a 1.5. 

I had inadvertently built a system that would require weekly maintenance. Who would handle updating each project, each week, for each PM? What were the consequences of my decision making should the system have inaccurate data and therefore give inaccurate results? Alas, the EQ eventually became unsustainable and abandoned. 

I tried the algorithm and it didn’t work. We also try dashboards, which can quickly become overwhelming, and what is meaningful to one may not be meaningful to another. How do we avoid “overconsumption” when the data seems to be infinitely accessible? After years spent working to figure this out, I have decided that it might be best to keep things simple. So, here is how I am framing it these days: I call it The Rule of 3 Plus 1. Let me explain. 

Grab 3 metrics that you watch at all times, and then add 1 to watch based on the position in which you currently find the business. OK, maybe it’s 4 and 1 or 3 and 2, but you get the idea. Determine what metrics to watch and watch them closely, dare I say daily, which is why they need to be simple. It cannot take an hour to compile this report. If it does, you will find yourself staring down the path of unsustainability and the system will lose its value. 

Allow me to walk you through what I used for my own 3, and then we will look further into the flexible 1. 

First, I always paid attention to leads volume, or more simply stated the number of leads per week. In using this number, I made a couple of assumptions, namely that I had a handle on our current closure rate and average job size.  

Here is a simple look at the math: if I took in 30 new leads in a week’s time and knew that my closure rate was at 50%, this meant that on average I would sell 15 of those leads. If I knew that my average job size was $5,000, then in that weeks’ time I could expect to add $75,000 (30 leads x 50% closure rate x $5,000) to the pipeline. This mental math allowed me to use the number of leads metric as a quick predictor of our production pipeline. 

Second, I always looked at a number I’ll call production rate. This metric; calculated by taking the number of days from the date a job was sold to the date the job was complete in production—yes, 100% complete, punch list and all. I would take this number of days and divide it into the overall job size, in dollars. So if a job started on March 1 and completed on April 15, it was calculated as 45 days “in production.” If these days were on a $10,000 project, it yielded a production rate of $222.22. If it were calculated on a $25,000 project, it would yield a rate of $555.55. 

I could then use the production rate to determine efficiency during the Work in Progress phase of a project and start to compare one job against another, or one PM against another, or an estimator, insurance company, or subcontractor… you get the idea. If I saw a number start to skew, I could jump into the specifics to learn more about what might be causing inefficiency. 

The third number I tracked was my daily deposit. Every day there was a report emailed to senior-level management with the deposits collected for the day. We had an established, well-published goal for weekly collections expectations. If by Wednesday the early-week deposits weren’t on pace to match the target, I could take action by diving into the receivables list to see what phone calls needed to be made. I could also look through jobs in production that were potentially close to a next draw and follow through on those. The daily deposit and it’s progress toward the goal allowed me to manage cash flow in real time, rather than look at the bank account balance to see if I could make payroll that week. 

The extra 1, the flex metric, was pulled from different areas, depending on circumstances. If cash flow seemed off, maybe I took a close look at invoice lag—the number of days between when a project is completed and when the customer receives their invoice. If job-level gross margins were off target, perhaps I took a dive into our labor hours and monitored overtime or “shop time.” If the average job size seemed low, I could look into whether we were using the correct number of pieces of equipment on the project per IICRC standards. 

The important thing is to use your 3 to get a healthy view of the company and then the 1 can fill in the gaps or provide an opportunity for refinement and improvement. 

In a world filled with numbers, it’s easy to develop systems and metrics so complex that the story of the business gets lost in the details. Take a step away and maybe get a fresh set of eyes to help you develop some simple, repeatable metrics that allow you to watch business behavior and predict trends. The Rule of 3 Plus 1 can provide a framework for doing just that.