Likelihood and Quality: The Two Questions at the Heart of Khorus

In the mid-2000s, I was running NetQoS, the network management software firm I cofounded with my wife in 1999. I was years into my CEO journey, and I’d gotten a pretty good feel for the unique challenges of the role. One challenge kept popping up again and again: keeping people aligned. In the business world, we talk about the alignment problem all the time, using well-worn—dare I say tired—phrases like “rowing in the same direction” or images like geese in a V. But we talk about company alignment a lot because it does actually matter. At NetQoS, I found myself surprised when I would give everyone our objectives for the quarter, but then people would start working on projects unrelated to the objectives. I needed a better system for aligning the team—and I decided to build one of my own. That system would evolve into Khorus, but not before one of my employees sparked an idea that became the system’s most distinctive feature: the weekly measurement of the Likelihood and Quality of goals. These two metrics would supplement the system’s aligning power in a way I hadn’t predicted. The result inspired me to bring the platform to the rest of the world, and give CEOs something they currently don’t have: a system of record for running their businesses as effectively as possible.

The Origin Story

The employee who crystallized the Likelihood and Quality metrics was a brilliant, PhD-holding kid named Mike. Mike had helped me build the backbone of my alignment system: a set of cascading goals that began with the top objectives of the company and branched into supporting goals for departments and employees. One day I called Mike into my office. “Let’s add status updates to the system,” I said. It helped for everyone to see the goal cascade, but as CEO, I needed to know how things were going, week by week. “Have an email go out to everyone prompting them to give me their progress on each goal.” Three or four days later, Mike came back into my office. “Joel, you don’t want status updates,” he told me. I wasn’t about to write off someone as intelligent as Mike. “Okay, what do I want?” “You talk all the time about how it’s the CEO’s job to look out into the future and avoid the icebergs,” Mike said, “but a status update isn’t going to really tell you anything. I could tell you I’m 80 percent done on a project and have no idea how I’m going to get the final 20 percent done. What you want is for me to predict the likelihood I’m going to complete the project.” It hit me that he was absolutely right. I’d been sitting in ops meeting for years and getting a lot of historical data that didn’t do much of anything. People talked about what had happened, not about what would. The only forecasting done in ops meetings was done by sales—and that was the number we spent 95 percent of the meeting talking about. If I followed Mike’s advice, we could have everyone offer predictive insight. I’d get a forecast of performance from the people who were in the best position to make the prediction: the people actually doing the work. Thus the Likelihood metric was born. Every week, we would ask people to answer the question “How likely are you to meet this goal?” That was information that would be of far more value to me than a simple status update or percent-complete number. But Mike wasn’t done. “You want to ask another question, too.” “And that is . . .” “You walk around the halls talking to people a lot. Even if you know the status of one of my projects, you’ll ask me how I feel about it. Sometimes I’ll tell you I feel great about it. Other times I’ll tell you we had to cut a bunch of corners and this thing is held together with duct tape and I just hope it works when we ship it. So the second question you want to ask is ‘How do you feel about the quality of the work done so far?’” Once again, Mike was absolutely right. With that, we had our second metric, Quality. It would offer me the benefits of Management by Walking Around—and I wouldn’t even have to walk around.

Why These Are the Metrics That Matter

We implemented Mike’s idea, and I started getting answers to those two questions—“How likely are you to meet this goal?” and “How do you feel about the quality of the work done so far?”—from employees every week, with employees rating each on a 5-color scale, from bright red to bright green. I found that this gave me a wealth of information, without burying me in irrelevant, backward-looking details. It offered me simplicity without being simplistic, cutting straight to the heart of what matters to the CEO:
  • Will we meet our targets for the quarter?
  • Are people feeling confident and empowered about the work they’re doing?
  • What teams or individuals need my support to keep performance on track?
And once we took the next step and started aggregating the answers in a simple 2×2 matrix, the data became even easier to interpret. With the matrix, I had a consolidated view of performance that felt less like the cockpit of a 747 and more like the dashboard of a Ferrari. Goals in the upper right are on track and going well, but as a goal starts trending to the lower left quadrant, the CEO can simply click on it for more insight, and then take the steps necessary to resolve the issue. .
The Predictions Matrix in Khorus
. I believe this is a game-changing way for CEOs to assess and drive performance. This system—in addition to aligning all employees with their team and with the whole company—helps CEOs understand the issues their teams face and deliver more predictable results. Based on the insight delivered straight to them by employees, they can make adjustments and realignments before problems escalate. And the act of logging in to the system each week (a process that usually takes no more than a few minutes) keeps all employees in sync with the wider goals of the organization, clarifies the strategy-execution plan, and helps everyone get the right sh*t done. If you’re interested in trying Khorus in your own company, I hope you’ll set up a demo with a member of our team.

Comments are closed.