The Solution to Metrics Overload

APRIL 20, 2017

How many performance metrics does your company track? If you’re a large national retail organization with thousands of stores and complex distribution channels, that number is shockingly large. It’s not uncommon for companies to have thousands of different metrics to track and be regularly updated. Now put yourself in the role of a District Manager—your job involves tracking thousands of performance metrics for each of the 10-20 stores that you supervise. To do this, if it were actually possible, you’d need to spend every waking hour combing through reports. That probably means logging in to all the different IT systems that host your reports, or digging through your inbox for that email attachment your boss sent you last month. And when exactly are you supposed to squeeze in the time for those store visits?
“CoEFFICIENT adapts itself to our users’ preferred workflows to proactively surface hidden operational problems..”

CoEFFICIENT® Metrics solves the problem of metrics overload by focusing on specific different metric types. Today, we’ll focus on three of those types, specifically: Focus Metrics, Related Metrics, and Recommended Metrics. First up are Focus Metrics—these are the top 5 metrics that everyone in your business knows by heart. They’re probably the metrics that determine most of your bonus, and they’re the metrics your boss is most likely to ask about. CoEFFICIENT Metrics keeps the focus on Focus Metrics. They’re displayed front and center for every user in the organization.

Next come 10-20 Related Metrics. These metrics are shown in simple, curated dashboards and allow users to drill-down to find more detail on what’s going on with their Focus Metrics. These metrics can be selected by analyzing historical correlations, or by interviews with subject matter experts.

At this point we’ve winnowed down our set of metrics to only the most important ones. Unfortunately, we’ve hidden a lot of complexity, and that complexity doesn’t just go away. It’s needed because business is inherently complex. This is where Recommended Metrics comes in. It would be a terrible waste of time to make a human scan through thousands of metrics looking for exceptions. This is the perfect job for a machine, however. We can turn loose the power of Machine Learning on the full suite of metrics loaded in CoEFFICIENT to bring to the surface only what’s important to you and what needs action now.

Recommended Metrics are really flexible. They can find key metrics that have suddenly begun to trend down, or they can scan for pre-defined exceptions based on the best practices in your industry. An area that I’m actively working on right now is the ability to learn which types of metrics our users care about from their past interactions with CoEFFICIENT. By doing this, I’m building up a personalized library of recommendations that’s learned from your specific tastes. CoEFFICIENT adapts itself to our users’ preferred workflows to proactively surface hidden operational problems in the same way that other consumer products adapt to your tastes to surface books, movies, or songs that you may not have otherwise found.

Because we don’t develop software in a vacuum, I’m constantly reaching out to our users who receive Recommended Metrics to get their feedback. In fact, we we send out an email to our users each week that contains their Recommended Metrics. At the bottom of each of these emails is a short survey. It specifically asks users whether or not users found the recommendations helpful and, depending on their response, they’re taken to a short 3-question survey to expand upon it. Throughout the entire history of this project, we’ve only ever received one response with negative feedback. I’m still working to improve the science behind the recommendations, but it’s clear that this is something our users love.

Every day we help District Managers understand store performance better with CoEFFICIENT Metrics. Interested in seeing what we could do for your team? Reach out to schedule a demo with us now.

As an astrophysicist, John used to look for black holes and dark matter in nearby galaxies. At Square Root, he gets to put that knowledge to work on our Data Science team. He’s a real data power lifter with an interesting perspective who makes our data science team even stronger. With a true passion for problem solving, John is an expert in breaking down complex ideas and data into information we can all understand. He holds a Ph.D. from the University of Texas and a B.S. from Rutgers University in astrophysics.