The gauges in your car have a purpose. They monitor the health of your engine. The original design was simple and effective. A knowledgeable driver could tell with a glance at the dashboard if there were any problems or potential issues.
Engineers, being what they are, couldn’t leave well enough alone. They knew that some people wouldn’t learn how to read the gauges. Surely, they could make it easier. And they did. Indicator lights replaced the gauges hiding potential issues until they became problems. Instead of a gauge that showed the engine’s temperature, a light flashed when the engine was overheating. The opportunity to stop the vehicle before the heat reached a critical level was lost. The new feature quickly became known as “idiot lights”.
Being an engineer, I completely understand the urge to make things easier, faster, and better. But, in my experience, every time I try to make something idiot proof, along comes a better idiot. Albert Einstein said, “Everything should be as simple as possible, but not simpler.”
And that’s the problem with most key performance indicators (KPI’s).
They are too simple to provide any meaningful information. The theory is sound. If you set goals with minimum and maximum benchmarks, the needle adjusts itself so management can monitor performance. But, what if the goals and benchmarks are wrong? What if they’re right, but the data is corrupted? How do you know?
If you are relying solely on KPI’s to manage your business, you are missing hidden opportunities and warnings. For example, you may be watching your customer base increase while the average order is holding steady. It looks good, doesn’t it?
But what if you are acquiring hit-&-run customers who place one or two orders and leave? Are they generating a return on investment or are they depleting your profits? If you aren’t monitoring them, you don’t know. And what you don’t know will kill your business.
Take some time this week to review the numbers behind the indicators. Look for opportunities to improve sales and reduce costs. Watch for anomalies that affect the data. You’ll be glad you did.
