apo 3 2a

Let me just share something with you that I learned the hard way about metrics. You can waste a lot of time tracking stuff that looks good but doesn't mean very much. We call these vanity metrics and I've had my fair share of dealing with them. There are numbers that look good, but they don't really help us make decisions. Now this concept, vanity metrics, it was introduced by Eric Reiss in his book the Lean Startup. He points out that raw numbers like total registered users, well that can be misleading since they only go up and never really tell us if people are actually using our product. Instead, he pushes for actionable metrics, metrics that help us understand cause and effect, like how many active users we have right now or what percentage of users come back each week. Reese says if a metric can't help you make a clear business decision, it's probably just vanity. They might make us feel good, but they don't help us make real decisions. And like vanity metrics, another place I see teams waste enormous amounts of time and energy are on output focus metrics. Teams get kind of like caught up in how much stuff they're cranking out. You know, like counting features they've shipped or my favorite, adding up story points or even tracking how many user stories they worked on. Sure, these numbers, they tell you if you're busy. But being busy isn't the same as making an impact. Instead, try to focus on outcome metrics. While output metrics just tell us how much stuff we're doing, outcome metrics show us if that stuff actually made a difference. For example, if you support a stock trading app built by a financial services company, well, your outcomes might be measured as monthly active traders or time to first trade or even session frequency. Whereas a county health department that supports a customer case management system, well, they might measure outcomes as time to case resolution, customer satisfaction score or system uptime. And a product owner for a cybersecurity scanning application, well, they would measure outcomes like zero day threat detection rate, remediation success rate or even false positive rate. Now, every company is different and so are their products. And that means the way you measure success, well it's going to depend on what matters for your specific company and what you're trying to achieve with your products or services. There's no like one size fits all for outcome metrics. I have a whole other lesson that goes deeper on this topic, including examples of a balanced metrics approach. So be sure to check out the video below called Metrics that matter for system optimization. Speaking of outcomes that Matter. This brings us to one of the trickiest kinds of metrics to get innovation metrics. They look at everything from how fast we learn from experiments to how well we turn new ideas into real value for customers. That's why measuring innovation requires a different mindset. Instead of just measuring successes and failures, we need to measure learning and discovery. Now, here's some practical innovation metrics I've found useful. How quickly can we go from idea to customer feedback? What new customer insights do we gain? Kill rate is how many ideas do we have the courage to stop? Now, this one has a pretty rough name, but it's interesting because sometimes success and innovation is about saying no to good ideas. So you can say yes to great ones. Pivot speed. That's how quickly can we change direction when we learn something new. The best innovators, they can turn on a dime when the data tells them to. And assumption to validation ratio. Now, this one's simply tracking how many of your initial assumptions turned out to be right or wrong when they're tested with real customers. Let me show you a practical way to track that assumption to validation ratio using two simple but powerful tools, the test card and the learning card. You'll find both of these references below this lesson. The test card is where you lay out what you think is true before you run your experiment. It's like saying, hey, here's what we think we know. You write down we believe that, whatever. And then you fill in your assumption. You spell out exactly how you're going to test it and what success looks like. Okay, so then the learning card, that's its partner. It's where you capture what actually happened. It's. It helps you document. Well, here's what we actually found out together. These cards make it dead simple to track your assumption to validation ratio. For each assumption you test, you've got a clear before and after. The test card captures your assumption. The learning card captures whether you were right or wrong. Keep these cards simple and small. One assumption, one test, one learning. That way, you can easily count up how many assumptions were validated versus how many taught you something completely different. The beauty of these cards is that they make it impossible to fool yourself. You wrote down what you thought would happen, and then you wrote down what actually happened. No revisionist history here. And the best part, over time, you build up this amazing library of learning. It's like having an innovation diary that shows exactly how your understanding evolved over time. My, my, how interesting. Okay, good metrics. They do three things. They help you make decisions. They're easy to understand and they drive the right behavior. The trick here is picking metrics that match what you're trying to achieve. For example, building a new product. Well, you want to focus on learning metrics. Maybe you're improving an existing service or working on an enhancement. Then look at usage and satisfaction metrics. The best metrics tell a story about your customers, and they help you understand if you're making life better for the people using your product.