To create effective performance metrics, you must start at the end point--with the goals, objectives or outcomes you want to achieve--and then work backwards. A good performance metric embodies a strategic objective. It is designed to help the organization monitor whether it is on track to achieve its goals. Performance metrics must be understandable. Employees must know what is being measured, how it is calculated, what the targets are, how incentives work, and, more importantly, what they can do to affect the outcome in a positive direction.
Complex KPIs that consist of indexes, ratios, or multiple calculations are difficult to understand and, more importantly, not clearly actionable.
We then have the best technicians meet with others to discuss strategy and techniques that they use to positively influence the metric," says a director of customer management at an energy services provider. Every performance metric needs an owner who is held accountable for its outcome. Some companies assign two or more owners to a metric to engender teamwork. Companies often embed these metrics into job descriptions and performance reviews.
Without accountability, measures are meaningless. Metrics should be actionable. That is, if a metric trends downward, employees should know what corrective actions to take to improve performance. There is no purpose in measuring activity if users cannot change the outcome. Actionable metrics require employees who are empowered to take action. You might find yourself at a decision point like Mike and worry about what investors will think, or other external influencers. For Mike and Circle of Moms, it was the right decision.
The company grew their user base back up to 4 million users and eventually sold to Sugar Inc. Leading and lagging metrics are both useful, but they serve different purposes. Most startups start by measuring lagging metrics or "lagging indicators" because they don't have enough data to do anything else. And that's OK. A great example of this is churn. Churn tells you what percentage of customers or users abandon your service over time.
But once a customer has churned out they're not likely to come back. Measuring churn is important, and if it's too high, you'll absolutely want to address the issue and try to fix your leaky bucket, but it lags behind reality.
A leading metric on the other hand tries to predict the future. It gives you an indication of what is likely to happen, and as a result you can address a leading metric more quickly to try and change outcomes going forward.
For example, customer complaints is often a leading indicator of churn. If customer complaints are going up, you can expect that customers will abandon and churn will also go up. But instead of responding to something that's already happened, you can dive into customer complaints immediately, figure out what's going on, resolve the issues and hopefully minimize the future impact in churn. Ultimately, you need to decide whether the thing you're tracking helps you make better decisions sooner.
Remember: a real metric has to be actionable. Lagging and leading metrics can both be actionable, but leading indicators show you what will happen, reducing your cycle time and making you leaner. A correlation is a seeming relationship between two metrics that change together, but are often changing as a result of something else.
Take ice cream consumption and drowning. If you plotted these over a year, you'd see that they're correlated--they both go up and down at the same time. The more ice cream that's consumed, the more people drown. But no one would suggest that we reduce ice cream consumption as a way of preventing drowning deaths. That's because the numbers are correlated, and not causal. One isn't affecting the other. The factor that affects them both is actually the time of year--when it's summer, people eat more ice cream and they also drown more.
Finding a correlation between two metrics is a good thing. Correlations can help you predict what will happen. But finding the cause of something means you can change it. You prove causality by finding a correlation, then running experiments where you control the other variables and measure the difference.
It's hard to do, but causality is really an analytics superpower --it gives you the power to hack the future. It's not the whole story to learn more see our presentations and workshops on Lean Analytics , but I'd encourage you to take a look at what you're tracking and see if the numbers you care the most about meet the criteria defined in this post.
Another popular metric is to measure how many followers or friends or likes an organisation receives through social media. However, this is only a popularity contest unless you can get those followers actually do something for you. Every organisation has an infinite amount of different numbers to be measured and collected. However, a smart organisation concentrates on the metrics that truly bring valuable and actionable information for them.
Like we all know, these actionable insights are in the core of gaining competitive advantage in todays business world. First, while revenue is fairly easily measurable and correlated with positive business performance, it's not predictive of future revenue.
Most metrics reflect past performance. This is particularly true of financials. For instance, revenue tells you how many deals your sales team closed - last quarter.
It doesn't tell you anything about what might happen in the coming quarter. Second, revenue is not totally isolated to the performance of the sales group. Sales teams are often impacted by conditions outside of their control, such as the quality of product, economic conditions, etc. If the product performs poorly, even the best salesperson might not be able to sell much. Conversely, a great product might rack up huge revenue even with a mediocre sales group.
Because low revenue doesn't necessarily correlate to a bad team or poor effort, it's not a great sales metric.
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