Nowadays everyone knows that if you cannot measure it you cannot improve it. Big data has become “the new oil” in the 21st century. At the same time, if you have ever worked with data you also know that it is not the size of your data set that matters, but rather the right questions.
When I worked at Stack Overflow, first as a community manager and then as an analyst, I saw the same thing again and again: While we had a huge amount of data and even more tools to work with that data, there was very little practice in making data-driven decisions. Why? Because few people could (or had time to) formulate a right question, choose the right metric, take measurements and interpret the results correctly.
On the one hand, there’s nothing wrong with that, not all of us are meant to be Data Scientists. On the other hand, we still need to get the job done. That is why I think it is important to build a reference guide with the most useful metrics so that any community manager, even if they are not a tech savvy person, can understand how to use it!
Our ultimate goal is to make Data Science for Community Management as clear and easy to use as possible. I think one of the first steps in this direction is to explain how metrics work. In this guide, I have included the metrics that we use to build reports for our clients. You can also find these and many other metrics in our metric explorer.
What does the guide consist of?
This guide consists of metrics, where each metric is described using the following components:
-
Metric description. In order for everyone to easily understand what exactly we are talking about when we talk about a metric, we need to be able to explain what this metric measures in simple, plain language. This is where the metric description comes handy.
-
Question answered by the metric. Without a question, an answer will almost never be useful. In fact, we are usually interested in the answer to a question, and not the metric itself. So each metric on our platform is associated with a question that is measurable and actionable.
-
Steps to measure. We expect you to use this guide to solve specific applied problems. Therefore, we include detailed instructions on how to calculate each metric.
-
Interpretation. Measuring a metric is only half the battle. The other half is interpreting the result correctly. If you have no experience working with data, this section will be a great starting point for understanding what the measured results mean.
-
Benchmarks (i.e. industry average). In addition, for each metric described, we measured (benchmarked) the industry average. Since we are constantly updating the data, instead of a particular number, I refer to the metric benchmark on our analytics platform, where anyone can see the current average value of the metric and compare their community’s performance to it.
Please help us make the guide better!
If you’d like to suggest a metric, discuss an existing one, or otherwise help develop metrics, please start a new thread in this section!
Metric collection and reporting automation
Oh yeah! If you want to automate metrics collection and get monthly health reports for your community, we’d love to see you among the happy customers of our advanced analytics platform for online communities.
Lessons learned while working on Stack Overflow
Also, I wrote a book about my days at Stack Overflow. It has a dedicated chapter about community metrics. You can read it here on the site, or in the form of a book on Amazon.