There’s a solid chance that if this article caught your eye, your role either includes some element of community analysis or it’s about to.
But before you open Excel or Google Sheets, reluctantly grab that giant CSV file, or start pondering bars vs. lines and dashes vs. solid lines, let’s chat about some guidelines that will make your foray or continued adventure into community analytics a bit easier.
Everyone who runs any element of a community initiative should be using community data.
In charge of the day-to-day community operations? Yes.
Designated planner of community content and programming? Yep!
Leader of the community moderation efforts? Absolutely.
Head of community overall? Oh yes. Definitely.
But why do you need to use data? The reality is that unless you’re managing a tiny community of a few dozen people, it becomes exponentially impossible to keep up with every single user, page, article, etc.
With data, we stand a chance. We’re able to see the activity, engagement, and consumption of millions of users and members. Community data is a versatile tool that helps community professionals make informed decisions, tailor initiatives, and direct our attention within our communities.
However, how we approach, process, and discuss data completely overrides who we are and why we’re using it. Let’s jump into the three guidelines I’ve learned over the years by trial and error (gosh, so much error).
I like to equate this one to going to the grocery store to figure out what to buy for dinner on an empty stomach. With a plan, you’d probably have bought just a few things — but then you walk through the bakery, your stomach distracts you, and suddenly you’ve spent $40 on dessert and still have no dinner.
Data is very much the same. If you import a CSV without having a plan, you’re either going to experience paralyzing panic (analysis paralysis!) or gleefully gravitate towards big meaningless numbers.
When you open a dataset without a clear question in mind, you risk getting lost in the sea of numbers and losing sight of the bigger picture. This can lead to unproductive hours of analysis and missed opportunities to make an impact in your community.
Before diving into the data, think about the community goals you want to achieve and frame your questions accordingly. This will not only save you time but also help you to focus on the most relevant information.
Always start with a clear question, which will help you focus your analysis, avoid rabbit holes, and provide valuable insights. Consider questions like, "What factors contribute to user engagement?" or "How does content type influence the time spent on the platform?"
Imagine you're in charge of a community's monthly virtual meetup. Instead of just looking at the total number of attendees, get specific and ask questions like, "How many first-time attendees joined this month?" or "What topics are generating the most engagement?" This targeted approach will help you create more effective and engaging events for your community.
Let me tell you about something that happened recently. We made a massive change to a process at Zapier, where I work, and we anticipated our community metrics positively reflecting the process change. But a week past this change, our week-over-week metrics were down — really down — when they should have been up by quite a bit.
Did our process change fail? The metrics sure looked that way.
But, no. Metrics alone aren’t telling the whole story.
The two weeks leading up to the process change also saw an avalanche of hype around AI, which created an unplanned spike in our metrics. So while we were down week-over-week in comparison to the AI hype, we were up from our previous benchmark and anticipated volume.
Relying solely on data can lead to false conclusions and misguided decisions. It's like judging a book by its cover — you might miss out on a fantastic story or end up with a dud. Data can be misleading, and there might be contextual factors that the numbers haven’t captured.
Always supplement your data analysis with contextual and qualitative information. Speak to community members, community staff, and other functions that share similar initiatives. Gather feedback and understand the unique culture of your community. Combining these insights with your data will paint a more accurate and nuanced picture.
Suppose your data shows a decrease in community engagement over time. Instead of immediately assuming that interest is waning, consider other factors, like recent changes in platform features, user experience, or even external factors like holidays or global events. By understanding the context, you'll be better equipped to address the situation effectively.
Back when I was a total newbie community professional, I got all sorts of excited to present that our support community had recently seen a massive spike in new members. I assumed that this spike meant that our recent efforts to attract new members meant our efforts were a success. Our community was growing, healthy, and thriving in my eyes, so off I went reporting our successes. Growth! Yay!
But a few weeks later, I also discovered that we had a massive subtle spam attack going on and a majority of the new members had the same domain, similar IP address… and they were posting spam. Nothing like a wee bit of back pedaling and playing down the previous success. Ugh.
In this case, I had assumed that the increase in new users meant our community growth related initiatives were working. Not only did I not check my work, I fell right into the correlation without causation pit of despair. I assumed that just because two variables appeared to be related, one directly caused the other — which wasn’t the case at all.
Mistaking correlation for causation can lead to misguided actions and conclusions. Just because two variables seem related doesn't mean one causes the other, and acting on this assumption can have negative consequences for your community.
When you identify a correlation, dig deeper to determine if there's a causal relationship or if it's just a coincidence. Consult other data sources, look for confounding variables, and use critical thinking to evaluate the true relationship between the variables.
Imagine you find a correlation between the number of posts a user makes and their overall satisfaction with the community. You might initially assume that encouraging users to post more will boost satisfaction.
However, on further investigation, you might discover that highly engaged users post more and are naturally more satisfied with the community. In this case, focusing on boosting overall engagement, rather than just post count, would be a more effective strategy.
And there you have it.
Three golden guidelines to being a good community data analyst. As you venture forth into the wild world of data analysis, remember to always start with a question, never assume data tells the whole story alone, and don't fall for correlation without causation.
With these guidelines in mind, you'll be well on your way to providing valuable insights and helping your community flourish.