The modern creative landscape is digital. Every blog post, podcast episode, Instagram story, and YouTube video leaves its unique digital footprint — and with it, a parallel stream of data flows from each day’s activities, waiting to be mined and put to use.
Depending on the publishing platform, many of us have access to more analytics, reporting, and statistics than we know what to do with — from average viewing time to conversion goal percentages; page, country, city, and referral source breakdowns; bounce rates and exits; open rates and click-through rates and every cursor scroll between.
All of this is supposedly the professional marketer’s domain— but increasingly, independent creators are taking it upon themselves to leverage these rich data streams toward achieving the business outcomes they care about.
The flip side?
Any modern creator that isn’t looking at their content performance by the data is leaving hours, dollars, and effort on the table.
For those who already have a strong data analysis, hypothesis, and testing flow going; keep on keeping on.
But for those of us looking to improve how we make use of the data already being collected for us, here is a 7 step flow that helped me when getting started.
The era of data-driven creation is here — and what a beautiful gift it is.
There’s always a problem. Even when things are going great. At least — learning how to frame our data setup in terms of solving a specific problem is critical.
The creative process is richer than simply maximizing profits, improving content engagement, or growing your mailing list though. And so the desired result isn’t always obvious. It may be earnings. It might be some direct correlate — views, watch time, engagement, subscriber count. Or it may be something else altogether — a sense of finding one’s voice, of reaching a creative milestone, of publishing in a particular space, of receiving a special flavor of recognition or acknowledgment.
As creators, it’s worth sitting with this basic question until something solid bubbles to the surface. We want to feel grounded. We want to know what we’re orienting ourselves toward. We want to know deeply why we’re putting in this effort — so that when the hesitations arise (and they will), we have a reliable heuristic for arriving back on solid motivational footing — (Oh, because I need to pay rent, that’s right 😌).
And when it comes to pointing data toward a clear objective, being 100% firm on the desired result will help to avoid getting lost in spurious correlations.
This is SMART goal-setting 101. I want to choose my metric concretely, be it earnings, views, shares, daily mood rating — whatever. Then give it a specific target.
In 2021, this is more about the process and strategic setup than manually going out into the field and collecting the data. For those seeking to write for a living, take a look at which data is already being collected on our content — views, reading time, exit pages, and so on. Where possible, it’s worth considering what data is being collected on our own activity, too.
For example: How often do I spend reading on social media and other platforms? And is this relevant to how my own content performs? (More about personal analytics and the creator economy coming soon).
Look for gaps. It helps for me to think about the data I wish I could collect, then ask where and with what fidelity it may currently be living. If there are ways to improve the fidelity, frequency, or quality of data at this step — do so.
The key takeaway is to leverage data collection tools and processes that are automated; those already being collected without manual input.
This sounds boring — clinical — the kind of thing students are forced to do to appease teachers and professors.
But it’s important, and it can be more intuitive than it sounds. Cleaning and preparing data just means taking some reasonable steps before doing the analysis. It means noticing any ridiculous outliers that will mess up the insights I care about — for example, if I know I spent last month intentionally away from publishing, I can omit data from that period for my analysis so that my larger set isn’t totally thrown.
That’s cleaning the data.
If I have data sets from different platforms or contexts — e.g. Medium statistics + Google Analytics data + HubSpot email data— then I may need to do a little preparation to ensure formatting is in sync.
For example, I’d want to make sure I’m looking at the same date ranges; lining up the same values (users vs. sessions vs. new users?), or making sure there aren’t any double entries boosting a post’s statistics.
That’s preparing the data.
It doesn’t need to be some highly rigorous process. Mostly, it’s about taking a common-sense view of the data you have at hand and what you’d like to achieve with it. Computers don’t really understand the data points we’re feeding them, so we need to put things into a common language first if we want to view useful outputs.
OK, so what exactly do we mean by “analyze”? If I check my Google Analytics dashboard each morning and say: “Oh, how about that? +2% new users yesterday, neat!” Does that count as data analysis?
Well, it depends. Google has been kind enough to do a lot of the analysis for me — so that all I need to do is check in and see if there has been a change in user activity. In this sense, we are simply fortunate to be digital creators in this modern era — there are so many tools already doing the lion’s share of analysis on our behalf.
But we can do more.
We’ve arrived at some charts, percentages, and colorful indicators. What does it all mean, though?
Google may have the stats, but it doesn’t know what I know about my business and my content. It doesn’t have intuition about why certain pages might be doing better than others. It doesn’t know how long or how much effort I invested into each piece. Doesn’t know my process for producing it. Doesn’t know my distribution strategy. Doesn’t know that I sent an outreach email promoting one and not the other. Doesn’t know how much or how little I enjoyed writing about that topic.
Interpreting the analysis given to you is where our creative, data science, and hypothesis-making minds really shine. It’s where we link abstracted numbers and statistics to real-world use cases; where we connect the dots; where we get the most out of that pre-frontal cortex everyone is always talking about. It’s where we finally ask — what can I actually learn from and do with this result?
For me, it often helps to lean on what I know about myself and my business that isn’t in the data. To let intuition formulate reasonable hypotheses about what I see in the data sets. Then, to set about testing those hypotheses rigorously.
Intuitive hypothesis-making. Rigorous hypothesis-testing.
For example:
Simple.
This may sound absurd as an independent creator: making a deck, and standing up to present the findings, in the mirror, to an audience of one.
But memory is a finicky thing. We’ve put in the effort of doing the analysis, interpreting the results, and coming to some insights which we feel will be important for business — it’s worth consolidating these findings in something with a longer half-life than short-term memory.
It doesn’t need to be elaborate — but it should be something that is pleasant to look back on. Something more interpretable than a few quickly scribbled notes jotted down as some exciting insight, or perhaps impatiently from a sense of obligatory diligence.
Taking an extra half hour to prepare a presentation, however, is typically worthwhile. I think of it as a gift to my future self — and often, that future self is grateful for the gesture.
“Data-driven creator” can sound a little… robotic. I get that. Where’s the magic? The muse? The creative flair?
But as soon as we begin to dive in, it becomes clear that a world with data can be just as complex, serendipitous, messy, and wonderful as a world without. The curtain isn’t pulled away from the creative process, we are simply given a new view. A new toolkit. A new vantage point from which to make better-informed decisions about what’s working (and what’s not) in our efforts as creators.
Carve out a half or quarter day each week for a dedicated review of content performance analytics. That’s all. We don’t have to think of ourselves as especially data-oriented or technical professionals — we can simply be data-curious creators.
Curious enough to discover insights that will improve our efficacy going forward. Because so long as we’re out here creating on our own, we can be sure no one else is going to do the analysis for us.
