In the creator economy, personal performance and business performance are uniquely linked. When you sleep in, business opens late. When you write for 6 hours, your business receives 6 hours of productive inputs. When you take care of your health and wellness, your business’ employee engagement (i.e. your engagement) is taken care of, equally.
With this unique linking, personal factors that are typically off-limits to companies and organisations suddenly become relevant and accessible. Your sleep patterns, for instance. Your optimal work schedule. Your social media activity. Your health and fitness behaviors. If it has a reliable influence on mood and productivity, it will reliably influence business performance also.
And while it would be inappropriate for an employer to ask for these personal data sets (even in service of generating helpful insights or interventions to improve performance) — when you are your business, these valuable data streams are yours and ready to be mined.
Sleep, fitness, circadian rhythms and screen time may sound a little too vague for definitive ‘business analysis’, but we are living in a new era of work and data. Odds are, you already have rich streams of data on each of the above-mentioned factors — only, you’re not putting them to any dedicated use.
Maybe it’s a sleep tracking app like Sleep Cycle. A wearable device, from Fitbits to Oura rings. Even a weekly screen time report on your phone has high-fidelity, low-friction information about you and your activities, specifically. The independent creator (or any individual-run business, for that matter) is in a rare position to:
If sleep, fitness and screen time are influencing your personal productivity — they’re necessarily influencing your business performance, too.
This simple formulation — when I do X, my business tends to Y — is specific to individual enterprises. Relevant in the gig and creator economies. Applicable to solopreneurs, not large organizations.
It has to be this way. In a company context, multiple agents influence outcomes. Multi-million dollar budgets and decade-old processes influence decisions. Salaries are agreed to in advance. Bonuses are far from straightforward — less to do with direct links of causation, and more to do with a hazy amalgamation of work history, experience, timing, internal politics and overall company performance.
As independent creators, however, this simple relationship between personal inputs and business performance is a feasible, causal link worth exploring in detail. And it is easy to overlook.
Some simple examples:
With better data sets, we can be more specific still:
Sleep. Music. Time of day. Social media activity. These can all seem a little too vague or indirect to be worth pursuing. Life is complex; ambiguous; messy — there’s no way we can play this game to a pinpoint.
Fortunately, we don’t need to.
If you allow yourself to set the bar at ‘interesting and significant relationship’ rather than ‘factor X leads to outcome Y with 100% predictability’, then suddenly you open yourself up to a whole new terrain of fruitful self-discovery and exploration. If you allow significant relations in the data to serve as a starting point — a prompt, a question, a curiosity — then you can better focus your attention on areas for improvement, rather than picking your starting point blindly.
How do we come to find ‘interesting relationships’ in our personal data?
Of course, there are many approaches — but each will involve at least these 3 elements.
There has never been more data collected about you on a daily basis. When it happens without your consent, is sold to third-parties and redirected back at you in adverts — I can see how this is something to be cynical about. But when those data are made freely available to you, the individual, it can become one of the more incredible assets and gifts of these modern digital times.
My one word of advice for anyone interested in upping their data collection game? Focus on apps and processes that record your data without you needing to manually input anything. A diary is a wonderful thing. But if you’re trying to log your sleep data, screen time, reading habits, diet and business outcomes in various spreadsheets (or worse, by hand!) — the data collection process will be a hassle that gets skipped more often than not, and the resulting data set will be far from reliable.
Automate data collection, wherever possible.
Okay, you’ve established your data collection flow. You’ve exported your raw data sets on your top 3 factors of interest — say, sleep, fitness and working hours.
So: what do you do with them? How do you begin making useful connections across such seemingly disparate contexts?
If you’re not a data-oriented person; not a spreadsheet extraordinaire; not into regression analyses and scatter plots; don’t stress. Fortunately, many apps will produce some basic charts and analytics for you within the context of the app itself (albeit behind a Premium subscription paywall).
Take popular sleep-tracking app, Sleep Cycle. Each morning, I wake up to a collection of charts, scores and sounds recorded through the night. This alone is a gift of data processing done on my behalf — all performed while I’m asleep, no less!
But the goal of this step isn’t just to view my Sleep Quality score and get on with my day. It is to make useful connections between this personal sleep information and the business outcomes I care about.
How can I, say, compare these sleep data with my productivity patterns from other sources, across the same time period?
A) The more rigorous, data-oriented approach is to export the relevant raw data sets into a combined Google Sheet, then create some charts which compare the variables I’m most interested in (say, comparing ‘Hours asleep’ with ‘Total Hours worked on All Projects’ through my Timely time tracking data, for the most recent 7 or 30 Days).
B) The more intuitive (and less spreadsheet-y) approach is to spend an hour viewing the charts produced by each independent app or source (e.g. Timely, Google Analytics, Sleep Cycle) and actively seeking out useful relations. This is a rather different attitude than briefly checking in with the statistics passively each morning, one app after the next.
This might mean having several tabs open at once. It may mean screenshotting the various charts and collaging them onto a single page or artboard. The goal is simply to place your data streams within the same view or context — so that you can begin connecting the dots for yourself.
So, suppose this process of data collection and analysis leads you to a promising insight: ‘When I sleep less than 7 hours, my business tends to see -15% productive hours, on average’.
What exactly should you do about it?
Approach 1 is to take this result and say, ‘Ohh, would you look at that? I guess I’d better get some more sleep then.’ This would be a more passive, prescriptive approach.
Approach 2 is to ask: ‘Are there any factors that may be contributing to my not getting 7 hours? If so, what actions can I take to course correct or intervene?’ This would be an intervention approach.
If the intervention approach sounds a little rigorous for something as straightforward as ‘getting more sleep’, you might be right. But this is a business decision, remember? Tell any executive about a factor that may be responsible for -15% in productive output (and possibly -15% gross revenue) and you can be sure they’ll spend the resources necessary to get to the bottom of the analysis — and promptly begin implementing the appropriate interventions.
By the way: an intervention stemming from this example might be as simple as: ‘Setting a bedtime reminder one hour earlier for 2 weeks’, then seeing if it has any effect on productivity for that period.
And this is the beauty of taking a data-driven approach to one’s own business as an independent creator.
Each intervention is both stemmed from, then becomes in itself, an experiment on a hypothesis. You generate an insight, which allows you to propose a reasonable hypothesis (“Whenever I stay up late, I get less than 7 hours of sleep — since I tend to wake up at the same time each morning”).
This predicts an intervention which seems promising — set a reminder to get to bed an hour earlier than usual. You try it out for a while. You collect more data, with a clear objective in mind (that might be more ‘productive hours’, more ‘posts published’, higher earnings, more website visits — whatever you are most interested in improving) and see how it performs.
If performance improves, run it again, refine it, or double down based on the new information. Simple, iterative and low-effort — this is what it looks like to put personal data you are already collecting toward the business outcomes you care about.
Depending on how rigorous you want to be, this process of using personal data to understand (and predict) business performance may be more of an art than science. And that’s fine.
By simply grounding our observations and intuitions in some of the rich data streams already collected on our behalf, we will at least be taking a step in the right direction. As creators, our decisions will be a little better informed. We can feel a little more solid in our footing. And the fast-growing population which will make up this decade’s exciting creator economy will be a little more stable and sustainable in its efforts — with the data on its side.
Disclaimer: I am in no way affiliated with any of the apps linked or mentioned in this article — other than using and enjoying them myself.
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