The gig economy is nothing new: In 1960’s Kentucky, my grandfather learned to weld by working “piecemeal” – an arrangement where you’re paid by the number of parts you finish. Even then, this was an old-school, pre-industrialization practice that he was only able to land in the ultra-rural part of the country where he (and I) grew up, outside of major industrial hubs. Today, apps like Uber and DoorDash have brought the piecemeal concept to the digital age as we see more workers and businesses shift away from traditional 9 to 5 work.

Plenty of companies are searching for ways to cater to this newly resurrected form of work, but it’s a hard thing to get right. Worker acquisition costs can spiral out of control, especially if worker retention isn’t high enough to recoup the up-front investment, and the whole thing blows up if you can’t get high-quality, well-trained work from a workforce of non-employees with lots of other things (and gigs) on their minds.

While building Blueprint Stats, we routinely had to scale up and down our “statistician network” – the people who we paid to watch basketball game film and use our software to tag statistical events. Because of the seasonality of that business, I had a unique opportunity to take several cracks at building a worker acquisition, evaluation, and retention machine. In the early attempts, we didn’t get a whole lot right, and often had to sacrifice unit cost and turnaround time in order to keep up the work quality. As we grew though, I was able to hone in on several factors that allowed us to create a successful, international “piecemeal” labor force of over 500, and here are my top 3:

1. Meet workers where they are

The header may sound obvious, but if you want a shot at keeping worker acquisition costs under control, you have to treat the endeavor in the same way that you treat CAC (customer acquisition costs). At Blueprint, we found workers in the same way that many consumer apps do: through online ad placements and crucially, referrals.

Before setting out on a new ad campaign, we would create a persona of the ideal statistician: where they lived, what they interacted with online, and what they were motivated by. Then we worked backwards. We created video ads that messaged to the motivations of our target audience (“watching basketball is cool, and get paid for it is cooler”), placed them before YouTube videos that we knew they were watching, and targeted the countries where the cost of living and interest in basketball were at ideal levels.

Once we had a base of statisticians from ads, we relied heavily on referrals for lower-acquisition-cost, higher-quality workers. To execute, we followed our 50/50 forumla: offer the top 50% of our existing workforce a bonus equal to 50% of our ad-driven WAC (worker acquisition cost) for each statistician that joined, completed their evaluation, and worked 5 assignments. This not only added an incentive for the workers to send a link to their friends, but to help them pass the evaluation and complete 5 assignments – a key metric toward long-term retention.

2. Evaluate fast, and only where necessary

Today’s workforce does not want to wait the 4-8 weeks of a standard hiring process to start working. Ideally, you would have sent them a task yesterday. Additionally, they won’t go through multiple rounds of interviews, several hours of tests, and a number of evaluation tasks before getting paid. I can hear folks screaming at their screens now that “workers expect too much these days” – yes. Get over it, or get left behind. Remember how I said you have to treat gig workers like customers when it comes to acquisition? That carries on throughout the whole process. You have to compete for them.

What this means on a practical basis is that you have to ruthlessly cut out any fat from your evaluation process. If it isn’t strictly necessary that a worker have a competency in a certain area, do not evaluate on it. Additionally, it helps to set up tiers to get workers through evaluations and earning money quicker, and to gamify the experience so that it feels like workers are “moving up the ranks” without the standard promotions that come with employment.

At Blueprint, we had a super-easy evaluation that most statisticians could pass on their first try without reviewing any training materials. While we preferred a statistician have skills outside of the accuracy of their work, that is ALL we evaluated on at this stage. Once they passed, they were able work on our lowest-paying tasks (from signing up to being assigned your first task could take as little as 45 minutes). Eventually, they were offered the opportunity to take a much harder evaluation with more exciting game assignments and better pay on the other side.

3. Pay often, pay fair

I’ll say the quiet part out loud: businesses find the gig economy attractive in part because they don’t have to offer benefits, pay employment taxes, or in some cases, even minimum wage. Plus, it filters out less-productive workers as they aren’t able to complete tasks in the amount of time needed to earn a livable hourly-equivalent (this is a double-edged sword, of course, because it also incentivizes rushing to complete tasks at the expense of quality).

Here’s my take, for what it’s worth: if you have any interest in sustained success, you once again have to treat gig workers like customers, not as disposable labor. Uncompetitive pay is a quick way to generate churn, and churn is your enemy. Lag time on pay is another great churn-generator. Gig workers aren’t employees, and they don’t expect to be paid weeks after a task has been completed. Same-day is quickly becoming the norm, and it’s what we went with at Blueprint.

There are so many benefits to the gig economy beyond the fleeting ability to exploit workers and get cheap labor, and further, between government intervention and the ever-increasing competition to hire gig workers (often from companies that you didn’t even think were your competitors!), if your model relies on that kind of behavior, it’s not a sustainable one.