Our broad goal at Sixty was to build a “LinkedIn for Freelancers”, where we had a majority of freelancers in the world listed. That way, clients could come to one marketplace and be able to sort through every freelancer -- similarly to how recruiters use LinkedIn right now. Any other product we felt would be too similar to a clone of Upwork, Fiverr, Toptal, or any one of the freelance marketplaces that existed.
To build this product, we determined the hardest part would be getting the best freelancers on the platform and retain them. The very best freelancers get most or all of their business from referrals. They also notoriously dislike freelance marketplaces.
We started in early 2018 by developed relationships with a handful of top freelancers that we felt were representative of the broad ecosystem. We asked them about their current experience with marketplaces, and what a marketplace that they would join looked like. Their responses:
From a product and growth perspective, the way to accomplish these was:
To accomplish matchmaking with this degree of specificity, we needed a larger volume of data across more parameters than any other marketplace that exists (still to this day).
We thought asking for that much data from already successful freelancers could be a fatal flaw in the business. So we ran an experiment:
We got the data!
Once we proved we could get the data, we reached out to a representative group of clients to ask about their current experience hiring freelancers, and clarify what was most important to them. Their responses:
From a product perspective, the way to solve this was
With broad product requirements in hand, we started targeting clients that our beta group of top freelancers would be interested in. We also constructed our v1 matchmaking and quote prediction algorithms. The growth plan was to expand out from the beta user group into a product for web designers, then one for knowledge workers freelancing overall.
When clients sent in project inquiries, we got on a phone call and manually collected data points to feed into our algorithms. Then shared the estimated quotes over the phone to make sure that budget worked. If it did, we'd intro the client to the top designers in the results.
I personally did the first few dozen calls myself to tune the algorithm logic. Then I hired an operations lead to take over while I continued to work with her on the product.
Once the product was stable, we built a front-end out of Vue.js for clients to self-serve with. To reduce time-to-ship, we used the Google Sheet as a microservice to deliver the algorithm results by sending and returning API requests through Zapier. (All described in the video above.)
For the first few clients using the interface, we sat on a phone call with them in real-time to gauge their reactions. More often then not they said:
"Wow, I had no idea something like this could exist".
At this point, we decided to open up the closed beta to the rest of the web designers, and within a few weeks we had over 60% of Squarespace designers (over 600) on our waitlist. To grow demand, we used the projects dataset powering the algorithm to create an SEO growth engine called the Showcase — which grew organic traffic by 30% WoW.
Unfortunately we ran out of runway before we hit breakeven though. So at this point, my hope is that I or someone else can build this matchmaking and quote prediction product one day.
 In the demo video, you'll notice the quote and matchmaking results change with each user input. This replaces some of the back-and-forth nature of scope refinement for clients, as it helps educate them as to which variables change the cost and scope of the project. That results in considerable time saved on both sides.