Onboarding

Kalo is leading the way in freelancer management, working with some of the biggest global brands including Thrillist, Airbnb and Expedia to manage their external workforce from onboarding through to payments.

At Kalo, I’ve led the design of a dramatically more scalable product as the company has seen significant growth – now with hundreds of teams using the web interface to manage thousands of freelancers daily.

I’ve been responsible for using data in our design process to help drive decisions, adopting user research and spending time speaking to our users. I’ve also defined and evolved our design system.

Another key part of my role at Kalo has been focused on hiring many talented people to work on design, user experience, and other product roles for both our London and San Francisco offices.

Building a smarter onboarding

I've been responsible for dedesigning Kalo’s web app onboarding. This project is the start of a bigger project around making Kalo a smarter application, with the ability to match the best freelancers to tasks through artificial intelligence.

Team Setup: Product Manager, Product Designer, 2 x FE Engineers, 3 BE Engineers.

Business Goals and Context:

Make sure we collect relevant data to start building Kalo ID's and a network effect.
Standardise how we collect data from a user and technical perspective so we can build a smarter app.
Improve onboarding experience for freelancers.
Encourage freelancers to engage with Kalo to complete their profile and optmise for working with other companies.
Move towards a more mobile-focussed approach.

Current Problems:

Today, a lot of the data we gather is unstructured and we don't have a way to use it intelligently.
The current onboarding doesn't engage the freelancer or encourage them to complete their profile.
The current onboarding isn’t responsive so only works on desktop.
We don’t differentiate types of onboarding between an "applicant" or a "worker" freelancer.

Research methods

Methods:

We conducted research sessions with clients to understand how they currently match freelancers to tasks.
We put together a freelancer survey which collected over 70 responses to understand decision making and pain points.
We analysed current data to understand how complete the current profiles are and give us clear benchmarks to track against.
We looked at the client-generated onboarding fields to understand where there were commonalities that we should standardise.

Key Findings:

Freelancers didn't know the value of Kalo beyond the one client they worked with.
Some of the top rated ways to incentivise freelancers were ease of finding work and the companies they could potentially work with.
Freelancers found it difficult to stand out from the crowd when applying for work.
Clients were asking for a lot of additional data around address since we were only capture city information.
Clients ask to see freelancer CVs and/or work history when onboarding freelancers.

Kick-off session

After gathering our initial data and defining the goals and needs, we arranged a kick-off session involving the Product Manager and a Tech Lead. The goal of the session was to give a run through of our project definition and current research to help us come up with ideas and the best approach.

Understanding the user flow

Once we had outlined our proposed approach we started mapping out the various user flows for each of the scenarios including notification triggers. We then ran the team through the flows to make sure we were all aligned from a UX and technical perspective.

Wireframes

Our next steps were to wireframe up and prototype our solution and conduct internal user testing to catch initial gaps or issues. During this phase we were continually iterating before progressing to UI exploration.

UI exploration

The UI exploration helped us gather more detailed requirements from a UI and tech perspective such as what API's would help us deliver the desired interaction. During this time we also worked with the engineering team to look at how we could break down and deliver the project in smaller chunks that we could quickly test and iterate on.

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