About the company
Torre.co is a job matching network startup focused primarily on remote jobs, with over 1.5M users. It has a powerful matching algorithm that uses 121 different factors to determine if a person’s genome is a good fit for a job and vice versa.
- Companies were not being able to hire great talent because they were not offering enough compensation.
- Recruiters had no automated way to know how much compensation was being requested by the top talent for that job.
If we give talent seekers an idea of how their compensation offers stack up with what job seekers are requesting, they will increase their offers to attract better talent.
Understand the opportunity
How it started
This experiment began with a hunch. Not mine, but from one of our sales executives who was tracing our decreasing growth all the way back to salaries posted on the site. Here’s the breakdown of that logic:
Companies and talent seekers want the best talent, as fast as possible, and (sometimes) as cheap as possible.
Job seekers consider monetary compensation one of the most important elements of a job post to decide if they’ll apply or not. When they see a platform filled with jobs that pay less than expected, they leave.
This starts a cycle of increasing churn for job seekers and making it harder for our sales teams to grow in clients looking to hire.
This issue started a big account management effort to assist recruiters in their decision for monetary compensation. Representatives would verbally explain to recruiters what level of compensation would be required to find the talent they were looking for.
This helped to some degree, but (A) it wasn’t very scalable, and (B) recruiters weren't fully buying it. They wanted to see real data that they could show to internal stakeholders to increase their budgets. They needed the numbers to believe it.
That’s where I came in.
What could we build to solve this?
I was essentially dealing with a SaaS B2B project. Users for this functionality would be internal stakeholders, recruiters, and other representatives of Torre-using companies who would on occasion look for talent.
After interviewing five internal stakeholders and five recruiters, I concluded there were two use cases in Torre that could be significantly improved by providing more information about salaries:
The search bar use case
Whenever a recruiter typed in the search bar to look for candidates that fit specific criteria of skills, we could give them statistics about how much their target market would be interested in getting.
The chart should feed only from the data of the people that showed up in the results for that specific search. It should provide the viewer with a general idea of how much money job seekers were asking for depending on how high they rank against the skillset that was searched.
The job post creation use case
Most importantly, when a recruiter is in the process of posting a job and inputs the compensation they will offer, we could show a similar graph.
In this scenario, the graph would take into consideration all the criteria the recruiter had already added to their job post. We could also add an indicator of approval or disapproval depending on the amount entered by the recruiter.
As it tends to happen, we weren’t the first ones to face a similar issue, so there were a few examples online of how other companies were reporting salaries. Some important adjustments would be required but the standard users are accustomed to was clear. Respecting Jakob’s law, I decided we should do a histogram too.
After seeing what others were doing I had to go back to consider what our explicit purpose was. Most of the tools I was looking at were made to assist job seekers in better understanding the market so they could know what to expect and ask for.
What we needed was a tool that convinced Torre talent seekers to increase the salaries they were posting because it would lead to better, faster hires for them and overall growth for Torre: a win-win-win. So that was what I would aim for.
Here are the designs:
The why behind some big decisions
Q: Why is there a gradient throughout the whole chart?
A: We wanted to direct the attention of users toward a specific section of the chart, where the optimal candidates would likely be located.
Q: Why does it say “top candidates” in the middle bar?
A: Again, we wanted to make it extremely clear where they should be aiming at. If you want great candidates, this is how much they are asking for.
Q: Why aren’t we marking the medium or the average?
A: Same reason. The medium is irrelevant when we want you to focus on the top candidates. For this, we chose the top 75th percentile within the candidates that matched the criteria for the search.
Q: Why are there two graphs for yearly vs hourly at once instead of an option to modify the info?
A: The first version had several customization options but we decided to reduce the engineering scope to the minimum for the initial launch. The team got it out pretty fast.
Q: What do the red and teal colored bars mean?
A: As you’ll see in the GIFs below, when a job post is being created, these colors show the user how the amount they are offering compares to the threshold of the 75th percentile. It is also accompanied by a text saying
These GIFs show the final results:
The job post creation use case:
The people search use case:
At the time of writing this case study, the experiment has just recently been released. More data will be needed in order to determine how this tool has affected the initial hypothesis. However, the reaction from stakeholders and clients is very positive!
Initial reports show that recruiters (together with our clients) are being able to create more job posts that offer monetary compensation within the standards some of the “top candidates” from specific markets have set in place.