Breaking Down Bias & Building a Better Outshine
We’re always learning at Outshine, and while this was especially true last month with some of our colleagues away at events like SaaStr Annual, our learnings don’t stop at marketing and SaaS. We also spend a great deal of time thinking about the type of company we are, and the type of company we want to become.
A large part of that is about taking our hiring practices seriously, and focusing on bringing the best possible colleagues on board.
But what makes “the best colleague?”
For us, it’s a lot of things—driven, high-performer, passionate, problem solver, people-person are a few of the qualities that come to mind. But what’s important to note is that none of these are characteristics of a specific type of person, be it gender, race, or otherwise.
We’re currently searching for a senior marketing consultant to join our team at Outshine. We’ve published the posting across the web, and were promoting it on LinkedIn and Indeed.
As the resumes roll in, we quickly realized that we needed a plan to start processing them in a structured, professional way. We knew it wasn’t enough to make an undocumented, unmethodical decision each time, and we know that reducing bias isn’t likely to just happen on its own.
If diversity and inclusion are important to you, as they are to us, you need to put proactive measures in place to check bias at the door.
And guess what? It’s work! It takes thoughtfulness, planning, and dedication.
So where did we start?
Shortlisting Key Evaluation Criteria
Without proper scoring criteria, it’s all too easy to look at an application and think, “This person probably isn’t qualified,” and move them to the reject pile. But what information was considered while making that decision? Was it a lack of related skills, or was it the person’s name, assumed ethnicity, or some other criteria?
We began by defining the top five or six pieces of experience the role really, truly requires. These became our shortlist of evaluation criteria, against which every candidate would be ranked. For example, we started with a list that included:
Paid advertising experience (specifically AdWords, Facebook, LinkedIn, Twitter, Bing)
Google Analytics / data analysis experience
3 years (or more) digital marketing experience
Each time an applicant has or meets one of the criteria above, they get a point. Each time an applicant has extensive knowledge or experience in the criteria above, they get two points.
Note that the evaluation criteria are an attempt to more objectively rank applicants by technical skills and experience, which we view as only stage one of a more lengthy process involving screening interviews, cultural assessment, and so on.
As evaluation criteria were being developed, we simultaneously hit print on the pile of resumes received. And then we realized we had a problem. Each of the printed resumes had the names, emails, and addresses of the applicants. It was going to be impossible to not let unconscious bias slip into our hiring process. We came up with a game plan to fix this.
Tip: Gathering resumes was a painless system—every application that comes through LinkedIn, Indeed, or via the website goes to a generic “careers@outshine” email address, which then populates a Trello board with a new card per candidate—making sorting and organizing candidates a matter of drag and drop.
Once printed, one of our team members not directly involved in the hiring process went through each and every resume, manually blacking out personally identifiable information that could engage bias on behalf of the hiring committee. Information removed included:
Address (we had candidates apply from all over the world, and didn’t want to exclude based on location)
First & last names (names/gender not relevant)
Photos (a surprising number of resumes came in with a photo of the candidate themselves, which tells you too much about age, gender, race, and so on)
Email address (containing the candidate’s name)
This process was a little bit clunky, and truthfully, time-consuming. But it was important to us, and as such we’ll work to iterate and refine as we continue with our search.
Developing a Scoring System
We then set up two Google Sheets:
One had the complete list of candidates with a number assigned (candidate 1, 2, 3, and so on). This sheet wasn’t shared with those involved in ranking candidates; the person responsible for anonymizing each of the applications also wrote the candidate’s corresponding number on their application.
The second sheet had the list of applicant numbers running vertically (no names), and the scoring criteria labelled horizontally, like so:
Each person on the hiring committee has a tab in this Sheet where, individually, they record their own evaluation of each candidate.
At the end of the process, each applicant has a score that can be compared against all other applicants. Those with the highest scores are reviewed again, and then may be selected for an initial screening interview done by phone. From there, candidates still of interest would move on to a cultural fit evaluation.
Removing bias, as much as possible, is hard work. We don’t have a fool-proof system, but we’re working on it.
As marketers who work with a global clientele, broadening the worldview of our team is the right thing to do for our work, our clients, and for each other.
PS—if you’re someone who wants to increase diversity in your workplace, here’s a great resource. It’s specifically based on hiring women in tech, but there’s some great, practical advice on things you may be overlooking, like how your job description is written, where you’re looking for candidates, and what your current company culture says about you.
We’d love to learn about what other companies are doing to reduce bias in their hiring processes. Leave a comment below!
Blog post written by Emily Stephen, head of marketing at Outshine.