Audience Analysis and Algorithms

When it comes to resumes and cover letters, everyone has an opinion. You’ll take your resume to a resume workshop on campus and get one piece of advice, then show it to your friend and get another, and ask a mentor and get told to ignore what the first two people told you. That’s because conventions about resumes and cover letters differ according to the job and the industry.

Resume and cover letter conventions also evolve over time. In the 1970s and 80s, many people included their height, weight and a photograph when they applied for the job. You can imagine how this led to discriminatory hiring practices. Thanks to Human Rights Tribunal cases and advocacy from workers who spoke out against the practice, employers stopped asking for photographs. (Some restaurants and bars still ask for photos when hiring servers, and while the practice isn’t technically illegal, it’s a sign that you should never work there).  Cover letters used to be formatted like actual letters, but today very few cover letters are actually sent through the mail. Today, cover letters and resumes are adapting to the fact that many companies using software that forces you to copy and paste your resume and cover letter into little text boxes.

In this section, we’ll focus on how to make effective decisions about your cover letters and resumes. We’ll avoid giving prescriptive advice and instead focus on helping figure out what your audience wants and how to market yourself effectively. In doing so, we’ll draw on a lot of the different skills we’ve learned this semester: analyzing an audience, persuasion, graphic design, plain language and more.

Audience Analysis: Communicating To An Algorithm

When you write a resume and cover letter, you have two audiences and one isn’t even human. Right now, an important context in workplace communications is that many companies use some form of artificial intelligence to make hiring decisions. An applicant uploads a resume and cover letter to the H.R. software, and an algorithm scans it for keywords, skills, progression (whether the person has been promoted and taken on experience), and other factors. This means that even if you’re qualified for the job, your application could be thrown out before a human ever sees it.

Algorithms can:

  • Search your resume and cover letter for specific keywords and phrases. For example, one keyword might be ‘Adobe Photoshop’ or ‘marketing manager.’ The algorithm can even assign different weights to different keywords. A resume will receive a certain number of points based on how closely it matches the keywords. So, if it’s really important that a candidate has a Ph.D., that keyword would be worth a lot of points.
  • Use the dates on your resume to determine how much experience you have and whether or not you’ve been promoted or taken on more responsibility. Clear formatting can help the algorithm see your career progression.
  • Use “linking text” and “expressed magnitude” to try to figure out if you have soft skills like leadership skills or project management.[1] Linking text means that the algorithm is searching for phrases (“managed a team of # people”). Expressed magnitude means that the software searches for adjectives and adverbs (such as ‘expert’ or ‘highly’) to figure out how good you are at a particular task. Algorithms will even analyze your word choices to try to figure out how passionate, positive or enthusiastic you are.
  • Search for “signals” about your competence. [2]For example, many algorithms will reject candidates who make spelling or grammar errors. Some algorithms even reject people who use certain software to create their resumes.

There’s a great case to be made that using artificial intelligence to make these decisions is highly problematic. Often, suitable candidates are excluded because they didn’t use the right keywords or because they took a non-traditional career path. More than that, however, software is never neutral because the unexamined biases of the people who created the software end up in the algorithm. So, if you’re white, you may create facial recognition software that works best on white faces. (Google Photos, Flickr and other companies have been criticized for accidentally labeling black people as “monkeys” or “apes,” for example). [3] Founder of the Algorithm Justie League Joy Buolamwini calls this the “coded gaze,”  which means that discrimination gets baked into software “by those who have the power to code systems” (Buolamwini 2016). And since companies often don’t test software for biases, it’s a hard problem to correct.

You may not have control over some of the ways that algorithms interpret your cover letter or resume, but here are some ways to meet this audience’s needs:

  • Find key phrases in the job posting and use them in your resume and cover letter [4]. When you read the job posting, underline key phrases, paying special attention to ones that are repeated. We’ll show you how to do this in the next section.
  • Don’t take too many design risks.[5] If you work in a creative field, you may be tempted to use different fonts, photos or Photoshop to show off your skills. Algorithms have a hard time with these. Use a clear, simple format that’s easy to read.
  • Be clear and specific[6]: Using plain language and setting your accomplishments in concrete terms will help you pass the algorithm. For example, instead of saying “I’m a highly experienced and passionate engineer,” you could say “I have 10 years of experience as a Mechnical Engineer and am an expert in creating CAD drawings and collaborating with Project Managers.”
  • Proofread both your resume and cover letter: Since many algorithms will throw out any resume or cover letter that has a spelling mistake, make sure to check your work carefully before you submit it.

This visual offers you an overview of the audiences involved in the hiring process.




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Business Writing For Everyone Copyright © 2021 by Arley Cruthers is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

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