The Science of the Job Search, Part VIII
By Claire Jaja
Talent Works, December 5, 2018 —
Torn between keeping your resume to one page and including as much as possible?
Is keyword stuffing a good thing or a bad thing?
We crunched the numbers, and turns out, longer is better — up to a point.
Once your resume exceeds 600 words, your chances of an interview plummet.
We analyzed 6,000+ job applications from 66 industries and found that:
- The sweet spot for resume length is between 475 and 600 words.
- There are exceptions though — even longer is better, if you’re an academic or industrial scientist, college professor, school teacher, or social service worker.
- “Keyword stuffing” your resume doesn’t make you any more likely to get an interview.
Keep your resume short and sweet (but not too short)
Job applicants with resumes over 600 words had significantly lower interview rates. Up until that point, longer is better — short resumes, less than 450 words, also had lower interview rates. Makes sense, since more words means more opportunities to sell yourself. Keep adding words beyond that though, and recruiters or hiring managers are likely to have their eyes glaze over.
Taken together, this means that the sweet spot for resume length is between 475 and 600 words. Unsurprisingly, this corresponds to a densely-packed single page resume. Interview rates for users with resumes in this range averaged 8.2% compared to less than 5% for shorter or longer resumes.
Longer resumes are better for certain professions
Wait a second, you might be saying, I’m an academic researcher, and I need 5 pages to include all my publications (kudos to you, if so) — are you saying that’s a bad thing? Turns out, there are some exceptions to the rule. Resumes over the 600 word threshold are better — if you’re an academic or industrial scientist, college professor, school teacher, or social service worker.
This makes complete sense: scientists and professors often have long lists of patents and publications, and, as we noted in a previous study, teachers and social service workers were some of the few professions where resume objectives helped their interview chances. If your industry really cares about all of your motivations or your exhaustive list of achievements, longer resumes are better.
No, really, keep your resume short, especially if…
On the other hand, most industries punish long resumes and some industries really punish long resumes. For example, in business, long resumes were a whopping 72% less hireable than those in the sweet spot. No surprise — if you’re in business, brevity wins. If you’re a Marketing Manager and can’t market yourself in 1 page, you have a big problem.
Don’t bother stuffing your resume with keywords
Maybe it’s not the number of words in your resume, maybe it’s the number of keywords. So we extracted keywords using a known qualification set and looked for a trend between the number of keywords in a user’s resume and their interview rate. Turns out, having more keywords in your resume doesn’t correlate with a higher interview rate.
At first, this seems surprising, since we know that there is often an initial filter using an ATS (Applicant Tracking System) where resumes without specific keywords don’t even get seen by a hiring manager (sad, but true). But I think that this is a case of quality over quantity — it’s not about how many keywords you have in your resume, it’s about having the ones that match the job.
Next time you’re working on your resume, remember:
- Keep it in the 475 to 600 word range.
- Unless you’re an academic or industrial scientist, college professor, school teacher, or social service worker — then let your verbosity shine!
- Don’t go out of your way to fit as many keywords as possible in your resume.Methodology
First, we randomly sampled 6,305 applications across 66 industries for 721 different users from TalentWorks. Then for each of those users, we extracted the word count and keyword count (of keywords from a known qualification set) from their resume and calculated their interview rate. Finally, we clipped outliers, then weighted (by number of applications per user) and smoothed the results to find the general trend. All analysis and graphing was done using python with pandas, sklearn, scipy, and bokeh.