How Data-Driven Experimentation Can Shape Your Job Search

Job hunting (like any marketing activity) is a numbers game. Much as you might hope that your “perfect” application to your dream employer that you spent four days perfecting might secure you an immediate interview, there are many reasons why that may not happen. Not on this occasion anyway. If you try again via a slightly different route in a week, you might have better luck. Data-driven experimentation can help you to make these choices.

Obsessing over the quality of a small number of applications may pay off if you are lucky. Most candidates soon realise that they need to increase the volume of applications before they start to get interview invites dropping into their inboxes. If you go “wide” as well as keep the quality of your applications high enough, opportunities will come.

According to a Jobvite survey, 12% of applicants are typically invited to the average interview (1 in 8 applications). Furthermore, 28% of interviewed applicants get the job (which equates to 1 in 4 applications). If you do the maths, the average applicant should cast out dozens of fishing rods to give themselves the chance of landing a job (and they still may choose not to go for it).

So, with all this activity required, how do you work out how to spend your job search time? How efficient is the time that you are spending on social media? Are you spending too much time on the content of applications when it might be more profitable moving on to others?

Data-driven experimentation laws

The law of diminishing marginal returns rules supreme, but where do you draw the line?

After what point is the additional effort not going to result in a worthwhile bump in the probability of securing an interview and (more importantly) having an impressive enough application?

In a job search, data-driven experimentation should consider two sets of data:

The first is the conversion rate of job search marketing activity to interviews (which depends on more quantitative metrics). The second is the eventual outcome of those interviews (where qualitative metrics come into play).

Both sets of data are related. You might apply for 100s of interviews and get a few, but if you have not spent enough time on each application, the quality will not be there to enable the interviews to lead to job offers.

So, where does a data-led job search start?

As with marketing, you must keep filling the funnel with valuable opportunities.

Experimentation variables

Only apply for roles that tick your boxes in terms of seniority, location, function and responsibilities. Every now and again, it might be worth applying for something more senior or junior in the hope of starting up a conversation. Then see what type of activity is converting into interviews and work out if the time that you spent on that activity is worthwhile.

How did you apply for the role? Was it a quick online application process? Did a recruiter put you forward for a role? Did you approach the hiring manager directly on LinkedIn? Was it a speculative CV sent through to a general HR inbox?

Then comes the effectiveness of your interviews themselves. Much of this will depend on how you tell your career stories and the rapport that you develop with the interviewer, so it is worth jotting down some detailed notes after each interview and then comparing them with the employer responses. There may be a few correlations.

It may also be the case that you are not progressing through interviews because the quality of your application was poor (because you rushed through the online application process, for example), so bear this in mind if you sense that this is the case.

Note the variables as you progress with each opportunity, consider the conversation rates and study the rejections. With a job search that can last a few months, the earlier you engage in data-driven experimentation, the sooner you will get that job offer.

Follow the data as it doesn’t lie. Don’t compromise on quality if you can help it.

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This blog is shared with Job Seeker Duetists. 

Written by former recruitment ghostwriter Paul Drury (not AI).