Cycle Time is irrelevant
Updated: Nov 8
November 2023 UPDATE: This was my first blog post, written in December 2018. It's fascinating now to look back on what I learned (and is presented on my website and subsequent blog post). But also see how most of what I saw back then is still relevant today.
I write this article I'm just after wrapping up my own job search. Yep, even talent acquisition folks have to look for a job sometimes! I've always taken an empirical approach to my work, so it was easy to take that approach as I flipped from employer to candidate. I mean, if you gotta be here, why not observe it?
While I had many learnings during my search, one thought keeps rising to the top. Is it time to ditch cycle time? Many of the TA programs I managed in the past put a fair amount of focus on the "average cycle time" KPI. You know: "on average, how long did it takes to fill our jobs this month". We also measured sub-cycles like "average time to submit" so we could optimize each step. This was a worthy if not pedestrian effort to make the programs better.
But from the outside, as a candidate, I see cycle time differently now. It implies there is an abundance of talent. And certainly for some roles that's true. But for the roles that really impact a business, or demand emerging skills or experience, that simply is not true. Thus my thought. Do we need to shift our KPI thinking from "optimizing the averages" to "zero-sum game". It seems that the former actually hides the many failures implicit in an average, while the later focuses only on the success.
Think of it this way. Assume you and your competition need scare talent. Also assume that your firm is the best employer in your industry, but you do have two or three worthy competitors. One day "Mary-rare and great talent" pops up on the radar screen. Maybe your competition coaxed Mary to look around, or maybe Mary is unhappy with her new boss, but Mary is looking. Who will win Mary?
The average cycle time KPI program puts Mary into their process. She's a data point inside a requisition data set. She is processed along with other candidates. She is asked to follow carefully designed and regulatory compliant steps so that risk is mitigated. Mary is one of many. The KPI measures the average process time. Not did we get Mary?
The zero sum game KPI program sees Mary as a singular and scare resource. She is not a data point to be processed, so they focus on speed because hiring Mary is not an average result, it's binary. You either hire her or you do not hire her. Process, compliance and risk are simplified and optimized using technology tools. They expect to hire all the Mary's. The KPI measures success or failure and the cycle time is a by-product.
I find it fascinating that in my search, this TA innovator, talking to innovative companies, didn't really finding wide-spread innovation. I mean, based upon all the things my tech tech vendors told me, you'd think the whole world has gone to AI Sourcing with automated everything. Sure, I saw some (and I liked it). But it was anything but widespread.
So if many firms need help, where should they start? That's easy, apply for a job with your own company (see my CX pages). But I'd also start exploring digital self-scheduling platforms. I encountered two companies that used them - both were really slick. One of them hired me!
So what's my point? Maybe we shouldn't measure or even track cycle time. It measures a process and drives incremental improvement thinking. Maybe in today's world of talent shortages, we need a Zero-Sum-Game KPI. How many times did we get the person we really wanted? And how long did that take?
What do you think?