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    Entries in Recruiting (179)

    Monday
    Jun192017

    Diversity and Inclusivity Starting with the Job Application

    I'm not a user of Snapchat. Mainly because I am an adult, I was never able to figure it out the two or three times my HR Happy Hour partner Trish McFarlane tried to explain it to me, and also because I am an adult.

    While 'maturing' as a platform, (I bet following the same pattern as Facebook, as the parents of the pre-teens, teens, and young adults who were the primary users of the network are 'forced' to sign up in order to keep and eye on what their kids are up to online), Snapchat is still by and large an app/social network predominantly used by people under 34. And this totally fine. I personally don't get it, and I look a little side-eyed when a 46 year old man asks if I 'Snap', but at the same time I totally understand why a 17 year-old would be on Snap all day long. That same 17 year-old would laugh at LinkedIn the same way I scoff at Snapchat.

    I thought about this after reading a piece on Business Insider about McDonald's plans to use Snapchat, in the form of something they call a 'Snaplication' as a launch point in the recruiting process that has a goal of hiring about 250,000 new employees this summer.

    The basic idea is that an interested candidate would log in to Snapchat, find the McDonald's careers 'page' or account or whatever it is you call such a thing on Snapchat, and view a 10-second video from McDonald's employees. The version of the process in Australia also allows candidates to record their own 10 second 'Snaplication' to send to McDonald's. From there, the app allows the candidates (via a swipe) to launch an actual job application process in the app.

    Sounds really cool and innovative, if a little cheeky. But I do applaud McDonald's for pushing the technology and candidate engagement envelope with this initiative. They (probably rightly), see that users of smart phones, (just about everyone), and who also use Snapchat, (probably lots and lots of people from 16 - 30), line up pretty well with their typical or targeted employee profile.

    But what I worried about when I read the story, (and after I stopped rolling my eyes at the concept of a 'Snaplication'), is that this kind of a 'front door' to the recruiting process would almost certainly screen out a pretty significant cohort of potential applicants who don't use Snapchat, would have no clue how to figure out how to send a 'Snaplication', and rather than try and figure it out, would just walk next door to Chick fil-A to apply there. That cohort would be made up of mostly older people, folks like me for example. 

    And if you were surprised to learn that a 'Snaplication' is a thing, you might also be surprised to learn that on average, fast-food workers are getting older too. There are a few different sources of this kind of data, and the numbers are not all consistent, but this example from the BLS suggests that median age of all food service workers is about 30. And I bet if you hit up a McDonald's for your McMuffin and coffee fix this morning you are likely to finds as many 30+ folks working the counter and grill as you are the more typical Snapchatter.

    Now I know that you don't 'have' to use Snapchat to apply for a job at McDonald's, and the traditional methods that older candidates would be more familiar with are still available, but that is not really the point.

    The point is that every decision an organization makes about how it will find, attract, and engage candidates has an impact on the organization in the long run, particularly its diversity and inclusiveness.

    Pushing 'Snaplications' will drive more applicants from a certain, younger demographic, just like working an on-campus recruiting event at the University of Pick Your State will drive more applicants from that particular school's demographic. Running targeted job ads on any website or social network also (by design), shapes, influences, and limits the candidates you are likely to attract.

    None of this is new thinking, smart HR and recruiting folks know this for sure. But I am not sure candidates do. 

    Or said differently, when I read about the 'Snaplication' program, the first thing I thought of was that there's no way I would ever do that. And that is ok I suppose, as I probably would not be applying to McDonald's anyway.

    But I bet there are at least some, maybe quite a few actually, interested and desirable candidates that McDonald's might be turning off with a program like this. And the real lesson is that we all need to be really careful and considerate about how the places, methods, requirements, and technologies that we use in the candidate attraction and application process can have downstream impacts on the organization overall.

    'Snaplications' sound dumb. But they matter. All the choices we make that impact who we bring in to the organization matter.

    Have a great week!

    Tuesday
    May302017

    CHART OF THE DAY: Which matters more, Google or Facebook?

    Apologies for not being more clear on the question in the post title, a better way to phrase it would be this:

    Which source send the most/best referral traffic to your online content - Google or Facebook?

    The answer, and the consultant in me loves this, is really 'It depends.'

    And what it depends on is the kind/type of content you are publishing, and is the subject of today's Chart of the Day.

    As always, and by popular demand, first the data, then some pithy, wise, and FREE comments from me:

    Here goes...

    Interesting, no?

    (Let's pretend it is interesting and proceed).

    1. I have to admit being a little surprised at the edge Facebook has over Google as a source of referral traffic for many of these categories. This surprise is driven and clouded by my own personal media consumption habits I guess. I would never imagine using or relying on Facebook as a source of information for anything other than family/close friend news. And I barely use it for that. Said differently, it is a good reminder that the way you/me consume content may not be the way most people consume content. I barely use Facebook, but I have to remember most of the rest of the world does.

    2. If you are pushing any kind of mainstream, general consumption type content, and you care about how many folks consume said content, you might need to think more about how you can up your presence/reach on Facebook, and maybe be a little less concerned about SEO, (which you never really understood anyway, but that is another story).

    3. BUT... Take a look at the last content category on the above chart - Job postings. In this category Google still dominates with 7x the referral traffic as Facebook. And it even dominates 'other' (sorry other). It seems like if you are in the Recruiting business you still do need to worry about SEO after all. And you probably need to get a handle of what Google is up to with its recent and early forays into the recruiting and job search space.

    This is totally fascinating data I think. And a reminder that job postings are not (yet) the same as the rest of the content on the internet. People look for them, and find them, much, much differently than many of the other forms of content that are all over your Facebook feed.

    Interesting stuff for sure.

    Have a great week!

    Monday
    May222017

    Learn a new word: The Optimal Stopping Problem

    I caught an interview over the weekend with one of the authors of Algorithms to Live By (can't recall which of the two co-authors I heard, but it doesn't matter. Kind of like it doesn't matter which of the two guys in Daft Punk plays a particular instrument on any given track. But that is another story.), and wanted to share a new word I learned from the interview that has some relevance to HR/Recruiting.

    For this installment of Learn a new word I submit The Optimal Stopping Problem.

    From our pals at Wikipedia:

    In mathematics, the theory of optimal stopping or early stopping is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost. Optimal stopping problems can be found in areas of statistics, economics, and mathematical finance (related to the pricing of American options). A key example of an optimal stopping problem is the secretary problem. Optimal stopping problems can often be written in the form of a Bellman equation, and are therefore often solved using dynamic programming.

    I bolded the 'secretary problem' which, despite its dated-sounding kind of name, is the example most commonly cited when discussing optimal stopping, and as luck would have it, is directly tied to HR/Recruiting.

    The secretary problem is essentially, the question of 'Given X number of job candidates for a given position, and also given you have to make a 'hire/decline' decision on each candidate before moving to the next one, how many candidates do you need to interview in order to maximize your probability of identifying the best candidate, while minimizing the risk of making a 'bad' hire, (say by waiting too long, rejecting too many candidates, and having to settle for a candidate that is left).

    Let's say you have 10 candidates for a position. You probably wouldn’t offer the job to the first candidate you interview, because you have no idea how that candidate compares to anyone else, or the general caliber of the candidates overall . But you probably don't want to wait until the 10th candidate, because if they’re the only one left you’re going to be forced to offer them the job (or keep it unfilled), regardless of how strong a candidate they are. Somewhere in the middle of the process there must be an ideal place to stop interviewing more candidates just to see what they’re like, and make a selection. But where to stop?

    Enter the Optimal Stopping Problem. You can dig into the math here, but it turns out there is an ideal place to stop interviewing candidates, (or dating different people in order to try and choose who to marry), and it's after you have interviewed (or dated), 37% of the contenders. After you get to 37%, make a note of the 'best' candidate you have seen so far, (let's call her Mary Jane). Then, continue interviewing candidates and when you find the first one that is 'better" than Mary Jane, stop all further interviews and immediately offer that person the job.

    How it works is related to the math behind estimating where the best candidate could be in the lineup. This number, expressed as 1/e, where 1/e eventually approaches 0.368, or about 37%. By analyzing the possible distribution of talent, it also turns out that if you interview the first 37 percent of candidates then pick the next one who is better than all the people you’ve interviewed so far, you have a 37 percent chance of getting the best candidate. 

    It's a really interesting way of looking at the hiring decision making process, (as well as other processes that involve trying to make the 'best' choice amongst a number of alternative). But it makes sense somehow, even if only on an anecdotal level.

    How many times have you slogged endlessly through an interview process where after some point candidate after candidate seem the same, and certainly no better than one you saw two weeks ago?

    Or how many of us have, (maybe even privately), thought about a past boyfriend or girlfriend that 'got away' and for some reason has never been eclipsed by the series of people that you have subsequently dated?

    Knowing when to stop, and understanding the probability that you have seen the best, or close enough to it, in any decision process is an enormously valuable thing.

    In the secretary problem, and in probably a bunch of other problems too, the answer seems pretty clear - once you hit 37% you have seen enough, you won't learn much if anything else useful, and you know how to make your decision.

    It is easy to apply in a job vacancy with 10 candidates. 

    It is a little tougher to estimate just how many people you are willing/able to date in order to know when to apply the 37% cutoff.

    Have a great week!

    Wednesday
    Apr122017

    It's better to have a job when you're looking for a job

    As the 2007-2008 financial crisis and subsequent economic recession fade further and further into the distance, we don't in 2017 talk about unemployment all that much. The sustained recovery in the labor market has pushed unemployment to near "full employment" levels of about 4.5% in the US, and in many sectors and job roles most employers would report 'good help is hard to find'. Until the robots take over. But that is a different story for another time.

    Back to unemployment though. In 2008 and 2009, there was plenty of discussion about the best ways to help the many, many folks who were out of work to get back into the labor force. Lots of job search gurus appeared online, plenty of networking and support groups were created, and certainly significant governmental support, (cars, banking, insurance), was marshalled to try and stop the bleeding in the labor markets and help get people back to work (or keep them in work).

    Around that time, as the unemployment rate topped at about 10%, one peculiar storyline emerged, and pretty consistently as well - namely that folks who were unemployed, and 'actively' looking for work, were often characterized as less desirable candidates than say someone who was currently employed, and may not even be actively looking for something new. The dream 'passive' candidate if you prefer that term. Lots of anecdotes about hiring managers passing on any candidate who was out of work were shared, and plenty of folks, (I possibly was one of them), opined about how unfair that this kind of (for lack of a better word) discrimination against the unemployed was seemingly more and more prevalent. And anecdotal or not, it certainly seemed that looking for a job when you did not have a job was much, much tougher than looking for one when you were already employed.

    But just how much tougher is it, really?

    A recent study by the Federal Reserve Bank of New York looks to put at least some data around these anecdotes by looking at job search activity by unemployed workers, by employed workers, (both passive and active), and people out of the workforce. The entire report is interesting and worth a read but I thought I would tease out two of the report's most interesting findings about job search, and more importantly, job search outcomes.

    1 - Lots of employed people are actively looking for work - almost one quarter of them 'actively' searched in the trailing four weeks of the survey period

    Not shocking I guess, but also the 23.3% doesn't account for the probably much larger number of employed workers that would be open to at least discussing new opportunities, even if they were not in active search. Said differently, one of the reasons contributing to a bias in favor of employed workers is the fact that just about all employed workers are still in the candidate pool anyway. At least partially in.

    So how does this perceived bias influence outcomes? Here's the money chart from the study, depicting how search behavior and application intensity translate into positive outcomes, i.e. job offers.

    I will help you with the fine print here. Unemployed workers make up about 7 percent of the survey sample. They send out 40 percent of the total job applications, but receive only about 16 percent of the total job offers.

    In contrast, folks who were employed and were actively looking for work make up about 20 percent of the sample but receive almost half of all offers. Further, the employed not looking for work (and who do not apply for any jobs), receive about one‑fourth of all the offers in our sample—more than the unemployed who are the most active searchers and applicants.

    So how much better is it to be employed when looking, (or in many cases not looking) for a new role?

    Well, according to this data, much, much better. Roughly it takes eight times the effort in terms of time spent and four times the application rate for unemployed folks to generate a similar rate of job offers that employed workers realize - many of whom are not looking for work at all.

    Hopefully we won't have another dramatic economic or market shifting incident like the financial crisis that drives up unemployment and will make these findings and their impacts top of mind again. But it is good food for thought for any of us who may not love the job we have now, and are looking for something better.

    We just might want to hold on to that crappy job as long as we can, because having it makes our odds of finding the next (hopefully less crappy) job that much better.

    Monday
    Mar132017

    Understanding your competition for talent

    There is a old adage, (not sure when and from whom this was first attributed to), that ascribes a breakthrough in an auto manufacturer's business strategy to them realizing that they were not in the 'car building' business, but rather they were in the 'helping people to get where they want to go' business. 

    This restatement in their fundamental purpose as a business became the key to thinking differently or more expansively about the business, their products, and the talent attraction and retention programs they would have to employ. This kind of thing is happening once again in the auto industry, as described in a piece I read over the weekend from Business Insider titled 'There's a raging talent war for AI experts and it's costing automakers millons'.

    Most of the major auto makers are now playing at some level or another in the nascent self-driving vehicle space - continuing the evolution of their business purpose and their strategy towards personal transport and away from just making cars. But, as you would expect, and the BI piece points out, these shifts have important implications for talent attraction and retention - most importantly even for those of us not in auto making, and are driving changes in the talent competition marketplace.

    From the BI piece:

    But automakers, in particular, are making massive investments in (AI) experts because they’ve begun their AI efforts late compared to traditional tech companies.

    Because deep learning has applications far beyond just self-driving cars, manufacturers are having to compete with each other and traditional tech companies.

    Only 28 companies have more than 10 deep learning specialists on staff, accounting firm KPMG wrote in a 2016 report. What's more, only six technology companies employ 54% of all deep learning specialists: Google, Microsoft, NVIDIA, IBM, Intel, and Samsung.

    "The traditional power and talent of the auto industry was based in their product development group," Gary Silberg, the head of KPMG’s automotive unit, told Business Insider. "So they would hire these amazing mechanical and electrical engineers at the top schools of engineering and they would be part of product development."

    "You can’t just turn on a dime and say, 'ok, now we are going to go recruit AI geniuses and computer scientists and expect them to come to work with us,'" Silberg continued.

    A shift in strategy, leading to the increased demand for a (apologies to Liam Neeson) particular set of skills, is changing how and with whom the auto makers are having to compete with in order to find the talent they need for these AI initiatives.  And they are not finding it easy. Instead of a GM or a Ford more or less having to only worry about each other, and maybe Chrysler, for the cream of the crop of mechanical engineers and industrial designers, they now have to compete with Google, Uber, Microsoft, Tesla and more for the really, really scarce pool of AI experts.

    In fact, as the BI piece points out, the pool of AI experts is so small at least in part due to the best AI professors themselves being recruited out of academia and into industry, leaving universities unable to meet the demand for educating more AI students.

    Want a great example of how a business strategy shift impacts your talent strategy, and requires that the talent strategy undergo a complete re-think? Look no further than this example from the auto makers. The lesson here? The next question your company needs to ask when assessing a business strategy shift, after 'Can we really do this?' is 'Can we find, attract, hire, and retain the kinds of people we need to do this?'

    Competing for talent against one or two competitors that do about the same thing as you do is fairly straightforward.

    Competing for talent against an ever-growing, deep pocketed, and fast moving ecosystem of often dissimilar companies is another thing entirely.

    Have a great week!