Quantcast
Subscribe!

 

Enter your email address:

Delivered by FeedBurner

 

E-mail Steve
This form does not yet contain any fields.
    Listen to internet radio with Steve Boese on Blog Talk Radio

    free counters

    Twitter Feed

    Entries in data (126)

    Monday
    Jul312017

    CHART OF THE DAY: The World's Most Valuable Brands

    Happy last-day-of the-month Monday!

    Quick shot for kicking off a busy summer week. Courtesy of our pals at Visual Capitalist, let's take a look at the list of the corporations owning the world's most valuable brands:

    The 'brand value' methodology is referenced on the infographic above, but the essential element is that it it is the intangible asset that exists in the minds of consumers, which is usually an image forged over time through exposure to branding, ads, publicity, and other types of personal experiences. Attaching a dollar value to this intangible asset is perhaps more art than science, but while the specific dollar values can be debated, it probably can't be debated that there is at least some value to the brand.

    So while the top companies for brand value are likely the ones that you'd expect, after I saw this chart I couldn't help noticing that these companies also seem to be the ones that show up on the various 'Best or Top of Most Awesome Companies to Work For' lists that float around on the internet.

    Take a look at just one example, from our friends at LinkedIn, on the '40 Most Attractive Companies in the World' (according to LinkedIn)

    I cut the Top 40 List off at 7 due to space concerns and also because that is all I needed to make my point

    Hey, what a surprise! The Top 5 Global Brands in terms of value, (Google, Apple, Microsoft, Amazon, Facebook), all show up inside the Top 7 of the LinkedIn 'attractiveness' list.

    And you'd find similar kinds of results on most of the other types of 'Best Places' lists - they are dominated by these mega-tech brands that make the coolest products, have the most incredible corporate campuses, and often are led by influential and charismatic leaders.

    All of this to make the point you already know - the thing we like to call 'employer brand' is inextricably tied up in what most people will call the consumer or public brand. The most powerful, valuable, and well-known consumer brands have such an advantage in the employer brand category that it is almost laughable.

    If you are one of the companies on the 'most valuable' list, congrats, things are always going to be easier for you to attract and recruit. If you are not one of those global, mega-brands, you have to know you are starting any competition for talent at a disadvantage. 

    Some brands have all the luck, I guess.

    Have a great week!

    Thursday
    Jun152017

    Learn a new word: Positive Predictive Value

    Predictive analytics and the application of algorithms to help make 'people' decisions in organizations has been a subject of development and discussion for several years now. The most common applications of predictive tech in HR have been to assess and rank candidates for a given job opening, to estimate an individual employee's flight risk, and to attempt to identify those employees with high potential or are likely to become high performers.

    These kinds of use cases and others and the technologies that enable them present HR and business leaders with new and really powerful tools and capabilities that can, if applied intelligently, provide a competitive edge realized from the better matching, hiring, and deploying of talent.

    But you probably know all this, if you are reading an HR Tech blog anyway, and perhaps you are already applying predictive HR tech in your organization today. But there is another side or aspect of prediction algorithms that perhaps you have not considered, and I admit I have not really either - namely how often these predictive tools are wrong, and somewhat related, how we want to guide these tools to better understand how they can be wrong.

    All that takes us to today's Learn a new word - 'Positive Predictive Value (PPV)'

    From our pals at Wikipedie:

    The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic.

    A good way to think about PPV and NPV is using the example of an algorithm called COMPAS which attempts to predict the likelihood that a convicted criminal is likely to become a repeat offender, and has been used in some instances by sentencing judges when considering how harshly or leniently to sentence a given criminal.

    The strength of a tool like COMPAS is that when accurate, it can indicate to the judge to give a longer sentence to a convict that is highly likely to be a repeat offender, and perhaps be more lenient on an offender that the algorithm assesses to be less likely to repeat their crimes once released.

    But the opposite, of course is also true. If COMPAS 'misses', and it sometimes does, then it can lead judges to give longer sentences to the wrong offenders, and shorter sentences to offenders who end up repeating their bad behaviors. And here is where PPV really comes into play.

    Because algorithms like the ones used to create COMPAS, and perhaps the ones that your new HR technology uses to 'predict' the best candidates for a job opening, tend to be more or less wrong, (when they are wrong), in one direction. Either they generate too many 'matches', i.e., recommend too many candidates as likely 'good hires' for a role, including some who really are not good matches at all. Or they produce too many false negatives, i.e. they screen out too many candidates, including some that would indeed be good fits for the role and good hires.

    Back to our Learn a new word - Positive Predictive Value. A high PPV result for the candidate matching algorithm indicates that a high number of the positives, or matches, are indeed matches. In other words, there are not many 'bad matches' and you can in theory trust the algorithm to help guide your hiring decisions. But, and this can be a big but, a high PPV can often produce a high negative predictive value, or NPV.

    The logic is fairly straightforward. If the algorithm is tuned to ensure that any positives are highly likely to truly be positives, then fewer positives will be generated, and more of the negatives, (the candidates you never call or interview), may have indeed been actual positives, or good candidates after all.

    Whether it is a predictive tool that the judge may use when sentencing, or one your hiring managers may use when deciding who to interview, it is important to keep this balance between false positives and incorrect negatives in mind.

    Think of it this way - would you rather have a few more candidates than you may need get screened 'in' to the process, or have a few that should be 'in' get screened 'out', because you want the PPV to be as high as possible?

    There are good arguments for both sides I think, but the more important point is that we think about the problem in the first place. And that we push back on any provider of predictive HR technology to talk about their approach to PPV and NPV when they design their systems.

    Wednesday
    Jun142017

    CHART OF THE DAY: The Aging Global Population

    I am just back from an extended trip that included stops in China for HR Tech China as well as Japan - two places, Japan in particular, who are dealing with the economic and social challenges of an aging population.

    Usually the 'aging' statistics of a country's people is represented by two statistics. One, the percentage of the population age 65 or older. And two, the ratio of people aged 18-64, (and expected, mostly, to be in the workforce), to people 65 and up, (who, mostly, are no longer in the workforce). This ratio is called the 'dependency ratio' and reflects about how many workers and contributors to a country's social insurance schemes are there for each possibly retired person, many of who need income support from these social programs. 

    Said differently, the higher the ratio, the more workers for each older person, the easier it is for a country to keep their social insurance programs funded and solvent.

    With all that said, I was thinking about this more lately after spending time in Japan, where this challenge is especially acute. But as the data below shows, this challenge of an aging population is more widespread than you might think - and, in time, will surface here in the US as well.

    Take a look at the data below on the dependency ratio worldwide, courtesy of Visual Capitalist, then some FREE comments from me after the chart:

    While many countries face obstacles with aging populations, for some the problem is becoming severe.

    A dependency ratio below 5.0 is generally considered to be the mark by which a country has an 'aging' challenge. Countries like Japan, Italy, Germany, Canada, France, and the United Kingdom all fall below this level.  The United States sits in a slightly better situation with about 27.9% of its population expected to hit 65 or higher by the 2050 – and a dependency ratio of about 9, but in time the US (and the 2nd largest global economy, China), will both face looming demographic issues.

    What does this mean or suggest for organizations and for HR pros?

    Well, depending on the location, industry, and global nature of your business, chances are pretty good that the average age of the workforce is trending up. And it is also likely that since your competitors will be facing these same kinds of challenges that the competition for newer/younger workers to replace retirees or folks transitioningto fewer working hours will become more intense. Lastly, you may sooner than later be forced into thinking about and implementing changes to work practices, structures, and technologies that can better support an older workforce.

    It is an interesting time for sure. I am feeling a little older each day. Good to know it is not just me.

    Have a great day!

    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!