Common Sense KPI’s Gone Wrong

I love dashboards.  I have a goals list on my phone tracking how many miles I’m supposed to run or ride on my bike.  I have a trending graph on the device that tells my how much I weigh.  The only reason I have not bought one of those fitness wristbands yet is because I just can’t stand things on my wrist!  I was just on the company call, and we do the company performance dashboard, we stack ranked the all time leaders for ideation at the company, and all sorts of other visual and gamified graphics.  As employees, we should be managing our goals and goal progress, and some systems now have cool mobile components that can visually show where we are with each performance goal.  It’s great to be able to track where we are at any given time in almost any area of our lives.

Sometimes our KPI’s go desperately wrong, even though they seem to make sense.  My current personal goal is to get back to 10% body fat.  For those of you who don’t know me, let’s just say I’m already one “skinny ass dude.”  The problem is that less fat for a person who is generally athletic and out of doors as often as possible sounds like a good thing.  The question is, is it the right thing?  We actually face the same problem in our HCM KPI’s.  Here are a few examples.

Employee Referral %:  

  • Employee’s who are referred to us by other employees are our best people.  Right?  Almost all of us would agree that this is true as these employees will have higher levels of engagement, are pre-screened as people we’d want to work with, are capable and smart.  The referrer has a stake in the person’s success, but their credibility is also on the line so they probably won’t be referring crappy people.
  • Often, we’ll see that companies want to achieve as high a referral % as possible.  This allows the company to get more great people, but also reduce recruiting costs.  The problem is that there’s also a tendency to refer people who are similar to us.  This is a problem on a couple of fronts.  First of all, there is an ideation and innovation problem.  If you recruit people who are similar to you, who have similar experiences, have worked in the same places, you are not getting your company’s due in diverse thinking.  Second, people like us are not demographically diverse all of the time.  if you have a lot of “white dudes” and you want a 100% referral rate, you’re still going to be a bunch of “white dudes” in 5 years.

Employee Turnover %:

  • This one is fun.  Some organizations are SO proud of the low turnover that they have.  I’ll walk into a new project and within day’s I’m inundated with how they have achieved 5% turnover.  I mean, having great employee engagement so much so that nobody ever wants to leave is a great thing, right?  We see targets of 8% and lower all the time.
  • Depending on which philosophy to subscribe to, there is such a thing as “desirable turnover.”  Those are the Jack Welch bottom 10%, or in your forced bell curve performance ratings the bottom 5-15%.  Let’s just say that there are 10% of people in your organization at any time that SHOULD leave, and you should be encouraging to leave.  So if your desirable turnover is up to 10%, and your target is less than 10%, something is pretty much wrong.  Right?
  • The key is to figure out how to shift the conversation to unwanted turnover rate instead of total turnover rate.  A very high performing organization could have a total turnover rate at 12%, but if their unwanted turnover is only 2%, I’d say they are doing fantastically.

We all want high referral rates, and we also want low turnover rates.  These are great KPI’s, but we take too much for granted and at face value.  Going to extremes just because there’s a number to hit impacts our organizations in a pretty negative way, and in HR, it usually means that we have some of the wrong people working for us.

How To Give All The Wrong Answers

As per my last post,at the end of 2012, I was doing a family vacation in Taiwan.  Being with family for 2 weeks is quite an expose into mannerisms that each of us have.  I was particularly intrigued by my brother’s questioning of my mother.  My brother would constantly ask my mother things like “why are we going to [city_name]?” instead of “what are we planning to do when we get there?” and “how much time will I need to prepare the kids to sit in the car?”  Luckily, we had my mother there fueling the ridiculous line of questioning.  90% of the time, her answers had nothing to do with the questions he was asking.

  • “Why are we going to [city_name]?” “Oh, let me tell you, when I was growing up, I used to play with my cousins there.”
  • “Mom, why are we going to [city_name]?” “Oh, did you see that beautiful view over there?”
  • “Mom, can you please just tell me why were are going to [city_name]?” “Don’t worry, you will love it.  It’s beautiful there.”

There are two items I’d like to diagnose.  First, are we actually listening to the question?  Second, did we understand the question?

The first is fascinating to me because I’m not sure we actually are listening.  Many of our reporting organizations are pure intake, create, output engines.  We grab the data that is asked for, create the report and send it out hoping we got it right.  Basically, we are spec takers.  Second question follows right after the first.  Much of the time, we don’t know why report requesters want the data at all.  We could be asking ourselves why they want to know, and if the data we are providing helps them solve a problem.  If we are really cool, we could be asking if they are even trying to solve the right problem or not.

Here are a few questions you should explore when data requests come your way:

  • How are you going to use the data?
  • What is the core problem you are trying to solve for?
  • Are there other data elements or analysis that we have that can help further?
  • Are there other correlated problems that we should try to answer at the same time?

For all intents and purposes, this post is the exact corollary of the prior on how to ask the right questions.  The problem with being a non-strategic reporting organization is that if the wrong questions get asked, the output is doomed to be the wrong information as well.  But even works, sometimes the wrong question gets asked and we still give the requestor the wrong data back.  All this does is create turn – another report request, or bad data going to managers (who in turn trust HR a little less the next time around).

In the case of my brother, he asked the wrong question in the first place.  It would have been much more advantageous had he explained why it was important for him to prepare the children for the outing, have the right clothes, have enough food along, and maybe get them extra sleep.  I’ll never know if my mother would have given him the right information in return, “yes it usually rains on that side of the island, it’s 40 minutes away, and we will be in a friend’s house so they can’t get too wild.”  But the crafting if the right answer is a tight collaboration of both sides creating understanding of what the objectives are.

 

How To Ask All The Wrong Questions

At the end of 2012, I was doing a family vacation in Taiwan.  When I say family vacation, I mean not just my wife and me, but my brother’s family along with my parents, visiting all of the senior members of the family (an important thing in Asian cultures).  There is an incredible exposure of habits and an interesting (but sometimes undesirable) analysis of where my brother and I got those habits from.  I was particularly intrigued by my brother’s questioning of my mother.  Let’s just say that getting 2 grown sons, their spouses, and our parents together creates a certain amount of strife.

Let’s also just say that my brothers’ hauling around of two young children may have added to the stress – he really needed to understand the daily schedules and what was going to happen when.  Back to the questions: my brother would constantly ask my mother things like “why are we going to [city_name]?” instead of “what are we planning to do when we get there?” and “how much time will I need to prepare the kids to sit in the car?”  (more on my mom’s response in the next post)

The problem in the questions was not the question itself, but in the thought process.  All too often, we ask questions about what we think we are supposed to know.  We want to know about turnover, headcount, spending per employee.  This is information that is useful, but does not actually inform us about what our next actions are.  Being “strategic” to me means that we have a plan, and we are actively managing our programs towards that plan.  If we’re using data that just skims the surface of information, we have no ability to adjust direction and keep going in the right direction.

I’ve often heard storied about HR executives who go into the CEO office for a meeting to present data, and all they get are questions back that cannot be answered.  Some HR teams go into those meetings with huge binders (sometimes binders that I’ve sent with them), and those teams come out still not having answered the questions.  The problem is not with the data.  The problem is that the team has not figured out what the actionable metric is, and what the possible actions are.  No CEO cares about the data – they want action that ties back to what the strategic objective is.  In other words, why do they care?

Here are a couple things you can do to craft better questions:

  • Always think about the root of the question:  HR tends to analyze at the surface more than some other functions.  We have finance doing complex correlations and marketing doing audience analysis.  We’re reporting headcount and turnover to executives.  What kind of crap is that?
  • Be a child:  Ask why/what/how up to 3 times.  Why 1: “Why are we going to [city_name]?”  Why 2: “Why do I want to know what we are going to do there?” What 3: “What do the kids need to be prepared with?”
  • Take action:  If you ask a question that can be answered in such a way that you can’t take action, you asked the wrong question.
  • Create an intake form that customers can request through: make sure you ask the right questions here to ensure they think through the process and understand what they need.

Many of the organizations I consult with have some pretty robust analytics organizations.  When I dig under the covers, they are reacting to create ad hoc reports for managers and HR business partners.  Once a quarter they scramble to create a CEO report card to depict the state of HR programs.  This state is sad to me.  We should be doing deeper analysis and diagnosis on a daily basis.  If we asked the HRBP’s what/why that wanted data for, we’d probably find there is a huge amour of quality analysis being performed in silos that could be leveraged organizationally.

 

HR Technology Conference Reactions: Predictive Analytics

I’ve always thought I was pretty good at analytics.Not being a practitioner who is sitting in the middle of data all the time, I get more time to just think about the type of analytics that it takes to really run the business.  It’s been a really long time since I discounted the usefulness of things like time to hire preferring things like quality of hire (efficiencies versus effectiveness measures).  But I’ve always fought with predictive analytics.  In my opinion, they don’t really exist in HR yet.  We can trend our data and draw a trend line, but that does not predict our future – it simply tells us that directionally, something is going to happen if we don’t change course.  I’ll admit that I walked into this session with a great deal of skepticism, I walked out with some great insights.

The panel was made up of some great speakers.   Moderator: Jac Fitz-enz, Ph.D., (CEO, Human Capital Source), Laurie Bassi, Ph.D., (CEO, McBassi & Company), John R. Mattox II, Ph.D., (Director of Research, KnowledgeAdvisors), Eugene Burke, (Chief Science & Analytics Officer, SHL), Natalie Tarnopolsky, (SVP, Analytics and Insights, Wells Fargo Bank).

Theme #1:  Descriptive, Predictive, Prescriptive. Let’s start with some definitions as the panel did, but I’ll use a tennis example.  I don’t know if anyone has been watching the last few grand slams, but they have been using a good mix of all these types of analytics.  Descriptive is simple.  Roger Federer has one 16 tennis grand slams.  (I’m guessing as I’m on a plane typing this).  Predictive is next and basically tells us what our destiny is going to be.  Roger’s record against Nadal in grand slam finals has not been particularly good.  If Rafa is on his game, hitting his ground strokes with the huge topspin he has, Roger is going to have to figure something out or lose again.  Here is where the last few opens has been interesting.  The broadcasters will sit there with the stats and say things like, “If Roger can get 67% of his first servers in, he has a 73% chance of winning” or “Roger needs to win 55% of Rafa’s second serves to have a 59% chance of winning.”  Now we have prescriptive – the specifics of what to do in order to change our destiny.

Theme #2:  Engagement. We probably focus in on this a bit too much.  It’s not because it’s not important, but it’s not specific or defined enough.  I mean, we all have a definition in our heads, but for 99% of us, it’s fluff.  My definition of engagement is the intangible quality that makes an employee want to provide that extra hour of discretionary work when other non-work opportunities exist.  Total fluff, right?  We can provide some correlations around engagement.  If engagement increases by 1%, then turnover decreases X% and so on.  What it provides is a great predictive measure, high level as it may be.  We know we need to increase engagement, and it is indeed important.  But it’s not the key measurement we have all been lead to believe will solve all our problems.

Theme #3:  Predict winning. OK, so if engagement is not the key metric, then what is?  Well, I have no idea.  I’m not being snide, I’m just saying that it will change for every single organization.  If you are (mall) retail organization, then having really good salespeople might be what hits the bottom line.  You could run the numbers and find out that if you rehire sales that worked for you the summer/holiday season last year, those salespeople are 20% more productive, whereas engagement reduces turnover by 1.3%.  Which metric are you going to focus on?  Right, how do you get those experienced salespeople back?  Instead of spending $1 on engagement, you could get 5 times the ROI on that same dollar elsewhere.  What we want to do is not predict outcomes.  We want to predict winning and understand what our highest contributors to winning will be.

Let’s take another example, this one from the panel.   Let’s say 5% of your workforce are high performers, but you can only give 3% of them promotions this year.  You also know that the 2% of top performers who don’t get promotions will likely leave the organization.  Now you have a problem.  You can’t afford to promote these people, but the cost of replacing top performers is extraordinary.  Analysis like this quickly leads you to decisions which are actionable.  At the end of the day, we need to compare our top drivers against our weaknesses to really figure out our greatest opportunities to invest in.

Theme #4:  HR can’t do it. This part sucks.  Towards the end of the session, we walked through a statistical model.  Yeah, we can end this post right here, but I’ll continue.  The rather brilliant by HR terms model was presented by Wells Fargo.  Go figure an ex-finance person working at a bank would have this all put together.  The point being, this was an ex-finance person, and the bak part is ot wholly irrelevant.  All the stuff I said above really makes great sense.  But when it comes down to executing it, HR in most organizations does not have the skillset to execute on it.  We don’t have very many statisticians in our HR staffs, and even if we did, HR executives would have a hard time seeing the vision and have the willingness to implement these technologies and models.  All is not lost however.  Finance has been doing this stuff forever.  I mean, I’ll bet you anything that if the interest rates drop by 1 basis point, Wells Fargo knows within seconds what the impact on profits are for savings, mortgages, etc.  Can’t we have/borrow/hire just a few of these guys?