Oct 12, 2012
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?