Linkedin Premium Search Traffic

I needed to send some inMail messages, so I signed up for Linkedin Premium.  You get a little bit more visibility in terms of who is looking at your profile and how they got there.  The most interesting thing for me is how little search traffic comes from anything about my functional job; only 1% of search traffic to my profile is based on the phrase "Big Data."  Almost all of my traffic is driven from what my SEO team would call "Branded" terms.  That is, derivations of my name.  Number one search term is "Kenneth", then "Rona", then "Ken Rona".

Standardizing our Interviews

My current team has grown to a bit over 50 people, including contractors.  We are constantly hiring for some function or another and some of my staff seem better at hiring than others.  Some teams seem to attract and retain great staff.  Some struggle a bit.  Even within a team, our hiring experiences vary.

I am not surprised that we have these challenges.  The SVP of "People Operations" at Google, speaking about their hiring practices said "Years ago, we did a study to determine whether anyone at Google is particularly good at hiring. We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert."

So what are we doing about it?  A couple of things.  First, we are putting together a small set of attributes that every candidate will be evaluated against and a set of questions that can be used to test for those attributes.  We are going to try to improve consistency of our interviews and see if we can get everyone to adopt best practices.

Second, I now interview every candidate.  As the leader of my organization, I need to be responsible for the quality of the staff.  Problem is, I am not scalable and I bring my own biases.  I know that the CEOs of some internet companies want to review all hires.  I get why.  And to be fair, I don't know that my involvement will fix the problem.  But I can make sure that we are hiring people that I can stand behind.

Ah, well.  First step is recognizing the problem.  I'll tackle the scaling issue when it becomes acute.

Amazon does something interesting.  As part of the interview loop, the candidate is evaluated on if they will make Amazon smarter.  And the person doing the eval is not part of the reporting structure.  I think they are part of HR.  I like the notion.

Thinking Fast and Slow Observations

     I just finished reading "Thinking Fast and Slow" by Kahneman.  My Phd was in behavioral economics and it was great to get a refresher from the master.  Given my now 13 years in business, I got to read the book with a different eye; one where I could think about common pathologies I have noticed in business decision making but brought back to first principles.  I highly recommend the book.  Some random observations: 

1.  If you have a choice between a for sure likelihood of a bad outcome if you stop a project or a small probability of a good outcome but a small likelihood of a disaster, take the bad outcome. You can explain a bad outcome.  It is much harder to explain that you decided to choose to go down a path that had a high probability of disaster.

2.  If you see a structural impediment to accomplishing a goal, don’t proceed.  See if you can fix it.  If not, do something else. It is really hard to overcome a structural governor on change.

3.  Take a look at the historical ability of a person, partner or team to do something.  If the historical probability is low, do something else.

4.  Organizational change is hard because someone always loses.  And the change hurts the losers more than helps the winners.  So the losers fight harder.

5.  Experts do a good job of figuring out the important drivers of some phenomena.  But we are not good at using those mental models in a consistent way, in the moment of making a decision.  Algorithms are much better at getting to good results.  Even imperfect algorithms.  Think about this in the context of hiring, or forecasting, or evaluations, or capital budgeting or ... 

6.  Don’t just evaluate one alternative.  Always put two down, even if the other one is do nothing.  I like to see if, when something is framed as a positive ("we are giving you a gift") I can reframe as a negative ("You are creating an obligation")

7.  People conflate liking with smart.  In a hiring context, managers wind up hiring nice people who they think are smart.  Not actual smart people.  As organizations get bigger, you wind up with a more likable, but less smart organization.  Next thing you know, you have a large group of people who have a limited skillset and can't adapt to change.