ldepoorter

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Peakon shows the power of people analytics beyond the level of the firm
ldepoorter
Posted on April 13, 2020 at 6:42 pm
By analyzing millions of employee survey responses, Peakon shows that employee engagement starts to drop about 9 months’ before an employee leaves. Peakon's analysis shows the true power of people analytics: being integrated in internal processes, using data beyond the [...]

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On April 15, 2020, ldepoorter commented on Evaluating WFH Productivity in the Age of Covid-19 :

Sarkis, what an insanely rigorous analysis of your own personal work pattern, this is great! I completely agree with everything you write at the end of your blog post: the crisis definitely affects everyone in a very different way and in order to make general conclusions you would have to repeat this analysis for a larger subset of the population.

My big question: how did you quantify your work pattern? I am especially interested in understanding how you measured your own productivity!

On April 15, 2020, ldepoorter commented on Do your employees laugh at your jokes? ? :

Catchy title! You definitely got me drawn in here!

You lost me a little bit with “it’s possible to build a relatively accurate Naive Bayes classifier that uses social action data, without the use of content or media-specific metadata, to understand if something is ‘funny’”. I tried to Google it, but I think I need a proper Googler to explain ;-).

Taking a step back, I wonder whether humor is really measurable without the use of voting buttons, polls, or perhaps audio measurement (laughs) and/or face recognition (smiles). I also wonder whether we actually need it. As you rightly point out, humor is used with a specific cause in mind:
– on a granular level, is this piece of communication good / positive / professional?
– on a higher level, how do you perceive this leader?
– on an even higher level, how would you rate our culture on these parameters?
Taking this all together, perhaps it’s easier to measure the effect rather than the input here?

On April 15, 2020, ldepoorter commented on Predicting who will fail and when – a Marine Corps study with broad implications :

Incredibly interesting article Blake – thanks so much for sharing!

What triggered me was your sentence “It will take very concrete and defensible analytics to help make decisions about who gets to enter training and who gets failed. Personality tests and surveys will never make the cut.” My question to you (as someone without a military background): would you ever want anyone to not be able to even enter training? Is there anything that would make the cut?

An interesting approach to this is the engineering entrance exam (which I helped design as an Ed Rep in my University). In Belgium, we are firm believers that everyone deserves a chance to enter University, so we do not have any admissions process. You just show up. However, the government wanted to tackle the problem of >50% drop-out rate after only 1 year in the engineering studies. It basically cost a lot of money… What we designed was an in-take exam which was compulsory but not binding. They show you what the likelihood is of you succeeding based on prior intakes with the same scores, but you are still allowed to enter the program. The interesting thing here is that you as an applicant know what you need to work on to get there. This might be interesting for the army as well?