Analytics used to predict who will leave a job

At first my reaction was obvious: "Well of course you can tell which employees are going to quit! If they're not getting paid well, don't feel like they have mobility, and don't have flexibility, they will move on!"

But so much of what you read about "what employees want" is both anecdotal and highly-personalized. Some employees want to work from home and others prefer face-time. Some folks want a decent salary, whereas others (like myself) choose something less lucrative in order to work at a startup. For some, upward mobility equates to a track to management, and for others a tech lead, and yet others just want to be left alone to write some darn code.

But if we can try and predict, using actual real data, what kinds of corporate knobs (raises, promotions, job rotations, etc) can decrease attrition, then that also means a company would be increasing job satisfaction in the ways which are important to employees. Why haven't we been doing more of this all along?

Analysts: See, they're no longer employed! Another successful prediction for the algorithm!

Any properly managed business group will have this covered because they treat their human "resources" like actual humans - not variables in a spreadsheet. A GOOD manager would know an employee was at risk as a result of doing their god damn job properly. You do not need a team of data scientists to figure this out.

Advice for younger engineers: if a company has grown past 100 employees, stay away from it. It's inevitably laden down by morons who would forego talking to you on a personal level in favor of a mechanical system with as little interaction as possible (liability and all).

Sounds like it's just a simple case of disparity "hey, I know I can get a much higher salary elsewhere given my experience and title. HP hasn't given it to me over the last few years, so it's time to go."

On a larger note, I wish "managers" used other simple means like talking to people openly about their aspirations, instead of blindly following the processes laid out by the company. Thankfully I don't work in such a place - but most big corps could just do better on the attrition rate if they made their appraisal process more employee friendly, focussed on encouraging a open culture and reduced needless hierarchy.

I'm guessing obvious websites like linkedin would pop up, but I wouldn't be surprised if other patterns would emerge.

[1] Companies, especially of this size, already do this kind of spying on their staff. Not necessarily with cats.

I think there is some doubt here that it is valuable, which fails to appreciate scale.

If someone said to you 'I don't know why Twitter need all these engineers - I made a site like twitter in 2 hours in an introduction to rails tutorial', you'd explain that scale was the problem.

The CEO of Toshiba oversees business units that do nuclear power plants, LCD screens, and business systems. His understanding of any of these units is fairly shallow - he knows less about nuclear power plants than the CEO of that unit, who knows less than his chief engineer. The chief engineer knows less about the workstation login page than the junior who implemented it.

When you are working at that scale, your tools and information are limited and broad. You can set budgets, issue directives, and try to create culture.

On the other hand, because you are working with large numbers of people (data points), it makes sense to make decisions statistically.

When N is 1, you need to make decisions subjectively - it doesn't matter that taking Statins after 50 slightly reduces the chance of heart disease for some people (possibly including you). You decide if you want to take a pill every day, and broadly if you want to do things that are healthy vs things that are enjoyable.

For the guy deciding whether to spend millions on Statins as part of your health insurance, it makes total sense - he can model how much heart disease costs, and how much statins reduce that, along with some value placed on an extra year of life, plus something about cost to acquire customers and policy retention, and decide if it's worth the money. So the British NHS decided to give everyone over 50 Statins.

The guy in charge of HP can model how much staff churn costs, and assign budgets that are sub divided down by business units. Using this data he can target that budget where it will help the most.

And that is all he can do.

Another thing is that it doesn't matter if it's a good idea, it matters if someone will pay for it. If HP can demonstrate measurable financial advantage to doing this they can land clients. That's a lot easier to measure = sell than 'not being a douche' training for managers.

The approach is seriously lacking in the factors it looks at for flight-risk. Every organization has a culture: a way of going about getting work done, dealing with petty administrative tasks, behaving, talking, etc. These factors are extremely difficult to convey when hiring someone. Unfortunately, some individuals aren't always a good fit for a culture.

Over time, I've found out from experience that I don't like large organizations. Large organizations have a certain bureaucracy and formality that I find irritating. I left one particular organization just for that reason. My current employer is still too large for my tastes. They treat me well, the pay is fine, the benefits are unheard of nowadays. I just don't like the bureaucracy and formality.

One more point is that the reasons for leaving may be different in our current era of entrepreneurship. People may simply leave to follow their own ideas about a business.

https://news.ycombinator.com/item?id=617533

This is only a preliminary result though, we need more funding to back up this radical hypothesis.