cpektas

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On April 13, 2021, cpektas commented on Debugging the Pulse Check Survey :

I’m very conflicted about this issue. On one hand, I agree with the author that pulse check surveys are ineffective, inefficient, and usually misinforming ways of understanding the workplace environment; yet on the other hand I don’t think his solution of “empowering your managers to be amazing” is a realistic solution that can be implemented across every team and organization.

One problem I see is the format of said surveys. We need to develop a better way of collecting more precise employee sentiment data, without turning it into a chore or formality. A possible solution is employing data collection methods that do not require employee feedback but instead analyze employee interactions, movements, tone, and similar behavioral variables. Yet as we’ve covered in several cases, this would most certainly lead to privacy issues as well as potential misuse of said data.

While I have no clear solution, creating an environment of trust and transparency would certainly be a good start. Establishing strong cultural norms at the startup phase could then potentially eliminate the need for pulse check surveys, or replace them with more ad-hoc feedback sessions, even as the company grows.

On April 13, 2021, cpektas commented on Should your employer care how much sleep you’re getting? :

There is definitely a strong argument around the privacy issues behind Whoop’s sleep tracking program. However, I believe that this program has the potential to be a win-win for employers and employees, creating both more productive employees and happier individuals.

Employers have been running company-sponsored wellness programs (either directly or through health insurance providers) ranging from smoking cessation to weight loss for a long time, and many employees (who would potentially not take any action otherwise) have been leveraging them to improve their overall well-being.

While there are possible risks around personal privacy and unwanted conversations, I think they can easily be solved by establishing ground rules and being more flexible around participation (e.g. being able to opt-in, yet remaining anonymous on the public leaderboards).

These practices are only in their infancy and we clearly don’t know how positive or negative the outcomes will be. However, as data collection and analysis capabilities improve over time, it is becoming increasingly important to start testing such programs and understand what’s working well and what isn’t.

On April 13, 2021, cpektas commented on Extending People Analytics to K-Pop :

This is very interesting – and a great example how the field of people analytics goes way beyond impacting the professional workplace setting!

A related concept that I find exciting is how the music industry is leveraging data to not only identify potential hits but to actively shape audience preferences. Starting from the early days of the radio, record labels have experimented with many factors beyond with song/artist characteristics. They would test the timing, order, and frequency of new songs to understand when and how often new songs needed to be heard in order to be recognizable and eventually turn into hits. There is also a network element to it, as the most cost-effective way to spread new music was through word of mouth.

In today’s era of subscription services, labels have even more tools at their disposal. They can access extremely precise metrics around listening habits and track in real-time how songs become viral. Furthermore, as subscription services themselves are concerned with growing and retaining their audiences, they employ many algorithms to ensure that the next song in our playlist is one that would keep us listening. As these tools are only going to improve, it is scary to think about whether we are being served songs that we actually like; or whether we actually are “tricked” to like songs that are served to us when we’re most likely to enjoy them. And as long as we enjoy them, does that even matter?

On April 13, 2021, cpektas commented on Extending People Analytics to K-Pop :

This is very interesting – and a great example how the field of people analytics goes way beyond impacting the professional workplace setting!

A related concept that I find exciting is how the music industry is leveraging data to not only identify potential hits but to actively shape audience preferences. Starting from the early days of the radio, record labels have experimented with many factors beyond with song/artist characteristics. They would test the timing, order, and frequency of new songs to understand when and how often new songs needed to be heard in order to be recognizable and eventually turn into hits. There is also a network element to it, as the most cost-effective way to spread new music was through word of mouth.

In today’s era of subscription services, labels have even more tools at their disposal. They can access extremely precise metrics around listening habits and track in real-time how songs become viral. Furthermore, as subscription services themselves are concerned with growing and retaining their audiences, they employ many algorithms to ensure that the next song in our playlist is one that would keep us listening. As these tools are only going to improve, it is scary to think about whether we are being served songs that we actually like; or whether we actually are “tricked” to like songs that are served to us when we’re most likely to enjoy them.