Category: hr

Predicting Employee Turnover at SIOP 2018

The 2018 annual Society for Industrial and Organizational Psychology (SIOP) conference featured its first-ever machine learning competition. Teams competed for several months in predicting the enployee turnover (or churn) in a large US company. A more complete introduction as presented at the conference can be found here. All submissions had to be open source and the winning submissions have been posted in this GitHub repository. The winning teams consist of analysts working at WalMart, DDI, and HumRRO. They mostly built ensemble models, in Python and/or R, combining algorithms such as (light) gradient boosted trees, neural networks, and random forest analysis.

13 Data-Driven Insights to Improve Your Job Search

Talent.Works is back, elaborating on the applicant characteristics that relate to landing an interview. While the majority of applicants has a meager ~2% chance of getting invited to an interview, some applicants do way better! What accounts for their success?

Original can be found in the Talent Works blog

Analyzing 4000+ applicants, Talent.Works found 13 factors that related to getting an interview.

There are some things outside of the applicants’ control:

Young applicants have higher chances (+25%).
Women applicants have better chances (+48%).
Applicants with a second degree have better chances (+22%).

Fortunately, applicants can boost their interview invitation rate using the following tricks:

Apply on Monday (+46%), between 6 AM and 10 AM (+89%), and within the first four days (+65%).
Start sentences with action-related verbs (+140%).
Use numbers to demonstrate impact (+40%).
Use occasional buzzwords / jargon (+29%) and skills (+59%).
Use leadership-related words (+51%) and avoid overusing words related to teamwork and collaboration (-51%) or personal pronouns (-55%).

Here are some of these effects visualized:

Predictive HR Analytics

Tilburg University has set up a masterclass Predictive HR Analytics. In 3 days, the Professional Learning program will teach you all you need to know to implement predictive analytics and take HR to the next level. More information can be found here.

What makes this program unique?

The masterclass Predictive HR Analytics goes beyond HR analytics and focuses on transformational people predictions. You learn how to embed predictive HR analytics into your HR Strategy and how to use your findings to convince others.
The masterclass is developed at the prestigious Human Resources department at Tilburg University, which has obtained international recognition with its high-quality academic research in the HRM field.
The mix of professors in conjunction with leading HR professionals leads to a strong academic program with a practical approach.
Your peer participants will make sure that the class opens up a high-quality network of HR specialists. The diversity of leading companies from different sectors in the classroom creates new insights for all the participants.
The program is like a 3-day pressure cooker. By combining online and offline components, we can create more in-depth discussions in the classroom.
You will experience a high impact on your daily practice, since the program is focused on direct implementation.

Your profile

This course is ideal for anyone in HR seeking to become more adept in using quantitative data for decision making. Typical participants are (future) HR analysts, HR managers, HR business partners, HR consultants and (financial) business analysts with a strong link on people resources. Participants are from various sectors, such as financial services, healthcare institutions, government agencies and business services.

HR Analytics: Een 7e zintuig voor de moderne HR-professional

Wat gebeurt er in Nederland op het gebied van HR Analytics? Dit nieuwe boek laat zien wat enkele Nederlandse organisaties de afgelopen jaren daadwerkelĳk hebben ondernomen. De verschillende auteurs, waaronder ik mij mag scharen, geven een kĳkje in de praktĳkwereld van het onderbouwen van HR-beslissingen aan de hand van diverse databronnen en analysetechnieken. Ze verklaren daarmee HR Analytics niet heilig, maar wie als HR- professional waarde wil toevoegen aan de business, kan er veel aan hebben. Het credo is dan: weet wat je moet doen, wees alert op de valkuilen en beschouw HR Analytics als een zevende zintuig naast je andere zintuigen. Met dit extra zintuig kun je als HR- professional scherper waarnemen wat het echte HR-probleem is, en wat mogelĳk de oplossing is.

Het boek ‘HR Analytics’ is voor de moderne HR-professional die nieuwsgierig is naar wat analytics kan bĳ dragen aan zĳn of haar professionaliteit. De voorbeelden en verhalen uit de praktijk leveren verschillende leerpunten en inzichten die helpen bĳ een meer analytische benadering van de diverse HR beleidsthema’s rondom recruitment, loopbanen, arbeidsvoorwaarden, training en opleiding of engagement. Het is een duwtje in de rug op weg naar HR Analytics als een toevoeging aan het HR-vak. Niet als vervanging.

Wiemer Renkema, recensist op managementboeken.nl, heeft het boek inmiddels gelezen en vat de inhoud mooi samen:

In de tien hoofdstukken van het boek komen de belangrijkste HR analytics voorbij, zoals die voor recruitment, carrièreontwikkeling, medewerkerstevredenheid en beloning. De lezer kan zelf de relevantie van ieder onderwerp bepalen en gericht de informatie zoeken die voor hem van belang is. Bij ieder onderwerp gaan de schrijvers in op alle kernvragen, wat het boek een overzichtelijke en makkelijk leesbare structuur geeft.

[…]

Je hebt geen lange adem nodig om HR analytics. Een 7e zintuig voor de moderne HR-professional te lezen. Wat een praktisch, compleet en goed geschreven boek is dit!

Wiemer Renkema, recensist [link]

Hier kun je een deel van het introductiehoofdstuk inzien om te kijken of het boek iets voor jou is.

Job-Switching Behaviors in the USA

Nathan Yau – the guy behind the wonderful visualizations of FlowingData.com – has been looking into job market data more and more lately. For his latest project, he took data of the Current Population Survey (2011-2016) a survey run by the US Census Bureau and Bureau of Labor Statistics. This survey covers many topics, but Nathan specifically looked into people’s current occupation and what they were doing the year before.

For his first visualization, Nathan examined the percentage of people switching jobs (a statistic he dubs the switching rate). Only occupations with over 100 survey responses are shown:

Nathan concludes that jobs that come with higher salaries and require more training, education, and experience have lower switching rates. The interactive visualization can be found on FlowingData.com

Next Nathan looked into job moves within job categories, as he hypothesizes that people who decide to switch jobs look for something similar.

Nathan concludes that job categories with lower entry boundaries are subjected to more leavers. Original on FlowingData.com

The above results in the main question of the blog: Given you have a certain job, what are the possible jobs to switch to? The following interactive bar charts gives the top 20 jobs people with a specific job switched to. In the original blog you can specify a job to examine or ask for a random suggestion. I searched for “analyst” in the picture below, and apparently HR professional would be a good next challenge.

The interactive visualization can be found on FlowingData.com

Nathan got the data here, prepared it in R, and used d3.js for the visualizations. I’d have loved to see this data in a network-kind of flowchart or a Markov-chain. For more of Nathan’s work, please visit his FlowingData website.

Talent Works: Data Science to improve Job Application Chances

Searching and applying to jobs can be a costly activity, requiring many hours upon hours of perfecting your motivation letter and CV. Hence, it can be very frustrating to get ghosted (not receiving a reply) for a job. Luckily, Talent Works is able to give us some general tips when it comes to improving the success of your applications. You might remember them from their Interactive Map of the US Job Market.

Using a sample of about 1600 job applications, Talent Works recently conducted all kinds of statistical analyses to look at the hiring process. For instance, they examined the time it takes to get from the application stage to your first day on the job. Split out for various jobs, it seems Mechanical Engineers spend quite a while in the interview stage whereas Software developers are put to work within three weeks.

estimated.mdf.png — The numbers of days spent in each application stage per job (Talent.Works)

In a different analysis, Talent Works examined how to minimize your risk of getting ghosted on a job application. For instance, they found that during the “Golden Hours” (the first 96 hours after a job gets posted), your chances of getting an invitation for an interview are up to 8 times higher than afterwards.

If you submit a job application in the first 96 hours, you’re up to 8x more likely to get an interview. After that, every day you wait reduces your chances by 28% (Talent.Works)

Based on the above they come to the following three timeframes in the application cycle:

“Golden Hours”: Applications submitted between 2-4 days after a job is posted have the highest chance of getting an interview. Not only is there a difference, there’s a big difference: you have up to an 8x higher chance of getting an interview during this period, even if you’re submitting the same application.
Twilight Zone: Chances quickly decrease from OK to really bad: every day you wait after the “Golden Hours” reduces your chances by 28%. The longer you wait, the higher the risk that employers have already checked their inboxes and setup interviews with candidates that met their “good enough”-bar.
Resume Blackhole: According to Talent.Works it’s nearly not worth applying after 10 days. On average, job applications during this phase have a meager ~1.5% of getting an interview. Put another way, if you send out 50 job applications, you might hear back from one (if you’re lucky).

Next, Talent.Works investigated on a more granular level what would then be the best time to apply for a job.This resulted in the following figure

what-best-time-apply-for-job — The best time to apply for a job is between 6am and 10am. During this time, you have an 13% chance of getting an interview — nearly 5x as if you applied to the same job after work. Whatever you do, don’t apply after 4pm (Talent.Works)

Again, they provide a summary of their conclusions:

The best time to apply for a job is between 6am and 10am. During this time, you have an 13% chance of getting an interview.
After that morning window, your interview odds start falling by 10% every 30 minutes. If you’re late, you’re going to pay dearly.
There’s a brief reprieve during lunchtime, where your odds climb back up to 11% at around 12:30pm but then start falling precipitously again.
The single-worst time to apply for a job is after work — if you apply at 7:30pm, you have less than a 3% chance of getting an interview.

If you want to see more, please visit Talent.Works. Here, you can let them process your CV and help you improve your hiring chances (see also this blog post).