Machine Learning in HCM: A Use-case

MachineLearningHR, HCM, MachineLearning

Been a while since I have written a blog :-)

I am taking the opportunity as I have some really fascinating insight to share.

At EVA.ai, we are constantly seeking routes to deploy automation solutions that transform how people work, within a process.

We believe that if it can be automated THEN it should be automated.

Saying that before employing automation capabilities - one should carefully explore, design and ensure the program improves the use-case in a lean, agile & user-centric way. 

Last week EVA.ai worked on a machine-learning powered, automation solution for an enterprise with tens of thousands of employees.

 

The Goal 

Our clients wanted to develop predictive outcomes that would better facilitate their business planning and strategic decision-making. The EVA solutions would work across 4 distinct use-cases:   

  1. Tenure: Predicted days in a role, before employees exited the company. This would cover the average time spent per position, the employee and the correlation between both variables.
  2. Efficiency: Measured output, specifically how efficient are employees at selling contracts (across different price points and product ranges)  
  3. Fraud: The count of fraudulent contracts signed within a given quarter 
  4. Contract Termination:  Whether the combination of either 1-3 prior use cases led to a contract termination (client or employee)

In essence, our clients are focused on reducing churn, driving hiring efficiency and retaining their brand value by plugging their existing historic data to power predictive ML via EVA to future-proof hiring decisions. 

 

Data Exploration

Our client gave us access to their HR data set, composed of 120 variables ranging from HR-specific contexts to employee information and performance output data. Some examples:

Employees: Division, Region, City, Position, Date hired, Date fired, Date leave, Status now, Efficiency, Disciplinary, Contract Fraud...

Candidates: Current Job position, Desired salary, Ready for a business trip, Email, Employment, Schedule, Work experience, Previous Job positions, Role Responsibilities, Education, Skills, Languages...

 

The Model

I would love to delve deeper into the exploration processes, modelling techniques and processes used to get to the output, but for blog purposes, I’ll keep to the good stuff  (if you are interested in the process, pls book some time with me for a conversation). 

We discovered 3 predictive models around the 4 business goals. The model for ‘Contract Termination’ presented the most limitations based on variables submitted  (Low R2 scores indicate little predictive value for the model as a whole. However :) We were able to develop models across the 3 other business goals, specifically 

  1. Tenure: We uncovered 58 predictors with significant effects and an R2 value of .43
  2. Efficiency: We uncovered 27 predictors and an R2 of .92
  3. Fraud: We uncovered  18 variables and an R2 of .59.

 

Interesting Findings

As we worked through historic data here are some insights we were found based on our clients’ historic data

  • Candidates who have “Honesty” in their behavioural characteristics (within their profile) have 1.6x higher tenure within the company than people who don’t

  • People whose previous job titles included Supervisor or Account Manager and Sales manager were 7% more efficient than their counterparts.

  • Employees displaying skills in ‘Telephone Calls’ or who had the phrase “Customer focus” within their profile evidenced 4% fraudulent behaviour than people who didn’t

 

The Roadmap 

The ultimate goal as we begin to work with this enterprise company is to facilitate a predictive algorithm that scores people based on a subset of variables available but is reinforced continuously. Specifically speaking: 

  1. Talent clusters / Long lists: EVA allows hiring teams helping to make data-driven decisions by intelligently recommending lists of candidates to interview in priority based on our clients’ historical data and candidate attribute.
  2. Two Supervised learning models that encompass variables required to predict employees potential “Tenure & Performance” or “Fraud & Contract Termination” 
  3. Psychometric tests (5-minutes long) delivered by EVABot, iterated in phases to coincide with the accuracy (validity) of the supervised learning predictors.
  4. Reinforcement Learning: An ongoing process, where EVA monitors and takes into account every decision of the Talent Acquisition / HR team members related to ‘job-fit’ and its algorithm +  the effects these decisions have on placement and HR performance. The real-time data is used to drive even more hiring accuracy.

 

EVA and Machine Learning

Our approach to building ML HCM solutions depends on our clients’ use-case. We deploy the best-in-class methodology to build custom solutions that help our clients thrive. To learn more about EVA.ai and our ML process click here

 

Read more




Recruitment 4.0

Recruitment is changing 

So,consider this. Talent acquisition managers are increasingly asking the strategic question “How is the workforce changing?”.

But not only how, but why it’s changing and at such a rapid pace?

----------------------------------------------------------

It’s in this context that HR executives and talent teams need to start considering Recruitment 4.0 – an emerging methodology designed to help companies thrive, not just survive in a fast-changing and complex human capital marketplace. 

But what is Recruitment 4.0? 

Taking inspiration from the rapid shift and adoption of process automation seen in corporate enterprises, it’s a method that’s challenging executives to consider the benefits and risks of automating the recruitment process.

Recruitment 4.0 represents a new era in of talent acquisition. It’s an automation-focused  approach to tackling the longstanding challenges, driven by:

  • Alarmingly expensive and complex software estates
  • Candidate experience underinvested 
  • Candidate attraction overwhelmingly misplaced and badly designed 
  • Lack of global alignment and standardisation for talent proceesses 
  • No measure specificity across the funnel and into post-hire 
  • Worryingly qualitative in the ‘big data’ age 
  • Missed opportunity because of dated technologies and their increasing redundancy 

It is in this context that Recruitment 4.0 is key to overcoming these challenges - helping great teams do what they’re already doing, even better. It’s not only about productivity gains, but also recruitment effectiveness. 

External factors driving Recruitment 4.0

When considering Recruitment 4.0, understanding the external factors and market context that create these challenges in the first place. Making it increasingly difficult for companies to differentiate themselves and get ahead. 

  • Global marketplace for talent - the internet has created a platform where companies all over the world are competing for the same qualified talent
  • Increased competition - The best candidates are on average only on the market for 10 days (Linkedin) 
  • The importance of candidate experience - Data, personalisation, content optimisation all coming into play. With candidates expecting on-demand access to information 
  • Manual tasks - Talent teams can often be weighed down by the sheer amount of paperwork they have to process on a daily basis 
  • Employer Value proposition (EVP) - With mobile + social media on the rise, talent can very easily share their dissatisfaction with your hiring process with other potential applicants (e.g Glassdoor, Twitter etc) 

Winning in Recruitment 4.0 

Companies that deploy automation technologies can realise substantial performance gains and take the lead in their industries, even as their efforts contribute to increases in business productivity. 

Employing recruitment 4.0 as a method for hiring success is a highly relevant and important way to evaluate how to overcome these challenges and move your team from good to great. Here are some more benefits 

  • Increased Revenue and lower costs 
  • Improved candidate and internal customer satisfaction 
  • Increased candidate and employee Engagement 
  • Reduction of risk 

Read more



How AI can help us eliminate recruitment bias

You’ve heard the trend - diversity is the new global mindset in recruiting this year.

73% of companies embraced diversity, while over half of companies are already tackling it head-on to eliminate recruitment bias, according to a recent survey by LinkedIn.

Read more