13 10 2014
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4 Ways To Add Value With HR Analytics

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Traditional performance analysis in most companies consists of retrospective analysis that provides a backward-looking view, reporting what happened in the past. To be able to add value, HR will need to evolve from descriptive to predictive analytics, working with statistical models and forecast techniques to understand the future and answer the question: ‘What could happen in the future?’  With predictive HR analytics you can: 1) predict the employee characteristics that are associated with good/bad performance and 2) analyse how groups or clusters of high/low performance employees differ depending on those characteristics.

By using predictive analytics HR will be able to predict the relationships between aspects such as employee characteristics, organisation context, training investments, management quality, employee engagement and employee performance. Here are 4 examples of performance-related HR analytical approaches:

1. Predict ‘Time-to-performance’

Time is money, as the old saying goes. Companies can’t afford to waste time when it comes to bringing a new recruit up to speed. Predictive algorithms can provide high-quality hiring experiences, on-boarding and training processes for new starters (such as call centre agents, sales reps, field service experts, machine operators and many others).

Unlike traditional fuzzy measures of hiring (e.g. time-to-hire), on-boarding and learning (e.g. cost of learning), those predictions can lead to faster time-to-contribution, lower cost-per-hire and ultimately, increased quality of hires. In turn, this leads to increases in retention, decreases in bad hires, and significantly higher levels of productivity. What are you waiting for?

2. Predicting Organisational Effectiveness

In many organisations, recorded event logs are already available and conceal an untapped reservoir of knowledge about the way employees conduct everyday business transactions (e.g. log data from call centers, CRM, field service, sales, banking, insurance, machine operations, financial trading, etc,). Performing data mining (called ‘process mining’) on event logs allows organisations to analyse and predict such critical challenges as employee compliance analysis, organisational inefficiencies, workflow management, detecting highly performing workflow communities, org-structure reviews and productivity & performance analysis. In other words, a powerful process-mining analysis provides organisations with a unique organisational people x-ray.

3. Employee Engagement Analytics 

HR can expand its potential as a strategic partner, given a more intelligent approach connecting HR and business data with the results of the engagement study. See also our other blog post on this subject. By linking the broader-scope engagement survey to other data available to HR such as sick leave, training, promotion, evaluation, turnover rates, customer satisfaction, sales figures, production figures, etc., HR can prioritise where to focus (driver analysis) and predict the business impact of areas like engagement.

An employee survey in combination with superior data analytics methodologies and a large engagement database can give you a unique competitive advantage. You can use standardised, (academically validated) survey analytics or specific survey solutions, tailored to your requirements.

4. Predicting Absenteeism or Work Accident Risks

Absenteeism and work accidents are costly and disruptive organisational challenges that are difficult to control. Absence may have an affect on productivity, customer service, employee morale and most importantly the organisation’s bottom line. Traditional employee absenteeism/accident analysis is typically executed with descriptive reporting.

Although this is definitely useful as a start, predictive analytics will dramatically enrich the insight. By mining past patterns of absenteeism and by including demographic variables and work context into the analytical model, you can predict future absenteeism and work accident risks in a so-called Risk Heat Map. These maps provides your organisation with an overview of high-risk employees or employee clusters and even (in case of good data) detailed forecasts of when absenteeism/accidents will most likely occur and the likely duration of the absence.

About the author

lukLuk Smeyers is an experienced senior HR executive who has lead complex transition projects for compelling Fortune 500 companies, such as PepsiCo, Starbucks and Nielsen. 

In 2008, Luk started a cutting-edge predictive HR Analytics consultancy, together with academic partner Dr. Jeroen Delmotte.



 Luk is widely recognized as one of the few European top HR analytics experts. He is revered as a leading thinker, educator, influencer and is a well-known content contributor, blogger, columnist and author of many articles. He is an invited speaker at international conferences and academic programs and helps clients set a higher ambition for strategic HR intelligence, leading consultative projects in the Benelux countries with such organizations as ING, KPN, ABN-AMRO, Philips, Rabobank, UWV, RealDolmen, Acerta, NS, BASF, Besix, Strukton, Bekaert, Randstad, Eandis, AG, Postnl, AON, Raet, etc.

Interested in using predictive HR analytics as a key component in your HR strategy? Get in touch with CEO and co-founder Luk Smeyers for more information ([email protected] or via Google+). Or follow iNostix on Twitter and/or Facebook for exciting international articles on HR analytics. And don’t forget to register for this blog!

 

 

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