Data Science in HR


It is one thing to know what has happened in HR – the majority of HR data to date has focused on the reporting of transactional outcomes – but another thing to know what will happen. For example, lots of HR groups report the percentage of people that had a performance review or who completed an engagement survey. This type of reporting relates to the process orientation of HR and, although interesting, does little to demonstrate the true value of the functions.

In addition, the opportunities to add value through HR practices come more from stopping the wrong outcome from happening, than from reporting on what has happened. For example, the cost of voluntary turnover has been established at approximately 1.5 times the annual base pay for salaried employees. Therefore, if you prevent two high-value employees, with salaries of 50,000GBP, from leaving the organization you have saved approximately 150,000GBP. In order to achieve this type of saving you need to know who will leave before they have left.

This is where sophisticated algorithms, that use historical data to determine the likelihood that someone will resign, come into play. There are a number of known actions that will prevent someone from leaving like signing bonuses, formal agreements around career progression, and learning opportunities. However, the crucial part is knowing to whom you should offer these incentives. When it is possible to focus on the right population, through powerful and validated statistical models, it leads to better outcomes for a lower cost.

Another place where prediction is becoming valuable and important for the companies we work with is in relation to retirement. The pattern of behavior relating to retirement is changing with more and more people delaying retirement or shifting to contract or part-time work than ever before. The prediction here is important as often the people retiring are in key roles or hold key relationships and are critical for the business to ensure continuity of performance. However, it is also challenging to keep a potential successor waiting if the incumbent chooses not to retire at the time expected.

Instead of using the old indicators of age and tenure to estimate retirement behavior modern analytics technology applies algorithms that take into account many additional factors such as recent changes in role, pay level, rates of change in pay and incentive eligibility to refine the prediction of who will retire. This allows companies to use this type of analytic approach to be more successful and effective in managing the retirement cycle and ensuring that key roles have a successor ready at the right time.

We are seeing this change already. The number of Directors of Workforce Analytics and Planning has increased dramatically in the last two years. These people are building out specialist groups that cover three primary areas: data management, analysis, and interpretation of data, driving the impact of the group on the overall business. These new groups are at the early stages of proving the value that can come from workforce analytics and planning.

The next change that will come is similar to the transformation in marketing. A decade ago the leadership in marketing needed to have a good idea and strong influencing skills. Now leadership in marketing is all about the tracking and analysis of which ideas worked, which ones did not, and identifying where data and results highlight that there are opportunities to meet targets.

In the same way, HR leaders will need to be able to build a cohesive talent strategy that is founded on a robust and detailed analysis of the organization’s people data. It will no longer be good enough to trust intuition, deliver what we delivered last year because no one complained, or to jump on the latest ‘buzz’ program. CEOs are already demanding that CHROs show up with an informed point of view on how to drive results for the business. Every leadership position within the HR group will need to be able to support this way of working throughout the HR organization.

In the future, the HR organization may or may not contain a data scientist; what will be true, however, is that those who lead the group will have to be well-versed in the interpretation and use of data and have the breadth of business understanding to link this to business outcomes. HR is moving away from being defined by its transactional volume and towards being valued for its strategic impact. This transformation cannot be achieved without a robust analytic orientation across the whole function.

HR driving store performance

Another great people analytics case study took place in a large restaurant chain that was in a downward spiral. The management team didn’t understand why. They had pieces of information but struggled to implement effective policies.

A team of consultants was asked to investigate and provide insight through data.

Because there was a lack of good data, the team decided to measure it themselves using a survey. What was interesting in this case study, was that they didn’t use a normal engagement survey. They instead first looked at the relevant business outcomes. The three key outcomes they identified were:

  • Customer count
  • Customer satisfaction
  • Employee retention

Business performance would increase if these three metrics would go up.

The company then deployed a business-focused engagement survey where they:

  • Linked employee outcomes to their real business outcomes
  • Prioritize on the factors that had the largest impact on business outcomes
  • Show the business impact of improvements of these factors
  • Focus front-line managers on the factors that showed the largest impact

By mapping these factors on their own scores and the impact they have on the business outcomes, the team could easily visualize which drivers contributed most to business performance – and which drivers front-line managers should focus on.

The six factors that would receive the most attention are in the blue square. By focusing on these 6 factors, line-managers would create the largest return.

Restaurant managers who had an average score of 4 or higher on the six key survey drivers were likely to see

  • a 16 % increase in customer satisfaction,
  • 18,000 more customers a year
  • 10% less staff turnover

A lot of these HR analytics case studies have focused on leveraging internal data. We can also find an analysis in which external data plays a big role.