HR predictive analytics is attracting a lot of attention. Last year, Towers Watson found that one in three organisations planned to increase spend on their HR function by more than 20 percent, and HR data and analytics tools rated as one of the top areas for investment. However, just looking at HR data in isolation does not represent the best opportunity to make an impact.

HR_ReviewIndeed, HR has always been driven by data. Traditional HR tasks such as performance reviews and staffing requests already create data that are used to support planning decisions, while processes such as hiring and firing create records of activity, as well. However, this data is often kept within the HR team as its own little island of information. At the same time, teams such as sales and marketing may have their own data on targets, performance and compensation that should also be considered. The challenge is getting all this data together in the context of the wider business.

At this point, it’s possible to feel jealousy for teams that seem to have it made when it comes to working with data. Sales and marketing professionals have huge amounts of choice when it comes to analytics tools available to them. However, this is a case where the grass isn’t necessarily greener on the other side.

For example, the link between investment in new tools and sales performance is not as easy as it might seem. Areas such as compensation planning and performance incentives can be analysed to encourage better results for the whole business. However, this depends on HR and other teams collaborating on how to make decisions with data.

Tactical uses of data for HR

Data can help planning around compensation, where HR has normally held a strong role for the business. However, it also involves the management teams within the business providing accurate information on how each individual is performing. If the sales team does not analyse its own results well, it’s possible that performance will not be accurately reflected in the compensation discussions that employees have.

What can cause this discrepancy? Well, consistency of data is one area where companies can improve. For example, even something as simple as headcount can be difficult to get consistent across departments and HR.

Rather than simply looking at how many employees there are in a team, there are now many more options for employment from full-time equivalent posts through to part-time employees, contractors and short-term employment roles. Each of these will require their own objective levels, rather than a ‘one size fits all’ approach, so that goals are managed and achievable.

For HR professionals, working with the sales team can be an opportunity to evaluate the whole use of data within both departments. For example, understanding the whole sales process and what value individuals create for the company can then help HR look at the total value that each person brings. This can and should be captured in the data that these employees create every day. Yet if it is not analysed well, then it will be lost.

What is the value that HR brings here? Well, the first point is that HR can help spot and retain better performers for the company. For teams with high turnover of staff, building up the right employee profile can help reduce this churn as well as improving results. This would normally tend to be based on anecdotal feedback rather than specific data points. Using HR predictive analytics, HR can look at what the reality is around certain roles and profiles to find out what made specific employees more successful in their jobs, and then guide decisions made around next hires. By helping them make decisions based on multiple data points rather than anecdotes, HR can help the sales management team understand and accurately value employees.

Likewise, it can be very expensive to recruit new hires into the business. Spending some time on internal development and keeping existing employees happy can save more in the long run compared to recruitment expenses and loss of productivity. However, investing in this way has to be justified. The right use of data can help, both for the HR team in its own allocation of budgets and for the business teams too.

Improving returns by predicting success

When you build up data over time, it’s possible to look beyond diagnosing what is taking place right now, and instead look forward at how to improve the chances of success around investments such as hiring. This ability to use data to improve the odds is called predictive analytics, and it has developed around sales and marketing teams.

Here is an example. For sales environments, traits such as the ability to form relationships quickly and an outgoing personality are typically highly valued in the recruitment process. Candidates who can’t demonstrate these characteristics tend to do less well in the process. However, it is worth evaluating the expectations against actual performance in the field. Do determination, willingness to learn and knowledge of the market actually produce better results than the typical “sales” personality?

By looking at performance in context, HR can use data to improve hiring decisions in the same way. It’s possible to see correlations between personal factors such as education, personality traits and previous experience and success in a specific role. This involves looking at multiple data sources over time to spot patterns of behaviour and how this influences results.

This may go against the conventional wisdom of the company or the industry as a whole. Spotting this kind of pattern can therefore provide opportunities for HR to have strategic discussions with other business teams on the results that are being generated and how to improve those results over time.

Thinking in this way and using analytics does not replace the people skills and insight that business managers and HR professionals have built up with experience. However, predictive analytics can help show where there are untapped areas of potential for hiring, such as looking at different personality types or experience areas when making decisions on who to bring in for interviews or further consideration.

Similarly, it’s possible to use predictive analytics around areas such as compensation and reward programmes. To pick on the sales function again, it’s typical that members of the team will focus their efforts where they will see the most personal return based on their targets. It’s possible to look at how compensation packages are put together to affect behavior. For example, increasing the commission rate on one product over another should incentivise members of the team to sell it.

By looking at each compensation package and product approach in context, it’s possible to structure activities so that they provide the most potential profitability for the business. At the same time, this can be modelled so that each activity brings the best chance of success for those within the sales team, as well. By only expending effort on those activities that are most likely to generate a sale or a response, businesses can improve their efficiency and make the most of their people.

Wrapping things together

By collaborating with other business units around their hiring patterns, HR can get more predictive around which kinds of hires should perform well and how to encourage those employees to perform. However, this use of analytics will be based on getting all the data from multiple sources together in one place so that it can be merged and used in the right ways.

The point to emphasise here is that HR can take a leadership role in defining key areas around employees and performance objectives based on actual data, rather than conjecture and opinion. This can automatically make HR more valuable to other departments, as it can be the centre of a network of interwoven analytic fabric about the company’s most valuable resources:  its people.

By understanding this process, HR can and should be back in the driver’s seat in making data-driven decisions. However, this is only possible through getting out into the business and using multiple sources of data, rather than HR data on its own.

The preceding article was originally published in HR Review on October 8, 2015.