Four ways HR analytics software can action your HRMS data
When asked as part of Deloitte’s 2018 Global Human Capital Trends survey, 84% of respondents agreed on the importance of HR analytics to their businesses. The analysts at Deloitte attribute this solid response to two main areas:
- The C-suite continues to look for leadership and management tactics that will boost productivity and engagement while also addressing current diversity issues.
- Ongoing investment in HR analytics software has generated new data sources – more raw material with the potential for more accurate analysis.
Predictive analytics combine and analyze datasets to produce possible future outcomes. Often the process is about looking for correlations between existing HR metrics. For example, connections between the number of hires, the quality of hires, and length of employment can point to improvements in the recruitment process. The following are four examples of HR analytics that can process your HR data to create new insights.
1. As part of your hiring process
Using AI-driven software, the early stages of a recruitment campaign can be automated, including online jobs postings, advertising, receiving and parsing resumes and applications, and candidate pre-screening. This leaves you free to devote your human resources to interviews and assessment exercises. AI recruitment platform Ideal suggests that the average work involved in filling a position using a manual approach to recruitment is 40+ hours; whereas by using predictive analytics, that investment of time falls to 17 hours. As a side benefit to the efficiency of your hiring practices, automation can also broaden the diversity – and quality – of your job candidates.
2. Retaining the talent you need
Having gone to the trouble to recruit the best, you understandably want to keep them; not least because of the cost of finding a replacement. HRMS analytics software can be used to spot turnover trends in your business, leading to more targeted retention strategies. Metrics that track turnover and leavers, including through retirement, resignation, and involuntary reasons, can be used to identify common factors over time, between teams and even connected to performance. The combination of the various related metrics leads to insights that can be used to enhance your retention strategies, keeping your higher performers and therefore contributing to productivity.
3. Connecting rewards to performance
Not only do you want to retain your best performers, but you also want to motivate them to consistently give that best performance. An HRMS that can delve deeper into the performance and compensation data can compare the reward packages of high performers compared to the average employee, which gives you a picture of the whole organization, and whether your high performers are, a) appropriately rewarded, and b) where you want them. Then, examining the reasons for the additional reward and determining whether those reasons align with your business priorities can tell you whether those high performers are working on the right things.
4. Retirement prediction
Retirement is no longer a simple case of an employee reaching a mandated age and the office planning a farewell party. Alongside age, the factors that influence a retirement decision include role, recent changes at work, salary level, and potential future incentives and possibilities for progression. This data tends to be available and comparisons and predictions can feed into how you manage retirement in general, and your succession planning for specific roles.
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