Digging into the Turnover Data

As Singapore moves up the value chain to grow its economy with more high value business activities, companies' ability to survive and succeed will be increasingly dependent on its quality of talent. This provides the motivation to analyse turnover data and derive more meaningful information, says Roy Chew

Talent retention is a key concern for many companies in Singapore as they compete to both keep and seek the best people to work for them in a tight labour market. This is apparent from the relatively high turnover rate of Singapore Professionals, Managers, Executives and Technicians (PMET), which averaged around 17.2% from the 2002-2011 period and the high hiring sentiments from employers.

As technology advances and businesses get more complex, knowledge workers are gaining importance, with their unique skill-set, knowledge, experience and professional network giving them a competitive advantage over others. As Singapore moves up the value chain to grow its economy with more high value business activities, companies’ ability to survive and succeed will be increasingly dependent on its quality of talent.

This provides the motivation to analyse and go further in-depth to understand turnover data and derive more meaningful information. For example, for a company in Singapore, it might be useful to compare its turnover data to that of a benchmark indicator such as the nation’s average turnover rate. Figure 1.1 provides such a comparison. At this stage, the company is performing well compared to general market conditions.

Looking at the annual turnover over the performance rating of its separated employees also provided positive signals (see Figure 1.2). Generally, the annual turnover decreases as performance increases. This implies that the company’s efforts at rewarding and incentivising its employees worked. The key message here is that the company is retaining its most valuable workers. However, there is a slight taper upwards which shows the turnover of the best performers increasing.

At this point, we could assume it to be within the normal variation and leave it at that or we could go further into the analysis to allay our suspicions. But I would recommend going further with the analysis because of the insights that we could potentially gain. For example, we might realise that there might be issues with the retention of high performing employees due to the taper upwards.

If for instance, this company is a multinational enterprise, a logical next step could be to look at comparing the Singapore turnover data against the global average as shown in Figure 1.3. Visually, both the Singapore and global data sets showed the same trend. Not much significant information can be deduced from there aside from the fact that the turnover is generally higher for Singapore over the entire performance band. Is this a wasted effort?

What if we take the ratio of the Singapore data over the Global data? This is shown if Figure 1.4. The visual impact is more apparent and significant now because we can clearly see that more high performers are leaving the company’s subsidiary in Singapore compared to the global branches.

Although the turnover of the worst performers in Singapore is less than 10% more than the global average, the turnover of the best performers is 80% more. This becomes an area of concern and the Singapore subsidiary would definitely need to evaluate the potential interventions that could help close the turnover gap in Singapore.

Hopefully this example has helped to illustrate the usefulness of HR analytics and the value that can be gained into going further with the analysis.
 
Written by Roy Chew Han Guan, former Senior Workforce Development Specialist, Micron Semiconductor Asia.

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