People analytics, integrated

Hiring the right kind of people was cited as the most important challenge by 98 per cent of HR and business leaders in a survey. The second most-cited concern was retaining this talent, backed by 93 per cent of respondents.

Workforce management solutions company Kronos, which conducted the Workforce Productivity India 2012 study with HR magazine People Matters , wants these findings interpreted correctly.

Dick Cahill, Vice-President and General Manager (International), Kronos Systems Ltd, explains, “They think their problem is hiring and retention. We believe that's not the problem — that's the result of the problems arising from lack of visibility into the work force. Unless you have visibility of what's happening on ground, how can you provide fairness, education, movement or development of employees?”

Kronos' argument is that accurate and real time workforce data — from absenteeism, time keeping, background, skills, workplace behaviours and more – can enhance productivity.

“We're convinced that the underlining factor is that they are not getting to grips with workforce productivity. An outcome of that is poor retention, so you then have to continue hiring,” adds Chris Marjara, Head of International Marketing, Kronos Inc.

Rather than have a manager allocate a task to a favourite, or whom he or she thinks is best suited, it must be allocated based on the skill match, based on data. If that sounds like a Kronos sales pitch for its solutions, it's a good one. And there are some examples to back that pitch.

Data to stem attrition

“Why does attrition in retail hover around 35 per cent in many markets?” asks Cahill. One obvious factor with a client company was a largely 16- to 18-year-old workforce in the stores. With attrition at 35 per cent, hiring and training was a big cost. Given the nature of industry, customer service could not be compromised.

Since this age group had a greater affinity to leave, Kronos started getting retail clients to look at staff from an older age group — they were found to stay longer and willing to offer flexible hours. Cahill admits that it wouldn't be suited for a lifestyle brand's ‘cool' store, but it is working in supermarkets in Western Europe, Australia and the US.

One didn't perhaps need much data from automated systems to pinpoint that a workforce this young was likely to move earlier. But a single point source of data across stores helped paint the macro picture — accurately.

And data was critical to find which kids were more likely to leave. Young employees from certain schools, for instance, were found to be more inclined to join university. There were those from other schools who tended to continue with the labour segment for longer.

This gave the retailer a clear direction on what strategies to adopt for which bunch. There were other findings to help retention too.

It was found that the attrition at a large retailer in Australia was much higher among those who had 9 to 12 hours of work a week. When the hours went up to 18 a week, the attrition rate was found to come down sharply.

“The pay for working around 18 hours a week was what they needed to be able to pay their bills. If they couldn't get that number of hours with the retailer, they would move on,” explains Cahill.

When this was mined out, the retailer cross-trained them across functions to increase their working hours. With more hours, and more pay, attrition dropped.

Integrating staffing data with sales

Kronos is also engaged with a telecom company in Europe, which has retail outlets of around 15 people each. With hundreds of stores, they had systems to get store data on sales and models sold. But they were missing a key element.

“We got the data on customer walk-ins and time. For staff, we looked at our data on when the staff was taking their breaks. We tied both in,” he says.

A majority of the staff was working 9 am to 5 pm. Their lunch break was taken between 11.30 am and 1.30 pm. Unfortunately for the company, a majority of people walking in to buy a phone, across many stores, was during this lunch slot for staff.

“All we did was enable better utilisation of those store resources. People took breaks earlier, and later. Same store sales increased simply by scheduling. It's very difficult to do this across stores without data at a broader level,” adds Cahill.

Marjara claims that the sort of labour analytics Kronos can provide hasn't existed before — not with this level of detailed granularity. But not all customers in all markets are ready to jump on to the evolved staff analytics bandwagon.

In India, where Kronos claims over 60 customers since operations began in 2007, new customers are coming on board — eight to 10 more will be signed on this quarter. New adopters will be in early stages of the ‘maturity cycle' of workplace solutions — enrolling for basic solutions such as automated attendance and time sheets. The company claims to be growing organically with client evolution.

Flexing to demand

Citing examples from the Indian market, Kronos' Country Manager, Mr James Thomas, says obvious problems can be identified and sorted even with basic workplace solutions. One of them is ‘unexplained absence'.

When only 850 of 1,000 people scheduled came in at a services company, it ramped up the head count to 1,100. There were no foolproof systems to capture this absence, he explains.

Says Thomas, “That added cost has an impact either on the bottom line, or on output pricing to the customer.”

In the case of organisations with a contract (temporary) workforce, the productivity data becomes even more critical. The idea here is to be able to predict and flex sourcing to demand — calculated based on accurate data on individual output.

Thomas cites an example of a plant where full-time employees sat and watched while contract employees finished the job.

“Without real time visibility, companies tend to create subjectivity and inequities leading to attrition,” he surmises.

> gokul.k@thehindu.co.in

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