Thursday, November 16, 2006
Catch 'em before they quit
16th November 2006 - Economic Times
Predicting who is going to leave by analysing attributes which make an employee more prone to leave, should find takers in sectors facing high attrition levels. For BPOs, such profiling makes a critical difference.
“We have predictive tools, which can be used at the recruitment stage, to profile a prospective employee. Using this, recruitment would be attributed rather than performance based,” Ramakrishna Reddy Dasari, director, EMEA, Fractal Analyticals, said.
While banks have been the fastest adopters of predictive tools such as these for customer acquisition and cross selling of services, Mr Dasari observed that employee retention could be a bigger business opportunity for the company.
He said that the assessment is based on attributes and judging how long an employee will stay, factoring in differences over issues like freshers over the more experienced. Another impact: salaries could be based on this data analytics.
Mr Dasari said data analytics involves using data from business intelligence and from data mining, processing it further, to come up with a comprehensive report. This report is based on the data collected and studied of consumer behaviour patterns and helps predict the future of their services with target groups of customers.
While banks, especially credit card issuers are the biggest users of such data, Mr Dasari said telecom, insurance, retail and FMCG also offered scope. Each sector has nuanced offerings but sectors like retail and FMCG need authentic third party data on usage patterns, Mr Dasari cautioned.
“There is data paucity in India, and there is also a need to change the mindset in organisations, for these practices to come into the country,” he said.
Mr Dasari cited the example of retailers like WalMart which might want data on known value items, that is, why does the customer prefer Store A over Store B. Such decisions can be made only when there is authentic third party data available on usage patterns.
For banks, it could be customer acquisition over customer retention, so the product is slightly different. However, there are seven to ten parameters where scores are assessed regarding the probability of customer default, in the case of credit cards.
“For banks and credit card issuers, customer acquisition has to be done more efficiently, there is intelligent cross selling and managing churn or retention. In case of telecom companies, churn can be voluntary or involuntary and we can assess the risk and set limits,” he noted.
Predicting who is going to leave by analysing attributes which make an employee more prone to leave, should find takers in sectors facing high attrition levels. For BPOs, such profiling makes a critical difference.
“We have predictive tools, which can be used at the recruitment stage, to profile a prospective employee. Using this, recruitment would be attributed rather than performance based,” Ramakrishna Reddy Dasari, director, EMEA, Fractal Analyticals, said.
While banks have been the fastest adopters of predictive tools such as these for customer acquisition and cross selling of services, Mr Dasari observed that employee retention could be a bigger business opportunity for the company.
He said that the assessment is based on attributes and judging how long an employee will stay, factoring in differences over issues like freshers over the more experienced. Another impact: salaries could be based on this data analytics.
Mr Dasari said data analytics involves using data from business intelligence and from data mining, processing it further, to come up with a comprehensive report. This report is based on the data collected and studied of consumer behaviour patterns and helps predict the future of their services with target groups of customers.
While banks, especially credit card issuers are the biggest users of such data, Mr Dasari said telecom, insurance, retail and FMCG also offered scope. Each sector has nuanced offerings but sectors like retail and FMCG need authentic third party data on usage patterns, Mr Dasari cautioned.
“There is data paucity in India, and there is also a need to change the mindset in organisations, for these practices to come into the country,” he said.
Mr Dasari cited the example of retailers like WalMart which might want data on known value items, that is, why does the customer prefer Store A over Store B. Such decisions can be made only when there is authentic third party data available on usage patterns.
For banks, it could be customer acquisition over customer retention, so the product is slightly different. However, there are seven to ten parameters where scores are assessed regarding the probability of customer default, in the case of credit cards.
“For banks and credit card issuers, customer acquisition has to be done more efficiently, there is intelligent cross selling and managing churn or retention. In case of telecom companies, churn can be voluntary or involuntary and we can assess the risk and set limits,” he noted.
Subscribe to Posts [Atom]