We have already written in the past about the use of data analytics to best route customers to agents based on demographics and other characteristics. The NY Times has an interesting article on the use of data analytics to improve retention and employee-employer relationships (“Big Data, Trying to Build Better Workers“)
The article discusses the broader appeal of these ideas, but focuses on applications to call centers. Why call centers? In contact centers, customer service agents, that are hourly workers handle a steady stream of calls under challenging conditions, yet their communication skills and learning capabilities play a crucial role in determining both the employee’s tenure and performance. The article discusses a new startup, Evolv, which helps firms find better-matched employees by using predictive analytics.
Transcom, a global operator of customer-service call centers, conducted a pilot project in the second half of 2012, using Evolv’s data analysis technology. To look for a trait like honesty, candidates might be asked how comfortable they are working on a personal computer and whether they know simple keyboard shortcuts for a cut-and-paste task. If they answer yes, the applicants will later be asked to perform that task.
It’s not that HR as a field has been devout of any data or analytics, but algorithms and data gathering have improved tremendously in the last few years. Furthermore, call centers pose an interesting opportunity for such algorithms, due to the sheer volume of employees and transactions, and the relatively high turnover.
In the call-center world, Mr. Rae says, 5 percent attrition a month — 60 percent a year — is stellar performance. Dropout rates are calculated at 30-day intervals, and it takes four to six weeks to train a worker. The cost of attrition — for hiring and training a replacement — is about $1,500 a worker, he says.” In the project with Evolv, Mr. Rae says, Transcom was able to hire fewer people — about 800 instead of a more typical 1,000 hires — to get 500 workers who were still on the job at least three months later. The big payoff, he says, should come in cost savings and better customer service with less worker churn in call centers.
The question is whether indeed these algorithms can find employees that can deal with the most challenging aspects of the call center job — low pay, lack of job security, high stress and constant verbal abuse from customers, or is it the case that as the economic situation worsens, turnover goes down significantly, and goes up as it improves. This is of course, not unrelated to the new trend of using Gamification tools in many call centers, as a way to improve performance and increase retention.
Another question is whether we are going to back to Taylorism where each and every aspect of the work is being measured. Using a data-driven approach is always a welcome change, yet it is seldom in and of itself a remedy for structural issues in the operations itself.