Using Big Data for More Effective Call Center Hiring, Other Improvement
Your frontline agents are the face and voice of your call center. They are the most direct point of contact that you have with your customers. A positive experience can leave a lasting impression, but a negative one can have just the opposite effect. In fact, one study by Accenture found that two out of three customers switched due to poor customer service.
The upshot of this insight, however, is that there is some opportunity here. Large companies can achieve big gains by improving that experience. In the same study, Accenture identified one North American telecommunications provider that boosted incremental margin by $1 million a month as a result of a 10 basis point improvement (e.g., moving from 0.5 percent to 0.4 percent) in its customer churn rate.
It is for this reason that many of the largest call centers are using big data to optimize their workforce and create a better customer experience. They do this by leveraging workforce and performance data from a variety of different sources to make better decisions about how to manage their talent through all stages of the employee lifecycle: Which job applicants will provide the best customer experience? What sort of training teaches them most effectively how to handle customer problems? How much overtime yields the best customer satisfaction scores? Data can provide more informed, predictive answers to all of these questions and can help you achieve the best possible outcomes.
Consider the hiring process. Traditionally, these decisions were made by hiring managers on the basis of intuition. However, research conducted by Evolv, done in conjunction with academic partners such as Yale School of Management, found that traditional indicators of success such as previous work experience were not nearly as predictive of good customer experience as an employee’s personality traits. Some of these traits include the extent to which someone has a strong service orientation, how relatively conscientious a person is, and attributes that suggest an individual is honest. How do you find these people? By measuring these traits in a systematic way at the point of application and then tying them to customer experience metrics to determine what types of applicants will provide the best possible customer satisfaction.
Let’s consider the real-life example of one call center – a BPO for a large telecommunications company – that was struggling with its customer service scores. Agents were not providing the level of service expected by the end client, and the BPO was being hit by financial penalties. It realized that it wasn’t hiring the right talent to hit those customer experience metrics, so it put in place a data-driven hiring tool that assessed all applicants and assigned them a score based on their responses, putting them into three categories: green (optimal employee), yellow (qualified, but may need additional training) and red (probably not ideal for the role). What it found was that the green applicants reached proficiency 18 days earlier than its historical hires. By focusing its hiring on the greens, it was able to achieve an aggregate of 1.5 percent improvement in customer satisfaction scores and eventually became the top-ranked BPO within that telecom company’s entire network.
Although this one example focuses on hiring, it’s important to realize is that call centers are using big data to generate insight throughout all stages of the employee lifecycle. They’re learning more about how to train their frontline representatives and uncovering who should supervise them, as well as how and who to promote or separate among their employee population. By injecting big data techniques and methodologies into talent management processes that were previously fraught with human error, they’re able to continually optimize the workforce and provide a better customer experience than ever before. The result is a more engaged and, ultimately, a more loyal customer base.
It is because of success stories like this one that these approaches are being rapidly adopted across the call center space and are increasingly becoming the rule rather than the exception. Much like the classic Moneyball example, it starts with just one visionary organization and gradually reaches a tipping point when everyone adopts. Eventually, no one will rely on the old approach to talent evaluation and management. Call centers can lead that transformation.
Michael Housman is vice president of workforce analytics at Evolv (www.evolv.net).
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