Call Center Analytics Move The Industry Into The 21st Century

call analytics
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Over the past few years, a lot of discussion has been centered on big data and predictive analytics in digital marketing. Conventional marketing departments have been slower to embrace this new technology, but they have also recently seen that it provides numerous benefits.

Call centers in particular have started exploring new avenues to use artificial intelligence and predictive analytics to improve customer satisfaction and streamline their operations. There are a number of ways that predictive analytics is helping improve the effectiveness of call centers around the world. They can be used for call analytics in the case of businesses that work with calls.

Identifying agent traits that yield to the lowest churn rates

Most organizations place a lot of emphasis on the importance of optimizing their training models. As important as this is, onboarding the right employees is equally vital.

This reality is particularly clear in the call center industry. No amount of training will make up for a lag in performance among employees that are not equipped with the necessary interpersonal skills or ability to cope with the anxiety that comes with working in the call center industry.


The problem is that the many of the specific traits that best prepare someone for a career as a call center representative are not immediately evident, even to managers with over a decade of experience under their belts. According to ICMI, new predictive analytics models have helped them identify the traits of the most successful call center representatives. They are able to find correlations between personality traits and behavioral tendencies of call center employees with the lowest churn rates.

Refining training models

Many call centers modify their training programs from time to time. They may not realize that variations in their training methodologies have unexpected differences in churn rates and customer satisfaction scores.

Predictive analytics models can track representatives based on the training programs that they were exposed to. This allows senior staff to assess the impact those programs had. This helps them develop more holistic training programs and abandon any training systems that are proven not to work.

Gauging future call center volume and assigning representatives accordingly

One of the biggest challenges of this profession is finding a balance between cost effectiveness and ensuring that the center is adequately staffed. Call centers have always relied on various models to estimate future call center volume. However, traditional staffing models usually relied on anecdotal experience and a limited array of useful variables.


Call center managers have found that there are actually thousands of different factors that affect call volume. Relying on sophisticated predictive analytics models helps them make much more accurate staffing decisions.

Improving CRM systems to reduce attrition

Customer relationship management software has played a very important role in most organizations. It influences customer satisfaction and engagement at every stage of the relationship and sales funnel.

Predictive analytics can have a profound impact on CRM strategies, so many call centers are finding ways to merge their CRM data into predictive analytics models. Here are some predictive analytics approaches that they are taking:

  • Carefully analyzing variables that contribute to customer dissatisfaction. This includes evaluating the number of calls that specific customers make, making multiple references to competitors, inquiring about the possibility of lower prices and making of her statements of dissatisfaction.
  • Having an automated system to route certain calls to representatives with different areas of expertise. Some call center employees are better equipped to handle certain calls than others. Predictive analytics models can be integrated into CRM systems to identify agents that are best able to handle the call.
  • Identifying signs that customers are open to upselling. Every brand intends to upsell their customers. CRM systems can collect data on customers and merge it with predictive analytics models that will help identify people that are most open to these pitches.

A growing number of CRMs will depend on predictive analytics in the coming years. The value of these systems will be evident to even today’s biggest skeptics of the potential of big data.


Predictive Analytics is Transforming the Call Center Industry in Fascinating Ways

There are a number of major benefits of predictive analytics, even in the aging call center industry. A growing number of brands will find new ways to utilize it to bolster customer satisfaction and reduce churn rates.

Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.