Call Center Performance Analytics | Predictive vs. Behavioral

We recently published an article that included a theoretical definition of both predictive and behavioral analytics. By leveraging Machine Learning to incorporate automated analytical computing, we can provide insight into current agent behavior, as well as the propensity for specific outcomes in the near-to-mid-term future.

That type of statistical analysis requires a lot of "heavy lifting", and for a contact center supervisor or executive, it means the difference between a lot of time pouring over data or, alternatively, automatically receiving actionable information that aids in managing the human and financial aspects of their operations.

Let's first take a moment to debunk the notion that Machine Learning is some kind of black magic or covert algorithm for spying on honest employees; I personally spent part of my career developing email and network security systems which relied on machine learning and heuristics to predict if a network was under attack, or a specific email contained a virus or malware. This was way back in 2008, and the concept of adaptive, self-learning computing has been around for even longer - by decades.

Machine learning has become a mainstream technology, and it happened virtually unbeknownst to consumers across a broad range of products, from the iPhone, to Amazon, and a large proportion of ERP/CRM business applications. This is due to the simple fact that there are myriad benefits to this type of computing across multiple industries spanning a variety of real-world use cases.




CEO Bill McDermott of SAP has stated that "intelligent applications will fundamentally change the way you do work in the enterprise in the next decade." 

Machine Learning in the Contact Center

Here are 5 common use cases  for an outbound sales contact center where analytics based on Machine Learning can add tremendous value:

Contact Center Performance Dashboard

Contact Center Performance Dashboard

  1. Measuring the effectiveness of incentives on sales
  2. Gauging the impact of a call center agent training program for sales on their respective call conversion rates
  3. How various HR policies or reward programs impact agent attrition
  4. Determining if a new on-boarding program is making sales agents productive in less time vs. the last fiscal period
  5. Measuring a new manager's impact on a contact center department's operational efficiency


Analytics to Improve Employee Engagement

Behavioral analytics can assist department managers to determine if, and/or when, a customer service or sales agent may be starting to show signs of losing their motivation, or level of engagement, at work.  By measuring trends in an agent's interaction with an application, such as frequency of logins, updates to activities being measured to gauge performance, and social activities such as messaging, sharing, and other online behavior, the algorithms in the underlying application start to "learn" a specific employee's baseline, or normal behavior, and can thereafter determine if there are any changes in that behavior. The application can then create new activities and challenges to increase engagement and encourage improvement and team collaboration.

Analytics to Reduce Attrition

Leveraging predictive analytics allows an organization to capture and analyze data from customer and employee interactions, in addition to other contextual information. 

Machine learning fuels predictive analytics and the decisions that occur "in the moment", by finding patterns in data and predicting results. This model produces a wealth of insights that can be shared across departments and assist with strategic planning at the highest levels within the organization. This allows companies to listen to their employees, and genuinely understand their needs, resulting in an improved customer journey.

Using the information gained from machine learning analytics, managers can reduce agent attrition, as timely and actionable data translates to tangible plans for improving agent performance. This concept is applicable to boosting revenue-generating activities, changing behaviors to improve customer service, and aiding in providing at-work training for handling calls more efficiently. .

Isn't that the whole point? 

Isn't that the whole point? 

Machine learning has become an essential technology to empower employees, gain invaluable insight into contact center operations, and most importantly, to provide clarity in an environment that is typically rich in data while presenting sometimes insurmountable obstacles to clear and concise analytics.

Learn how nGUVU customers apply gamification and machine learning insights to drive performance.