How To Use Data Effectively In Call Centers?

Call center customer experience (CX) leaders say adapting to customer needs is the number one goal driving their digital transformation activities, according to a recent report from Aberdeen.

Meeting these needs allows for customer focus, creating a positive experience and, ultimately, a loyal and lasting relationship. But many organizations fall short of their digital transformation goals, and the main reason, according to the Aberdeen report, is a lack of data.

While companies collect a great deal of user data through various digital channels, including chat, email, phone and text messages, the vast majority of contact centers (78% of them, according to the CX survey Aberdeen Executive Agenda 2021) indicates using the data collected to improve employee productivity and make better business decisions in contact centers.

Why Should Companies Use The Data Collected?

While nearly a quarter of CX and call center leaders surveyed by Aberdeen said they rely on data and analytics to make strategic decisions, a number of factors get in the way of their ability to use the data they collect effectively.

• 34% think the data is insufficient.
• 33% believe that data quality is not fit for purpose and execution.
• 32% think that the qualified human resources necessary to manage the data are lacking.
• 28% lack the necessary technologies to manage the data.
• 27% believe that data sources make it difficult to create a unified view of data.

According to the Aberdeen report, not all the data that companies have in their systems is relevant to every interaction. In addition, the data that organizations may have in their systems may be out of date and may not reflect current customer behavior and needs.

How Artificial Intelligence (AI) Is Changing The Way Contact Centers Use Data?

In some cases, the lack of employees with technical skills impedes the use of data by call centers. Contact centers not only need to bring agents up to speed with new technologies, but also equip them with tools capable of extracting information and analysis faster. Increasingly, AI powers those tools.

Aberdeen defines the range of Artificial Intelligence capabilities in the call center as follows:

• Artificial Intelligence (AI): Automated reasoning and decision-making capabilities based on insights discovered through machine learning algorithms.
• Machine Learning (ML): Technological applications that learn by themselves by analyzing a pattern of historical and recent data.
• Prescriptive Intelligence: Tools used to analyze structured and unstructured historical data to make predictions and suggest decision options.
• Predictive Analytics: Tools to predict the future behavior of customers.
• Automation: Tools used to automate the execution of tasks such as customer routing, agent scheduling, and quality control.

How AI Helps Achieve Customer Experience (CX) Goals

These AI tools can be used to see how specific activities can help address customer issues, helping organizations create a personalized customer journey.

Additionally, predictive analytics tools can show the activities of current customers who share similar characteristics and help the contact center better anticipate how to address their needs. In fact, call centers using AI capabilities experience superior CX performance improvements, according to the Aberdeen report, including:

• 3x year-over-year increase in customer retention rates (10.5% vs. 3.2% among non-AI call center users).
• 5x year-over-year increase in customer satisfaction rates (10.1% vs. 2.9%).
• 8x year-over-year increase in customer effort score improvement (8.8% vs. 1.1%).

In conclusion, AI capabilities can help call centers translate collected data, observe agent behaviors, and quickly learn whether interactions have a negative or positive impact on overall CX, leading to better competitive advantage in digital transformation.