Data Discovery: Optimizing Customer Service

Customers share a great deal about themselves, including their needs, preferences, and behaviors, in their interactions with contact centers.

This provides opportunities for companies to use data analysis and data discovery to better understand the channels that different types of customers use for different types of interactions (e.g. voice, IVR, chat, email, mobile).

As Molouk Y. Ba-lsa notes in a recent article, “Contact center operations need to be aligned to the overall vision of an organization, carefully taking into account the nature of the service offering and the needs of the target customers.”

Not only that, but the contact center should also be operated in a way that “dovetails” with the brand personality of the organization, Ba-lsa continues.

“For instance, an online gaming website should be handling customer queries in a very different manner to that of a bank, but they both should be fulfilling customer action items rapidly to ensure customer satisfaction,” he says.

Companies can use data analysis and data discovery tools in a number of ways to identify problem points in contact center interactions and to act on those issues.

For instance, some credit card customers call into a provider’s IVR (interactive voice response) system to pay by phone. But not all customers are the same.

One customer may just want to pay the minimum amount due while another would like the flexibility to pay a higher amount. Data discovery can help credit card companies identify the types of transactions customers are making and ensure that the system is designed to make the transactions as effortless as possible for these customers.

Understanding the reasons why customers are reaching out to contact centers, the channels they’re using to do so and the times they tend to initiate these interactions can also help contact center leaders with agent scheduling.

Contact center managers can use data visualization tools to observe customer patterns that are taking shape and then schedule agents based on anticipated customer requirements to effectively match the right agents to the right customers. Doing so can help contact centers improve first call resolution rates and boost customer satisfaction.

Data discovery tools can yield additional insights from customers’ contact center interactions. Contact center managers can drill down on customer interactions in the call center, or with chat, email, or mobile customer support to ascertain the attributes of top performing and poorly performing agents.

This information can be used to identify opportunities for coaching and training agents who may need help in certain areas. Pinpointing gaps in agent skills and then working with agents to improve on these skills can drive greater productivity as well as improve customer experiences.

Forward-looking organizations can also benefit from data analysis to identify opportunities for providing customers with proactive service.

By design, the contact center is a reactive environment where agents are often dealing with disgruntled customers who are looking to resolve problems. Through the use of data analysis tools, companies can identify opportunities for providing customers with proactive service by anticipating the types of things that could benefit customers.

One company that does a great job of this is Walgreens. The retailer reaches out to its pharmacy customers to let them know when prescriptions need to be refilled or to warn them that combining certain medications may have harmful side effects.

Companies that are able to anticipate customer needs and preferences demonstrate that they’re looking out for their customers’ best interests. This can help strengthen customer trust and lead to longer, more profitable customer relationships.