Solving the Mystery of Poor Customer Experiences
The symptoms of troublesome customer experiences are all too familiar: declining customer satisfaction, low self-care containment rates, and poor sales conversion rates. Contact centers are hammered with calls from upset customers. The evidence of less-than-satisfactory customer experiences abounds, but not the reasons behind them.
Management’s response, if any, has been varied. Some rely on an experience-based approach, using observational techniques, speech application and channel expertise, industry best practices, and assumptions about customer behavior. Others focus only on channel- or application-specific metrics, such as IVR containment rates, speech confirmation rates, and IVR satisfaction. These do not provide a full picture of what influences customer behavior.
Many companies lack the tools and expertise to conduct complex data analysis. Others fail to pursue a multichannel focus. Even those with strong Web or IVR analytical capabilities have a siloed view of the interaction channels; while valuable, they do not provide a full view of the end-to-end customer experience.
A data-driven approach, on the other hand, offers decided benefits. It entails transforming voluminous amounts of complex data, usually in the form of speech or Web application log files, into a visual representation of customer behavior, allowing for an analysis in a specific channel or across multiple channels.
The data-driven approach comprises the following:
Visibility into Customer Behavior By transforming complex data into actual customer behavior, companies can pinpoint customer problems in speech applications and other contact channels. This helps answer questions such as: What are customers really doing? Where are customer needs not being met? Why are customers calling multiple times for the same problem? Are the problems bigger than the speech application? Understanding the answers to these questions will improve and maximize the use of speech applications. The analysis also allows support for both personalization and optimization. Understanding customer behavior and being able to act on this knowledge at the time of interaction maximizes the value of each customer. The industry is developing new tools to help companies achieve this.
Segmentation Analysis A visual model of customer behavior provides a valuable tool for segmentation analysis. Customers can be segmented by behavior in a speech application or across channels. If high-value callers are routed directly to specialized agents and low-value customers are driven to self-service channels, companies can understand if the predefined paths, by segment, are effective. Once a customer segment has been defined, companies can create inputs to a centralized rules-based/real-time decision engine that creates personalized experiences for each customer segment and enables a dynamic and proactive interaction between customers and the care environment. Personalization is a differentiator in delivering a superior service experience.
Analytical Decision Making Expanding the scope of the analysis to include data collected from all interaction channels and leveraging the model of actual customer behavior significantly reduces the subjectivity of research results. Using actual behavioral data suppresses any bias in recommendations for speech application improvement. In addition, a data-driven approach augments experience-based analyses. Data analysis supports or refutes recommendations based on experience, expertise, or best practices. By the same token, experience-based analysis helps focus the data-driven analysis by identifying the usual suspects in poor customer experience and speech application design. Together, a data-driven and experience-based approach is very powerful.
Cross-Channel Analysis A data-driven approach must be applied across channels. Analysis of a single channel, while powerful, does not offer a full view of customer behavior. In essence, you aren’t interrogating all the suspects. By performing data-driven analysis of customer behavior across interaction channels, companies receive a full view of end-to-end customer behavior.
Adding a data-driven approach to analysis of the customer experience is critical to an effective relationship management strategy. In simple terms, an effective relationship management strategy optimizes how an organization interacts with its customers and employees to drive value from those relationships. The key to unlocking the power of data is to analyze, model, and apply actions based on the analysis.
Jo Ann Parris is vice president of relationship technology management at Convergys. She is responsible for sales, marketing, and solution management for Convergys’ portfolio of self-service and advanced automation solutions. She can be reached at email@example.com.