Web Interaction Analytics

Break the silos around web-self service and contact centers

Have you ever wondered how many customers that use your web site for self-service end up calling your contact center after failing to achieve their goal online? What are the root causes of this deflection from self-service to assisted service? How do they impact business metrics such as customer satisfaction and operational efficiency? You can find these insights with web interaction analytics.
 

Web interaction analytics is a key capability of NICE Interaction Analytics. It captures and analyzes an authenticated customer’s web session interactions (e.g. bank account status inquiries, airplane tickets purchases, loan application submissions, or cellular service upgrade requests). This information is automatically categorized, cross-referenced and compiled with other information gathered by NICE’s Cross-Channel Interaction Analytics specific to the customer.

The aggregated insights are compiled into a holistic profile of customer wants and needs. Web interaction analytics pushes this information to agents’ desktops as they interact with the customer, providing them real-time guidance on how to best handle the call based on the customer’s past attempts to receive service, as well as the context of the current interaction. Additional benefits include the ability to track self-service deflection rates across time and perform root-cause analysis to determine points of web self-service failure for correction.
 

Web interaction analytics improves operational efficiency by correlating calls to the customer’s prior web interactions and performing root-cause analysis for deflection into the contact center. It also improves Service-to-Sales by providing agents with real-time recommendations for optimal offers for calling customers based on a combination of an analysis of the customer’s prior web interactions, real-time speech analytics and recommendations that may have been calculated by a campaign management system. Furthermore, web interaction analytics optimizes churn reduction, providing organizations with early churn predictions by combining information from real-time speech analytics with “hot” web patterns – i.e. a flow of specific web pages that may represent a customer churn signal.