If you needed further evidence that customer support operations are overwhelmed by data, look no further than the joint research paper released today by the Technology Services Industry Association (TSIA) and Coveo entitled, “Enterprise Search 2.0-Powered Analytics: Transforming Data into Actionable Knowledge.” New data revealed within the report includes this eye-opening statistic: TSIA members receive, on average, 51,000 support incidents per month. These include phone, email, Web chat, and online incidents, each filled with critical information about products and services that could be mined for trends.
I was pleased to have contributed to this report. As the title suggests, the report focuses on Enterprise Search 2.0-powered customer service analytics, a topic relevant to today’s customer service organizations who are awash in oceans of data, and one where we have much expertise to offer. The aim of the report is to help readers understand how support teams are leveraging analytics to deliver real business value in the areas of operational impact, knowledge management, multi-channel management and voice of the customer.
The report outlines how the amount of data flowing through support organizations is increasing every year due to rising interaction volumes and social media activity. The report also reveals interesting figures regarding customer satisfaction scores by channel. The averages follow the same curve as cost: the more human interaction, the higher the satisfaction. The low ratings for self-service are particularly troubling, showing that first-generation knowledgebase and full-text search tools are not keeping pace with customer demand.
The report provides readers with a plan of attack to migrate traffic to the most effective channel to please their customers, which can mean serious financial savings, as well as real-world case studies of organizations who have measured significant business benefits with a unified, 360 view of customer information across multiple channels.
One solution to this data overload is the adoption of analytics in the form of 360-degree views of data centered on what matters most: the customer and the customer base, product and sales information, and customer support performance metrics. The ability to consolidate and correlate data from multiple sources enables the detection of customer trends and the identification of new operational and financial insights.