Turn Disparate Customer Data into Actionable Knowledge
Enterprise Search 2.0 Powers Dynamic Customer Service Analytics

Today's support executives are awash in an ocean of data. According to the Technology Services Industry Association (TSIA), on average, its members receive more than 51,000 support incidents across phone, email, Web chat and online outlets, every month! (See Figure 1. Page S4 in the Downloadable PDF) Each of these customer interactions are filled with critical information about your products and services that could be shared across your support organization and mined for trends.

Your customer systems—CRM, incident management, telephony systems, knowledgebase, product documentation, bug databases, download libraries and online training materials-contain valuable knowledge and provide clues on what customers are struggling with and what knowledge is referred to most frequently.

What's more, new social media tools are creating even more customer interactions across social networks, online communities and microblogs. Add into the mix online customer communities and discussion forums, and you've got huge libraries of information that require new and sophisticated "enterprise search 2.0" technology to help make sense of the unstructured data, combine it with structured data and create actionable knowledge.

Dynamic Customer Service Analytics

Customer service executives know there is value in all of the data and metrics they collect, but identifying this value using legacy tools has proved frustrating, if not impossible. CRM, multi-channel and telephony solutions typically include strong operational reporting, which is useful to determine a top performer for a certain metric, or to measure organizational performance by key performance indicators such as customer satisfaction scores or average response or resolution time.

Missing in operational reporting is the ability to analyze data across multiple data sources, and therefore identify linkages and trends, enable root-cause analysis and provide insight to better understand the operational report numbers. This more sophisticated approach to reporting is known as analytics. Analytics, according to Wikipedia, is the process of obtaining an optimal or realistic decision based on a complete view of all existing data. Enterprise search 2.0 enables far more, in fact: Dynamic and interactive analytics which explain the "why" in addition to the "what" of traditional reporting and analytics.

What should you look for in an enterprise search 2.0-powered customer service world?

• Interactive, knowledge 360 dashboards. These are graphical representations of multiple data sets from multiple information systems. They provide analyses for an instant understanding of the state of operations, and they allow users to interact with the data to learn the "why" behind the "what." As an example, a dashboard may track all operational metrics, giving a consolidated view to a shift supervisor so they can easily spot which metrics are out of the normal range, with the ability to drill down into problem areas to identify possible reasons for the variance—and hence a real-time solution.

  
  With customer service operations averaging a dozen or more disparate systems, it is critical that the consolidated views provided by management dashboards and operational consoles are easily accessed from anywhere within the ecosystem and are not tied or integrated with any single system.

• Ease of integration. Given the issue described above—data stored in multiple systems containing customer information, as well as multiple accounting, inventory and logistics programs—enterprise search 2.0-powered analytics must quickly and easily integrate with any front- or back-office system, and include a library of documented, high-volume application program interfaces (APIs). Packaged integrations popular to CRM and multichannel systems also speedintegration activities. The new breed of enterprise search 2.0 technologies enables such integrations with packaged connectors that bring the data, both structured and unstructured, into a common index, in effect a type of "virtual" integration.

• Dynamic data. Mainstream analytics tools use data warehouses, which replicate data from various systems, and then the tool analyzes it. However, data in a warehouse is structured, and, depending on how old the data is, insights from data warehouses may not be actionable—the problem or problems have passed. While data warehouses are valuable for historical data and therefore may predict future operations, it is key to combine this information, now possible with enterprise search 2.0 tools, with real-time data to identify problems and trends instantly. Dynamic navigation of the information helps identify solutions quickly.

• Business—user targeted controls. While administrators may be required to design the interactive solution, the system itself should be intuitive enough for end users to personalize, navigate and decipher information without administrative assistance.

Enterprise Search 2.0-Enabled Analytics

While most knowledgebase products include operational reporting to identify frequently accessed content, or unused articles to review, update or delete, these capabilities only apply to the content within that knowledgebase—not the dozen or more content sources both employees and customers navigate trying to find information. Analytics can deliver business value for knowledge management practices through:

Dynamic FAQs. What content, structured or unstructured, across all data repositories, is most frequently accessed, and by whom? Creating dynamic FAQs can easily boost self-service success rates by prompting customers with the most useful content for their product, their geography, or by any available customer demographic;

Trusted third-party content. In a Web 2.0 world, not all relevant content is internally authored. Enterprise search 2.0 tools are necessary to mine and analyze third-party content, such as trusted content from blogs, forums and other third-party suppliers in the social media world outside your enterprise; and

Content quality, applicability and accessibility. Most analytic platforms can look at content usage, articles tagged as resolving incidents and the actual workflow of content creation to identify the best content in the knowledgebase, and how it got that way. Enterprise search 2.0-powered analytics allows deeper insight into knowledge by consolidating information from outside a single knowledgebase. This enables predictive analysis that can avert a snowballing issue before it happens by ensuring that agents have access to the knowledge they need.