Extending Customer Insight to the Rest of the Enterprise
Introduction
The customer contact center has become the primary channel for organizations to provide information, assistance, and customer service to prospective and existing clients. The information flow between this department and a company's customer base influences the external perception and provides early insight into buying behaviors, competitive threats and market opportunities.
More than 80% of all data in an enterprise-including the contact center-is unstructured information such as telephone conversations, emails, word processing documents, Internet pages, paper documents and electronic forms. Unfortunately, attempts to understand and take advantage of this strategic resource traditionally fail. Many businesses lack the technology to leverage the vast majority of content that resides outside the scope of the structured databases.
The inability to capture, process, and access strategic customer information and relate it to other information across the enterprise can limit a company's perspective and understanding of its customers. In doing so, the contact center is marginalized and established as a difficult-to-penetrate information silo. Rather than viewing the contact center as simply 'the cost of doing business' for maintaining customer relationships, organizations need to recognize the value of the contact center. With mechanisms to tap into the wealth of unstructured information, the contact center can become a company's most strategic asset. This white paper will show you how to create an intelligent contact center for your enterprise.
What is an Intelligent Contact Center?
An intelligent contact center leverages both the traditional structured data and the unstructured information that flows through a contact center to deliver a much deeper understanding of the customer. Customer intelligence is based on multiple dimensions, from data such as basic customer contact information to valuable feedback that helps companies understand client needs, behaviors, responses and sentiments. This is what it takes to deliver true value across the business. Yet, thus far, traditional customer relationship management (CRM) applications have fallen short in their attempts to capture the voice of the customer for business advantage.
In order for a customer contact center to evolve into an intelligent contact center, organizations must have the means to capture, share, and analyze the critical unstructured information that is often overlooked in decision-making processes. This requires a unified technology platform that facilitates bi-directional information sharing with the rest of the enterprise and enables Meaning Based Computing in the contact center.
Bi-directional Information Sharing
Bi-directional information sharing-the exchange of information from the contact center to the enterprise and vice versa-is critical to the transformation into an intelligent contact center. For example, the recording of customer interactions can be used to influence a strategic business plan. These recordings not only contain basic customer information such as names and account numbers, but also insight into their attitudes, concerns, and intentions that can be leveraged to optimize performance, service, and quality. Conversely, customer service agents need a way to access relevant information from other departments such as Sales, Marketing, Research and Development and Operations to support their responses and fulfill their core mission-to satisfy the customer as quickly and efficiently as possible.
| Departments such as Sales and Marketing can leverage information collected at the contact center (calls/email/chat) to better understand: |
Contact Center Agents can leverage corporate information (product/process/promotions) such as: |
| • Customer Behavior |
• Product and Services Updates |
| • Product Improvement |
• Pricing Changes |
| • Promotion Success/Failure |
• Order Process Documentation |
Employees need access to more of the data available within their companies and better technologies for understanding it and delivering it to their desktops. The answer lies far beyond legacy keyword search engines capable of accessing only static documents or data inside relational database systems (RDBMSs). To satisfy this need, organizations must implement Meaning Based Computing technologies that enable bi-directional information sharing across a wide range of applications in the enterprise, particularly those that handle highly dynamic content.
Meaning Based Computing Technology in the Contact Center
Meaning Based Computing enables users to search and access unstructured data from disparate and distributed sources. This technology includes enterprise search, content classification, categorization and clustering, taxonomy creation and management, information presentation, and desktop search tools. Using Meaning Based Computing technology, users can locate and invoke an organization's data and intelligence. In other words, Meaning Based Computing technology helps users understand what matters amid fast growing volumes of information scattered across multiple silos in the enterprise.
Meaning Based Computing goes beyond keyword search to enable computers to understand the relationships that exist between disparate pieces of information and perform sophisticated analysis operations with real business value. By automatically analyzing, identifying and prioritizing the main concepts within a document, Meaning Based Computing provides users with relevant data they didn't even know existed, which facilitates the flow of information between the contact center and the enterprise. Some of the key functionality of Meaning Based Computing, such as hyperlinking and clustering, connects users to a range of related information that are contextually linked to the original text. This provides call center agents or line-of-business managers with valuable supporting material and even richer customer intelligence to apply to their business objectives.
Meaning Based Computing technology also supports the strategies of customer-focused organizations by giving employees direct and immediate access to all relevant information. This is critical to creating a bi-directional flow of information between the contact center and the rest of the enterprise.
The Solution: A Single, Integrated Technology Platform
Autonomy etalk holds the keys to capturing the voice of the customer. Autonomy, positioned in the Leaders Quadrant of Gartner, Inc.'s Magic Quadrant for Information Access Technology, 2006, pioneered the Meaning Based Computing movement that enables bi-directional information sharing across the enterprise. Autonomy's platform for managing unstructured data, the Intelligent Data Operating Layer (IDOL), is the foundation for Meaning Based Computing. Autonomy etalk includes a suite of technologies that gives businesses the power to create an intelligent contact center in their enterprise.
Multi-channel Interaction Analysis
Most enterprises now record customer conversations in their contact centers for regulatory or quality purposes, adding to the organization's mass of unstructured data. At an intelligent contact center, the unstructured data that is collected through agent-customer interactions can be leveraged by the rest of the enterprise to understand customer behavior. Analytical views of these recordings create insight into customer value. Speech analytics tools powered by unstructured data platforms enable organizations to automatically organize, categorize, and access call center recordings based on the meaning of the recorded conversation. This data can then be distributed throughout the organization for analysis and decision support purposes. The information generated in the contact center can be leveraged for customer insight into corporate operations.
- Sales Effectiveness
- Marketing Campaigns
- Competitive Research
- Win/Loss Analysis
At the most basic level, speech analytics can help uncover customer insight by providing word or phrase searching that removes the burden of manually listening to recorded calls. Autonomy etalk's speech analytics solution is able to take advantage of Meaning Based Computing to extract otherwise hidden information that can significantly enhance an organization's business intelligence. Users can create and save search criteria in order to "train" the system to automatically search for specific types of recordings. Advanced functionality such as clustering identifies trends and other data that users may not have thought to search for. Automated trend spotting identifies common themes in calls to help highlight previously missed opportunities. The Alert feature enables supervisors to spot trends in scripts, procedures, and agent performance so they can immediately address opportunities or customer issues that may arise. By utilizing speech analytics to understand customer data, organizations can gain valuable insight into customer and corporate issues to evaluate current practices, generate sales opportunities, reduce customer churn, and build new outlets for revenue.
Real-time Agent Support
While the possibility of leveraging customer data for enterprise-wide strategy is a key development, the primary goal of the contact center agent remains customer service. For agents to provide the best service possible, they must have access to the latest product, process, and sales information to enable them to respond to customers quickly and accurately. However, this information is often located in dozens of repositories across the enterprise, making it difficult for employees to find and act on relevant content. Autonomy etalk's agent assistant tool addresses this problem with real-time analytics that leverage dynamic data in the enterprise. This solution is a powerful desktop query tool that enables real-time information access for contact center agents. This tool uncovers conceptual matches to spoken or typed queries to provide agents with the most relevant answers while on the line with a customer. In addition, etalk can listen to the conversation and automatically return results that are related to the customer's inquiries.
The more data an agent has access to, the better prepared that agent will be to respond to customer questions quickly and effectively. By providing agents with real-time access to data across the enterprise, and not just within the contact center, organizations can experience a 30 percent reduction in call times, resulting in increased productivity and enhanced customer service. In turn, any information generated through an agent-customer interaction is automatically added to the enterprise knowledge base. This solution closes the loop between the customer and the enterprise-just as the information flow into the enterprise began with the customer calling the contact center; it ends with information being communicated back to the customer.
Summary
The contact center has traditionally been neglected as a strategic part of the organization because there has previously been no way to extract value from the unstructured information generated inside it. However, this information, including emails, documents, and phone conversations, is a valuable asset that can be analyzed and shared throughout the enterprise by utilizing Meaning Based Computing technology. By extending Meaning Based Computing into the contact center, an organization can tap into critical unstructured information and facilitate bi-directional communication that delivers customer insight to the rest of the enterprise.
If you would like to learn more about how this can be achieved at your business, please visit www.promero.com today.
Source: Autonomy
The Intelligent Contact Center: Extending Customer Insight to the Rest of the Enterprise is published by Autonomy. Editorial supplied by IBM is independent of Gartner analysis. All Gartner research is © 2007 by Gartner, Inc. and/or its Affiliates. All rights reserved. All Gartner materials are used with Gartner's permission and in no way does the use or publication of Gartner research indicate Gartner's endorsement of Autonomy's products and/or strategies. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The reader assumes sole responsibility for the selection of these materials to achieve it intended results. The opinions expressed herein are subject to change without notice.
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