Contact Centers and the Age of Analytics

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For years, enterprises have been using analytics applications in many functional areas, including finance, marketing, and sales. Analytics applications use online analytical processing to facilitate multidimensional analysis that, combined with other capabilities, allows users to rapidly identify issues and opportunities. Analytics is not just “reporting on steroids” or the ability to deliver pretty reports. Reporting simply delivers data from one or many systems. Analytics solutions find patterns in the data and make this information readily available.

Contact Center Analytics are Coming on Strong
Contact centers have entered the age of analytics. Applications such as performance management and speech analytics use analytical functionality to rapidly identify customer, contact center, agent, and enterprise issues, trends, insights, and much more. These solutions go far beyond delivering data about application performance, such as the number of transactions captured or calls evaluated. Instead, they analyze the data, find trends, and deliver actionable recommendations to improve the performance of the operating area, in addition to highlighting many other tactical and strategic issues.

While most of the contact center analytics solutions available today address data that is at least a day old, there are also real-time solutions that make recommendations while customers are still on the phone. The concept behind real-time analytics is to give either the agent or supervisor the information necessary to positively influence the outcome of a call while the customer is still on the line. Examples include a predictive analytics solution that tells the agent exactly which offer to make to a customer; a real-time speech analytics application that advises the agent about the emotional profile of a caller so that the agent can select an approach designed to effect a better outcome; or a real-time performance management system that streams data to an agent to inform her how well she’s doing her job. A few real-time analytics solutions are available for contact centers and are in the early stages of adoption, but, in general, this is an area that needs work. (Among other issues, processing information in real time requires a great deal of peak-period capacity in the central processing unit.) DMG Consulting expects to see significant innovation in the area of real-time analytics solutions in the next three years.

Contact Center Analytics Overview
Contact center analytics is a group of solutions that provides managers with tactical and strategic actionable insights and recommendations. These analytics tools capture, structure, and analyze data to find patterns, and provide guidance or recommend actions to address issues, challenges, and/or opportunities. There are two primary categories of contact center analytics: internal analytics that are targeted at the performance of the contact center and its agents, and externally oriented applications that focus on customers.

Internally oriented analytics applications include:

• Quality scoring/assurance—measures how well agents adhere to internal policies and procedures.
• IVR analytics—captures and assesses the performance of IVR applications to determine how well they are working and what options need to be enhanced.
• Performance management—improves the performance of the contact center by aligning its departmental goals with those of the enterprise. These applications also produce dashboards and scorecards that can measure the performance of every individual, group, and site in the contact center.
• Desktop analytics—a new application that measures and provides transparency into how well agents interact with their desktop servicing applications, and assesses the overall performance of these supporting systems.

Externally oriented analytics applications include:

• Speech analytics—takes recorded phone conversations, structures the unstructured content, and systematically identifies the root cause of customer issues, needs, and wants, providing insight into the actions enterprises should take in response.
• Predictive analytics—uses predictive algorithms to identify in real time the most appropriate way to service, sell, and/or retain customers. These solutions eliminate the guesswork in identifying the best way to serve the needs of each customer while the interaction is in progress.
• Real-time analytics—applications that structure the unstructured data in captured customer emails, faxes, feedback forms, chat sessions, or other text-based transactions to identify customer needs, wants, and insights.
• Web analytics—captures, assesses, and measures how effectively customers interact with the organization’s Web self-service environment. These solutions identify the functionality that is performing well, in addition to detecting areas where there are opportunities for improvement, making the Web site easier for customers to use.
• Customer feedback—involves surveying applications that capture and measure customer satisfaction with a company’s products and services.
• Customer value analytics—measures and communicates to agents the value of each and every caller. These applications have been used by marketing for years and have now entered the contact center, where they are being used to prioritize call routing and assist agents in deciding how to handle each customer contact.
• Customer experience analytics—measures the customer experience during interactions with self-service applications, live agents, and all follow-up/fulfillment activities.

Leading vendors have already started to enhance some of the traditional contact center applications, such as quality assurance (QA), with analytical capabilities. QA is a highly valuable activity even without analytics, as it identifies customer trends and measures how well agents are adhering to internal policies and procedures. By integrating analytics into the QA process, these applications can deliver additional benefits to organizations. Embedding speech and real-time analytics into the QA workflow transforms QA from a reactive solution into a proactive one that acts as an early warning system for the contact center and enterprise. It also extends the value and benefits of QA by capturing information in a timely fashion about customers at risk of defecting and new revenue opportunities.

Another example of embedded analytics is the integration of performance management into traditional workforce management applications, as a number of vendors are doing, in response to end-user requests to improve automation and make traditional solutions more actionable.

Final Thoughts
DMG Consulting expects the next 18 months to be very challenging for many technology companies, including contact center vendors. During difficult economic times, most enterprises freeze all but the most essential technology investments; any investment that can be postponed generally is. The good news is that a number of these analytical applications can be considered essential, if presented properly to senior management.

The best way to justify an investment is to do a return on investment analysis and show the payback, net present value, and internal rate of return. If a technology can quickly make a substantial contribution to either revenue generation or cost reduction, it will be considered essential even in tough economic times. After all, assuming the organization has cash to spend, it’s hard to resist an investment that can earn back double its cost in a year. Many of the analytical applications have the potential to quickly reduce operating expenses and/or increase revenue generation while improving the customer experience and contributing to other important contact center goals.

Donna Fluss (donna.fluss@dmgconsult.com) is founder and president of DMG Consulting LLC, a leading provider of contact center and analytics research, market analysis, and consulting.

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