Measuring Contact Center Effectiveness

From Reporting to Analytics

Contact centers have always been challenged to balance efficiency and effectiveness. Traditional contact center data supports the drive for efficiency by measuring average handle time, labor cost, staffing, cost per contact, occupancy and the like. Effectiveness presents more of a challenge. It demands a careful analysis of data drawn from enterprise applications (e.g., CRM, CIS, ERP and other core data systems) as well as the contact center’s ACD, WFM and QM systems. It’s a tall order!

Some vendors respond to the challenge with sophisticated tools that analyze data from multiple sources to reveal trends, anomalies and cause-and-effect relationships. Others claim “Me, too” status while offering little more than consolidated reports. Somewhere between industry confusion and the pressure to move forward with effectiveness metrics, a clear and coherent strategy that covers all reporting and analytics requirements gets lost.

Metrics processes and tools—and the associated reporting and analytics—must evolve to account for the center’s exigency to be efficient and effective. To that end, we’ll review the foundational elements that prepare the center to meet the challenge. We’ll also discuss the tools that can provide a more complete picture of the customer experience and some criteria for evaluating options.

Strategy First

Success with reporting and analytics, like many contact center technologies, starts with strategy. A good strategy achieves three objectives: (1) It gets the right data to the right person at the right time; (2) it leverages the right tools; and (3) it presents the information in a way that is actionable for the role (see Figure 1).

Our four-step approach to developing and implementing a reporting and analytics strategy starts with an understanding of the current state, including metrics, reports and how they are used, and the data sources for them. We then craft a vision that defines the business goals for the customer experience, and the accountabilities, processes and resources to achieve that vision. This step sets the stage for the technology needs and helps differentiate the use of reports, scorecards/dashboards, and analytics (see Figure 2). After defining the vision, we assess the gaps and develop a plan to close those gaps—whether using existing tools in different ways, or adding tools. We also consider the resources required to use the tools effectively. The final step is the implementation plan, which is likely phased. We typically address current reporting issues first to lay the foundation for adding new analytics capabilities.

Basic CC Reporting—the Foundation of Analytics

While it’s easy to get caught up in the novelty of customer experience analytics, centers still need to excel at real time and historical ACD reporting to manage operations efficiently and attend to workforce management (WFM).

The real time Supervisor interface should be leveraged to establish performance thresholds, monitor performance against targets, and provide notification when action needs to be taken, highlighting ACD metrics such as queue status, service level and agent work state. Ideally, the WFM adherence metrics integrate into the Supervisor interface for a consolidated view. Once thresholds are established, an individual or team must be assigned responsibility for the “command center” to monitor and react to issues as they arise.

To get the most out of the historical reports, you need to understand the canned reports available by category from each relevant system (ACD, CRM, WFM, as well as QM, IVR or others). You also need to know how to build/configure custom reports and schedule their delivery. When they arrive, use them! Look for issues, such as not meeting targets, as well as trends and anomalies. Then you must be prepared to do something with this information. If a report doesn’t provide actionable insights, it doesn’t need to be prepared and distributed.

Finally, make sure leadership and support staff understand what you measure, why you measure it, and which actions are expected in response to the information. They need to be well-versed in the Supervisor interface and reporting tools available in your current technology. And they need to be empowered and encouraged to share their views on how to further optimize reporting and the use of data to drive actions and optimization.

Contact Center Analytics

Most contact centers know they need a more analytical approach to understanding the customer experience. In our most recent WFO survey (see the Special Report on the full survey results in this issue), a high percentage of centers indicated that Analytics falls within the “Have but do not use effectively” category. Through the survey responses, our client work and industry forums, we also see many centers currently pursuing analytics while others place it on their “wish list.”

Admittedly, it can be confusing to sort out what “analytics” really means. Some vendors call their standard reporting package “analytics” to make it seem as though they’ve gotten on the bandwagon. Others associate that word with consolidated reporting or an updated interface with the ability to build graphical layouts with widgets. Some analytics solutions deal with a single source (e.g., call recordings in speech analytics), while others tap multiple sources of data (e.g., CC data from the ACD, WFM and QM, and enterprise sources such as CRM) and offer a scorecard/dashboard tool.

Here’s the straight scoop. Analytics tools should deliver visibility into the data with configurable dashboards and other visualization tools. They should offer the ability to configure graphics that reveal relationships between data elements (e.g., through drill down or through identifiers) as well as cause and effect. They should enable users to get a larger picture of where and how the contact center adds value through multiple touchpoints with its customers. And a graphics capability should enable trending and looking at the data from a historical perspective with indicators for up or down.

There are several types of analytics applications used in the contact center. All of them play a valuable role in serving the customer:

• Speech analytics uses either speech to text transcriptions or word, phrase, or phoneme searches of call recordings to provide a deeper understanding of the customer experience.
• Text analytics provides the same insights as speech analytics but parses the text-based media (e.g., email, chat, text/SMS, social) to reveal customer insights.
• Desktop analytics captures events on the agent desktop, looking for patterns, most frequently used applications, etc. Desktop analytics reveals insights such as compliance or efficiency of the desktop contact-handling processes and after-call work.
• Self-service analytics evaluates IVR, mobile and web-based application usage with an eye toward reducing customer effort and increasing success rate. It also reveals opportunities to expand self-service, and thereby reduce the load on the CC agents.
• Cross-channel analytics tracks customer behavior in all channels to track their journeys, identify patterns or changes to patterns, and optimize channel use. For instance, when a customer who was on the web calls for assisted service, the agent is prompted to show the customer how to complete the current request on the website next time.

F or the most comprehensive view of the customer experience, data analytics or business intelligence (BI) pulls data from all the other sources together into one visualization application (see Figure 3). It is often used for decision-making and planning at the enterprise level, but can be used to support contact center data analysis and outcomes. The success of this technology depends on the degree to which it focuses on areas relevant to the CC. A lack of focus—both in data and resources—can compromise its fit and value to a center. Predictive analytics looks at a wealth of historical trends to anticipate customer behavior going forward. It draws from data within the organization (e.g., web interactions, transactions, etc.) as well as external sources (e.g., the Internet of things, social media, weather patterns and future events). It uses this data to customize future web, mobile, IVR and contact center interactions. For example, customized hints or suggestions may be offered to a customer as she moves through a retail website. Customized greetings and prompts in the IVR may reflect the customer’s history, or anticipate a need based on current events or other customers’ behavior. Dynamic routing, screen pop and scripting may match the customer with an agent with specialized expertise or experience and equip that agent to respond to issues more effectively.

Analytics Solutions

When evaluating contact center analytics solutions, look for offerings that offer an easily understood user interface and support configurable dashboards and scorecards to present data and reveal trends. Agent dashboards and scorecards should capture the metrics for which they are held accountable as well as their current performance compared to team average and individual goal. These metrics should also roll up to supervisor, manager, other leadership or support roles. Here’s an overview of the main types of solutions on the market.

• Traditional contact center technology vendors offer analytics components as part of their WFO suites (e.g., Aspect, Genesys) that pull data from ACD, WFM and QM, and can import data from other data sources.
• Stand-alone WFO suites (e.g. Calabrio, Verint and NICE) offer a variety of analytics components and functionality.
• Stand-alone analytics vendors (e.g., Zoom International, CallMiner, Numerify, etc.) zero in on specific areas, such as sales closures, speech analytics or IT system performance.

Most CRM applications deliver an ability to pull data from other sources for combined reporting but may lack the dashboard capabilities of an analytics component from a WFO suite. Some have reacted to that gap with an added component (e.g., Salesforce’s Wave Analytics).

A crucial element of vendor selection is the future direction of the product offering. For example, automated interactions using virtual assistants need to be informed by predictive analytics so they can be responsive to what the customer wants and needs. Likewise, integrated, real-time speech analytics could detect when a customer is on the verge of a specific behavior (e.g., getting angry) and prompt the agent to take the right action. “Big data” may identify potential impacts on the contact center that can be addressed with associated business rules. For instance, an increase in social media complaints could trigger the contact center to expect more calls or tweets, and adjust resources accordingly. It may also activate an emergency message as part of the phone greeting to let callers know that the center is on top of the issue (e.g., a service outage).

Next-generation analytics combines artificial intelligence, the capability of a machine to imitate intelligent human behavior, and machine learning, the ability for computers to learn without specific programming. These capabilities enable the system to analyze data, envisage customer behavior and initiate an “intent-driven engagement” where the contact center adjusts service based on predicted sentiment (e.g., frustrated or happy) and transactional need (why are they calling). They can also reduce the agents’ burden and enable customers to successfully self-serve for an ever-increasing spectrum of tasks.

In sum, whatever tool you are considering, make sure the vendor’s offering:

• Delivers the ability to pull data from multiple sources.
• Has canned reports that emphasize the contact center’s role in the organization.
• Has a user-friendly, flexible interface to create reports and build graphical dashboards (combination of widgets).
• Truly offers analytics capabilities like trending, drill-down and relationships.
• Includes some next-generation emphasis in their roadmap.

See Your Center in a New Light

Analytics can help centers be more effective. Take the first step to improving your center’s performance by solidifying your reporting. Then implement the appropriate analytics tools to take a more detailed look at the customer experience and leverage rich data visualization to aid in exploration. With committed, skilled resources routinely executing analytics processes, you will begin to see new things, or old things in different ways. That investment can lead to actions that truly transform the customer experience and your center’s efficiency and effectiveness.