The Straight Scoop on Contact Center Analytics


Contact Center Analytics in Action: Using VoC to Create a Better Experience
Illustration by Matt Brooks

Most contact centers know they need a more analytical approach to understanding the customer experience. In our 2017 Workforce Optimization Survey, a high percentage of centers indicated that analytics falls within the “Have but do not use effectively” category. We also see many centers evaluating analytics offerings while others place them on their wish lists.

It can be confusing to sort out what “analytics” really means. Some vendors call their standard reporting package “analytics.” Others associate that term with consolidated reporting or an updated interface with the ability to build graphical layouts with widgets. Some 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 contact center 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.

For 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 (click on the figure below). 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 contact center. A lack of focus—both in data and resources—can compromise its fit and value to a center.
Contact Center Analytics Architecture
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.

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.