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Workforce Optimization Ushers in the Real-Time Contact Center

By Donna Fluss

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It’s hard to overstate how much the contact center technology sector has progressed in the past 15 years. The call center liability recording/quality assurance market has evolved into the contact center workforce optimization (WFO) market, encompassing 13 application sectors: omnichannel recording; quality management; workforce management; coaching; e-learning; surveying/voice of the customer (VoC); performance management; speech, text, and desktop (and, increasingly, predictive) analytics; gamification; customer journey analytics; and the newest IT sector to become part of WFO suites, robotic process automation.

Thirteen years ago, DMG Consulting published the book The Real-Time Contact Center. Its theme was (and still is) simple—to use the real-time capabilities of contact centers to assist people (customers, prospects, partners, the public, etc.) who interact with them. Only now, more than a decade later, have leading organizations begun to adopt many of the concepts in the book, but the real-time transition is finally under way.

The service economy has undergone many changes, all of which benefit from real-time capabilities. Social media depends heavily on real-time responses; omnichannel service requires companies to respond to a variety of media, such as chat, SMS, and video, in real time; and globalization has opened the door to worldwide resources and requires immediate responses for customers worldwide. These innovations have come hand in hand with megatrends that include Big Data, analytics, mobility, increased server processing speeds (and decreased costs), the market influence of Millennials (the “smart device” generation), the gig economy, and, of course, the cloud.

Over the past 15 years, total WFO revenue has gone from $803.4 million in 2003 to approximately $1.7 billion for the first half of 2018. But market growth is just a part of the story. We have progressed from interactive voice response (IVR) systems to intelligent virtual assistants (IVAs), from process optimization to process automation, and from technology as an enabler to technology as a partner. We’re at a major and exciting inflection point. The pace of change is startling, and the innovation (and fun) is just beginning.


Artificial intelligence (AI) is seeing rapid adoption across industries. In the context of autonomous cars, IBM Watson, algorithmic trading, software-defined networks, self-healing applications, healthcare diagnostics, and more, AI is playing an increasingly influential role in today’s world. It’s not just about productivity improvements, but game-changing innovation that is opening up doors to new possibilities.

Contact centers are typically conservative and slow to change, and DMG expects this to continue. However, AI-enabled contact centers are the next phase for service organizations. AI, machine learning, IVAs, robotic process automation (RPA), desktop process automation (DPA), knowledge management, and more will be instrumental in helping companies improve the service experience. As contact centers are people-intensive organizations where agents account for approximately 65 percent to 75 percent of departmental costs, it makes sense that businesses are looking for ways to reduce their dependence on live agents and help them be more productive.


The RPA and DPA industry is in its infancy, although the concepts have been around for decades and were previously referred to as workflow automation or business process optimization. What didn’t exist until recently were out-of-the-box applications and frameworks that can be inserted into an attended or unattended process and quickly implemented to automate some or all steps of the workflow.

Automation initiatives are priorities for executives in most industries. RPA and DPA excel in automating routine, repetitive tasks. If a company can save 10 seconds to a few minutes by automating an activity that is performed hundreds or even thousands of times daily by the same employee or hundreds of employees, it can cut many thousands of dollars per day from its budget. Today bots can handle a growing number of common and unique front-office, back-office, and shared business activity services.


Enterprises increasingly recognize the need to listen to customers and prospects across voice and digital channels to obtain a comprehensive view of their experiences. Speech and text analytics, also known collectively as interaction analytics, provide a firsthand, unfiltered view of what transpires between customers and an organization. Interaction analytics collects data from free-form, open-ended dialogues; it can also mine customer data from the web and social media to extract information about customer sentiment. Enterprises are leveraging interaction analytics to replace outdated methods (surveying, focus groups, etc.) of capturing feedback from customers.

Interaction analytics has become an increasingly important source of data for customer journey mapping because it provides a multidimensional view of the customer experience. It reveals which touchpoints customers used to interact with an organization, what route they took, how long each part of their journey lasted, and where the journey ended. Interaction analytics provides the soundtrack for understanding customers’ perception of what happened as they traversed channels by capturing sentiment and emotion and, by extrapolation, the amount of effort expended. As an input to customer journey analytics (CJA) solutions, interaction analytics enables companies to listen to their customers and take a data-driven approach to identifying the appropriate course of action. CJA solutions paint a holistic picture of each customer’s interaction with an organization, from the first touch through the last, allowing enterprises to evaluate a substantial portion of the customer journey.

Another emerging strategy for managing a personalized customer experience is the use of predictive analytics. Using data mining, statistical techniques, and machine learning to identify relationships, patterns, and trends, a predictive model can be built to anticipate future events or behaviors, as well as their potential business impacts. Speech and text analytics are being enhanced with predictive analytics capabilities to enrich and personalize each customer interaction. Predictive analytics can be used to understand what customers need and want, and then kick off real-time agent guidance, next-best-action recommendations, or optimal marketing/sales offers. Predictive analytics can also be used internally to identify and understand the drivers of agent churn and to recommend intervention.


Contact centers are not going away in the foreseeable future, but DMG expects them to change. Here are a few of our predictions about contact centers and the likelihood of each:

  1. RPA/bots will automate an increasing amount of work currently done by agents in contact centers within the next five years (1.0 probability).
  2. Machine learning will be incorporated into many contact center applications to improve their performance and reduce dependence on IT resources within the next five years (0.9).
  3. Speech analytics will replace the traditional QA process in the next eight years (0.7).
  4. CJA solutions that capture, analyze, and identify opportunities for improvements will emerge in the next six years (0.65).
  5. In the next six years, AI will drive omnichannel routing to ensure all interactions get to the right people in the organization for resolution, while also taking into account the cost of handling each transaction (0.35).
  6. Contact centers and back offices will merge in the next 10 years (0.3).
  7. Self-service solutions will eliminate the need for live agents in the next 10 years (0.1).

The overarching themes shared by these seven trends are productivity improvement, enhanced quality, and reduced costs, driven by AI-related functionality. What’s new today is the use of technology to work smarter, not harder, instead of just motivating agents and supervisors to perform their jobs faster and do more with less.

For contact centers, AI and automation will reduce the need for low-value agents. AI-enabled bots and IVAs will deliver information and provide answers more quickly and accurately than poorly trained agents, and they’ll do so more cost-effectively; this will profoundly change market dynamics. As the quality and effectiveness of self-service solutions improve, the number of contact center seats will decline. This will reduce dependence on outsourcers, particularly low-end offshore providers.

Companies will need to rethink and redefine the role of “agent,” as they will need fewer of these resources. The remaining agents will handle sensitive and complex service and sales situations. Transaction handling will be personalized, matched to the ideal resource and intelligently routed to highly sophisticated agents who have a new generation of servicing solutions at their fingertips to achieve optimal outcomes for the customer and the organization. It’s been a great run for the WFO sector, and the best is yet to come.