IP contact center technology: eliminating the risks (Part IV).
One of the most "future-proofing" attributes to look for in today's solutions is "adaptive" IP contact center technology--technology that empowers real-time optimization of even the most granular technology-driven business processes in real time, at no cost. Adaptive technology is integrated by design to incorporate all of the IP contact center technologies that typically power a Web-enabled call center, including phone, fax and Internet technologies, as well as technologies related to quality assurance. The difference lies in the "integration-by-design" approach; an approach that enables technology-driven business processes and their associated technologies to be provisioned and modified in real time, without sacrifice.
Adaptive solutions are driven by the same needs-analysis questions that integrators rely on to define "scope of work" for traditional point solution deployments; but here the entire range of standard outcomes is pre-programmed, and changes are implemented in real time from Web menus. (If an outcome hasn't been pre-programmed because it represents a new and unique idea, Web services are also available to empower the addition of new potential outcomes.)
What's The Value In This Approach?
Historically, contact centers cobbled together as many as 24 different point solutions to create their multichannel customer service communications infrastructures. The result was expensive to buy, expensive to maintain and notoriously rigid; it required significant time and money to implement changes. As a result, companies that have deployed technologies in this paradigm typically shy away from implementing expensive changes to their software investments and, instead, often opt to postpone resolving many problems until the life cycle of their current investments are fully depreciated.
By contrast, companies that have deployed "adaptive" solutions can fix problems in real time and, therefore, run in a more optimized state more often, enabling them to enjoy competitive advantages in both productivity and customer satisfaction. Academic research has validated productivity gains of more than 30 percent for companies that deploy adaptive technology, often increasing to many times that number depending on the size of the company (larger companies tend to have a history of fewer poor "gut infrastructure decisions") and how much time has passed since the company's last upgrade. Of course, it should be intuitively obvious that the ability to fix infrastructure problems in real time not only constantly renews the lifecycle of legacy investments, but also inevitably yields significant productivity benefits.
Perhaps just as important is the effect on customer satisfaction. Studies validate that businesses which deploy multichannel solutions--those that can adapt to changing customer needs in real time--enjoy higher customer satisfaction rates. (No surprise here.) What isn't as obvious is this fact: those companies that have the greatest customer loyalty rates also enjoy much higher growth rates compared with the average growth rates for their industries. This means that customer satisfaction is directly related to business growth. This is what Harvard University Business School calls the "Loyalty Effect." That's why you care.
A Look Into The Future
While adaptive technology leverages the business intelligence of contact center managers by empowering them to implement granular infrastructure changes in real time across all communications channels, it nevertheless relies on the intelligence of the individual to get there. In the future, the marriage of analytics and adaptive IP contact center technologies will result in the emergence of a new paradigm for contact center efficiency: the self-optimizing contact center.
What Is Analytics?
One can hardly work in contact center management today without hearing about the compelling benefits of analytics. Analytics technology provides contact center managers with the insight necessary for them to proactively optimize contact center performance, reduce operational costs and improve customer satisfaction. Of course, while proactive insight obviously has tremendous value, a great many operational bottlenecks might not be foreseeable by human or machine--and these unpredictable bottlenecks will inevitably take their toll on revenue production if they cannot be addressed in real time.
In the past, conventional wisdom held that analytics could only provide proactive solutions for business process optimization because contact center communications technologies simply couldn't be adapted in real-time to offer the possibility of effective reactive solutions. In the era of "point solutions aggregated via systems integration," that was most certainly the case. The argument about real time change today has been definitively settled, with world-class companies and service providers leveraging the benefits of adaptive technology every day to optimize their operations. There is no longer any reason to assume that analytics engines will continue to be limited to the role of advisor, relying on human beings to implement their recommendations over an extended period of time across diverse systems.
In the not-so-distant future, analytics will not only analyze historical data and suggest proactive changes to such things as call logic, call treatment and the content of future customer communications--analytics will actually implement those changes itself, in real time, via linkages to adaptive IP contact center platforms. This vision of the "self-optimizing" contact center, which we like to call the "Analytics Auto-Pilot," promises to change the metrics by which we assess technology investments.
A Compelling Illustration
If you were to call a company's call center and its system offered up an estimated wait time of 30 seconds, you would very likely wait on the line for an agent. However, if estimated wait time was presented as being 30 minutes, depending on the nature of your call, you might very well hang up and make your next call to that company's competitor.
One of the first things that a real-time analytics engine would have to identify is whether you, the caller, are likely to hold or hang up based on historical patterns for your particular queue and/or your personal patterns. It could then balance the demands of your queue and your call against the demands of other queues and callers, taking into account their relative priorities. For those customers likely to hang up, the analytics engine could instruct the adaptive platform to avoid telling the caller the estimated wait time, while at the same time addressing the bottleneck in one of a variety of ways to dramatically reduce callers' estimated wait time.
One course of action might be to dynamically adjust the overflow rules to route you to an agent in a different group; that is, assuming you belong to the class of customers deemed worthy of that kind of escalation.
Another approach might be to let you enter your telephone number, hang up and receive an automated callback when an agent is available, without losing your place in queue. The analytics auto-pilot might also dynamically adjust the contact center's customer priority routing rules to reprioritize your relative importance in the context of the kind of transaction you are waiting to process. It might go even further and take into account the relative value of your prospective transaction as compared with the transactions of others who are also in queue--to dynamically reassign the assigned customer priorities of those in queue.
The analytics engine might also consider dynamically changing the skills-based routing algorithms governing your queue in order to speed up average handling times. For example, if the analytics engine identified that an overall increase in average transaction processing times was connected to increased traffic in a particular language, such as Spanish, the correct remedial action could be to dynamically adjust the skills-based routing algorithm to favor native Spanish speakers (who would presumably be more proficient in Spanish).
Of course, without analytics discipline, that might be the correct answer to the wrong problem. What if the inefficiency is actually being driven by a particular product defect specific to Spanish-speaking customers (for example, poor assembly documentation in Spanish)? Unlike a human "guess," the analytics engine would identify the root cause by leveraging its access to both CRM and communications infrastructure and make a different decision; perhaps adjusting the skills-based routing algorithm to begin favoring agents who have dealt with the identified issue before--perhaps in combination with playing a recorded message to those in queue, describing the resolution of the problem.
There are literally dozens of different reactive actions that an analytics engine might take depending on the context underlying the bottleneck, each potentially highly effective or ineffective depending on the root cause of the problem. In empowering analytics to react directly to out-of-norm states in a context-sensitive manner, the marriage of adaptive IP contact center technology with analytics clearly looks like a winner.
Eli Borodow is the CEO of Telephony@Work, a leading provider of adaptive, multitenant IP contact center technology for contact centers and service providers.
Mike Betzer is vice president at Siebel Systems, a leading provider of customer-facing solutions that deliver demonstrable business results and long-term competitive advantage. Siebel Systems leverages Telephony@Work technology for its Contact OnDemand multichannel hosted solution.
Kevin Hayden is the director of Integrated Contact Center Solutions at TELUS Communications Inc., a tier-1 telecommunications carrier in Canada and a Canadian leader in hosted contact center services.
A Special Editorial Series Sponsored by Telephony@Work
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|Title Annotation:||INNOVATIVE IDEAS FROM THE NEXT-GEN CONTACT CENTER EXPERTS|
|Publication:||Customer Interaction Solutions|
|Date:||May 1, 2005|
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