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Feature Article
May 2000

 

What's Next In Monitoring Technology? Data Mining Finds A Calling In Call Centers

BY LINDA DILAURO, DICTAPHONE CORPORATION

With over 70 percent of business transactions taking place over the phone, call centers are important cogs in the customer retention wheel. Consider also that customer interactions are becoming increasingly complex. Today, companies are likely to segment their customers into more categories than there are flavors of Ben and Jerry's ice cream, and route customers to the ideal agent based on virtually any conceivable criteria.

Given the growing complexity of customer interactions, how can a call center get to the root of the problems that can cost it money and customers?

Typical monitoring systems that employ either a random or rules-based approach to monitoring increasingly do not seem to be the answer. Such systems, by design, either miss problems or force the manager to magically guess what the problems are.

Let's explore the evolution of monitoring systems and see how call centers can leverage customer-centric monitoring technology and data mining tools to automatically identify hidden patterns in call center data that can point to opportunities for improving customer service and operational effectiveness.

The Evolution Of Monitoring Technology
In the early days, monitoring took the form of over-the-shoulder agent observation, and then evolved to live monitoring of agents' calls using the service observance feature of the call center's ACD.

While this "service observe" approach was more efficient, it had its own set of drawbacks for call center supervisors, who still had difficulty finding the time just to "listen in" on a handful of calls during the course of a busy day. The supervisor was often forced to stay on the line through extended "dead time," waiting for an agent to begin a call. Then, once the call began, frequent interruptions could force the supervisor to start the process over again.

The introduction of the automated monitoring system -- complete with voice and data screen recording, built-in evaluation forms and reporting packages -- made monitoring more efficient and eliminated many of the drawbacks of live monitoring. Automated monitoring systems recorded agent conversations so supervisors could go back and "QA" calls at their leisure. A CTI link to the call center's switch let supervisors program the system to record a percentage of each agent's complete calls. Second-generation automated systems even allowed call centers to use CTI-driven record commands to intelligently target which calls to sample (e.g., calls associated with a specific DNIS).

In addition to saving time, this technology also helped supervisors be better coaches, because they could now use recorded calls to show agents what worked best, rather than just telling them. It also removed the biases inherent in monitoring and evaluation because multiple supervisors could now listen to and score the same recorded calls, and then compare their results -- a process called calibration.

But this technology had limitations, too. It only recorded individual agent conversations -- not complete customer experiences. If a customer was transferred several times, the first segment of the call might be recorded, but the customer's experience as he passed from one agent to another, then possibly another, would be lost. By monitoring the first conversation, the supervisor might conclude that agent Number 1 did a good job -- but what he couldn't know was that the customer's overall experience was dismal. Absent this information, the call center would be doomed to repeat the mistakes of the past.

Last, because these systems were designed to record a random sample of agent calls for monitoring (and throw away the rest), it was easy to miss many of the problems that could lead to customer dissatisfaction.

Customer-Centric Monitoring Technology
Next in the evolution of monitoring technology was the customer-centric monitoring system. The underlying premise of this technology was to create a customer experience recording and analysis tool.

The customer-centric monitoring system captured the various touch points of the agent/customer interaction -- IVR menu selections, hold times, transfers, multiple conversations and the like -- and then tied all of the pieces together for a complete "cradle-to-grave" customer experience.

This approach viewed recorded agent/customer interactions not as random events, but as a pool of information to be mined. Instead of randomly recording calls, the system recorded all interactions. Then, using the CTI data captured along with the recordings, supervisors could identify the calls that were likely to result in a bad customer experience -- for example, calls with long hold times or durations, or excessive transfers. By mining customer interactions intelligently, call centers could directly address the root causes of customer dissatisfaction. For example, by listening to calls with excessive transfers, a supervisor might learn that an agent was not equipped to handle the call (a training issue) or that the customer didn't reach the right agent in the first place (a call routing issue).

The only limitation of this approach was that it relied on the supervisor to use his or her intuition to determine what types of interactions were likely to be problematic.

It's true that what you don't know can hurt you, but the opposite is also true. What you don't know can help you, but you have to know how to spot it. Surely, there are problems that the call center supervisor would never uncover relying solely on his or her intuition.

How to solve this perplexing problem? The answer can be found in the marriage of customer-centric monitoring and data mining.

Data Mining: What's That?
To understand how data mining techniques can be effectively applied to call center monitoring, let's first understand what data mining is.

In its simplest form, data mining is a way of giving meaning to data, a method of transforming bits and bytes of data into knowledge, which, when acted on, can produce strategic business results. Data mining tools can uncover hidden patterns, trends, relationships and predictive indicators in any type of data. In the call center, there are many potential sources of data that could provide rich insights into customer experiences and operational effectiveness.

While data mining is now emerging as a popular technology, the widespread use of data mining was slow to take off, primarily because the technology had been geared toward analytic researchers and experts, not toward mainstream business.

A recent report by GartnerGroup predicts explosive growth for data mining. "In the next decade, the number of data mining projects will grow dramatically (over 300 percent) to 'improve customer relationships' and help enterprises 'listen to their customers.'"

GartnerGroup classifies data mining tools into six key categories, ranging from the generic, application-independent tools (the best-known class) to application-specific tools. Generic tools offer a wide range of data mining techniques and can address many different types of problems, but they require skilled statisticians to prepare the data, determine what analytical techniques to use and validate the results.

With the introduction of application-specific, user-friendly data mining tools, data mining is no longer just the domain of PhDs, statisticians and computer scientists. Application-specific tools are designed -- as the name implies -- with specific applications in mind. Less expensive and easier to deploy than generic data mining tools, application-specific tools are customized, turnkey solutions that require little expertise on the part of the user.

Practical Applications Of Data Mining And Monitoring
Embedding application-specific data mining tools within the customer-centric monitoring system is an extension of the philosophy that customer interactions, and all of the associated data surrounding them, are not mere random events, but information to be mined.

How can call centers benefit from the marriage of these two technologies? Through a querying interface, a call center supervisor with no statistical expertise can ask "what if?" questions of call center data to identify hidden patterns that can point to operational and customer service problems. Once these patterns are identified, the supervisor can immediately listen to the associated voice recordings to drill down to the source of the problem.

Following are a few examples of applications of this technology in the call center.

Example 1: Mining CTI data for operational effectiveness.
CTI call pattern analysis can help the call center maximize productivity while sustaining high-quality customer care by improving training and call processing practices. For example, analysis of a test site's CTI data revealed a counter-intuitive trend that caused the call center supervisor to reexamine call escalation procedures. The data showed that calls that were transferred three times were actually shorter in total duration than calls that were transferred twice. Listening to the associated recordings pointed out that although the goal of the "first call resolution" had been to quickly move calls through the center, its actual effect had been to prolong call duration, costing the call center a great deal of money and potentially frustrating customers. A new escalation policy or additional training of first-level support representatives was needed to solve the problem.

Example 2: Mining CTI data and agent evaluation scores for a true picture of agent performance.
Historically, call center supervisors have evaluated agents based on theoretical responses associated with an ideal call. However, the path that certain calls take through the call center (e.g., the number of times the caller is transferred) can influence the relative difficulty in handling the call for each successive agent.

By mining the CTI data that describes the path of the call -- hold times, transfers, etc. -- along with the agent evaluation scores for specific calls, the data mining tool can propose an adjustment to an agent's score that would reflect that call's actual difficulty (based on the customer's prior experience in the call).

CTI-driven agent performance evaluation can inject a new level of fairness into the monitoring and evaluation process. It could also be used by call centers for automated calibration analysis -- to see if calls of similar types tend to be scored the same or differently. Last, call center managers might use this information to create new incentive plans to reward agents who consistently take and resolve calls of higher difficulty, since this will now become visible.

Example 3: Mining customer experience evaluation data.
In many companies, 90 percent of the revenue is generated by 10 percent of the customers. The use of customer experience assessment statistics (customer profile data, and data describing the quality of the customer's experience in terms of routing, transfers, hold times and stress levels) provides another layer of data for analysis. By mining these data, call centers can determine if they are providing adequate levels of services to customers based on the same criteria they use to route calls. They can listen to actual recordings of interactions to determine how to do a better job next time.

Future Applications Of Data Mining And Monitoring: As Limitless As Everything You Don't Know
The future applications of these combined data mining and customer-centric monitoring technologies are even more compelling.

Soon, call centers will be able to incorporate "outcome variables" into their data mining analysis. This might include capturing customer rating scores from a back-end IVR application. For the first time, call centers would have a true, real-time assessment of a customer's experience (provided by the customer), linked to an actual "cradle-to-grave" record of that customer's experience, including the voice recording. Call centers would have a tool for understanding what types of customer experiences actually produce satisfaction. Listening to the voice recordings of highly customer-rated interactions (as well as poorly rated ones) would let them confirm or refute their hypotheses on how to adjust their strategies to win and keep customers.

This information could also be mined relative to other outcome or customer segmentation data captured from the call center's various CRM systems. These data could come from external sources such as account databases or screen data field entries. By mining these data with other data sources, it would be possible to analyze what types of customer experiences produced desirable or undesirable business outcomes. For example, was the customer's complaint resolved? Did the call result in a sale or cross-sale? Or did the customer close his account? If so, why?

One of the most powerful planned uses of embedded data mining technology will be to intelligently push information to upper-level management within the call center for immediate review and response. Imagine being informed by your monitoring system when key customers are put at risk due to poor call center experiences. With this knowledge comes a chance to repair the damage and retain key customers, a critical capability given that it costs far less to retain a customer than to acquire a new one.

So, what's the last frontier for this new combined technology? How can it help call centers bridge the customer experience information gap and improve operational effectiveness and customer satisfaction?

The answer lies at the very foundation of data mining itself. It's what we cannot guess and what we do not know.

Linda DiLauro is a senior marketing manager for the CRS division of Dictaphone Corporation, headquartered in Stratford, Connecticut.







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