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When considering information acquisition, information quality and data integration, it is hard to imagine a more interesting battleground than the call center. Let’s focus specifically on the inbound call center, ignoring our outbound callers for the time being. In the past, transactions took place in a physical location and were brokered by a real person. This tradition still continues, although the explosive growth in online electronic commerce has started to transition collective purchasing patterns from brick and mortar locations to web-based activities. Consequently, in many of these e-commerce transactions, the only time a customer speaks to a corporate representative is via the call center.
What about utility and service providers? When it comes to initiating service, the first step is executed at the call center, especially when it comes to recurring utilities (such as your home’s electricity, heating, telephone service, cable TV, etc.). So in many forward facing situations, the call center truly is the business’s front line with the customer. More importantly, customer issues and complaints are directly routed to the staff at the call center as well. Unfortunately, customer experiences with the call center are often negative; the customer is already in a bad mood to begin with if they are calling to complain, and hold times, interactive voice response (IVR) systems with their unending menus, call transfers and disconnections only serve to increase the level of frustration.
This positions the call center as a critical point of customer interaction, requiring high quality information to support the resolution of customer issues. The call center also serves as an important conduit for the collection of customer data. On the other hand, it seems that many businesses discount the value of their call center, hiring low-paid temporary workers or even outsourcing it to uncontrolled offshore environments. Does the combination of customer frustration and limited staff training establish the call center as a point of information failure? In many cases, the answer is probably a resounding “yes.”
A high stress position, combined with the need to extract relevant information from customer conversations and requirements for maintaining short call times, while simultaneously maintaining high call volume, all contribute to potentials for introducing flaws into the data stream. Customer names, locations, account numbers, etc. are likely casualties of the information capture process.
Yet, if the customer service representative (CSR) is the only human interface in the entire customer interaction, the call center becomes a key articulation point for the business. Many customer business decisions are made at that point, such as product purchases, elongation of service agreements and reduction in service commitment and attrition. Providing high quality data to the call center seems to be pretty important also.
This brings me to this month’s question: Should there be a call center information governance strategy? Call centers are the subject of performance management, so the productivity data is continuously being fed into a data warehouse for oversight of what are accepted as key performance indicators (more on this in a moment). The CSR needs to accurately capture customer identifying information, while that data must be used to uniquely identify the customer and present a profile back to the CSR’s desktop. In turn, all interaction information must also be supplied. These two facts suggest the incorporation of a master data management (MDM) program to support call center activity. Customers become a key master data object, as do customer actions and profiles. And since the quality of that data is critical, there should be oversight (along with incentives for good behavior and repercussions for nonconformant behavior) to ensure high quality data.
The driving factor here is that more and more business is going to happen over the phone, especially in the process of problem resolution. In addition, telephone interactions become key business decision points, so the importance of maintaining high customer satisfaction is significantly increased. But are these kinds of statistics and performance criteria being measured for call center productivity? Probably not; in many cases, the only metrics are based on technical issues, such as hold time, wait time and call time – metrics that are easily measured. But these metrics don’t necessarily correspond to customer satisfaction. In fact, I recently read about a situation in which CSRs felt obliged to hang up on customers in order to keep call times within the desired range and to track with call volume numbers. Perhaps this is an extreme case, but probably not too far from the mark.
Instead, organizations should reconsider their business objectives in relation to the limited live customer interaction. For example, let’s start at the beginning. Instead of measuring call duration, look at the number of repeat calls per customer. If the customer’s issue is not resolved the first time he calls, that caller will try again until the issue is properly addressed. Are CSRs taking the opportunity to upsell and cross-sell products in real time? This, in turn, suggests that problem resolution is a key business imperative, as are increased sales and increased customer satisfaction.
Here are two ideas. The first has to do with resolution, and the speed and efficiency with which customer issues can be addressed. To minimize customer frustration, the CSR must be armed with both the ability to access all the data relevant to addressing an issue as well as being empowered to make the right decision at the right time. Information access and delivery can be facilitated via a master data management program to consolidate customer data and make both demographic and profile data available as a shared resource. Qualifying and training CSR staff and providing incentives for resolving issues during a single call may initially result in longer calls, it will ultimately decrease the number of calls, since when the issue is resolved, there is no reason for the customer to call back.
The second idea has to do with embedding predictive analytics within the call management process. By incorporating statistical analyses to help in providing more automated guidance to CSRs in a way that empowers them to provide better service, on the average, they will be able to exploit sales opportunities more frequently as well as reduce customer attrition.
As an endnote, there is one byproduct of these improvements, which is that the customer service representative, through providing better service, may gain some degree of respect in the eyes of the customer. In turn, this will improve the experience for both the customer and the CSR staff, whose incentives to provide better service will be supported through embedded technology. By allowing them to do a good job, it improves their morale and encourages better interactions, a feedback loop that is a win/win/win for customers, staff and the organization as a whole.
David is the President of Knowledge Integrity, Inc., a consulting and development company focusing on customized information management solutions including information quality solutions consulting, information quality training and business rules solutions. Loshin is the author of Master Data Management, Enterprise Knowledge Management – The Data Quality Approach and Business Intelligence – The Savvy Manager's Guide and is a frequent speaker on maximizing the value of information. David can be reached at firstname.lastname@example.org or at (301) 754-6350.
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