A customer call center can be a big generator of customer satisfaction and loyalty, but this requires access to analytical intelligence by call center agents and shrewd marketing forethought. The fusion of customer marketing with customer care—backed by IT's contributions using data warehousing—presents a new opportunity for call center managers and data warehouse owners to work together to contribute to the top and bottom lines. Many opportunities exist to make the call center a part of your company's move to active enterprise intelligence, as opposed to a silo of independent activity.

Making it happen
One approach to facilitate communication among the IT and business groups is to focus on what is important, what is measured and what is valued by the call center organization. IT employees must understand that call centers—like all business groups—are under increasing pressure to perform more efficiently and effectively. For many call centers, efficiency focuses on measuring criteria such as call attributes and agent productivity.

enlarge The key performance indicators (KPIs) focus on both customer and marketing metrics, tying them together in a way that controls the number of offers to only those that are relevant. It also reduces costs for delivering offers and grows a share of wallet and customer satisfaction.

At the most basic level, call center scoreboards measure information about calls, such as:

	Percentage of calls by inquiry type
	Percentage of calls by initial status (if calls were answered, on hold, not answered or dropped)
	Average call duration and average hold time
	Calls by origin, hour and origination time period
	Number of transfers

Most customer call centers also measure information about agents:

	Average duration per call, by agent
	Agent utilization over time
	Pick-up time
	Cost per call

While both sets of metrics are good examples of a corporate focus on operational excellence and efficiency, they do not necessarily deal with what is most important. In fact, optimizing these operational call metrics may decrease customer satisfaction, because they encourage quick but possibly incomplete resolutions to customer calls.

A better approach is to measure the impact of the call center on customers. This is where a data warehouse can help. Moving to a customer-focused approach requires three important tasks, often performed jointly among the call center owners, marketing and IT:

1.	Constructing multi-channel customer-centric metrics
2.	Designing multi-channel customer-centric experiences or dialogues
3.	Testing both the metrics and the experiences, and learning from the results

Focus on customer-centric metrics
Customer-centric metrics include statistics that measure, from a consumer perspective, whether the call center:

	Was a good use of his or her time
	Solved the problem or answered the question
	Made the customer want to interact with this company again

These metrics focus on the fact that customers evaluate your company overall, not just the interaction with the call center. In fact, a 2005 survey of call center users by the Yankee Group found:

"To comprehend customers, one must understand how they interact across all channels. Analysis techniques are readily available to derive insight and draw understanding and actionable conclusions about products, services and customers' aggregated experience.

"Therefore, the next milestone is to optimize this rich customer interaction information and move from tracking information within channels to sharing and integrating it across channels."

It is important to develop holistic customer metrics that span channels and continuously collect and analyze overall customer opinions. With all information from all channels in a single place, this becomes easier to do.

Figure 1 (see above) highlights holistic metrics within a good metrics game plan. The key performance indicators (KPIs) in the figure focus on both customer and marketing metrics, tying them together in a way that controls the number of offers to only those that are relevant, reduces costs for delivering offers and grows a share of wallet and customer satisfaction.

Designing interactive customer experiences
This process is still an art form. Instead of isolating inbound and outbound channels to customers, your call center should view itself as one of several channels, all of which are carefully orchestrated to seamlessly maximize the customer experience. The corporate-wide goal should be to build systems that are highly responsive, relevant and profitable for the customers and the company to use. The interactions should include the right dialogue at the right time to the right customers over any combination of channels.

One Teradata customer in the financial services sector reports that approximately 85% of the time, contact is initiated by customers, as opposed to the bank contacting them. Additionally, according to a 2006 McKinsey Quarterly article, a recent study of North American banks finds that successful efforts to cross-sell during inbound service calls can boost a retail bank's sales of new products by 10%. There is tremendous opportunity to improve business and customer service if you are ready and know how to respond.

Sadly, many companies are not capitalizing on the potential of mapping customer dialogues. And if they have done so, it has been in isolation, not factoring in that customers have other communications options, such as the Web. While many interactions with a business can be automatically satisfied over the Web, not many companies have planned for cross-channel synchronization.

Supporting this integrated customer view is not difficult with active data warehousing and real-time analytics. In the past, data warehouses handled mostly strategic applications, which did not require instant response time or tight integration with operational systems such as the Web or call centers. But today's information can be used to drive real-time customer interactions over all customer channels. Call centers can use active data warehousing in two ways:

	To react to customers who call an "inbound" call center because of problems
	To proactively communicate with customers when appropriate via outbound call centers

In the reactive case, suppose a customer, James, calls a wireless company's customer service center complaining about dropped calls. Through the call center application, the customer service representative (CSR) can access specific details in the data warehouse about all of James' interactions with the company, resolve his phone service issue to his satisfaction and cross-sell or up-sell a new calling plan based on the details in James' profile and company interaction information.

In the proactive case, it is possible for dropped-call records flowing into the active data warehouse to automatically trigger outbound apology calls, with some kind of compensation, so that when James checks messages, he finds a pleasant surprise: This company knows it did not deliver and is trying to do better.

Testing metrics and customer dialogues
The third and final step in the equation is learning dialogue impacts. Consider another customer, Amanda. During Amanda's call to her bank, the CSR discovers that Amanda plans to help her niece with a college tuition payment using funds currently in a non-interest-bearing account. The CSR might use this dialogue to suggest that Amanda co-sign a student loan. The benefits are two-fold: Amanda's money would remain with the bank and her niece would become a new customer.

Because Amanda initiated the conversation and is already thinking about banking, this gives you the opportunity to gracefully grow that relationship. However, to do this you will need to determine when and how to interject key messages into the conversation or gather information that can be used for selected marketing campaigns.

You'll know what information is worth measuring and which dialogues are the most effective if you have captured that information in a data warehouse. By analyzing dialogue impacts by customer segment, you can quickly spot trends. The call center, with its wonderful human touch, can be a great channel for gauging reactions to offers, and a smart marketing department can use the agent inputs to record, test, listen to and fine-tune dialogues over time.

Data warehousing can help your customer call staff and systems work smarter and faster. By better focusing on the right customer metrics, you can create a true customer-centric interaction strategy, complete with instrumented cross-channel dialogues that include relevant and timely product and service messages.

Activating the call center with active enterprise intelligence can improve your customer metrics and exploit customer insights from marketing in near real time, while simultaneously leveraging your data warehouse investment. T

SERVICE CALL CENTER Backing a service call center with data warehousing and operational intelligence has the potential to differentiate your company via great cross-channel service synchronization. The enterprise data warehouse now captures information in real time, so should a customer switch from the Web site to calling an 800 number out of frustration, the customer context is in the data warehouse and next steps can be dynamically planned. In this case, caller ID can be used to transfer the person directly to a Level 2 agent with the right skills. With an understanding of where the customer was on the Web site, plus a screen full of help tips, the customer bypasses the interactive voice response (IVR) and Level 1 call center agents. Doing so speeds customer calls through the system, avoids context repetition and improves customer service satisfaction dramatically, since it's more likely that the problem can be resolved quickly and completely on a first call. —D.S.

SALES CALL CENTER A customer sales call center can take advantage of insights from customer purchases at the store as well as recent browsing behaviors on the Web to make next-purchase recommendations. The technology behind the scenes involves data warehousing and customer management analytics to match customers to purchase categories, individual past purchase history and predictive models, and uses active integration with call center technologies so that the recommendations can be dynamically formulated as screen pops for call center agents. Operational intelligence contributes to faster, smarter marketing—at the right time, individualized for each customer. —D.S.

Dave Schrader is director of Strategy and Marketing for Teradata.

Teradata Magazine-March 2007