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RE: Compliance Technologies & Solutions
March  2004

In The Face Of Do-Not-Call, Creating Profit In The Call Center

By Colin Shearer, SPSS, Inc.

When the do-not-call list was conceived, many thought it sounded a death knell for telemarketing organizations. For the smart ones, however, it's a chance to ratchet up response rates. For while the long list of names used to be the marketer's Holy Grail, the do-not-call list changes the rules. In the old days, response rates of one to three percent were the norm. And so the longer the list, the more sales it generated.

What marketers have ignored are two fundamental principles: a) untargeted pitching alienates 97 to 99 percent of their list; and b) better targeting of marketing messages leads to increased conversion and more satisfied customers (who do not opt-out of marketing contact).
The do-not-call list presents a unique opportunity for marketers by forcing them to know customers a little better and specifically tailoring products and offers to the demonstrated preferences of each individual customer. This is a big change from the carpet-bombing approach of yesteryear, and not only yields better conversion rates, but also keeps customers from opting out of receiving marketing messages. But how?

Evolving The Call Center
From the ashes of mounting restrictions against unsolicited telemarketing, namely the do-not-call list, a phoenix may well rise in the form of the call center. Industry analysts and experts are in agreement that marketers need to change their methods of reaching customers. The days of casting a wide net and hoping for a decent catch are over. Marketers must now transfer their time and resources from getting the best call list to truly understanding their customers and prospects and making them the right offer. Much of this begins at the customer's main point of contact ' the call center.

Shifting Gears From A Cost Center To A Profit Center
The call center business model has historically been based on reducing the amount of time spent on each call to reduce costs and personnel. The quantity and not the quality of each call was the driver. Operational efficiency, in this model, was the overriding goal, not customer satisfaction, customer retention, nor cross-selling or upselling for more revenue. Rarely was a call center's operation tied in with an organization's business goals or strategies.
With the advent of customer relationship management (CRM) initiatives in the late 1990s, call centers took on a more strategic role. CRM systems promised to bring all the threads together. When a customer called in, an operator in a call center could quickly see the entire history of an organization's relationship with that customer ' what they'd bought, when they'd bought it, what issues had come up and how they had been resolved. Service representatives could see what sales representatives had done and to which direct marketing campaigns a customer had responded. Everyone could see who was interacting with the customer.
Although CRM systems increased operational efficiencies, these initiatives have failed to deliver significant value to customers and, as a result, have not generated high returns. Operational CRM systems improve connections to customers, but they don't do much to improve customer understanding, and without good customer understanding, organizations can't be sure of the best thing to say to each customer at a given point in time. Operational CRM systems generate huge amounts of customer data, but have not been designed to transform those data into information organizations can effectively act on. Raw data has no real value until it is turned into information.
Put another way, CRM systems by themselves are great at facilitating communication and at illuminating what customers have done in the past, but by themselves, they do not tell an organization what to communicate. They cannot predict what the customer phoning into your call center, at this moment, would like to do but isn't telling you ' and that is the information that every customer-facing channel, such as a call center, needs to sell more efficiently and improve bottom line profitability.
The missing link is predictive analytics, which can turn the raw data generated by CRM systems into usable information. The predictive analytic process discovers the meaningful patterns and relationships in data ' separating signals from noise ' and provides decision-making information about the future. Predictive analytics solutions, properly deployed and seamlessly integrated with a CRM solution, do several things:
' They enable an organization to analyze all of its customer data and identify patterns that predict customer behavior.
' They enable organizations to understand that certain kinds of customers behave in ways that return a higher value to the organization ' and not necessarily because they have purchased the most expensive product or service (but because, for instance, they are likely to repay a loan over a longer term, or are more likely to maintain a balance on a credit card).
' They also enable organizations to understand which types of customers pose the greatest risk to the organization or stand to decrease their value to the organization through default, high claims or fraud.
Because a solid predictive analytics solution can be integrated with a CRM system for real-time use of in-session data, such a solution can enable call center agents to act ' in real time ' in ways that hold the greatest potential for increasing the value of the call center to the organization. Not only does this predictive information enable customer-facing agents to make upsell and cross-sell offers that are likely to be accepted by the customer and that return the highest value to the organization, but it also enables them to avoid pursuing offers with customers whose behavior suggests significant risk.
Generally, predictive analytics incorporate techniques from four categories:
Statistical analysis refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends. Although there are a number of statistical analysis procedures, the two most commonly used are classification and segmentation. Classification uses predictor fields to predict a categorical target field, such as which groups of people will respond to an offer. Segmentation divides subjects, objects or variables into a number of relatively homogeneous groups (e.g., segmenting consumers into usage pattern groups). Use of statistical analysis to classify and segment can help to increase the likelihood that the right offer is made to the right person at the right time.
Online analytical processing (OLAP) enables data to be easily and selectively extracted and then viewed from different perspectives. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's widgets sold in Wyoming in the month of August, compare revenue figures with those for the same products in October and then see a comparison of other product sales in Wyoming in the same time period. To facilitate this kind of analysis, OLAP data are stored in a multidimensional database, which considers each data attribute (such as product, geographic sales region and time period) as a separate 'dimension.' OLAP enables marketers to quickly review history and trends to take advantage of emerging opportunities and take corrective action on developing problems.
Data mining discovers the meaningful patterns and relationships in data and provides decision-making information about the future. Data mining procedures include: association, looking for patterns where one event is connected to another event; sequence or path analysis, looking for patterns where one event leads to a later event; classification, looking for new patterns; clustering, finding and visually documenting groups of facts not previously known; and forecasting, discovering patterns in data that can lead to reasonable predictions about the future. Data mining provides a clear picture of what is going to happen in time to change it. For example, who the best customers might be, which customers are likely to defect or if the right data are gathered, which carries the risk of adverse reaction to marketing offers.
'Creating Profit In The Call Center'
continued from previous page
Text mining analyzes unstructured textual data by finding and discovering the patterns and relationships within thousands of documents, such as e-mails, call reports, Web sites and other information sources. Text mining extracts terms and phrases, and then automatically classifies the terms into related groups, such as products, organizations or people, using the meaning and context of the text. Text mining can be used to analyze call agent notes and to provide real-time feedback, such as scripts that can be used to pitch cross-sell and upsell offers. With the combination of text mining and data mining, call center scripts can be changed instantly to reflect how the caller matches the pattern of previous calls. As the customer speaks or writes, the agent is immediately able to analyze that customer's current and future needs.
All of these are powerful techniques, but analysis alone ' no matter how sophisticated ' does not deliver value until the results can be used to make decisions wherever and whenever they need to be made. For call centers, this means predictive models have to integrate with the operational systems supporting interactions and deliver recommendations based on content and context of the call as well as on historical customer information. This has to be done in real-time; no matter how smart the recommendation, the customer conversation can't be stalled waiting for a response from the model.

Call Centers As Profit Centers ' Real-World Examples
Consider the case of a major financial services provider. This firm has moved from a product-focused to a customer-focused sales model and relies heavily on its knowledge of customer behavior to facilitate upsell and cross-sell efforts. Once they integrated a predictive analytical solution with their CRM system, they were able to mine their data, develop an understanding of different kinds of customer behaviors and use those models in real-time to present in-calling customers with highly targeted offers. As a result, the firm increased its conversion rate by 50 percent, even as it decreased its marketing costs by 40 percent.
Another financial services provider wanted to find a way to turn a service center that supported more than 2 million customer calls each year into a profit center ' one that could facilitate the conversion of incoming customer calls to sales opportunities. Through the use of predictive analytics, service center operators were able to increase conversion rates by a factor of 100. This contributed as much as $40 million to the firm's revenues in the first year, even as it enabled the firm to decrease the amount it was spending on other marketing endeavors. The net result was an increase in bottom-line profits on the order of several million dollars each year.
Other companies have had similar, some even more dramatic, successes. Organiza-tions are increasing conversion rates for direct mail, Web, direct sales and other channels ' and they are doing it by enabling their customer-facing agents to work, in real-time, with customer behavioral models discovered through the integration of predictive analytics software and their CRM systems. Not only are these efforts improving bottom-line profits for these companies, but they are also enabling these companies to show real return on CRM systems that had not shown strong return in the past.

The Customer Is King Again And There's No Turning Back
The backlash against unsolicited telemarketing and e-mails coupled with increasing competition has forced more precise methods of selling and marketing to customers. Customers are ' as they should have always been ' in the driver's seat and are forcing organizations to work harder to win and keep their business. Companies that have relied strongly on the carpet-bombing approach to marketing are now using their call center to better target customers at the right time with the right offers.
Achieving the right balance between providing good customer service and discovering and acting upon every existing sales opportunity in the call center will require more than a paradigm shift; it will require equipping the call center agents with the tools and intelligence necessary to understand customers. By turning the voluminous customer data available into useful customer intelligence, the value of each customer interaction is increased in real-time. This results in a higher degree of customer satisfaction and increased call center profitability and a rapid return on investment.

Colin Shearer is vice president of customer analytics at SPSS Inc. A pioneer of data mining in the early 1990s, he was a founder of Integral Solutions Ltd. (ISL) and the architect of ISL's Clementine system, the first data mining tool aimed at non-technologist end-users. In December 1998, ISL was acquired by SPSS Inc., a provider of predictive analytics. Shearer joined SPSS to establish its data mining business and now oversees the customer analytics business center. He is responsible for the product management and marketing of products and solutions related to the analysis of customer data. Colin can be reached at [email protected].

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