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CRM: Profiting from understanding customer needs Anne Stringfellow Assistant Professor of Marketing, Thunderbird—The Garvin School of International Management, Glendale, Arizona (
[email protected])
Winter Nie Associate Professor of Operations Management, Thunderbird—The Garvin School of International Management, Glendale, Arizona (
[email protected])
David E. Bowen Dean, Faculty and Programs, and Professor of Management, Thunderbird—The Garvin School of International Management, Glendale, Arizona (
[email protected])
Customer relationship management requires the alignment of three building blocks: insight into customer decision-making, information about customers, and information-processing capability. However, its emphasis on the latter has outpaced the first two, so that CRM rarely realizes its full potential. The guidelines presented here can help managers build a fullspectrum information portfolio for CRM that, through the thoughtful integration of existing tools, information properties, and communication channels, can provide a more complete picture of customers and form the basis for longlasting and profitable relationships with them.
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T
he essence of customer relationship management (CRM) is understanding customer needs and leveraging that knowledge to improve a company’s long-term profitability. When successfully deployed, CRM can have a dramatic effect on bottom-line performance. In a span of 18 months, Lowe’s Home Improvement Warehouse achieved a 265 percent ROI on its $11 million CRM investment. Virgin Wines achieved a 12 percent customer conversion rate (the percentage of customer visits to its website that resulted in sales) compared to a 4 percent rate before adopting the program. These examples clearly demonstrate CRM’s potential. Despite these impressive achievements, however, the Gartner Group estimates that 55 percent of CRM projects are not expected to produce results. Some organizational issues linked to CRM failures have been addressed, such as a lack of company-wide commitment to the system. But failures due to a lack of customer focus are less well understood. In a recent survey of 1,500 companies in Marketing Week, almost 30 percent of respondents equated CRM to a database primarily used for tracking transaction behavior. Only 5 percent recognized its full potential as a mechanism for maintaining and developing customer loyalty (“Blinded…” 2002). Transactional data is an inadequate basis for CRM. What is needed is a clear understanding of customer needs. So we present a framework that can help managers achieve this understanding, beginning with the three building blocks of CRM. The first building block is an understanding of customer decision-making. Because 75 percent of all buying decisions have an emotional component, according to Tehrani (2002), understanding customers’ emotional needs is vital for predicting and influencing their purchasing behavior. There is a difference between knowing about customers and knowing customers. Despite the fact that CRM centers on customers, “companies rarely understand what customers want,” commented Jason Goodwin, head of CRM at the SAS Institute (“Blinded…” 2002). 45
A. Stringfellow et al. / CRM: Profiting from understanding customer needs
The second building block is customer information. As discussed by Hennig-Thurau and Hansen (2000) and illustrated in Table 1, the information presently collected for CRM may be divided into three categories. Personal information, such as customer demographics, is useful in basic customer segmentation and for selecting advertising media. Customer history is a record of purchase transactions and such non-purchase transactions as complaints and service records. Profitability information expands on customer history by permitting the estimation of customer lifetime value. The most important category, customers’ deep-seated needs, is often ignored. But such information can provide crucial insights into what drives customers’ decision-making processes. The third building block is information-processing capability. CRM systems need to integrate information from multiple sources and across different functions. Data must be organized by customer so that decisions can be made at the customer or segment level. Fast processing allows the information to be used in real-time, point-of-contact decision-making. For example, Capital One believes that the micro-segmentation of its customers is essential for identifying and keeping its most valuable customers. When a customer calls, the full history of the account appears on the computer screen, and information on general customer characteristics and spending tendencies is available to guide the sales representative. Advances in information technology (IT) have made dramatic improvements in information-processing capabilities. Data integration problems have been addressed
through the use of data warehouses. However, most CRM systems fail to recognize the emotional component of customer behavior, with the result that many are technologyrich but knowledge-poor. With such a data-centric emphasis, instead of asking what information a firm needs for a complete picture of the customer, marketers use easily available superficial customer information without adequately probing its relevance and completeness. Their CRM databases record customer demographics and transaction numbers, but are not very revealing about people.
What’s wrong with CRM?
T
here are three major reasons why current CRM systems fail to capture crucial information about customer needs. First, many companies are not fully aware of the importance of knowing customer needs in the purchase decision-making process. Second, even though some companies realize the importance of this information, they are challenged by the difficulty and cost of collecting and interpreting it. Core needs are often hidden and hard to articulate. Third, companies fail to harvest intuitive, interpretive, or ambiguous information about customers that may be lodged in employees’ heads rather than somewhere in a database. Existing CRM databases and modeling techniques do well in describing the “whats” of customer behavior, but fall short of understanding the “whys.” For example, a transaction database fails to capture situations in which a product
Current CRM ignores this…
Current CRM collects this…
Table 1 CRM information requirements and value added
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Typical information collection methods
Information requirements
Examples
Personal information
Gender, age, income, size of household, etc.
Warranty card; survey
Basic segmentation and channel selection
Customer history
Purchase information; complaint information; customer satisfaction
Transaction database; customer service database; survey
Timing of marketing communication; avoiding customer defection; service recovery; future marketing
Profitability information
Current profitability for customers/customer segments; potential profitability
Transaction database; analysis and judgment from transaction and personal information databases
Optimizing marketing expenditure and efforts; predicting customer lifetime value
Customer needs
Functional needs; emotional needs
Survey, if the right questions are asked; qualitative methods
Cross-selling; up-selling; New product development; deepening and extending relationships; capturing customer lifetime value
Value added by information
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was picked off the shelf and then put back. Models developed from transaction data, where the decision process remains a black box, work well in describing the status quo but lack the ability to predict behavior under altered circumstances. This incomplete understanding of customer decisionmaking can lead to decisions that hurt business performance. Benson, Smith, and Thearling (1999) describe a wireless phone provider in the UK that modeled its customers’ likelihood to “churn,” or terminate service, based on their contracts, their length of service, the tariffs they were paying, and a number of other variables. Although the model succeeded in predicting customer churn for a couple of months, its predictive power rapidly waned as market competition changed. Some customers may simply have been open to trying a new provider and thus were especially likely to switch. Because the model lacked any personality trait variables, it was incapable of performing in a changing environment. The firm wasted resources in continuing to extend special offers to customers who were unlikely to churn. What is needed to predict long-term customer behavior is the recognition of the influence of customer needs on purchase decisions. According to Alan Ferber, VP of Marketing for US Cellular, it is only when one understands the needs of a given customer segment that one can design a loyalty program to successfully meet those needs (Levine 2001).
Benefits of understanding customer needs Purchase decisions are driven by two kinds of needs: (1) functional—those satisfied by product functions; and (2) emotional—deeper needs associated with the psychological aspects of product ownership. As Schneider and Bowen (1999) put it, when a man buys a Ralph Lauren polo shirt that costs twice as much as a similar shirt from L.L. Bean (and is of lesser quality, according to Consumer Reports), his functional needs are not paramount in decision-making. He pays extra for the polo logo to fulfill his need for self-esteem. Only by understanding such deeper needs are firms able to offer true value to the customer. As Godin (1999) suggests in the subtitle of his book on building customer relationships, firms need to “turn strangers into friends and friends into customers” by learning more about them and then using the detailed information gained to give them what they need. To fully comprehend customer purchase behavior requires viewing a customer as a human being first and a customer second. Kotler and Armstrong (2001) maintain that “the most basic concept underlying marketing is that of human
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needs.” Researchers Schneider and Bowen suggest that failing to meet basic needs for security and justice leads to customer dissatisfaction or outrage, while exceeding the need for self-esteem leads to customer delight. Meeting customer needs is vital, so need-based segmentation holds the key to building and sustaining more effective customer relationships.
The women’s sense of frustration with their lack of power over the men in their lives was alleviated by being able to use the spray to take direct action against cockroaches. Insecticide disks failed to fulfill this emotional need.
Need-based segmentation, also known as benefit segmentation, entails dividing up a market on the basis of the needs satisfied by the product, then creating the appropriate value proposition for one or more market segments. For example, some customers buy chewing gum to freshen their breath, others for their enjoyment of the flavor. Products targeted to the former group might incorporate a medicinal component, while products targeted to the latter would have a variety of candy-based flavors. While need-based segmentation is more difficult to achieve than simple demographic segmentation, meeting precise needs has the potential to create more satisfied long-term customers. MBNA Bank is an example of a company that profits from understanding the needs of a particular market segment. Well known for its ability to retain customers in a very competitive credit card market, MBNA has elected to serve only the market segment consisting of customers who require good service and have a history of paying their bills on time. It retains its customers not by offering them more favorable interest rates but by meeting their emotional needs to be treated fairly, politely, and with respect. At MBNA bank, every incoming phone call is answered on the second ring by a polite and helpful customer service agent who is able to solve the problem. Such excellent service results in long-term loyalty to the firm.
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Table 2 Understanding customer needs Customer needs
Information characteristics
Communication requirements
Data collection methods
Functional needs
Well-defined; explicit; tangible
Lean channels
Transaction records and clickstream data; survey (if right questions are asked)
Emotional needs
Latent; fuzzy; subconscious; hard to articulate; intangible
Rich channels
Semi-structured interviews; archetype research; storytelling; picture drawing
Collecting information on customer needs
H
ow should companies go about collecting information about what customers need? Two important factors guide the choice of communication channel. First, to reach customers and maximize response rates, firms need to match customers’ diverse preferences for communication by providing multiple communication channels. Second, the channel must match the type of information being collected. Because functional needs are tangible and easy to grasp, simple channels can convey this information; however, eliciting emotional needs requires the use of more complex channels.
Possible communication channels The most common communication channels are face-toface, telephone, email, and database. Face-to-face commu-
nication is an example of a rich channel, which Daft and Lengel (1984) define as having the ability to transmit verbal and nonverbal cues simultaneously and provide instantaneous interaction and feedback. Psychologist Albert Mehrabian (1971) found that non-verbal communication could convey considerably more information than that contained in words. Because of the multiple cues from body language, facial expression, and tone of voice, rich channels are capable of communicating even complicated emotional concepts.
Daft and Lengel discuss two important characteristics associated with communication channel richness: multiple modality and immediate feedback. A multiple-modality channel can transmit information of several types. Face-toface interaction, for instance, allows the simultaneous observation of cues such as body language, facial expression, and tone of voice that convey information beyond the spoken message and are capable of communicating a wide range of ideas and concepts. With immediate feedback, rich channels provide instantaneous interaction, helping to reduce ambiguity and probe for information on the emotional needs required to complete the customer picture. On the other hand, lean channels, such as email and databases, are convenient for customers and are efficient at communicating well-understood and unambiguous topics. However, they provide delayed feedback at best, and lack such non-verbal cues as body language.
Discovering emotional needs A number of data collection methods focus on probing people’s unconscious minds to discover their true feelings. Two of the more interesting of these methods are described below. Archetype analysis This technique seeks to elicit the emotions sparked by a product concept or prototype. Ball (1999) reports that medical anthropologist Dr. G.Clotaire Rapaille used the technique for DaimerChrysler in the development of the PT Cruiser. In a series of focus-group sessions, with dimmed lights and mood music, participants were asked to drift back to their childhoods and jot down the memories invoked by the prototype PT Cruiser parked in the room. Dr. Rapaille and his team screened the emotions behind these stories, ending up with a product that exceeded all sales expectations. Metaphor elicitation This technique, pioneered by Harvard Business School professor Gerald Zaltman (1997), aims to discover metaphors that capture a customer’s experience with a firm or product. Participants in these studies spend a few weeks thinking about how to visually represent their experience with the company and its products. They cut out pictures from magazines and then come to Zaltman’s lab to tell stories about them. In one study, women collected pictures representing their feelings about pantyhose. Not surprisingly, frustration with their tendency to develop runs emerged early on. What was more interesting, however, was the emergence of another unexpected theme: that of sensuality and attractiveness to men. This yielded a powerful advertising message that is more likely to produce sales in this segment than emphasizing the robustness of a particular brand.
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Identifying emotional needs Lean channels are perfectly adequate for collecting information on customers’ functional needs, but they allow marketers to capture only a small fraction of potential information. To build a complete picture of the customer, marketers need to go beyond traditional data collection methods and low-bandwidth channels (see Table 2). The traditional marketing research assumption is that people are able to articulate their needs and motivations. However, according to Sheehy (1999), recent neurological research suggests that people have a limited conscious knowledge of their needs. Conventional information collection techniques that access attitudes may fail to capture them. Instead, to access customers’ hidden purchase drivers, rich information channels are required. Motivation researchers are forced to employ rich channels in nondirective depth interviews, projective techniques, archetype research, metaphor elicitation, and picture drawing to delve deeper into customers’ emotional needs. (The sidebar on the opposite page describes two of these). It seems illogical that cockroach sprays would outsell more convenient insecticide bait stations in some markets. In fact, consumer interviews seemed to support the view that bait systems were more convenient. To investigate the paradox, the McCann-Erickson advertising agency took a rich channel approach. According to McDaniel and Gates (2002), they invited 100 low-income women who used a lot of roach spray to draw pictures of cockroaches and describe them in their own words. In the drawings, cockroaches were invariably portrayed as men and the use of the spray was associated with having control over them. The women’s sense of frustration with their lack of power over the men in their lives was alleviated by being able to use the spray to take direct action against cockroaches. Insecticide disks failed to fulfill this emotional need. This
Information characteristics
Communication medium
Face-to-face
Complex
Rich
Figure 1 Cost-benefit trade-off
Telephone
Simple
Lean
Internet Database Low
Cost
Source: Adapted from Daft and Lengel (1984)
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High
example illustrates that even a simple product may have emotional implications, and it is only by exploring customer emotions that firms can truly understand purchase behavior and use the insights to fulfill customer needs.
Cost-benefit considerations Matching channels with information characteristics is an important consideration; however, another consideration is the relative cost of channels. Email can rapidly reach a broad audience at minimal cost. Using it to survey customers’ book preferences is an appropriate and low-cost channel choice. On the other hand, communicating via email about a complex product, such as an automobile, may leave many questions unanswered. In the latter case, email may be inexpensive but ineffective. Generally, says Kathleen Valley, the “electronic negotiator” (Moruca 2000), people are more willing and able to share information through face-to-face communication than by email. Talking face-to-face can provide rich, in-depth information and allows for dealing effectively with complicated and unstructured information. However, the training required for sophisticated psychological face-to-face techniques renders them expensive and time-consuming. Figure 1 illustrates the relationships among information characteristics, communication channel, and cost. The decision boils down to a cost-benefit trade-off, weighing the expenditure on rich channels against the potential long-term benefits of a deeper understanding of customer behavior.
Building a full-spectrum information portfolio
A
full-spectrum information portfolio uses a combination of data collection approaches to provide a wide range of customer information. It makes use of inexpensive lean channels for collecting well-defined information, such as functional needs. Richer, more expensive channels, such as telecommunications, are applied to uncover customers’ emotional needs. Firms that correctly use the full-spectrum approach gain a better understanding of their customers than those that limit information collection to well-defined data. This allows them to differentiate product and service offerings from competitors and effectively identify their target audience. Unlike a transaction database, which a company can install and use immediately, a full-spectrum approach is much more complex. It has to be designed for the particular firm, so it takes time and effort to implement. People need to be trained to collect rich information and a system needs to be in place to capture, store, assemble, disseminate, and exploit the information. Done correctly, this provides a long-term advantage that is hard for other
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Using the rich information already available
Table 3 Guidelines for building a full-spectrum information portfolio Channels
Situations
Use rich channels
In complex or ill-defined situations, such as • obtaining data on new customers or new product viability • eliciting feedback on complex products • understanding emotional needs In situations with high profit potential, such as • collecting information on potential lifetime customers • collecting information on users of high-margin products
Use lean channels along with powerful modeling techniques
To substitute for rich communication channels, e.g., • collaborative filtering
Use lean channels
For simple and recurring problems, e.g., • eliciting functional needs • tracking changes in customer information
firms to copy. Table 3 lays out some summary guidelines for designing an optimal full-spectrum information portfolio. We expand on a number of these guidelines below.
Using rich channels in highly equivocal situations When a firm is entering a critical new market, it should use a rich channel to capture customer needs. The resulting information is invaluable in forming the market entry strategy. In the short run, the cost of the rich channel may outweigh the immediate financial benefits. Numerous examples have shown, however, that firms entering a new market without understanding the customers and the market ended up losing millions of dollars.
Allocating rich channels where they produce the best return When customers have a high lifetime value potential and the product and accompanying service have a relatively high profit margin, the use of rich channels to understand customer needs more than pays for itself. Dallas automobile dealer Carl Sewell (1998) estimates the average potential lifetime value of his customers at $332,000. Many firms cannot afford regular face-to-face conversations with every customer. However, by carefully segmenting customers by needs as well as potential lifetime value, a firm can focus on gathering rich information on those who are most likely to yield an increase in lifetime value that far outweighs the cost of collecting the information. Banks use rich channels to foster one-to-one relationships with their most valuable high-end clients, but they cannot afford to do this with lower-tier customers.
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One technique is to harvest rich information that already exists within the firm but has not yet been integrated into the customer information system. Salespeople and front-line service employees who typically interact with customers via rich communication channels have access to a wealth of information on customer needs. Call-center employees who field inbound calls, for instance, may become aware of customer needs long before those needs show up in market research reports. Unfortunately, this type of information may be hard to codify, and front-line employees may be reluctant to share what they view as trade secrets.
Most firms have little means or impetus for capturing and using this unique information. Some, however, have realized its importance and, according to Greco (1999), have created “learning histories” by encouraging employees to get together specifically to share this type of tacit knowledge. Thus, they gain valuable information at a fraction of the cost of collecting it from scratch. As Meredith Peabody (2003), former Managing Director for Channels and Distribution for DBS Bank in Singapore, expressed it: It is critical to have the CRM system integrated and accessible at every customer touch point. The system is designed to help facilitate the information flow of customers across the organization, so that no matter where the customer touches the company, the information is there. All our sales and service staff were required to document every interaction with a customer. The basis for CRM is to capture this data so that there are no gaps in service provided. It created a better customer experience and helped make it easier for our customers to do business with us.
Substituting for rich channels With the advent of sophisticated modeling techniques, some situations that normally require rich channels can now be dealt with efficiently by using lean channels in combination with analytical tools. Companies such as Amazon.com use collaborative filtering to record customer preferences and then use the information to recommend products to customers with similar tastes. The uses of rich and lean channels are not mutually exclusive. In reality, it is often more efficient to use a combination of both in view of their utility and cost differences.
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Integrating different types of information
Nordstrom does it right
Nordstrom has long set the standard for customer-focused marketing, and A company can integrate the results from it provides an excellent example of the benefits that accrue from a fullsimple attitudinal surveys with transacspectrum approach to data collection. Long before CRM was in vogue, tion data. For tracking changes in cusNordstrom women’s clothing departments used index cards to record sizes tomer information and eliciting funcand preferences. With the advent of computers, they developed their Pertional needs, the use of lean channels sonal Touch program, described by Davenport, Harris, and Kohli (2001). serves the purpose and is cost-effective. They record customer purchase history in a database (lean channel with an Firms using CRM systems typically coninexpensive method of collecting simple and structured data), then supplesider transaction data on purchase frement this with telephone interviews and face-to-face conversations to collect information about customer likes, dislikes, lifestyles, and clothing needs quency as a measure of behavioral cus(rich channels with relatively expensive methods of collecting more comtomer loyalty, an important predictor of plex and less structured information). With all this information in place, customer value. However, data on attitudiwouldn’t it be cost effective to use kiosks in the store? The company knows nal loyalty—the customer’s feelings about that a fashion consultant, well trained and knowledgeable about colors and maintaining a long-term relationship with fashions, can extrapolate better from past purchases to current styles than a the firm—is rarely measured or considkiosk. The consultant can also meet customers’ emotional needs for an effiered. Attitudinal loyalty, note Reinartz and cient, pleasant, reassuring, and friendly shopping Kumar (2002), is linked to customers’ experience. This illustrates that Nordstrom undertendencies to recommend a product to stands customer needs, translates them into different others. Such word-of-mouth recommenlevels of information requirements, and matches the dations are very effective, says Vence information with appropriate communication channels and data collection methods. The end result allows (2002); not only are they free, they are personalized service and builds a long-term relationviewed as credible because they come ship with customers. from a trusted source. Tracking attitudinal loyalty can help firms achieve more precise estimates of total customer value, since the indirect effect of customer recommendations can be included in the calculation. nderstanding customers does not necessarily Consider the Ritz-Carlton hotel chain. A clear mission require new methods of data collection and drives the company: to build a seamless customer-driven analysis. Rather, as Nordstrom realizes (see the service system that anticipates and satisfies a guest’s needs. sidebar above), what may be required is the thoughtful How do its managers achieve this mission? As Klein, integration of existing tools, based on a clear understandSasser, and Jones (1999) explain, first they realize that ing of customer value, the properties of information, and human interaction is at the heart of customer service. the pros and cons of communication channels. We sugPatrick Mene, corporate director of quality, notes, “[O]ur gest a full-spectrum portfolio of approaches to underpeople are caring, relaxed, and refined. And they know standing customers, taking into consideration channel that our customers expect to be waited on and do not appropriateness and cost effectiveness. The portfolio of want to have to wait for service.” Second, Ritz-Carlton channels and methods we suggest provides a more commanagers rely on two information systems: COVIA, which plete picture of customers, forming the basis for long-lasthandles worldwide reservations; and Encore, a local sysing and profitable relationships with them. Unlike prodtem that tracks reservations and current guest preferences uct attributes that can be readily copied, this in-depth cusby individual hotel. Guests are explicitly asked about their tomer knowledge, and the emotional bonds that go along preferences and dislikes, and the data are made available with it, represent a unique source of sustainable competito all hotel staff. Equally important, staff are expected to tive advantage. ❍ update guest files by talking with guests, listening to comments, and acting on complaints. Everyone in the RitzCarlton organization knows that Encore and COVIA proReferences and selected bibliography vide information that facilitates better service, but mere Ball, Jeffrey. 1999. But how does it make you feel? Wall Street reliance on the databases is insufficient. Jim Veil, general Journal (3 May): B1-B4. manager of the Ritz-Carlton Buckhead in Atlanta, Barrett, Lucy. 2002. Blinded by science. Marketing Week (7 Februexplains: “The technology serves us well, but it is our peoary): 45-47. ple who work at listening so that we can record every cusBerson, Alex, Stephen J. Smith, and Kurt Thearling. 1999. 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