T he essence of customer relationship management(CRM) is understanding customer needs andleveraging that knowledge to improve a com-

pany’s long-term profitability. When successfully deployed,CRM can have a dramatic effect on bottom-line perform-ance. In a span of 18 months, Lowe’s Home ImprovementWarehouse achieved a 265 percent ROI on its $11 millionCRM investment. Virgin Wines achieved a 12 percent cus-tomer conversion rate (the percentage of customer visits toits website that resulted in sales) compared to a 4 percentrate before adopting the program. These examples clearlydemonstrate CRM’s potential.

Despite these impressive achievements, however, the Gart-ner Group estimates that 55 percent of CRM projects arenot expected to produce results. Some organizationalissues linked to CRM failures have been addressed, suchas a lack of company-wide commitment to the system.But failures due to a lack of customer focus are less wellunderstood. In a recent survey of 1,500 companies inMarketing Week, almost 30 percent of respondentsequated CRM to a database primarily used for trackingtransaction behavior. Only 5 percent recognized its fullpotential as a mechanism for maintaining and developingcustomer loyalty (“Blinded…” 2002).

Transactional data is an inadequate basis for CRM. Whatis needed is a clear understanding of customer needs. Sowe present a framework that can help managers achievethis understanding, beginning with the three buildingblocks of CRM.

The first building block is an understanding of customer deci-sion-making. Because 75 percent of all buying decisionshave an emotional component, according to Tehrani(2002), understanding customers’ emotional needs is vitalfor predicting and influencing their purchasing behavior.There is a difference between knowing about customers andknowing customers. Despite the fact that CRM centers oncustomers, “companies rarely understand what customerswant,” commented Jason Goodwin, head of CRM at theSAS Institute (“Blinded…” 2002).

Customer relationship management requires the alignment of three building blocks: insight into customer decision-making, information about customers, and information-processingcapability. However, its emphasis on the latter has outpaced the first two, so that CRMrarely realizes its full potential. The guidelinespresented here can help managers build a full-spectrum information portfolio for CRM that,through the thoughtful integration of existingtools, information properties, and communi-cation channels, can provide a more completepicture of customers and form the basis for long-lasting and profitable relationships with them.

Anne Stringfellow Assistant Professor of Marketing, Thunderbird—The GarvinSchool of International Management, Glendale, Arizona(stringfa@t-bird.edu)

Winter NieAssociate Professor of Operations Management, Thunder-bird—The Garvin School of International Management,Glendale, Arizona (niew@t-bird.edu)

David E. Bowen Dean, Faculty and Programs, and Professor of Management,Thunderbird—The Garvin School of International Manage-ment, Glendale, Arizona (bowend@t-bird.edu)

CRM: Profiting fromunderstanding customerneeds

The second building block is customer information. As dis-cussed by Hennig-Thurau and Hansen (2000) and illus-trated in Table 1, the information presently collected forCRM may be divided into three categories. Personal infor-mation, such as customer demographics, is useful in basiccustomer segmentation and for selecting advertisingmedia. Customer history is a record of purchase transac-tions and such non-purchase transactions as complaintsand service records. Profitability information expands oncustomer history by permitting the estimation of customerlifetime value. The most important category, customers’deep-seated needs, is often ignored. But such informationcan 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 multiplesources and across different functions. Data must beorganized by customer so that decisions can be made atthe customer or segment level. Fast processing allows theinformation to be used in real-time, point-of-contact deci-sion-making. For example, Capital One believes that themicro-segmentation of its customers is essential for iden-tifying and keeping its most valuable customers. When acustomer calls, the full history of the account appears onthe computer screen, and information on general cus-tomer characteristics and spending tendencies is availableto guide the sales representative.

Advances in information technology (IT) have made dra-matic improvements in information-processing capabili-ties. Data integration problems have been addressed

through the use of data warehouses. However, most CRMsystems fail to recognize the emotional component of cus-tomer behavior, with the result that many are technology-rich but knowledge-poor. With such a data-centric empha-sis, instead of asking what information a firm needs for acomplete picture of the customer, marketers use easilyavailable superficial customer information without ade-quately probing its relevance and completeness. TheirCRM databases record customer demographics and trans-action numbers, but are not very revealing about people.

What’s wrong with CRM?

T here are three major reasons why current CRM sys-tems fail to capture crucial information about cus-tomer needs. First, many companies are not fully

aware of the importance of knowing customer needs inthe purchase decision-making process. Second, eventhough some companies realize the importance of thisinformation, they are challenged by the difficulty and costof collecting and interpreting it. Core needs are often hid-den and hard to articulate. Third, companies fail to har-vest intuitive, interpretive, or ambiguous informationabout customers that may be lodged in employees’ headsrather than somewhere in a database.

Existing CRM databases and modeling techniques do wellin describing the “whats” of customer behavior, but fallshort of understanding the “whys.” For example, a transac-tion database fails to capture situations in which a product

Informationrequirements

Personal information

Customer history

Profitability information

Customer needs

Value added by information

Basic segmentation andchannel selection

Timing of marketing com-munication; avoiding cus-tomer defection; servicerecovery; future marketing

Optimizing marketingexpenditure and efforts;predicting customer life-time value

Cross-selling; up-selling;New product development;deepening and extendingrelationships; capturingcustomer lifetime value

Typical informationcollection methods

Warranty card; survey

Transaction database;customer service data-base; survey

Transaction database;analysis and judgmentfrom transaction andpersonal informationdatabases

Survey, if the rightquestions are asked;qualitative methods

Examples

Gender, age, income,size of household, etc.

Purchase information;complaint information;customer satisfaction

Current profitability forcustomers/customersegments; potentialprofitability

Functional needs;emotional needs

Cur

rent

CR

MC

urre

nt C

RM

igno

res

this

…co

llec

ts t

his…

Table 1CRM information requirements and value added

was picked off the shelf and then put back. Models devel-oped from transaction data, where the decision processremains a black box, work well in describing the statusquo but lack the ability to predict behavior under alteredcircumstances.

This incomplete understanding of customer decision-making can lead to decisions that hurt business perform-ance. Benson, Smith, and Thearling (1999) describe awireless phone provider in the UK that modeled its cus-tomers’ likelihood to “churn,” or terminate service, basedon their contracts, their length of service, the tariffs theywere paying, and a number of other variables. Althoughthe model succeeded in predicting customer churn for acouple of months, its predictive power rapidly waned asmarket competition changed. Some customers may sim-ply have been open to trying a new provider and thuswere especially likely to switch. Because the model lackedany personality trait variables, it was incapable of per-forming in a changing environment. The firm wastedresources in continuing to extend special offers to cus-tomers who were unlikely to churn.

What is needed to predict long-term customer behavior isthe recognition of the influence of customer needs onpurchase decisions. According to Alan Ferber, VP of Mar-keting for US Cellular, it is only when one understandsthe needs of a given customer segment that one candesign a loyalty program to successfully meet those needs(Levine 2001).

Benefits of understanding customerneeds

Purchase decisions are driven by two kinds of needs: (1)functional—those satisfied by product functions; and (2)emotional—deeper needs associated with the psychologi-cal aspects of product ownership. As Schneider andBowen (1999) put it, when a man buys a Ralph Laurenpolo shirt that costs twice as much as a similar shirt fromL.L. Bean (and is of lesser quality, according to ConsumerReports), his functional needs are not paramount in deci-sion-making. He pays extra for the polo logo to fulfill hisneed for self-esteem.

Only by understanding such deeper needs are firms ableto offer true value to the customer. As Godin (1999) sug-gests in the subtitle of his book on building customerrelationships, firms need to “turn strangers into friendsand friends into customers” by learning more about themand then using the detailed information gained to givethem what they need.

To fully comprehend customer purchase behavior requiresviewing a customer as a human being first and a customersecond. Kotler and Armstrong (2001) maintain that “themost basic concept underlying marketing is that of human

needs.” Researchers Schneider and Bowen suggest thatfailing to meet basic needs for security and justice leads tocustomer dissatisfaction or outrage, while exceeding theneed for self-esteem leads to customer delight. Meetingcustomer needs is vital, so need-based segmentation holdsthe key to building and sustaining more effective cus-tomer relationships.

Need-based segmentation, also known as benefit segmen-tation, entails dividing up a market on the basis of theneeds satisfied by the product, then creating the appropri-ate value proposition for one or more market segments.For example, some customers buy chewing gum tofreshen their breath, others for their enjoyment of the fla-vor. Products targeted to the former group might incorpo-rate a medicinal component, while products targeted tothe latter would have a variety of candy-based flavors.While need-based segmentation is more difficult toachieve than simple demographic segmentation, meetingprecise needs has the potential to create more satisfiedlong-term customers.

MBNA Bank is an example of a company that profits fromunderstanding the needs of a particular market segment.Well known for its ability to retain customers in a verycompetitive credit card market, MBNA has elected to serveonly the market segment consisting of customers whorequire good service and have a history of paying theirbills on time. It retains its customers not by offering themmore favorable interest rates but by meeting their emo-tional needs to be treated fairly, politely, and with respect.At MBNA bank, every incoming phone call is answered onthe second ring by a polite and helpful customer serviceagent who is able to solve the problem. Such excellentservice results in long-term loyalty to the firm.

The women’s sense of frustrationwith their lack of power over themen in their lives was alleviatedby being able to use the spray totake direct action against cock-roaches. Insecticide disks failedto fulfill this emotional need.

Collecting information oncustomer needs

H ow should companies go about collecting infor-mation about what customers need? Two impor-tant factors guide the choice of communication

channel. First, to reach customers and maximize responserates, firms need to match customers’ diverse preferencesfor communication by providing multiple communica-tion channels. Second, the channel must match the typeof information being collected. Because functional needsare tangible and easy to grasp, simple channels can con-vey this information; however, eliciting emotional needsrequires the use of more complex channels.

Possible communication channels

The most common communication channels are face-to-face, telephone, email, and database. Face-to-face commu-

nication is an example of a rich chan-nel, which Daft and Lengel (1984) de-fine as having the ability to transmitverbal and nonverbal cues simultane-ously and provide instantaneous inter-action and feedback. Psychologist AlbertMehrabian (1971) found that non-ver-bal communication could convey con-siderably more information than thatcontained in words. Because of the mul-tiple cues from body language, facialexpression, and tone of voice, rich chan-nels are capable of communicating evencomplicated emotional concepts.

Daft and Lengel discuss two important characteristics asso-ciated with communication channel richness: multiplemodality and immediate feedback. A multiple-modalitychannel can transmit information of several types. Face-to-face interaction, for instance, allows the simultaneous ob-servation of cues such as body language, facial expression,and tone of voice that convey information beyond the spo-ken message and are capable of communicating a widerange of ideas and concepts. With immediate feedback, richchannels provide instantaneous interaction, helping toreduce ambiguity and probe for information on the emo-tional needs required to complete the customer picture.

On the other hand, lean channels, such as email and data-bases, are convenient for customers and are efficient atcommunicating well-understood and unambiguous topics.However, they provide delayed feedback at best, and lacksuch non-verbal cues as body language.

Customerneeds

Functionalneeds

Emotionalneeds

Data collection methods

Transaction records andclickstream data; survey (ifright questions are asked)

Semi-structured interviews;archetype research; story-telling; picture drawing

Communicationrequirements

Lean channels

Rich channels

Informationcharacteristics

Well-defined;explicit; tangible

Latent; fuzzy; sub-conscious; hard toarticulate; intangible

Table 2Understanding customer needs

Discovering emotional needs

A number of data collection methods focus on probing people’s unconscious minds to discover their true feelings. Two ofthe 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 inthe development of the PT Cruiser. In a series of focus-group sessions, with dimmed lights and moodmusic, participants were asked to drift back to their childhoods and jot down the memories invokedby the prototype PT Cruiser parked in the room. Dr. Rapaille and his team screened the emotions behind these stories, end-ing 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 thatcapture 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 maga-zines and then come to Zaltman’s lab to tell stories about them. In one study, women collected pictures repre-senting their feelings about pantyhose. Not surprisingly, frustration with their tendency to develop runsemerged 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 toproduce sales in this segment than emphasizing the robustness of a particular brand.

Identifying emotional needs

Lean channels are perfectly adequate for collecting infor-mation on customers’ functional needs, but they allowmarketers to capture only a small fraction of potentialinformation. To build a complete picture of the customer,marketers need to go beyond traditional data collectionmethods and low-bandwidth channels (see Table 2). Thetraditional marketing research assumption is that peopleare able to articulate their needs and motivations. How-ever, according to Sheehy (1999), recent neurologicalresearch suggests that people have a limited consciousknowledge of their needs. Conventional information col-lection techniques that access attitudes may fail to capturethem. Instead, to access customers’ hidden purchase driv-ers, rich information channels are required. Motivationresearchers are forced to employ rich channels in non-directive depth interviews, projective techniques, arche-type research, metaphor elicitation, and picture drawingto delve deeper into customers’ emotional needs. (Thesidebar on the opposite page describes two of these).

It seems illogical that cockroach sprays would outsellmore convenient insecticide bait stations in some mar-kets. In fact, consumer interviews seemed to support theview that bait systems were more convenient. To investi-gate the paradox, the McCann-Erickson advertising agencytook a rich channel approach. According to McDaniel andGates (2002), they invited 100 low-income women whoused a lot of roach spray to draw pictures of cockroachesand describe them in their own words. In the drawings,cockroaches were invariably portrayed as men and the useof the spray was associated with having control over them.The women’s sense of frustration with their lack of powerover the men in their lives was alleviated by being able touse the spray to take direct action against cockroaches.Insecticide disks failed to fulfill this emotional need. This

example illustrates that even a simple product may haveemotional implications, and it is only by exploring cus-tomer emotions that firms can truly understand purchasebehavior and use the insights to fulfill customer needs.

Cost-benefit considerations

Matching channels with information characteristics is animportant consideration; however, another considerationis the relative cost of channels. Email can rapidly reach abroad audience at minimal cost. Using it to survey cus-tomers’ book preferences is an appropriate and low-costchannel choice. On the other hand, communicating viaemail 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 shareinformation through face-to-face communication than byemail. Talking face-to-face can provide rich, in-depth infor-mation and allows for dealing effectively with complicatedand unstructured information. However, the training re-quired for sophisticated psychological face-to-face tech-niques renders them expensive and time-consuming. Fig-ure 1 illustrates the relationships among information char-acteristics, communication channel, and cost. The decisionboils down to a cost-benefit trade-off, weighing the expen-diture on rich channels against the potential long-termbenefits of a deeper understanding of customer behavior.

Building a full-spectruminformation portfolio

A full-spectrum information portfolio uses a combi-nation of data collection approaches to provide awide range of customer information. It makes use

of inexpensive lean channels for collecting well-definedinformation, such as functional needs. Richer, more ex-pensive channels, such as telecommunications, are ap-plied to uncover customers’ emotional needs.

Firms that correctly use the full-spectrum approach gain abetter understanding of their customers than those thatlimit information collection to well-defined data. Thisallows them to differentiate product and service offeringsfrom competitors and effectively identify their target audi-ence. Unlike a transaction database, which a company caninstall and use immediately, a full-spectrum approach ismuch more complex. It has to be designed for the particu-lar firm, so it takes time and effort to implement. Peopleneed to be trained to collect rich information and a sys-tem needs to be in place to capture, store, assemble, dis-seminate, and exploit the information. Done correctly,this provides a long-term advantage that is hard for other

Figure 1 Cost-benefit trade-off

Face-to-face

Telephone

Internet

Database

Ric

hLe

an

Low HighCost

Co

mm

un

icat

ion

m

ediu

m

Simp

leC

om

plex

Info

rmatio

nch

aracteristics

Source: Adapted from Daft and Lengel (1984)

firms to copy. Table 3 lays out some summary guidelinesfor designing an optimal full-spectrum information port-folio. We expand on a number of these guidelines below.

Using rich channels in highly equivocalsituations

When a firm is entering a critical new market, it shoulduse a rich channel to capture customer needs. The result-ing information is invaluable in forming the market entrystrategy. In the short run, the cost of the rich channel mayoutweigh the immediate financial benefits. Numerousexamples have shown, however, that firms entering a newmarket without understanding the customers and themarket ended up losing millions of dollars.

Allocating rich channels where they produce the best return

When customers have a high lifetime value potential andthe product and accompanying service have a relativelyhigh profit margin, the use of rich channels to understandcustomer needs more than pays for itself. Dallas automo-bile dealer Carl Sewell (1998) estimates the average po-tential lifetime value of his customers at $332,000.

Many firms cannot afford regular face-to-face conversa-tions with every customer. However, by carefully segment-ing customers by needs as well as potential lifetime value,a firm can focus on gathering rich information on thosewho are most likely to yield an increase in lifetime valuethat far outweighs the cost of collecting the information.Banks use rich channels to foster one-to-one relationshipswith their most valuable high-end clients, but they cannotafford to do this with lower-tier customers.

Using the rich informationalready available

One technique is to harvest rich infor-mation that already exists within thefirm but has not yet been integratedinto the customer information sys-tem. Salespeople and front-line serv-ice employees who typically interactwith customers via rich communica-tion channels have access to a wealthof information on customer needs.Call-center employees who field in-bound calls, for instance, may be-come aware of customer needs longbefore those needs show up in marketresearch reports. Unfortunately, thistype of information may be hard tocodify, and front-line employees maybe reluctant to share what they viewas trade secrets.

Most firms have little means or impetus for capturing andusing this unique information. Some, however, have real-ized its importance and, according to Greco (1999), havecreated “learning histories” by encouraging employees toget together specifically to share this type of tacit knowl-edge. Thus, they gain valuable information at a fraction ofthe cost of collecting it from scratch. As Meredith Peabody(2003), former Managing Director for Channels and Dis-tribution for DBS Bank in Singapore, expressed it:

It is critical to have the CRM system integrated andaccessible at every customer touch point. The sys-tem is designed to help facilitate the informationflow of customers across the organization, so thatno matter where the customer touches the com-pany, the information is there. All our sales andservice staff were required to document every inter-action with a customer. The basis for CRM is tocapture this data so that there are no gaps in serviceprovided. It created a better customer experienceand helped make it easier for our customers to dobusiness with us.

Substituting for rich channels

With the advent of sophisticated modeling techniques,some situations that normally require rich channels cannow be dealt with efficiently by using lean channels incombination with analytical tools. Companies such asAmazon.com use collaborative filtering to record cus-tomer preferences and then use the information to recom-mend products to customers with similar tastes. The usesof rich and lean channels are not mutually exclusive. Inreality, it is often more efficient to use a combination ofboth in view of their utility and cost differences.

Channels

Use rich channels

Use lean channelsalong with powerfulmodeling techniques

Use lean channels

Situations

In complex or ill-defined situations, such as• obtaining data on new customers or new product viability• eliciting feedback on complex products • understanding emotional needsIn situations with high profit potential, such as• collecting information on potential lifetime customers• collecting information on users of high-margin products

To substitute for rich communication channels, e.g.,• collaborative filtering

For simple and recurring problems, e.g.,• eliciting functional needs• tracking changes in customer information

Table 3Guidelines for building a full-spectrum information portfolio

Integrating different types of information

A company can integrate the results fromsimple attitudinal surveys with transac-tion data. For tracking changes in cus-tomer information and eliciting func-tional needs, the use of lean channelsserves the purpose and is cost-effective.Firms using CRM systems typically con-sider transaction data on purchase fre-quency as a measure of behavioral cus-tomer loyalty, an important predictor ofcustomer value. However, data on attitudi-nal loyalty—the customer’s feelings aboutmaintaining a long-term relationship withthe firm—is rarely measured or consid-ered. Attitudinal loyalty, note Reinartz andKumar (2002), is linked to customers’tendencies to recommend a product toothers. Such word-of-mouth recommen-dations are very effective, says Vence(2002); not only are they free, they areviewed as credible because they comefrom a trusted source. Tracking attitudinalloyalty can help firms achieve more pre-cise estimates of total customer value,since the indirect effect of customer rec-ommendations can be included in the calculation.

Consider the Ritz-Carlton hotel chain. A clear missiondrives the company: to build a seamless customer-drivenservice system that anticipates and satisfies a guest’s needs.How do its managers achieve this mission? As Klein,Sasser, and Jones (1999) explain, first they realize thathuman interaction is at the heart of customer service.Patrick Mene, corporate director of quality, notes, “[O]urpeople are caring, relaxed, and refined. And they knowthat our customers expect to be waited on and do notwant to have to wait for service.” Second, Ritz-Carltonmanagers rely on two information systems: COVIA, whichhandles worldwide reservations; and Encore, a local sys-tem that tracks reservations and current guest preferencesby individual hotel. Guests are explicitly asked about theirpreferences and dislikes, and the data are made availableto all hotel staff. Equally important, staff are expected toupdate guest files by talking with guests, listening to com-ments, and acting on complaints. Everyone in the Ritz-Carlton organization knows that Encore and COVIA pro-vide information that facilitates better service, but merereliance on the databases is insufficient. Jim Veil, generalmanager of the Ritz-Carlton Buckhead in Atlanta,explains: “The technology serves us well, but it is our peo-ple who work at listening so that we can record every cus-tomer want and need.”

U nderstanding customers does not necessarilyrequire new methods of data collection andanalysis. Rather, as Nordstrom realizes (see the

sidebar above), what may be required is the thoughtfulintegration of existing tools, based on a clear understand-ing of customer value, the properties of information, andthe pros and cons of communication channels. We sug-gest a full-spectrum portfolio of approaches to under-standing customers, taking into consideration channelappropriateness and cost effectiveness. The portfolio ofchannels and methods we suggest provides a more com-plete picture of customers, forming the basis for long-last-ing and profitable relationships with them. Unlike prod-uct attributes that can be readily copied, this in-depth cus-tomer knowledge, and the emotional bonds that go alongwith it, represent a unique source of sustainable competi-tive advantage. ❍

Nordstrom does it rightNordstrom has long set the standard for customer-focused marketing, andit provides an excellent example of the benefits that accrue from a full-spectrum approach to data collection. Long before CRM was in vogue,Nordstrom women’s clothing departments used index cards to record sizesand preferences. With the advent of computers, they developed their Per-sonal Touch program, described by Davenport, Harris, and Kohli (2001).They record customer purchase history in a database (lean channel with aninexpensive method of collecting simple and structured data), then supple-ment this with telephone interviews and face-to-face conversations to col-lect information about customer likes, dislikes, lifestyles, and clothing needs(rich channels with relatively expensive methods of collecting more com-plex and less structured information). With all this information in place,wouldn’t it be cost effective to use kiosks in the store? The company knowsthat a fashion consultant, well trained and knowledgeable about colors andfashions, can extrapolate better from past purchases to current styles than akiosk. The consultant can also meet customers’ emotional needs for an effi-cient, pleasant, reassuring, and friendly shoppingexperience. This illustrates that Nordstrom under-stands customer needs, translates them into differentlevels of information requirements, and matches theinformation with appropriate communication channelsand data collection methods. The end result allowspersonalized service and builds a long-term relation-ship with customers.