- Email: [email protected]
Simple database marketing tools in customer analysis and retention
ARTICLE IN PRESS
International Journal of Information Management 23 (2003) 291–301
Simple database marketing tools in customer analysis and retention Yu-Hui Taoa,*, Chu-Chen Rosa Yehb b
a Department of Information Management, I-Shou University, Kaohsiung County, Taiwan, ROC Institute of Human Resource Management, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC
Abstract Many businesses practice database marketing to increase effectiveness in their marketing activities. The database marketing approach uses current advanced information technology to collect customer data, analyze it and use it to provide recognition and services to customers for the purpose of increasing customer loyalty and repeat sales. Over the years, the practice of database marketing has evolved into a costly implementation of highly sophisticated information technology and application, which requires elaborate planning and organizational skills for successful adoption. Consequently, the need has been recognized for a human-centred approach which focuses more on the business problem rather than on the technology. This paper introduces two simple yet essential database-marketing tools—usage segment code and net revenue equation—and describes their application in the credit-card business. The promising results obtained further demonstrate that simple yet creative ideas can be converted into powerful database marketing tools to increase the return on investment in a marketing database. r 2003 Elsevier Ltd. All rights reserved. Keywords: Database marketing; Marketing tools; Customer analysis; Credit-card companies
1. Introduction In recent years research in information systems has led to the development of theories and techniques for the exploitation of organizational knowledge of the customers. See, for example, Dyche (2000) and Wigand, Picot, and Reichwald (1997). It is, however, still possible for any company with a modest customer database to start their own database marketing activities with simple tools in-house, which can be successful if the focus is on the business problems instead of the technology problems. This article delivers the experience of a top-ten US credit-card issuer *Corresponding author. Tel.: +886-7-657-7711x6562; fax: +886-7-6577056. E-mail address: [email protected] (Y.-H. Tao). 0268-4012/03/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0268-4012(03)00052-5
ARTICLE IN PRESS 292
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
who developed two simple marketing tools, Usage Segment Code (USC) and Net Revenue Equation (NRE), both proven useful in understanding customer behaviors and in various other marketing activities. As Section 2 of this article will explain, USC is the spending pattern of a customer while NRE is the retrospective estimated profit from a customer during a certain period. Two major applications of these two marketing tools are introduced: a backend customer migration analysis in Section 3 and a customer retention management system in Section 4. These two tools can be applied to other membership-type business, such as retail banking, mortgage, mail order, department stores, insurance services, auto clubs, airlines, and hotels. The marketing database, if done correctly, can assist marketing managers in tasks ranging from daily operation, resource allocation, and budget planning, to strategic decision processes. As the marketing world braces for changes that call for new tools and approaches, database marketing is emerging as an important tool (Palmquist & Ketola, 1999). Database marketing is an approach to generate integrated and accessible customer information to help the marketers better target their marketing efforts to existing customers or prospects. The ultimate goal of database marketing is to create a win–win situation for both the marketers and the customers by reducing marketing costs, increasing sales and profits, and building customer loyalty. Over the decades, many database-marketing tools were developed and used in various stages of marketing. Some of the most popular tools include: the Recency-Frequency-Monetary (RFM) Formula, the lifestyle segment of the existing customers and the lifetime value of a customer (Stone, 1988; Dwyer, 1997). Database marketing tools are now evolving to the point that the credit-card industry can actually deliver its long-standing promise of providing products tailored to each individual customer. It might however be a technological game limited to the largest of players (Demery, 1999). Database marketing becomes more complex as technology continues to grow (Weber, 2000). As organizations grow more dependent on this new technology, they invest more in the hardware and the software that subsequently contribute to the complexity of their systems. Somewhere along this process, organizations may start to lose their focus. Lehaney, Clarke, Kimberlee, and Spencer-Matthews (1999) supported the demonstrated value of technology-enabled database marketing, but also concluded that its success rests on participative, human-centered approaches to development. A marketer tackling specific marketing challenges should not focus on technology, but rather, the business problem. When solving business problems, human experience and creativity provide critical insights and at times viable solutions without costly investment or expansion in the technology. This paper introduces two simple yet essential database-marketing tools—usage segment code and net revenue equation— and their applications in the credit-card business. The promising results further demonstrate that simple yet creative ideas can be converted into powerful database marketing tools to increase the return on investment in a marketing database.
2. Database marketing tools: USC and NRE USC and NRE are described in this section, along with their applications in a US top-ten credit-card company.
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
293
2.1. Usage segment code (USC) USC is a way to cluster existing customers (cardholders) into distinct groups based on customer history, such as the total spending, type of spending (e.g., purchase or cash advance), delinquent status and history, other status code, and so on. The criteria of each segment should be fine enough so that they can be either used individually for a certain marketing activity which requires very fine clusters, or further combined into larger groups/segments for appropriate marketing activities. Table 1 illustrates part of a 40-segment scheme. The complete 40-segment scheme can be further grouped into larger groupings such as new customers vs. old customers, or transactors vs. revolvers. In this case, we define transactors as those customers who paid their balances off every month; and revolvers as those who paid at least one-month interest over the last 12 months. Revolvers are preferable customers because they positively contribute to the credit-card company’s bottom line. Table 1 demonstrates the old customer segments (USC from 25 to 40) which include customers who have been on the books for over 12 months (Months On Book (MOB)X12). 2.2. Net revenue equation NRE is a formula used to calculate the net revenue from an existing customer over a period of time, such as a month or a cycle. The accuracy of NRE depends on the ongoing revenue dynamics of the customers and various costs of the company processes. Due to the complexity of the calculation and the complicated nature of a business, it is difficult to accurately determine NRE. However, once the guidelines are made and agreed upon, NRE can be a very useful tool to show how profitable a customer is. Table 1 A sample usage segment code table and corresponding NRE for each segment USC
Description
Criteria
Avg. NREa
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Old Old Old Old Old Old Old Old Old Old Old Old Old Old Old Old
MOBX12 and (>0 60 DPD, >4 30 DPD, or >10 5-DPD) MOBX12 and (>0 30 DPD or >5 5-DPD) MOBX12 and >2 5 DPD MOBX12 and no interest paid and no balance MODX12 and no interest paid and avg. bal. X$250 MOBX12 and no interest paid and avg. bal. o $250 MOBX12, Paid 1–7 mon. of int. and cash bal. >20% MOBX12, Paid 1–7 mon. of int. and avg. bal. X$250 MOBX12, Paid 1–7 mon. of int. and avg. bal. o $250 MOBX12, Paid 8–11 mon. of int. and cash bal. > 20% MOBX12, Paid 8–11 mon. of int. and avg. bal. X$1000 MOBX12, Paid 8–11 mon. of int. and avg. bal. o$1000 MOBX12, Paid 12 mon. of int. and avg. bal.=$0 MOBX12, Paid 12 mon. of int. and cash bal. >90% MOBX12, Paid 12 mon. of int. and avg. bal. X$2000 MOBX12, Paid 12 mon. of int. and avg. bal. o$2000
$20.86 $26.46 $28.49 ($0.14) $1.36 ($0.05) $9.07 $8.07 $0.89 $14.88 $25.18 $5.57 $26.85 $32.66 $48.18 $17.33
a
severe delinquency problem payer mild delinquency never active high bal. transactor transactor low revolver-cash user low revolver-high bal. low revolver med. revolver-cash user med. revolver-high bal. med. revolver high revolver-paying down high revolver-cash user high revolver-high bal. high revolver
As of January 1996.
ARTICLE IN PRESS 294
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
NRE is based simply on a customer’s periodical contribution to the company’s profit. A sample cycle-day NRE may include the following components: 1. Fee income: includes all possible income due to fees, such as purchase fees, cash advance fees, etc. 2. Interchange income: includes both the purchase and the cash advance each with a different interchange rate. 3. Functional cost: includes costs in all functional areas for supporting the product on a cost per person basis. 4. Loss: is mainly the total amount of charge-off’s. 5. Reward cost: is related to the cost in supporting reward prizes during product promotion. NRE is the sum of those components and can be expressed in a formula as follows: NRE ¼ Fees Income þ Interchange Income þ Functional Cost þ Loss þ Reward Cost:
3. Application I: customer migration analysis Combining USC and NRE allows us to look at the USC migration patterns over a certain period and learn how the corresponding NREs change. Once the USC/NRE migration patterns are clear, the marketing/product managers can better target their marketing resources to retain those segments that migrate into preferable segments and to shift adverse migration behaviors toward preferable patterns. The power of USC and NRE is best shown in back-end analyses, which are usually performed offline and in batch mode, when these two tools collaborate. We will demonstrate how a creditcard product manager used this joint application in a migration analysis to learn both the customer profit dynamics and ways to better target different customer groups. When customers migrate from one usage segment to another, the value of their NRE changes. The first step in the migration analysis is a 40 40 USC matrix of the customer counts and the corresponding NRE changes, which serves as the master migration data map. The data sets can be a quarter, 6 month or a year apart in order to see the percentage changes over time. Then, we partition the master matrix into easily analyzed groupings. For example, Table 2 illustrates part of the USC-count% matrix with the USC data in January 1995 and 1996 for the old customers group. For easy reference, this table was arranged so that the highlighted figures lined up in a diagonal fashion. We quantify the gain or loss in each cell by the average NREs from the original 40 40 USC matrix. For example, nearly 30% of the high-balanced high revolvers (USC 39) turned into paying-down high revolvers (USC 37) over the year of 1995 which rendered a loss of nearly $22 per cardholder (see Table 1, under Avg. NRE column, $48.18–$26.85), a vivid evidence of the lack of effective retention effort to keep the high-balanced high revolvers in their most profitable status of a year ago. The cells in bold print are the target of investigation during our analysis. For example, 62.5% of the inactive group (USC 28) in 1995 remained inactive in 1996, which indicated the need to promote the usage of their credit cards. Also, over 50% of those transactors (USC 30) in 1995 remained transactors a year later, which indicated the need to motivate these customers to
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
295
Table 2 Customer migration analysis—old accounts from 1/95 to 1/96 1996
USC 26
USC 27
USC 28
USC 30
USC 33
USC 36
USC 37
USC 38
USC 39
USC 40
0.3 0.5 0.7 3.2 2.4 1.7 3.5 3.1 2.2 3.1 3.8 2.8 3.6
0.1 0.5 0.5 4.7 4.3 2.0 4.8 5.5 3.7 6.5 0.5 0.9 1.5
62.5 2.3 14.3 7.3 3.3 12.1 4.4 2.8 4.3 0.2 0.1 0.1 0.1
8.9 52.3 57.7 12.3 17.6 27.8 5.4 5.6 6.1 0.6 0.2 0.1 0.3
3.3 19.4 13.9 22.8 28.4 30.8 18.1 16.1 24.8 8.9 5.7 4.8 6.8
0.6 0.7 1.6 13.3 9.8 7.1 16.7 15.3 18.1 9.3 8.2 6.5 10.5
0.0 0.1 0.2 9.5 10.3 3.0 14.4 20.4 19.9 47.1 27.9 29.9 25.6
0.0 0.0 0.0 6.4 0.4 0.2 9.2 0.8 1.1 2.4 28.0 1.2 4.0
0.0 0.0 0.0 1.9 1.2 0.1 2.8 4.3 0.5 4.1 2.0 29.5 8.0
0.0 0.0 0.0 2.5 1.1 0.4 4.4 2.0 3.3 2.7 3.3 0.9 17.9
1995 USC USC USC USC USC USC USC USC USC USC USC USC USC
28 29 30 31 32 33 34 35 35 37 38 39 40
All figures are percentages
revolve; otherwise, to prevent further losses to the company, strategies were needed to let these customers leave. For those high revolvers (USC 37–40), some stayed where they were or revolved more, but more were moving into less revolving status, which indicated that the company might be losing its best customers. Usage-segment migration analysis such as the one shown in Table 2 provides a clear and complete picture of how customers migrate over time. With this information, marketing/product managers can then develop marketing strategies to correctly target each individual migration pattern. Combining USC and NRE, the migration analysis puts customers’ consumption behaviors in concrete and quantifiable terms, making it a much easier task to earn upper management support for the marketing/product managers’ campaign. To understand more about why preferable and less preferable USC migration patterns occur, more detailed analyses can be conducted with other customer data in the customer database. For example, to analyze customers who migrate from heavy revolvers (USC 37–40) to mild revolvers (USC 34–36), profiles of customer lifestyle and demographic data can be made of these customers. These can then be compared with the marketing activities applied to understand what the company did (or did not do) to change these customers’ NRE from high to low. The company can then adjust marketing strategies to customers with similar lifestyle or demographics. Using the same method, the company would also learn what prompted the transactors (USC 30) to migrate to revolvers (USC 31–40), so that the company continues to focus on those marketing activities that propelled customers toward this more desirable migration pattern. The case company has also used USC and/or NRE with other tools in the following applications with satisfying results: 1. Determined how to adjust customers’ annual membership fees. 2. Determined how to promote revolvers by lowering their interest rate to proper levels.
ARTICLE IN PRESS 296
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
3. Selected customers for an extensive customer survey and an important focus group. 4. Determined how to target customers with fee-based insurance marketing campaigns, such as the credit insurance program and some membership programs. 5. Profiled different levels of transactors and revolvers to understand how to solicit new customers with preferred profiles.
4. Application II: retention management system USC and NRE are also used for non-marketing activities, such as those in the retention area whose primary goal is to keep valuable customers from leaving the company. Early information support for this retention process is in the primitive form of a few mainframe screens with scattered customer data. The representative interacts with the customer while pulling necessary information in and out of layers of screens and making decisions simultaneously. The retention process can become tedious and consequently ineffective. USC and NRE can be used as a building block in constructing a useful customer retention system. 4.1. A prototype retention management system For years the customer-service department at the case company handled customer retention via character-based terminals without dedicated screens and organized information. A PC-based retention computer system was developed, and a retention unit launched when the company realized that it was maintaining a product which lost 12% of its customer base annually, and was spending over 4 times the amount of money to acquire a new customer than to retain an existing one. The new environment applied client–server technology, and was a knowledge-oriented system via a user-friendly graphical user interface. It organized the necessary information from mainframe database servers over the network. The main components of this system include the database, the model base, the user interface, and the knowledge base as illustrated in Fig. 1. Key components such as the customer-value index and the knowledge component are described in greater detail. 4.2. Customer-value index The customer-value index is an indication of the value of an individual customer to the company and is a simple indicator resulting from a complex modeling process. Customer-value index has to be simple and easy for the company’s representatives so that they can quickly focus on the appropriate approach to retain the desired customers. The proposed customer-value index produces one value for each customer. The value ranges from 1 to 6, representing highly valuable to not valuable. The customer-value index used by the case company was derived from various pieces of information about individual customers, including usage segmentation code (USC), average 12month NRE behavior score (internal risk-based score), RPM (revolving propensity score from the credit bureau), and ROA (return on asset ratio). The actual calculations and derivations may vary in different businesses or companies.
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
297
User Interface (UI) • Graphical and windowed UI • Hierarchical level of details of the customer profile and corresponding business strategies
Data Base • Customer monetary • Non-monetary information
Model Base • Customer-value index
Knowledge Base • Business strategy for retention process • Competitor information • Cross-sell information
Fig. 1. Components of the Retention Management System.
Table 3 Matrix of customer-value index Index\USC 1 Min. 2 Min. 3 Min. 4 Min.
28
29
30
31
32
33
X $4
NRE Hurdle X $3
NRE Hurdle
X $5
34
35
36
37
38
39
40
X $3
X $3
X $3
X $3
X $3
X $3
X $3
X $4
X NRE Hurdle X NRE Hurdle
To illustrate the USC vs. value-index matrix, Table 3 presents part of a complex matrix from USCs 28 to 40, where 28–30 represents the transactor groups, 31–33 the low revolver groups, 34–36 the medium revolver groups, and 37–40 the high revolver groups. The matrix lists only those customers who are considered worthy of the retention effort (customer-value index 1–4). In the credit-card business, companies profit more from customers who revolve; it is therefore not difficult to figure out why medium to high revolver groups (USC 34–40) are marked as highly valuable as indicated by the value index 1. 4.3. Knowledge component The knowledge base holds three sets of major knowledge: competitor information, retention strategy and cross-sale information. Competitor information includes all the product information of the competitors in the market, and provides a direct feature-to-feature comparison between the competitors and the case company (see Table 4). Retention strategy includes all the possible offers of the case company’s product as illustrated in Table 5. Cross-sale information contains possible
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
298
Table 4 Sample Feature-to-Feature Product Comparison Feature\Credit Card a
APR Annual fee Fee waiver Introduction rate Interest adjust period a b
Case Company b
L +12.6% $25-Classic 1 year 7.9% first 6 months Monthly
Diners Club
GE Vacations
N/A $80-Classic No N/A N/A
L+11.49% $20 1 yr 6.9% first 6 months Monthly
APR=Annual Percentage Rate. L=London InterBank Offered Rate, which is usually between 12% and 13%.
Table 5 Sample business retention strategy for customer-value index=1 Offer
APR
AMF
Misc.
1 2
7
L+5.9
Waive AMF for 1 year ($25–$100)a Waive AMF for 1 year ($25–$100) if Bal. Trf. $1,000 Waive AMF for 1 year ($25–$100) if reverse 2500–8000 bonus points Pay AMF($25–$80) receive 250–800 bonus points Waive AMF for 1 year ($25–$100) if Bal. Trf. [email protected] until 1/97 Waive AMF for 1 year ($25–$100) if Bal. Trf. [email protected] until pay off Waive AMF for 1 year ($25–$100) and 2500 bonus points if Bal. Trf. $3000
$50 Misc fee waivedb Bonus points of 1000
6
L+8.75 L+8.75/Bal. Trf. of $500+ L+8.75/Bal. Trf of [email protected]% until 1/97 L+7.9/Bal. Trf. Of $500+ L+5.9/Bal. Trf. Of $500+ L+7.9
3 4 5
Bal. Trf. $3000 and receive 2500 bonus points
APR=Annual Percentage Rate; AMF=Annual Membership Fee; Bal. Trf.=Balance Transfer. L=London InterBank Offered Rate, which is usually between 12% and 13%. Bonus points=reward-based points, used to exchange for benefits provided by the product, accumulated by using the card. a Supervisor approval required. b Supervisor approval required (Must use to meet customer half way on disputed balance, not to exceed $50).
alternative offers or additional products to the customers, such as a different credit-card product or other banking products (e.g., discounted mortgage rate, certificate of deposit, securities, etc.). The strategies are also linked by the value index to the reason(s) why the customer is leaving. Table 5 illustrates a list of business strategies corresponding with customer-value index 1, which is also what the representative sees on the computer screen during a retention call. Business strategies are developed to retain customers within value indexes 1–4. The customer-value index practically drives the entire retention process. Combining Tables 3–5, the retention representative is able to quickly recognize the importance of the customer and use the corresponding business strategy to effectively persuade the customer to stay with the company.
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
299
4.4. The retention process When a customer calls to cancel her account, the phone call is automatically routed to the retention unit. The representative brings up a retention main screen which shows the customer’s profile with the value index of the given account number. If the value index indicates that this is a preferred customer, the strategy is a series of offers targeted at the customer’s needs. The representative starts with the least attractive offer going up to the most attractive one to persuade the customer to stay. In the case described, a great percentage of customers were leaving because of a known competitor. Thus the competitor information usually led the representative to the right offers to create a win–win situation for both the company and the customer. Once the customer is retained successfully, the representative further persuades the customer to transfer balances with other competitors to the case company. On the other hand, if the customer still wants to leave, the representative would try offering other products in the cross-sale list. The ultimate goal of this knowledge-oriented retention approach is to convert a possible loss into a potential opportunity for the company, which is also the spirit of this credit-card issuer’s corporate business strategy. 4.5. Early results Initial results from the retention unit of the product were extremely promising. In the first three months after launching the prototype retention management system, the retention unit successfully retained half of the leaving customers who fell into customer-value index 1–4. Moreover, about one-fourth of those who stayed agreed to a balance transfer of an average amount of $1500. A look at the distribution of the total population who called in to cancel their accounts showed 28% of those were in the value indexes 1–3 categories, 50% in value index 4, and 22% in value indexes 5–6. In other words, the collaborate work of the retention unit and the knowledge-oriented retention system had helped the case company obtain a 39% retention rate and successfully converted potential loss into increased business through a good amount of cross sale. Informal observations of the retention operations indicated that the retention system did smooth the workflow of the retention representatives. With the aid of the built-in strategies, they could calmly proceed with the negotiation. They spent less time hurrying through screens, but focused more on the task of retaining the leaving customers. To the case company, part of the success came from the representatives’ increased satisfaction with the company. The main reason was that the applied technologies both enabled the representatives’ retention task and improved their working environment, which was long overdue from the company.
5. Conclusion 5.1. Contributions USC and NRE were developed and tested in many projects in a top-ten US credit-card issuer with satisfactory results. These ideas are simple to understand and to implement, and as
ARTICLE IN PRESS 300
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
illustrated, they can be used to derive rich customer information that provides vital input to important marketing strategies. This case proves that simple yet powerful database marketing tools can be developed in-house even with a less than perfect data warehouse that encompasses company-wide data over time or with marts that store department-wide data over time. On the road to on-going database marketing processes, many companies invest millions of dollars in a customer data warehouse. Simple ideas such as USC and NRE can increase the return of this investment. 5.2. Limitations In making the above point, we caution the readers also to consider the limitations of what we have proposed, particularly problems with the use of data, information, or knowledge from an historical perspective. This database marketing application aims to influence existing customers by reference to their past behaviors. Consequently, a company can only react in this way to retain customers in a somewhat limited fashion. Marketers need to consider how this may affect the strategic direction of the company. Ideally, a database-marketing application should complement an overall strategic system which encourages more proactive use of customer data.
Acknowledgements The authors would like to thank Debbie Sandgren and Diane McCowin, colleagues at the credit-card company where USC and NRE were developed and used, for their support in making these two database marketing tools possible. They are both working for a different US credit-card issuer now with higher responsibilities.
References Demery, P. (1999). The decade of marketing. Credit Card Management, 11(11), 74–84. Dwyer, F. R. (1997). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 11(4), 6–13. Dyche, J. (2000). e-Data: Turning data into information with data warehousing. Boston, MA: Addison-Wesley Publishing Co. Lehaney, B., Clarke, S., Kimberlee, V., & Spencer-Matthews, S. (1999). The human side of information development: A case of an intervention at a British visitor attraction. Journal of End User Computing, 11(4), 33–39. Palmquist, J., & Ketola, L. (1999). Turning data into knowledge. Marketing Research: A Magazine of Management & Applications, 1(2), 28–32. Stone, B. (1988). Successful direct marketing methods (4th Edition). Lincolnwood, IL: NTC Business Books. Weber, A. (2000). Marrying technical and marketing skills. Target Marketing, 23(2), 144–159. Wigand, R., Picot, A., & Reichwald, R. (1997). Information, organization and management: Expanding markets and corporate boundaries. Chichester, England: Wiley. Yu-Hui Tao is an associate professor at the Department of Information Management, I-Shou University, Taiwan, ROC. He received his master’s and Ph.D. degrees in industrial and systems engineering from the Ohio State University,
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
301
Columbus, OH, USA. His current research interests include tools and applications in relationship and database marketing, web-based applications, and e-commerce. Chu-Chen Rosa Yeh is a seasoned management consultant and currently a Ph.D. student at the Institute of Human Resource Management, National Sun Yat-sen University, Taiwan, ROC. She received her master’s degree in instructional design and technology from the Ohio State University, Columbus, OH, USA. She has worked with numerous Fortune 500 companies in the US and Taiwan in the areas of relationship marketing, service quality, and human performance improvement.
International Journal of Information Management 23 (2003) 291–301
Simple database marketing tools in customer analysis and retention Yu-Hui Taoa,*, Chu-Chen Rosa Yehb b
a Department of Information Management, I-Shou University, Kaohsiung County, Taiwan, ROC Institute of Human Resource Management, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC
Abstract Many businesses practice database marketing to increase effectiveness in their marketing activities. The database marketing approach uses current advanced information technology to collect customer data, analyze it and use it to provide recognition and services to customers for the purpose of increasing customer loyalty and repeat sales. Over the years, the practice of database marketing has evolved into a costly implementation of highly sophisticated information technology and application, which requires elaborate planning and organizational skills for successful adoption. Consequently, the need has been recognized for a human-centred approach which focuses more on the business problem rather than on the technology. This paper introduces two simple yet essential database-marketing tools—usage segment code and net revenue equation—and describes their application in the credit-card business. The promising results obtained further demonstrate that simple yet creative ideas can be converted into powerful database marketing tools to increase the return on investment in a marketing database. r 2003 Elsevier Ltd. All rights reserved. Keywords: Database marketing; Marketing tools; Customer analysis; Credit-card companies
1. Introduction In recent years research in information systems has led to the development of theories and techniques for the exploitation of organizational knowledge of the customers. See, for example, Dyche (2000) and Wigand, Picot, and Reichwald (1997). It is, however, still possible for any company with a modest customer database to start their own database marketing activities with simple tools in-house, which can be successful if the focus is on the business problems instead of the technology problems. This article delivers the experience of a top-ten US credit-card issuer *Corresponding author. Tel.: +886-7-657-7711x6562; fax: +886-7-6577056. E-mail address: [email protected] (Y.-H. Tao). 0268-4012/03/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0268-4012(03)00052-5
ARTICLE IN PRESS 292
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
who developed two simple marketing tools, Usage Segment Code (USC) and Net Revenue Equation (NRE), both proven useful in understanding customer behaviors and in various other marketing activities. As Section 2 of this article will explain, USC is the spending pattern of a customer while NRE is the retrospective estimated profit from a customer during a certain period. Two major applications of these two marketing tools are introduced: a backend customer migration analysis in Section 3 and a customer retention management system in Section 4. These two tools can be applied to other membership-type business, such as retail banking, mortgage, mail order, department stores, insurance services, auto clubs, airlines, and hotels. The marketing database, if done correctly, can assist marketing managers in tasks ranging from daily operation, resource allocation, and budget planning, to strategic decision processes. As the marketing world braces for changes that call for new tools and approaches, database marketing is emerging as an important tool (Palmquist & Ketola, 1999). Database marketing is an approach to generate integrated and accessible customer information to help the marketers better target their marketing efforts to existing customers or prospects. The ultimate goal of database marketing is to create a win–win situation for both the marketers and the customers by reducing marketing costs, increasing sales and profits, and building customer loyalty. Over the decades, many database-marketing tools were developed and used in various stages of marketing. Some of the most popular tools include: the Recency-Frequency-Monetary (RFM) Formula, the lifestyle segment of the existing customers and the lifetime value of a customer (Stone, 1988; Dwyer, 1997). Database marketing tools are now evolving to the point that the credit-card industry can actually deliver its long-standing promise of providing products tailored to each individual customer. It might however be a technological game limited to the largest of players (Demery, 1999). Database marketing becomes more complex as technology continues to grow (Weber, 2000). As organizations grow more dependent on this new technology, they invest more in the hardware and the software that subsequently contribute to the complexity of their systems. Somewhere along this process, organizations may start to lose their focus. Lehaney, Clarke, Kimberlee, and Spencer-Matthews (1999) supported the demonstrated value of technology-enabled database marketing, but also concluded that its success rests on participative, human-centered approaches to development. A marketer tackling specific marketing challenges should not focus on technology, but rather, the business problem. When solving business problems, human experience and creativity provide critical insights and at times viable solutions without costly investment or expansion in the technology. This paper introduces two simple yet essential database-marketing tools—usage segment code and net revenue equation— and their applications in the credit-card business. The promising results further demonstrate that simple yet creative ideas can be converted into powerful database marketing tools to increase the return on investment in a marketing database.
2. Database marketing tools: USC and NRE USC and NRE are described in this section, along with their applications in a US top-ten credit-card company.
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
293
2.1. Usage segment code (USC) USC is a way to cluster existing customers (cardholders) into distinct groups based on customer history, such as the total spending, type of spending (e.g., purchase or cash advance), delinquent status and history, other status code, and so on. The criteria of each segment should be fine enough so that they can be either used individually for a certain marketing activity which requires very fine clusters, or further combined into larger groups/segments for appropriate marketing activities. Table 1 illustrates part of a 40-segment scheme. The complete 40-segment scheme can be further grouped into larger groupings such as new customers vs. old customers, or transactors vs. revolvers. In this case, we define transactors as those customers who paid their balances off every month; and revolvers as those who paid at least one-month interest over the last 12 months. Revolvers are preferable customers because they positively contribute to the credit-card company’s bottom line. Table 1 demonstrates the old customer segments (USC from 25 to 40) which include customers who have been on the books for over 12 months (Months On Book (MOB)X12). 2.2. Net revenue equation NRE is a formula used to calculate the net revenue from an existing customer over a period of time, such as a month or a cycle. The accuracy of NRE depends on the ongoing revenue dynamics of the customers and various costs of the company processes. Due to the complexity of the calculation and the complicated nature of a business, it is difficult to accurately determine NRE. However, once the guidelines are made and agreed upon, NRE can be a very useful tool to show how profitable a customer is. Table 1 A sample usage segment code table and corresponding NRE for each segment USC
Description
Criteria
Avg. NREa
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Old Old Old Old Old Old Old Old Old Old Old Old Old Old Old Old
MOBX12 and (>0 60 DPD, >4 30 DPD, or >10 5-DPD) MOBX12 and (>0 30 DPD or >5 5-DPD) MOBX12 and >2 5 DPD MOBX12 and no interest paid and no balance MODX12 and no interest paid and avg. bal. X$250 MOBX12 and no interest paid and avg. bal. o $250 MOBX12, Paid 1–7 mon. of int. and cash bal. >20% MOBX12, Paid 1–7 mon. of int. and avg. bal. X$250 MOBX12, Paid 1–7 mon. of int. and avg. bal. o $250 MOBX12, Paid 8–11 mon. of int. and cash bal. > 20% MOBX12, Paid 8–11 mon. of int. and avg. bal. X$1000 MOBX12, Paid 8–11 mon. of int. and avg. bal. o$1000 MOBX12, Paid 12 mon. of int. and avg. bal.=$0 MOBX12, Paid 12 mon. of int. and cash bal. >90% MOBX12, Paid 12 mon. of int. and avg. bal. X$2000 MOBX12, Paid 12 mon. of int. and avg. bal. o$2000
$20.86 $26.46 $28.49 ($0.14) $1.36 ($0.05) $9.07 $8.07 $0.89 $14.88 $25.18 $5.57 $26.85 $32.66 $48.18 $17.33
a
severe delinquency problem payer mild delinquency never active high bal. transactor transactor low revolver-cash user low revolver-high bal. low revolver med. revolver-cash user med. revolver-high bal. med. revolver high revolver-paying down high revolver-cash user high revolver-high bal. high revolver
As of January 1996.
ARTICLE IN PRESS 294
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
NRE is based simply on a customer’s periodical contribution to the company’s profit. A sample cycle-day NRE may include the following components: 1. Fee income: includes all possible income due to fees, such as purchase fees, cash advance fees, etc. 2. Interchange income: includes both the purchase and the cash advance each with a different interchange rate. 3. Functional cost: includes costs in all functional areas for supporting the product on a cost per person basis. 4. Loss: is mainly the total amount of charge-off’s. 5. Reward cost: is related to the cost in supporting reward prizes during product promotion. NRE is the sum of those components and can be expressed in a formula as follows: NRE ¼ Fees Income þ Interchange Income þ Functional Cost þ Loss þ Reward Cost:
3. Application I: customer migration analysis Combining USC and NRE allows us to look at the USC migration patterns over a certain period and learn how the corresponding NREs change. Once the USC/NRE migration patterns are clear, the marketing/product managers can better target their marketing resources to retain those segments that migrate into preferable segments and to shift adverse migration behaviors toward preferable patterns. The power of USC and NRE is best shown in back-end analyses, which are usually performed offline and in batch mode, when these two tools collaborate. We will demonstrate how a creditcard product manager used this joint application in a migration analysis to learn both the customer profit dynamics and ways to better target different customer groups. When customers migrate from one usage segment to another, the value of their NRE changes. The first step in the migration analysis is a 40 40 USC matrix of the customer counts and the corresponding NRE changes, which serves as the master migration data map. The data sets can be a quarter, 6 month or a year apart in order to see the percentage changes over time. Then, we partition the master matrix into easily analyzed groupings. For example, Table 2 illustrates part of the USC-count% matrix with the USC data in January 1995 and 1996 for the old customers group. For easy reference, this table was arranged so that the highlighted figures lined up in a diagonal fashion. We quantify the gain or loss in each cell by the average NREs from the original 40 40 USC matrix. For example, nearly 30% of the high-balanced high revolvers (USC 39) turned into paying-down high revolvers (USC 37) over the year of 1995 which rendered a loss of nearly $22 per cardholder (see Table 1, under Avg. NRE column, $48.18–$26.85), a vivid evidence of the lack of effective retention effort to keep the high-balanced high revolvers in their most profitable status of a year ago. The cells in bold print are the target of investigation during our analysis. For example, 62.5% of the inactive group (USC 28) in 1995 remained inactive in 1996, which indicated the need to promote the usage of their credit cards. Also, over 50% of those transactors (USC 30) in 1995 remained transactors a year later, which indicated the need to motivate these customers to
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
295
Table 2 Customer migration analysis—old accounts from 1/95 to 1/96 1996
USC 26
USC 27
USC 28
USC 30
USC 33
USC 36
USC 37
USC 38
USC 39
USC 40
0.3 0.5 0.7 3.2 2.4 1.7 3.5 3.1 2.2 3.1 3.8 2.8 3.6
0.1 0.5 0.5 4.7 4.3 2.0 4.8 5.5 3.7 6.5 0.5 0.9 1.5
62.5 2.3 14.3 7.3 3.3 12.1 4.4 2.8 4.3 0.2 0.1 0.1 0.1
8.9 52.3 57.7 12.3 17.6 27.8 5.4 5.6 6.1 0.6 0.2 0.1 0.3
3.3 19.4 13.9 22.8 28.4 30.8 18.1 16.1 24.8 8.9 5.7 4.8 6.8
0.6 0.7 1.6 13.3 9.8 7.1 16.7 15.3 18.1 9.3 8.2 6.5 10.5
0.0 0.1 0.2 9.5 10.3 3.0 14.4 20.4 19.9 47.1 27.9 29.9 25.6
0.0 0.0 0.0 6.4 0.4 0.2 9.2 0.8 1.1 2.4 28.0 1.2 4.0
0.0 0.0 0.0 1.9 1.2 0.1 2.8 4.3 0.5 4.1 2.0 29.5 8.0
0.0 0.0 0.0 2.5 1.1 0.4 4.4 2.0 3.3 2.7 3.3 0.9 17.9
1995 USC USC USC USC USC USC USC USC USC USC USC USC USC
28 29 30 31 32 33 34 35 35 37 38 39 40
All figures are percentages
revolve; otherwise, to prevent further losses to the company, strategies were needed to let these customers leave. For those high revolvers (USC 37–40), some stayed where they were or revolved more, but more were moving into less revolving status, which indicated that the company might be losing its best customers. Usage-segment migration analysis such as the one shown in Table 2 provides a clear and complete picture of how customers migrate over time. With this information, marketing/product managers can then develop marketing strategies to correctly target each individual migration pattern. Combining USC and NRE, the migration analysis puts customers’ consumption behaviors in concrete and quantifiable terms, making it a much easier task to earn upper management support for the marketing/product managers’ campaign. To understand more about why preferable and less preferable USC migration patterns occur, more detailed analyses can be conducted with other customer data in the customer database. For example, to analyze customers who migrate from heavy revolvers (USC 37–40) to mild revolvers (USC 34–36), profiles of customer lifestyle and demographic data can be made of these customers. These can then be compared with the marketing activities applied to understand what the company did (or did not do) to change these customers’ NRE from high to low. The company can then adjust marketing strategies to customers with similar lifestyle or demographics. Using the same method, the company would also learn what prompted the transactors (USC 30) to migrate to revolvers (USC 31–40), so that the company continues to focus on those marketing activities that propelled customers toward this more desirable migration pattern. The case company has also used USC and/or NRE with other tools in the following applications with satisfying results: 1. Determined how to adjust customers’ annual membership fees. 2. Determined how to promote revolvers by lowering their interest rate to proper levels.
ARTICLE IN PRESS 296
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
3. Selected customers for an extensive customer survey and an important focus group. 4. Determined how to target customers with fee-based insurance marketing campaigns, such as the credit insurance program and some membership programs. 5. Profiled different levels of transactors and revolvers to understand how to solicit new customers with preferred profiles.
4. Application II: retention management system USC and NRE are also used for non-marketing activities, such as those in the retention area whose primary goal is to keep valuable customers from leaving the company. Early information support for this retention process is in the primitive form of a few mainframe screens with scattered customer data. The representative interacts with the customer while pulling necessary information in and out of layers of screens and making decisions simultaneously. The retention process can become tedious and consequently ineffective. USC and NRE can be used as a building block in constructing a useful customer retention system. 4.1. A prototype retention management system For years the customer-service department at the case company handled customer retention via character-based terminals without dedicated screens and organized information. A PC-based retention computer system was developed, and a retention unit launched when the company realized that it was maintaining a product which lost 12% of its customer base annually, and was spending over 4 times the amount of money to acquire a new customer than to retain an existing one. The new environment applied client–server technology, and was a knowledge-oriented system via a user-friendly graphical user interface. It organized the necessary information from mainframe database servers over the network. The main components of this system include the database, the model base, the user interface, and the knowledge base as illustrated in Fig. 1. Key components such as the customer-value index and the knowledge component are described in greater detail. 4.2. Customer-value index The customer-value index is an indication of the value of an individual customer to the company and is a simple indicator resulting from a complex modeling process. Customer-value index has to be simple and easy for the company’s representatives so that they can quickly focus on the appropriate approach to retain the desired customers. The proposed customer-value index produces one value for each customer. The value ranges from 1 to 6, representing highly valuable to not valuable. The customer-value index used by the case company was derived from various pieces of information about individual customers, including usage segmentation code (USC), average 12month NRE behavior score (internal risk-based score), RPM (revolving propensity score from the credit bureau), and ROA (return on asset ratio). The actual calculations and derivations may vary in different businesses or companies.
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
297
User Interface (UI) • Graphical and windowed UI • Hierarchical level of details of the customer profile and corresponding business strategies
Data Base • Customer monetary • Non-monetary information
Model Base • Customer-value index
Knowledge Base • Business strategy for retention process • Competitor information • Cross-sell information
Fig. 1. Components of the Retention Management System.
Table 3 Matrix of customer-value index Index\USC 1 Min. 2 Min. 3 Min. 4 Min.
28
29
30
31
32
33
X $4
NRE Hurdle X $3
NRE Hurdle
X $5
34
35
36
37
38
39
40
X $3
X $3
X $3
X $3
X $3
X $3
X $3
X $4
X NRE Hurdle X NRE Hurdle
To illustrate the USC vs. value-index matrix, Table 3 presents part of a complex matrix from USCs 28 to 40, where 28–30 represents the transactor groups, 31–33 the low revolver groups, 34–36 the medium revolver groups, and 37–40 the high revolver groups. The matrix lists only those customers who are considered worthy of the retention effort (customer-value index 1–4). In the credit-card business, companies profit more from customers who revolve; it is therefore not difficult to figure out why medium to high revolver groups (USC 34–40) are marked as highly valuable as indicated by the value index 1. 4.3. Knowledge component The knowledge base holds three sets of major knowledge: competitor information, retention strategy and cross-sale information. Competitor information includes all the product information of the competitors in the market, and provides a direct feature-to-feature comparison between the competitors and the case company (see Table 4). Retention strategy includes all the possible offers of the case company’s product as illustrated in Table 5. Cross-sale information contains possible
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
298
Table 4 Sample Feature-to-Feature Product Comparison Feature\Credit Card a
APR Annual fee Fee waiver Introduction rate Interest adjust period a b
Case Company b
L +12.6% $25-Classic 1 year 7.9% first 6 months Monthly
Diners Club
GE Vacations
N/A $80-Classic No N/A N/A
L+11.49% $20 1 yr 6.9% first 6 months Monthly
APR=Annual Percentage Rate. L=London InterBank Offered Rate, which is usually between 12% and 13%.
Table 5 Sample business retention strategy for customer-value index=1 Offer
APR
AMF
Misc.
1 2
7
L+5.9
Waive AMF for 1 year ($25–$100)a Waive AMF for 1 year ($25–$100) if Bal. Trf. $1,000 Waive AMF for 1 year ($25–$100) if reverse 2500–8000 bonus points Pay AMF($25–$80) receive 250–800 bonus points Waive AMF for 1 year ($25–$100) if Bal. Trf. [email protected] until 1/97 Waive AMF for 1 year ($25–$100) if Bal. Trf. [email protected] until pay off Waive AMF for 1 year ($25–$100) and 2500 bonus points if Bal. Trf. $3000
$50 Misc fee waivedb Bonus points of 1000
6
L+8.75 L+8.75/Bal. Trf. of $500+ L+8.75/Bal. Trf of [email protected]% until 1/97 L+7.9/Bal. Trf. Of $500+ L+5.9/Bal. Trf. Of $500+ L+7.9
3 4 5
Bal. Trf. $3000 and receive 2500 bonus points
APR=Annual Percentage Rate; AMF=Annual Membership Fee; Bal. Trf.=Balance Transfer. L=London InterBank Offered Rate, which is usually between 12% and 13%. Bonus points=reward-based points, used to exchange for benefits provided by the product, accumulated by using the card. a Supervisor approval required. b Supervisor approval required (Must use to meet customer half way on disputed balance, not to exceed $50).
alternative offers or additional products to the customers, such as a different credit-card product or other banking products (e.g., discounted mortgage rate, certificate of deposit, securities, etc.). The strategies are also linked by the value index to the reason(s) why the customer is leaving. Table 5 illustrates a list of business strategies corresponding with customer-value index 1, which is also what the representative sees on the computer screen during a retention call. Business strategies are developed to retain customers within value indexes 1–4. The customer-value index practically drives the entire retention process. Combining Tables 3–5, the retention representative is able to quickly recognize the importance of the customer and use the corresponding business strategy to effectively persuade the customer to stay with the company.
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
299
4.4. The retention process When a customer calls to cancel her account, the phone call is automatically routed to the retention unit. The representative brings up a retention main screen which shows the customer’s profile with the value index of the given account number. If the value index indicates that this is a preferred customer, the strategy is a series of offers targeted at the customer’s needs. The representative starts with the least attractive offer going up to the most attractive one to persuade the customer to stay. In the case described, a great percentage of customers were leaving because of a known competitor. Thus the competitor information usually led the representative to the right offers to create a win–win situation for both the company and the customer. Once the customer is retained successfully, the representative further persuades the customer to transfer balances with other competitors to the case company. On the other hand, if the customer still wants to leave, the representative would try offering other products in the cross-sale list. The ultimate goal of this knowledge-oriented retention approach is to convert a possible loss into a potential opportunity for the company, which is also the spirit of this credit-card issuer’s corporate business strategy. 4.5. Early results Initial results from the retention unit of the product were extremely promising. In the first three months after launching the prototype retention management system, the retention unit successfully retained half of the leaving customers who fell into customer-value index 1–4. Moreover, about one-fourth of those who stayed agreed to a balance transfer of an average amount of $1500. A look at the distribution of the total population who called in to cancel their accounts showed 28% of those were in the value indexes 1–3 categories, 50% in value index 4, and 22% in value indexes 5–6. In other words, the collaborate work of the retention unit and the knowledge-oriented retention system had helped the case company obtain a 39% retention rate and successfully converted potential loss into increased business through a good amount of cross sale. Informal observations of the retention operations indicated that the retention system did smooth the workflow of the retention representatives. With the aid of the built-in strategies, they could calmly proceed with the negotiation. They spent less time hurrying through screens, but focused more on the task of retaining the leaving customers. To the case company, part of the success came from the representatives’ increased satisfaction with the company. The main reason was that the applied technologies both enabled the representatives’ retention task and improved their working environment, which was long overdue from the company.
5. Conclusion 5.1. Contributions USC and NRE were developed and tested in many projects in a top-ten US credit-card issuer with satisfactory results. These ideas are simple to understand and to implement, and as
ARTICLE IN PRESS 300
Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
illustrated, they can be used to derive rich customer information that provides vital input to important marketing strategies. This case proves that simple yet powerful database marketing tools can be developed in-house even with a less than perfect data warehouse that encompasses company-wide data over time or with marts that store department-wide data over time. On the road to on-going database marketing processes, many companies invest millions of dollars in a customer data warehouse. Simple ideas such as USC and NRE can increase the return of this investment. 5.2. Limitations In making the above point, we caution the readers also to consider the limitations of what we have proposed, particularly problems with the use of data, information, or knowledge from an historical perspective. This database marketing application aims to influence existing customers by reference to their past behaviors. Consequently, a company can only react in this way to retain customers in a somewhat limited fashion. Marketers need to consider how this may affect the strategic direction of the company. Ideally, a database-marketing application should complement an overall strategic system which encourages more proactive use of customer data.
Acknowledgements The authors would like to thank Debbie Sandgren and Diane McCowin, colleagues at the credit-card company where USC and NRE were developed and used, for their support in making these two database marketing tools possible. They are both working for a different US credit-card issuer now with higher responsibilities.
References Demery, P. (1999). The decade of marketing. Credit Card Management, 11(11), 74–84. Dwyer, F. R. (1997). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 11(4), 6–13. Dyche, J. (2000). e-Data: Turning data into information with data warehousing. Boston, MA: Addison-Wesley Publishing Co. Lehaney, B., Clarke, S., Kimberlee, V., & Spencer-Matthews, S. (1999). The human side of information development: A case of an intervention at a British visitor attraction. Journal of End User Computing, 11(4), 33–39. Palmquist, J., & Ketola, L. (1999). Turning data into knowledge. Marketing Research: A Magazine of Management & Applications, 1(2), 28–32. Stone, B. (1988). Successful direct marketing methods (4th Edition). Lincolnwood, IL: NTC Business Books. Weber, A. (2000). Marrying technical and marketing skills. Target Marketing, 23(2), 144–159. Wigand, R., Picot, A., & Reichwald, R. (1997). Information, organization and management: Expanding markets and corporate boundaries. Chichester, England: Wiley. Yu-Hui Tao is an associate professor at the Department of Information Management, I-Shou University, Taiwan, ROC. He received his master’s and Ph.D. degrees in industrial and systems engineering from the Ohio State University,
ARTICLE IN PRESS Y.-H. Tao, C.-C. Rosa Yeh / International Journal of Information Management 23 (2003) 291–301
301
Columbus, OH, USA. His current research interests include tools and applications in relationship and database marketing, web-based applications, and e-commerce. Chu-Chen Rosa Yeh is a seasoned management consultant and currently a Ph.D. student at the Institute of Human Resource Management, National Sun Yat-sen University, Taiwan, ROC. She received her master’s degree in instructional design and technology from the Ohio State University, Columbus, OH, USA. She has worked with numerous Fortune 500 companies in the US and Taiwan in the areas of relationship marketing, service quality, and human performance improvement.