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Simple Database Marketing Tools for Great Marketing Results Yu-Hui Tao Department of Information Management I-Shou University [email protected] C. Rosa Yeh Business Management Consultant [email protected] ABSTRACT Database marketing is an important approach for many businesses in their marketing activities. It uses current advanced information technology to help businesses be more effective and profitable. Many tools used in a marketing database derive information from the existing customer information in their data warehouse. This paper introduces two simple but essential tools--usage segment code and net revenue equation--and their application in the credit-card business. The powerful result and the rich application of these two simple tools will prove that simple yet creative ideas can be converted into database marketing tools to increase the return of investment in such a marketing database. Keywords: credit card; database marketing; data mining; marketing tools; OLAP 簡易資料庫行銷工具建行銷大功 陶幼慧 義守大學資訊管理學系 [email protected] 葉俶禎 企業管理顧問 [email protected] 摘要 資料庫行銷是現今許多企業行銷活動的重要方法之一。它運用先進的資訊科技來提昇企 業的效率與獲利率。許多行銷資料庫的工具都是從現有資料倉儲中的顧客資料推導出 來。本文介紹兩個簡單但基本的工具—使用區隔代碼及淨盈利公式--及它們在信用卡業 的應用。它們表現的效能與豐富的應用範圍印証了即使是簡單但具有創意的觀念也可以 被應用到資料庫行銷來增進其投資報酬。 關鍵字:信用卡,資料庫行銷,資料探掘,行銷工具,線上分析處理。 1. Introduction Marketing is the “process of executing the conception, pricing, promotion, and distribution of ideas, goods, and services to create exchanges that satisfy individual and organizational goals“ [1]. 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. In other words, 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. The marketing database, if done correctly, can assist marketing managers from daily operation, resource allocation, budget planning, to strategic decision processes. Some of the well-known database marketing tools are: the Recency-Frequency-Monetary (R-F-M) Formula, the lifestyle segment of the existing customers and the lifetime value of a customer [5]. RFM formula is often used to identify the best customers who had bought most recently, who had bought most frequently within a specified period, and who had spent specified amounts. Lifestyle segment is a customer profile composed of the geo-demographics, psychographics, lifestyles, attitudes and purchase behaviors. Lifetime value is an estimate of the profit a customer can generate over his/her relationship with the company. There are many commercial database marketing tools available, such as Metromail’s DNA [3,4] and EQUIFAX’s MicroVision [2]. However, any company with a modest customer database can start their own database marketing activities with simple tools in-house. Usage Segment Code (USC) and Net Revenue Equation (NRE) are simple existing customer-based marketing tools, which had been proven useful in a top-ten credit-card issuer in understanding customer behaviors and in various other marketing activities. USC is the spending pattern of a customer while NRE is the retrospective estimated profit from a customer during a certain period. This article introduces and illustrates these two marketing tools and their applications in a US credit-card issuer. However, these two tools can be applied to any membership-type business, such as retail banking, mortgage, mail order, department stores, insurance services, auto clubs, airlines, and hotels. 2. Database Marketing Tools We will define and illustrate USC and NRE in each of the following sections. The illustration is based on, but not limited to, the application in the credit-card business. 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 last 12-month of total spending, type of spending (e.g., purchase or cash advance), percentage of type of spending (e.g., cash advance over the sum of purchase and cash advance), number of months paying interests, 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 a partial 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. Table 1 demonstrates the old customer segments (USC from 25-40) which include customers who have been on the book for over 12 months (Months On Book (MOB) >= 12). The young customer segments (USC 9-24) which are not shown in Table 1 have similar definitions as the old customer segments except their MOBs are smaller than 12. 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. Usage Segment Code Table USC Description Criteria 25 Old Severe Delinquency MOB >=12 and (>0 60 DPD, >4 30 DPD, or >10 5-DPD) 26 Old Problem Payer MOB >=12 and ((>0 30 DPD or >5 5-DPD) 27 Old Mild Delinquency MOB>= 12 and >2 5 DPD 28 Old Never Active MOB>= and No Interest paid and No Balance 29 Old High Bal. Transactor MOD>=12 and No Interest paid and Avg. Bal. >= $250 30 Old Transactor MOB>=12 and No Interest paid and Avg. Bal. < $250 31 Old Low Revolver-Cash User MOB>=12, Paid 1-7 Mon. of Int. and Cash Bal. > 20% 32 Old Low Revolver-High Bal. MOB>=12, Paid 1-7 Mon. of Int. and Avg. Bal. >= $250 33 Old Low Revolver MOB>=12, Paid 1-7 Mon. of Int. and Avg. Bal. < $250 34 Old Med. Revolver-Cash User MOB>=12, Paid 8-11 Mon. of Int. and Cash Bal. > 20% 35 Old Med. Revolver-High Bal. MOB>=12, Paid 8-11 Mon. of Int. and Avg. Bal. >= $1,000 36 Old Med. Revolver MOB>=12, Paid 8-11 Mon. of Int. and Avg. Bal. <$1,000 37 Old High Revolver-Paying Down MOB>=12, Paid 12 Mon. of Int. and Avg. Bal. = $0 38 Old High Revolver-Cash User MOB>=12, Paid 12 Mon. of Int. and Cash Bal. >90% 39 Old High Revolver-High Bal. MOB>=12, Paid 12 Mon. of Int. and Avg. Bal. >= $2,000 40 Old High Revolver. MOB>=12, Paid 12 Mon. of Int. and Avg. Bal. <$2,000 2.2 Net Revenue Equation may include the following components: Net revenue equation is a formula used to calculate the net revenue of an existing customer in a periodical interval, 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. NRE = NRE bases simply on a customer’s periodical contribution to the company’s profit. That means, we quantify a customer by his/her contribution in revenue without other risk factors. A sample cycle-day NRE Fees_Income + Interchange_Income + Functional_Cost + Loss + Reward_Cost Note that: 1. Fee income includes all possible income due to fees, such as purchase fees, cash advance fees, insurance fees, late fees and other fees. Normally this is a positive item. 2. Interchange income includes both the purchase and the cash advance each with a different interchange rate. Normally this is a positive item. 3. Functional costs include costs in all functional areas for supporting the product on a cost per person basis. The functional cost rates are usually different for a delinquent account and a non-delinquent account. Normally this is a negative item. 4. Loss is mainly the total amount of charge-off’s. Normally this is a negative item if there is any charge-off. Most accounts have this item equal to zero. 5. Reward cost is related to the cost in supporting reward prizes during product promotion. For example, the reward can be a cash rebate, bonus points, or coupons. Each reward has a cost structure associated with it. Normally this is a negative item if the product is a reward based product. For non-reward based products, this item is set to zero. 3. Applications The power of USC and NRE is best shown in back-end analyses when these two tools collaborate. Results of these back-end analyses help marketers better understand customers’ consumption behaviors and how these behaviors impact the company’s profit. They are also used for non-marketing activities, such as those in the retention area whose primary goal is to retain those valuable customers who are leaving the bank. We will illustrate this joint application, and show you how a credit-card product manager used it in a migration analysis to learn both the customer profit dynamics and ways to better target different customer groups. 3.1 Usage Segment 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 who migrate into preferable segments and to shift adverse migration behaviors toward preferable patterns. Before looking into the migration patterns, we profiled each usage segment in the old customers group in Table 1 with their average NRE, % of accounts, Return On Assets (ROA), Average Purchase Balance, Average Cash Balance, and % of Net Revenue. (See Table 2.) This profile gave the managers and the analysts a better idea of the characteristics of different customer segments before any further analyses. Table 2. Customer Profile by USC as of January 1996 USC 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Total* Avg. NRE % of Accts ROA Avg. Pur. Bal. $20.86 0.1% 12.8% $1,425 $26.46 2.5% 15.9% $1,513 $28.49 3.1% 15.9% $1,649 ($0.14) 10.5% N/A $0 $1.36 2.4% 2.1% $792 ($0.05) 12.7% -1.1% $55 $9.07 1.3% 11.2% $349 $8.07 3.8% 9.3% $1,033 $0.89 5.5% 8.7% $99 $14.88 1.5% 9.3% $607 $25.18 1.2% 12.5% $2,371 $5.57 2.0% 12.1% $471 8.3% 14.2% $1,802 $26.85 $32.66 1.1% 12.5% $767 1.0% 14.2% $3,448 $48.18 $17.33 1.0% 14.0% $1,174 $6.46 100% 10.9% $494 Avg. Cash Bal. % Net Revenue $534 0.2% $483 11.7% $503 15.8% $0 -0.3% $0 0.6% $0 -0.1% $626 2.1% $8 5.5% $24 0.9% $1,322 4.0% $37 5.3% $83 1.9% $475 39.3% $2,364 6.5% $617 8.5% $316 3.0% $217 100.0% *: The total includes numbers from USC 1 to 40, which is not completely shown here. 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 by 40 USC matrix of the customer counts and the corresponding NRE changes, which served 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 partitioned the master matrix into easily analyzed groupings. For example, Table 3 illustrates the partial USC-count% matrix with the USC data in January 1995 and January 1996 for the old accounts group. For easy reference, this table was arranged so that the highlighted figures lined up in a diagonal fashion. We quantified the gain or loss in each cell by the average NREs from the original 40 by 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 with the loss of nearly $22 per cardholder (See Table 2, $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 a year ago. The cells in bold prints were 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 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 we might be losing our best customers. Table 3. Customer Migration Analysis - Old Accounts from 1/95 to 1/96 1996 1995 USC 28 USC 29 USC 30 USC 31 USC 32 USC 33 USC 34 USC 35 USC 35 USC 37 USC 38 USC 39 USC 40 USC 26 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% USC 27 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% USC 28 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% USC 30 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% USC 33 USC 36 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% For a marketing manager, or a product manager who has the responsibility of promoting his/her product, usage-segment migration analysis such as the one shown in 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% USC 37 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% USC 38 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% USC 39 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% USC 40 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% Table 3 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), we profiled customer lifestyle and demographic data, comparing them with the marketing activities applied to these customers to understand what we did (or did not do) to change their NRE from high to low. We could then adjust our marketing strategies to customers with similar lifestyle or demographics. Using the same method, we would also learn what prompted the transactors (USC 30) to migrate to revolvers (USC 31-40), so that we could continue to focus on those marketing activities which propelled customers toward this more desirable migration pattern. 3.2 Other Applications As demonstrated above, the collaboration of USC and NRE alone can be a very powerful weapon for the marketing function. But the application of USC and NRE does not stop there. When used with other data, each tool can solve a number of marketing or product positioning problems. In the category of marketing analyses, these two tools can be classified as the non-risk factors and thus need to be combined with other risk-based information, such as the behavior score or credit bureau’s fico score for any campaign. We have used USC and/or NRE with other factors in the following applications with satisfied results: 1. Determined how to adjust customers’ annual membership fees. 2. Determined how to promote revolvers by lowering their interest rate to proper levels. 3. Selected customers for a customer survey and a focus group. 4. Determined how to target customers with fee-based insurance marketing campaign, 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. 6. Used in retention group to identify customer values and to apply appropriate strategies to retain customers. 4. Conclusion USC and NRE were developed and tested in many projects in a top-ten US credit-card issuer with satisfied results. They are simple to understand and to implement, and as illustrated, they derived rich customer information which provided vital input to important marketing strategies. More importantly, this case proved that simple yet powerful database marketing tools can be developed in-house even with less than perfect data warehouse or marts. 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. Acknowledgment I would like to thank Debbie Sandgren and Diane McCowin, my 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 [1] Bennett, P. B., (Ed.) Dictionary of Marketing Terms, American Marketing Association, Chicago, USA, 1988. [2] Equifax, Microvision Workshop, USA, 1996. [3] Metromail, DNA Demographic Super Cell User’s Guide, Illinois, USA, 1996. [4] Metromail, DNA Demographic The 104 Cells, Illinois, USA, 1995. [5] Stone, B., Successful Direct Marketing Methods, 4th Ed., NTC Business Books, Lincolnwood, Illinois, USA, 1988.