The Present and Future of IMC and Database

Debra Zahay Charlotte H. Mason John A. Schibrowsky Northern Illinois University The Utiiversity of Georgia The University of Nevada, LasVegas

INTRODUCTION With the list of the electronic media and their usage growing ever day, the challenge of this digital media is The discipline of database marketing is at an impor- for marketers to find ways to integrate these new tools tant stage. Rather than trying to justify the application into their existing communication plans. Since inte- of analytical techniques based on high-quality data, grated marketing communications is nearly impossi- database marketers are instead finding themselves ble in the digital era without an up-to-date, accurate much in demand. A recent spate of practitioner books database, database marketing has changed IMC plan- on data analytics indicates that the widespread accept- ning and execution. As we shift from traditional IMC ance if not adoption of these techniques is upon us. tools (broadcast media) to those that are interactive The accountability that databases facilitate has been (narrowcast media), the role of customer databases be- a mainstay of applications. With the comes even more crucial (O'Regan 2003). It is in- development of commercial relational databases in the creasingly important that IMC practitioners and late 1970s, managing databases became an essential researchers have an understanding of where database element of direct marketing. In order to effectively marketing is and where it is headed. Consider the fol- and efficiently mail offers and catalogs to specific in- lowing quotation from Stan Rapp and Tom Collins: dividuals and companies, direct marketers needed to be able to address their customers individually and The common wastefulness of the mass advertis- predict response rates. The definition of direct market- ing of the past is giving way to the newly afford- ing as developed by the Direct Marketing Association able ability to locate and communicate directly includes the ability to track customer information in a with a company's best prospects and customers. suitable database. By the new millennium, the new . . . and, in effect, create a database that becomes technology in terms software and hardware resulted in your private marketplace. large numbers of traditional marketers becoming at- tracted to the use of databases and individual level This quote is not from 2005, or 2000, or even 1995. data (Sehultz and Patti 2009). Gradually, all types of Rapp and Collins made this observation in their book marketers came to realize that they were able to de- MaxiMarketing (pages v-vi) in 1987, and their predic- velop better relationships with customers and provide tion has come true: Database marketing is the fastest better, more relevant offers by trapping and analyzing growing area of marketing. individual level data. While it has been a bumpy ride, In spite of the recent focus on customer information database marketing is alive and thriving. Building of gathering, customer databases and data-based market- customer databases has become a major trend in mar- ing have been around almost since the beginnings of keting. Most marketers now believe the future of mar- business. In its eariiest form, information was stored keting is in database. in the minds of shopkeepers who used that informa- This change has occurred because more than any tion to build lasting relationships with their customers. other area of marketing, customer databases affect the Over time, it became customary to keep a written way an organization communicates with its customers. record of things like the customer's birthday or fa- 13 vorite product model. In the 1950s and 1960s, there customer, what he/she bought, how valuable he/she is was a concerted effort to formalize the.se database ef- to the firm, what he/she are likely to buy/do in the fu- forts with filing systems, such as the now famous ture, etc. With individual-level data, a firm can do in- Rolodex. dividual-level marketing. The concept of "Individual By the eariy 1970s, many firms had moved away Marketing" includes (1) communicating one on one from teletypes to "computerized" ordering and billing with customers and prospects; (2) remembering inter- that resulted in the first electronic databases. These ests, past requests, past purchases, past complaints, mainframe computers were the first real drivers of and lifestyles; (3) customization of products and serv- customer databases because they enabled customer ices; and (4) creating individual relationships with tracking, which made calculations such as RFM feasi- each customer. ble even for huge groups of customers. In 1979, Ora- Database marketing is not only a business tool; it is cle introduced the first commercial relational database a business philosophy based on the marketing con- software, and the second generation of database mar- cept. Database marketers have a different view of keting was bom. By the early 1990s the use of per- business and, therefore, marketing. Once an organiza- sonal computers equipped with Windows 3.0 was tion adopts a database mentality, the approach to at- growing exponentially. In 1992, Microsoft introduced tracting and retaining customers changes. Table 1 its first version of ACCESS, a relational database soft- displays some of the difference between traditional ware program that could be mn on a PC. Eventually and database marketing. this allowed individuals and small businesses to pro- Over the past 30 years, database marketing itself has duce and use their own customer databases. evolved (See Figure 1) from primarily a list manage- In 1991, the first version of the World Wide Web ment funcfion to a sophisticated program for managing (WWW) was released by CERN. By 2000, there were customer communications, providing customized of- more than 100 million North American Intemet users. fers, predicting responses to those offers, and identify- The Intemet led to major changes in the way individu- ing the best way to build relationships with the als gather and share information and has changed the organization's most valuable customers. Once a cus- way most marketers interact with their customers. tomer database is developed, it becomes the backbone These changes led to the current generation of data- of the firm's marketing programs. Relationship market- base marketing, which incorporates both traditional ing, differentiated service, and personalized integrated and online information into databases. In the early marketing communications are virtually impossible 2000s, database marketing experienced a setback, as without employing previously collected data on indi- large numbers of CRM implementations were deemed vidual customers. Table 2 displays fifteen benefits of failures. In 2004, Gartner reported that that 50% of all building a customer database. U.S. CRM implementations and 80% of European As Figure 1 illustrates, the discipline of database projects were considered unsuccessful. However, with marketing has three primary functions: Database Con- the recent recession, database marketing is once again a hot topic. In part this resurgence is due to the disci- stmction. Data Analysis, and Data Mining. When con- pline's effectiveness and efficiency. Most important, Figure 1: Database Marketing Begins as noted above, is the accountability database applica- with Building the Database tions facilitate.

DATABASE MARKETING: Database Marketing THE MAJOR APPLICATIONS The key concept and value associated with database Database Data Data Mining marketing is that the organization identifies and col- Construction Analysis • Neural Nets lects data on individual customers. It is this individ- • Internal • Segmentation • Decision Trees ual-level data that provides database marketers with a • External • Clusters • Working w/o competitive advantage in terms of measurement and • Data Quality •RFM Priors accountability. Aggregate-level data tells about our average customer and results in the identification of target markets. Individual-level data tells about each

14 International Journal of Integrated Marketing Communications Table 1: The Differences Detween Traditional and Database Marketing

Traditional Marketing Database Marketing Market-level data Individual-level data Aggregated Survey Research Individual-level research

Target Markets Individual customers Behavioral, psychographic, and demographic data Demographic segments combined to form segments

Targeted advertising Personalized communications Targeted Products and offers Customized products and offers

All customers treated alike -^ the customer is king Best customers treated the best, etc. Focus on acquisition Focus on retention of the profitable customers

Table 2: Fifteen Benefits of Building a Database

1. Determine how much each customer is worth. 2. Identify best customers and worst customers. 3. Offer best customers special deals etc. 4. Increase Lifetime Value of customers by reducing defections, increasing the amount of money spent by customers, reducing marketing costs, and increasing referrals. 5. Use the information to build better relationships with your best customers. 6. Profile customers using a combination of behavioral, demographic, and psychographic variables. 7. "Clone" best customers. 8. Be more targeted in acquisition efforts by determining the best predictors of a profitable customer. 9. Provide different levels of service depending on how valuable a customer is to your organization. In essence you can discriminate between customers. 10. Model and segment customers in a variety of ways to make marketing more effective. 11. Communicate in a personal way with each customer. Build a dialog or conversation with customers. 12. Customize offers to the individual. 13. Gather information quickly, cheaply and accurately. 14. Determine the effectiveness of any marketing effort. 15. Rent the database to others. structing a customer database, the information in a These techniques help the firm identify its top cus- customer database falls into three categories: Internal, tomers and tailor marketing programs per customer External, and Modeled Data. Internal customer infor- segment. These analytical techniques are often based mation is information, such as order information and on the firm's prior modeling efforts and are likely to basic customer data. Internal information is aug- lead to decisions as to how to treat customers differ- mented external information, which is often pur- ently according to one-to-one and interactive market- chased, such as credit ratings, purchase history with ing concepts. other vendors, size of firm, etc., and must be com- Another critical aspect of database marketing in- posed with the highest possible data quality in mind. volves what is known as data mining. It means work- Firms using data modeling further augment their ing without prior hypotheses to find patterns in the databases with modeling and analytical applications, data. In marketing, decision- tree classification algo- such as segmentation and clustering models and RFM rithms such as CHAID or CART are useful to narrow (Recency, Frequency, Monetary Value) analysis. down how customers make choices and which cus-

Fall 2009 15 Figure 2: Customer Database Have Become of improving customer satisfaction while optimizing Central to Many Marketing Functions the current and future value of the customer base. Organizations interested in building relationships with customers must get close to their clientele (Jack- son 1985). This goes back to the old corner grocery store example discussed by Hughes (1991). How did the local grocer build strong loyalty with his or her customers: by getting close to them. The grocer knew all about the customers, knew what they liked, stocked products for them, made them feel important, solved their problems, provided them with special informa- tion and deals, and gave them credit. A firm that wants to add value for their customers and build lasting rela- tionships with them, needs to know who the customers are, what they want, and what is important to them. The relationship marketing paradigm is built on the premise of leaming everything relevant about the cus- tomer and then using that information to serve them. This approach requires a customer database. The major justification for relationship marketmg is the research finding that it is easier, cheaper, and more tomer factors are most likely to lead to purchase deci- profitable to retain current customers than to acquire sions. Neural networking applications look at relation- new ones. Leigh and Marshall (2001) estimated across ships in the data to develop predictive models that can a wide range of industries that it cost from five to be used, for example, to optimize campaign perform- seven times more to acquire new customers than to re- ance. These types of applications arc particularly com- tain those with which the firm was already doing busi- pelling in these times of discontinuous change where ness. In addition, studies indicate that the longer a firm the ideas of the past might not always be the ideas that retains customers, the more profitable they become work in the future. (Reichheld 1993, 1996). Except for those smaller Once a database has been established, a number of businesses, where the firm's employees are able to applications can ensue from this collected information. personally keep track of customers' interests, data- A review of the database marketing literature and re- bases are absolutely essential for relationship market- cent practitioner publications uncovered the following ing. Relationship marketing also posits that customers common functions and applications using customer are not equally profitable (Berry 1983). Some cus- data for relationship development and communication tomers are very profitable while others are not. Many (See Eigure 2). companies find that 20% to 40% of their customers This paper provides an overview of database mar- are unprofitable. Customers that are the most prof- keting and offers insight as to where database tools for itable merit the most attention when it comes to build- marketing might be heading in the Integrated Market- ing relationships. A customer database is a necessity ing Communications (IMC) context. Next we discuss for this to happen. each of the above areas in Eigure 2. The common thread throughout the relationship marketing literature is an emphasis on building lasting Managing Customer Relationsbips relationships with individual customers. Research Berry (1983) defined relationship marketing as, "at- suggests that this is accomplished through the use of tracting, maintaining and enhancing customer rela- relationship bonding activities. Berry and Parasura- tionships." Relationship marketing is focused on man (1991) identified three hierarchical bonds perti- developing ties with existing customers with the goal nent to strengthening relationships in an effort to of retaining them as customers (Jain 2005). The strat- increase customer retention. The bonds are financial, egy is to use customer information to build customer social, and structural bonds. Retention is positively re- loyalty and relationships, applying differential atten- lated to each of these bonding levels and in their tion to more valuable customers with the ultimate goal strongest form, bonds will include elements from each

16 International Joumal of Integrated Marketing Communications Figure 3: Customer Lifecycle Stages

Universe of High Value Retained or Customers Former Prospects Responders New Repeat Customers Customers < High Potential Customers

Low Value

of the three levels. Once again, building relationships stages—over time—of the relationship between a cus- with customers cannot be accomplished without a tomer and a business (see Figure 3). Early in the cus- database. The data allows the company to learn about tomer lifecycle there is limited information available, its customers and create that private marketplace that but the amount of customer data accumulates as the Rapp and Collins (1987) described. To build financial, lifecycle progresses. A summary of the types of data social and structural bonds, a firm needs to be able to typically acquired in different lifecycle stages is customize communications and offers based on the in- shown in Table 3. formation they have in their database. Moving for- ward, the battle to build relationships with customers Customer Acquisition will be won by firms that can do the best job of cus- Having a customer database dramatically changes the tomization and personalization. Those will be the way a firm goes about acquiring new customers. Data- firms with the best customer data and the ability to use base marketers have long used information pertaining such information in a meaningful way. to the organization's best customers to provide insight Managing customer relationships begins with acqui- into who to target as the firm's best prospects. By sys- sition and continues with development and retention. tematically tracking and analyzing responses of current Acquisition focuses on finding the right customers at a customers, models are developed so that marketers can reasonable cost. Development focuses on increasing target offers to those prospects that have an acceptably the value of customers through the cross-selling of ad- high probability of responding and becoming long term ditional products, the up-selling of higher-margin profitable customers (Roberts 1997). The customer products, or reducing marketing costs via initiatives database can be segmented, the most profitable seg- including campaign optimization or migrating cus- ments profiled, and that information used to identify tomers to lower-cost channels. Retention focuses on prospects. While a large number of firms practice pro- extending the duration of the relationship by reducing filing and "cloning" their best customers with the help voluntary attrition of valued customers. The customer of large data warehouses like NDL, there is a definite lifecycle has emerged as a framework to describe the lack of academic work in this area.

Fall 2009 17 Table 3: Customer Data Builds Over the Customer Lifecycle

Customer Lifecycle Stage Customer Data Prospects Purchased data: Name, address, phone Socio-demographic Geodemographic data (e.g. PRIZM cluster profiles) Lifestyle data Campaign History data

Responders Reported data: Product category interests Financial or credit information Socio-demographics and lifestyle data Product ownership

New Customers Amount of first purchase Goods or services purchased Payment type (cash, check, credit card) Source of customer (direct mail, saw ad, referral)

Retained or Repeat Customers Purchase History Payment History Product Usage Customer Contacts Responses to marketing campaigns Channel preferences Clickstream data Satisfaction data

Former Customers Reason for termination

Recent research has begun to investigate different mate the number of real versus current customers acquisition strategies. Research has begun in cus- when loyalty programs are implemented. These areas tomer acquisition pertaining to the evaluation of the offer a large number of research opportunities. long-run profitability of customers based on the chan- nel of distribution they used to make their initial pur- Customer Development chase and the method used to attract them. Database research on customer development tends to Villanueva, Yoo and Hanssens (2008) compared cus- fall into two areas: modeling and communication strate- tomers acquired through marketing activities, such as gies (Malthouse and Eisner 2006). We will discuss direct mail, with those acquired by word-of-mouth modeling here and communication approaches in the and found that marketing-induced customers add IMC section. Database information on prior purchase short-term value but word-of-mouth acquired cus- behavior is commonly analyzed to suggest additional tomers have nearly twice as much long term value. products, to recommend higher margin products, or to Musalem and Joshi (2009) found that firms should remind customers to renew or repurchase. Tao and Yeh pursue prospects that that are likely to exhibit moder- (2(X)3) demonstrated the effectiveness of database mar- ate responsiveness to the firms' CRM efforts. Schu- keting for increasing customer loyalty and repeat macher (2006) explored the use of the "Butterfly . Yet Reinartz, Thomas, and Bascoul (2008) con- Effect" developed by Ronald Aylmer Fischer to esti- cluded that cross-buying is largely a consequence of

18 International Journal of Integrated Marketing Communications loyalty and not the other way around, and they ad- a positive link between the collection of sales contact vised firms to emphasize cross-selling to customers information and firm performance in the context ofthe later in the customer lifecycle once the relationship is entire Customer Information System (CIS). Database well established. Pass (2009) found that the predic- marketing as a managerial discipline has yet to fully tions of likelihood to buy are improved when the ac- exploit the new forms of many-to-many marketing ap- quisition order of purchases by customers is modeled. plications (Deighton and Kornfeld 2009). Malthouse and Derenthal (2008) found that the pre- dictability of scoring models was improved when Customer Retention multiple scoring models were used and the results The key concept in relationship marketing is that im- aggregated. proving retention rates has a compounding cumulative effect. Database marketers take a completely different Although information about a customer accumu- approach to customer loyalty than do traditional mar- lates throughout the relationship, firms often have lit- keters. Database marketers are able to calculate reten- tle data on what their customers are purchasing from tion rates and therefore can analyze the phenomenon, other competing firms. For example, a bank will have develop strategies to reduce defections and test those detailed information about their customers' transac- strategies to see if they are working. tions with them—but not with other financial services providers. Kamakura, Wedel, de Rosa, and Mazzon Frequency or loyalty programs are often imple- (2003) showed how database information can be aug- mented by many organizations as a mechanism to col- mented with survey data to predict service ownership lect data and establish a database. While there is a large with both the service provider as well as competitors, number of firms that implemented some type of fre- which can then be used to identify cross-selling quency or loyalty programs (grocery store cards, air- opportunities. line frequent flier programs), the amount of academic Neslin et al. (2006) demonstrated the importance of research pertaining to their long-term effects is some- methodological factors in estimating defections from what limited. Wagner et al. (2009) found that cus- customer databases. McCarty and Hastak (2007) com- tomers in loyalty programs that were demoted to a pared the results of segmenting a database with a vari- lower-level status due to reduced purchases were more ety of traditionally employed analytical techniques. likely to leave the relationship than those customers They found that CHAID was superior to logistic re- who were never given the upgraded status. This finding gression and simple RFM models in a few situations, suggests that loyalty programs might have a number of but in most cases the results were comparable. unintended consequences. Contact management is another important aspect of Following Reichheld and Sasser's (1990) arguments customer development. Contact management systems for the value of loyal customers and improved reten- that record all interactions with customers are growing tion, research has examined ways to quantify the value in popularity and provide input to decisions about of customer retention, to identify antecedents of attri- when to contact customers with what frequency and tion or customers "at risk" for attrition, and to investi- via which channel(s). One area of research focuses on gate the relationship between retention and other improving predictive models. Direct marketing con- lifecycle stages. Hogan, Lemon, and Libai (2003) tacts, such as catalogs or solicitations for donations, sought to answer the question, "What is the tme value are costly due to the low response rates, so predictive of a lost customer?" Pfeifer and Farris (2004) develop models help firms decide who will receive a market- methods to quantify the economic benefits of increased ing contact by scoring customers based on likelihood customer retention and develop a measure of "reten- of response. Researchers have compared different tion elasticity." methodological approaches including RFM (recency, Other researchers have focused on factors that pre- frequency, monetary) models, regression models, de- dict retention (or attriüon). While customer satisfaction cision trees and neural networks. Recent research by has long been known to impact retention. Lemon, Malthouse and Derenthal (2008) showed that an ag- White, and Winer (2002) showed the value of also in- gregated model that averages predictions from multi- cluding the customer's future orientation—both antici- ple scoring models can yield significant gains in pated future benefits and regret. Verhoef and Donkers profits. Another important and under-researched area (2008) investigated the effect of relationship-marketing in the area of database marketing is contact manage- instmments on customer retention. Boehm's (2008) em- ment systems. Zahay and Griffin (2004) demonstrated pirical study of a bank found a strong positive im-

Fall 2009 19 pact of Internet use on customer retention and suggests customers. Customer lifetime value calculations typi- migrating customers to the Internet channel. cally involve discounting the value of the customer A substantial body of research focuses primarily on over time to reflect future as well as current profitabil- building more sophisticated models in this area. For ity and is therefore suggested as a better approach than example. Van den Poel and Lariviere (2004) combined more naïve methods such as RFM to identifying best several types of predictors including time-varying co- customers (Kumar 2006). This financial approach to variates from longitudinal customer data. Jamal and evaluating the profitability of customers makes it Bucklin's approach (2006) also employed time-vary- much easier for marketers to justify the investment ing covariates as well as heterogeneity. Neslin et al. made in marketing (Gleaves et al. 2008). (2006) focused on how methodological factors con- Customer lifetime value can involve a variety of tributed to the accuracy of customer churn predictive different mathematical models ranging from relatively models. simple discounted cash flow models to Markov While retention is a priority for most businesses, re- Chains and Bayesian approaches (Berger and Nasr tention should be not viewed in isolation. Thomas 1998; Dwyer 1989; Fader and Hardie 2007; Gupta et (2001) demonstrated that customer acquisition and re- al. 2006; Jain and Singh 2002; Pfeifer and Bang 2005; tention are not independent, and that failure to take that Pfeifer and Carraway 2000). Different approaches into account can lead to bias and misleading decisions. have been proposed for industrial versus consumer The growth in customer databases has led to in- markets, for contractual versus noncontractual set- creasingly sophisticated analyses to identify prospects tings, and for situations where social effects such as to target, to suggest opportunities for up-selling and word-of-mouth are prevalent. Once developed, a com- cross-selling, and to predict which customers are at mon use of these models is to determine what change risk of attrition. While these models have proved use- in the way the customer is treated or other marketing ful for managing customer relationships, there is much actions will likely lead to a change in customer life- room for improvement. In most instances, the overall time value. explanatory power is not strong. A limitation is that Even though the term is referred to as "lifetime" the databases, while rich in detailed transactional and value, the typical calculations are conducted over a behavioral data, often have little or no attitudinal data shorter time frame, often three to five years. These sim- and often lack information about customers' interac- pler models perform well and are not necessarily supe- tions with competitors. There is ample opportunity to rior to more sophisticated models (Donkers et al. 2007). better understand how to leverage customer databases However, some research shows that more sophisticated for the benefit of both consumers and marketers alike. models take into account the differences between cus- These databases can be used to manage integrated tomers, such as those that are actively purchasing and communications strategies over the entire customer those that drop out early can provide more insight than lifecycle. simpler calculations (Fader et al. 2007). Does lifetime value need to be calculated for each Customer Lifetime Value customer in order for the firm to make the best deci- As a consequence of developing customer relation- sion? Lemon and Mark (2006) suggested that for ships and maintaining data about the customer, many some industries, CLV calculations might be more cost firms are now focusing on identifying and nurturing effective if performed on a segment rather than an in- their most profitable customers, an activity that would dividual customer basis. In any case, before any cus- not be possible except through the use of customer in- tomer lifetime value calculation is conducted, the formation collected in the customer database. The organization needs to determine what information search for customer profitability is again based on Re- needs to be collected, by whom and what types of ichheld's idea that long-time customers are less ex- analyses will be more meaningful (Lemon and Mark pensive to serve and generate more profits over time 2006). (Reichheld 1993). These lucrative customers then re- Customer lifetime value calculations are not an ceive targeted marketing communications to nurture easy proposition. Because of differences in purchasing and develop the relationship. patterns, customer behavior, and channel usage, calcu- Customer lifetime value (CLV) uses information lations will vary across firms and industries (Aeron et from the customer database as well as mathematical al. 2008). For example, Boehm (2008) demonstrated a modeling techniques to determine the profitability of strong relationship between Internet use, retention.

20 International Journal of Integrated Marketing Communications and customer lifetime value. Lee et al. (2006) has IMC perspective is to personalize communications demonstrated that for low-revenue customers, cus- and customize products for individual customers. In- tomer word-of-mouth can have a positive impact on formation from customer profiles, including expressed CLV Sharma (2006) suggested other strategies for preferences and past purchases, is used to personalize maximizing the customer equity of low CLV cus- offers. A variety of definitions for personalization tomers, such as lower acquisition and transaction exist, ranging from one-to-one marketing (Allen, costs. Kania and Yacckel 2001 ; Peppers and Rogers 1997) to The benefit of CLV calculations extend beyond the customer intimacy (Wiersema 1998) to permission individual customer to broader decisions such as firm marketing (Godin 1999). Roberts (2003) provides a resource allocations and channel selection (Kumar clear definition of personalization as the "process of 2006). Gupta and Lehmann 2006), More research op- preparing an individualized communication lor a spe- portunity exists to demonstrate the benefits of CLV in cific person based on state of implied preferences." contributing to and maintaining firm value. In other One need only to look at large online retailers such as words, can the use of CLV as a management tool be Amazon.com to see the underlying databases at work identified as a competitive advantage? to create relevant offers for individual customers. Blattberg et al. (2009) pointed out some of the real Zahay and Griffin (2003) researched the database complexities in developing a CLV calculation, such as antecedents of personalization and customization and selection of the appropriate discount rate. Their article found that customization and personalization im- suggested a number of areas for future research, such pacted customer-based performance metrics (CLV, re- as whether loyalty programs and other customer con- tention rate, share of wallet, etc.) and, ultimately, tacts increase CLV and whether the effect is lasting business growth (sales and net income). They con- and what is the relationship between REM and CLV. cluded that the ability to personalize and customize is The notion of long-time customers being more prof- ultimately related to firm performance. In addition, itable than more recently acquired customers has been they found that the quality of the information was im- challenged by Reinartz & Kumar (2002), whose study portant in the ability to personalize communications of 16,000 customers over four industries indicated that and customize products, indicating the important role retention itself is not the best metric with which to of data quality in personalization and customization gauge profitability. Indeed, the longstanding customer activities (Zahay and Griffin 2004). may be the least profitable (Keiningham et al. 2006). Overall, personalization of communications and Certainly researchers have just begun to explore the customization of offers and products has been under- area of CLV and how such metrics can best be used by researched. This gap in the literature will become managers and by the firm. more important as companies expand their customiza- tion capabilities through technology. Einally, mobile applications are likely to see an explosion in personal- INTEGRATED MARKETING ized communications and will provide ample research COMMUNICATIONS material in this area. Much of what was discussed in the previous sections Peltier et al. (2002b) demonstrated the effectiveness pertaining to relationship marketing is eminently re- of a longitudinal communication plan that was se- lated to IMC. Once you have modeled, segmented, or quenced based on the recipient's response to previous scored your customers, it is the execution of an interac- communications. This research demonstrated that tive integrated marketing communications (IIMC) pro- message strategies could influence buyers in different gram that determines the successfulness of the effort. ways and at different stages of the relationship-building To maximize the benefits of personalized communica- process. They argued that the unique characteristics of tions, it is critical to have detailed data on who cus- the new media require that a database-driven segmenta- tomers are, what they buy and why they buy (Peltier et tion approach to communications strategy be employed. al. 2002a; 2002b). This allows the firm to segment cus- Even though database segmentation is the linch pin to tomers in ways to capitalize on the benefits associated personalization of communications and customization with the personalization of communications. offers and products, less academic research has been Once knowledge of the customer has been obtained done in this area than one would expect. Very few stud- and valuable customers identified, one of the most ies exist that develop and test database segmentation useful applications of the customer database from the methods that combine various types of customer data.

Eall 2009 21 Most of the work in this area is proprietary work done proves the firm's ability to personalize communica- by firms trying to personalize their messages and cus- tions and customize offers. tomize offers and products. However, the amount re- Moon and Russell (2008) investigated a product search being done in this area is increasing. recommendation model ba.sed on the priticiple of cu.s- Peltier et al. (2002a, 2003) made a case for the im- tomer preference similarity of prior purchases. Their portance of incorporating psychographic/attitudinal approach resulted in excellent predictability when information in segmentation of the customer database. compared to traditional benchmaik numbers. Gariand They proposed the use of "interactive" psychograph- (2005) demonstrated the way banks are improving ics to identify motivation-based segments that could revenues and profits and reducing costs by segmenting be targeted with timely, relevant, personalized com- their customer database and personalizing and cus- munications. They demonstrated that customers can tomizing their marketing program. This is the basic be segmented across psychographic dimensions in- premise of all database-driven IMC programs. cluding values, buying motives, beliefs and attitudes, With the burgeoning business of collecting large and lifestyles and that membership in these psycho- amounts of individual-level digital data such as scan- graphic segments can be accurately predicted by a ner purchase data, clickstream data, and other online limited set of demographic and other related database analytics, the Naik et al. (2008) article that looked at information. They introduced an "Interactive Inte- the challenges and opportunities of this type of data is grated Marketing Communications" model that linked significant. They identify some of the is.sues of dealing the use of detailed database information to the cre- with high dimensional data.sets. Cui et al. (2006) re- ation of integrated interactive communication plans. searched the use of machine learning methods to data- Fader and Hardie (2009) recently argued for the use mine large noisy databases, such as those that rely on of probability models for customer base analysis. scanner data. Their results provide strong support for Malthouse and Eisner (2006) found an improved ef- Bayesian networks as a robust tool for modeling cus- fectiveness of communications and offers to con- tomer responses. sumers who were segmented using complementary Malthouse (2002) compared the use of performance- basis variables. Peltier et al. (2006) determined that based variable selection process to the more tradi- transactional and relational data need to be included in tional fit-based variable selection approach when the segmenting of customers for the purpose of creat- building a direct mail response model. He found that ing personalized communications and customized of- the performance-based variable method outperformed fers and products. Reutterer, Mild, Natter, and Taudes the more traditional method. (2006) proposed a segmentation approach that ad- The evolution of the Internet is having a major im- dressed both who to target and with which offers. pact on the way communications between companies Dreze and Bonfrer (2008) investigated the impact and consumers are conducted and maintained in the of contact frequency on customer spending and reten- ever-evolving marketing communication landscape tion, and they showed that suboptimal communication (Ozuem et al. 2008). The extension of IMC into the "in- frequency impacts the value of the customer database. teractive" marketing realm has placed great value on They concluded that the frequency of communications bring together data from multiple touchpoints, media, impacts both retention and customer spending, and and messages (Peltier 2006). Jenkinson (2007) noted they advised marketers to err on the side of less fre- that an integrated customer database that collects data quent rather than more frequent contacts. from a variety of sources allows marketers to identify Another under-researched aspect of customer com- and map touchpoints to better understand how where munication is the impact of communications not just and when an organization and its customers are com- between the company and the customer but between municating. The ability to create a tailored set of com- customers in social networking or other peer-to-peer munications for online customers has become very models. sophisticated (Montgomery and Smith 2009). Some on- Berry (2003) mapped out a process and tools to im- line companies have established the proof that person- plement a "Next Best Activity" program as part of a alized content on web sites improves customer personalized IMC program. Meanwhile, Albert (2003) retention and satisfaction, cross-selling, and profitabil- demonstrated the importance of employing need- ity (Noble 2009). As personalization has expanded rap- based variables in segmenting business databases. The idly in the realm of Internet marketing, it has raised inclusion of these nontraditional variables greatly im- privacy issues and some concerns for customers. Col-

22 International Journal of Integrated Marketing Communications laborative filtering systems such as those used by Ama- to analyze customer information that is captured in a zon and Pandora that make recommendations based on variety of ways. prior purcha.ses are commonplace. Marketers, however, A notable opportunity for data analysis that takes would very much like to use more customer informa- advantage of the interactive potential of the Intemet is tion than perhaps the consumer is willing to tolerate. the analysis of clickstream data. Such data comes Although customers generally have a positive approach from a variety of sources, including raw server files as toward interactive communication, they are likely to be well as aggregated Internet panel data that can be ob- reluctant to fully accept all aspects of personalized tained from Comscore or Netratings (Bucklin and Sis- communication (Vlasic and Kesic 2007) and may in- meiro 2009). Clickstream research can include how deed 'push back' if the communication is perceived as users browse within a site (Huberman et al. 1998; "too much, too soon" (White et al. 2008). The con- Johnson, Bellman, and Lohse 2003) in terms of the sumers' reaction to personalized communication and re- length of the visit, page requests, and page view dura- lated privacy concerns continue to provide fertile tion. This type of analysis is ideally suited to database ground for research in this area. marketing applications because of the large amount of data available for these analyses. The growth in the importance of employing an IMC perspective is in part due to the emerging media tech- Researchers have also looked at how consumers nologies that empower businesses with the opportu- browse between web sites. Research to date has indi- nity to tailor how they communicate with (not to) cated that consumers actually look at very few sites in customers and the communications channels used making their shopping comparisons, indicating an op- (Peltier 2006). The new electronic media is changing portunity for those who have developed a strong brand identity (Johnson et al., 2004; Smith and Brynjolfsson the way we have traditionally viewed IMC and there- 2001). Since most large purchases today begin with In- fore will change the way it will be performed in the temet searches, this information is useful for Integrated future. A customer database affords an organization Marketers. Marketing communications can direct cus- the ability to adapt to the situation and communicate tomers to web sites relevant to their current search. with customers in a relevant timely manner (Finne and Of course, integrated marketers marketing over the Gronroos 2009; Noble 2009). Interactivity can only web are also interested in using clickstream data to de- occur when marketers develop information intensive termine how to convert searchers and shoppers to con- strategies that adapt their product offerings and com- sumers. Research to date is still evolving in terms of munications to meet the ever changing needs of their how to predict purchase likelihood and this area pro- customers (Glazer 1999). This can only happen when vides an important area for future research. Another a firm has a real-time customer database. An IMC pro- area for research potential is the area of word-of- gram should not be considered an end goal but rather a mouth (WOM) communications. To the degree that starting point in the development and implementation marketers can capture social networking data, the in- of a marketing program that evolves over time (Peltier formation can be used to develop specific communica- et al. 2003). tions campaigns. Targeting influencers identified through the analysis of clickstream and other online CLICKSTREAM DATA information can have significant benefits for online marketers seeking to mine information from their cus- As more and more customers migrate to the Intemet, tomers and WOM referrals can have a longer carry- online data is becoming prevalent. With this new vol- over than other traditional marketing actions (Trusov, ume of "clickstream data" come new opportunities for Bucklin and Pauwels, 2009). database marketers. Clickstream data is data that is the electronic activity of a particular user on the Intemet. As is demonstrated by personalized communications MULTICHANNEL MARKETING discussed above, one of the benefits of a customer Although the ability to analyze clickstream data holds database is the ability to interact with customers by promise for database marketing, an unresolved chal- addressing them individually, remembering what was lenge for direct and intemet marketers comes from op- said, and addressing them again in a way that reflects erating increasingly in environments that include what was said in the past interaction (Deighton, 1996; multichannel marketing opportunities. In fact, one of Glazer, 1999). Interactivity in tum leads to the ability the leading books on direct marketing now includes

Fall 2009 23 multichannel marketing in its title (Stone and Jacobs Through the collection, consolidation, and analysis 2008). It is not uncommon for retailers such as J.C. of customer information, database marketing has the Penney to operate in several channels at once and to potential to benefit both firms and consumers by offer- make catalog offers that can also be redeemed online. ing timely, relevant communications and products to Various studies have shown that the multichannel customers and by avoiding irrelevant or inappropriate shopper spends more and is more valuable than the offers. The era of "Big Brother" is upon us, and mar- consumer using a single channel (Neslin, Grewal, keters must grapple with the balance of personaliza- Leghorn, et al. 2006; Shankar and Winer 2005; Kumar tion and customization versus the right to privacy of and Venkatasen 2005; Venkata.sen and Kumar 2004). the individual customer. Within today's digitally con- Empirical studies have also demonstrated that not nected world it is increasingly difficult to be anony- every channel combination is equally profitable mous and, in the course of daily life, consumers reveal (Thomas and Sullivan 2005). much about their activities, interests, and purchases With the prevalence of multichannel retailing, the that is eageriy gathered up by marketers for future use challenge for those in database marketing who are ad- and, in some cases, for sale. Yet consumers seem to vocating an integrated marketing communications ef- have a love/hate relationship with marketers' use of fort across multiple channels is how to best create a their information. In one Yankelovich study, 55% of single view of the customer and all of his or her pur- respondents said they would be willing to pay extra chasing pattems (Neslin and Shankar 2009, Zahay et al. for more personalized marketing (Vollmer, Frel- 2006) and then determine how to best target marketing inghuysen, and Rothenberg 2006). At the same time, efforts and dollars across channels. In spite of the ex- concerns about consumers' privacy have been raised. pansion in multichannel retailing, many retailers are Business press reports that hotels are recording still stmggling with matching online purchases back to such information as room service leftovers or observ- catalog offers to determine the effect of promotions ing when a guest has a cold may delight some cus- across multiple channels. The challenge is building at- tomers but will alarm others (Johnson, 2006). Online tribution models to determine the effect of each particu- retailers are particulariy well suited to sophisticated lar channel—online, versus catalog, versus retail store, promotional targeting. By tracking the location, time etc., and to do so in a way that is measurable and ac- of day, and search engine queries, marketers make in- tionable for retailers. Right now models are typically ferences used to tailor offers to specific consumers built per industry and per customer and cannot be gen- (Vascellaro 2006). Since this information is captured eralized across multiple industries and customer types. without consumers' knowledge or consent, some will As such, these models are time consuming to develop see this activity as a violation of privacy. Consumer and difficult to maintain over time. Research opportuni- backlash is most likely when customer information is ties exist in generalizable models to determine the most used to charge some customers higher prices or to cost-effective channel choices. Additional research is offer inferior service to some as Netflix did when rout- also needed in why consumers seek to choo.se one ing heavy renters to the end of queue for the most- channel versus another and the various utilitarian bene- wanted DVD's that were in short supply and fits received (Balasubramanian et al. 2005). "throttling," or delaying shipments, to protect profits (Associated Press 2006). PRIVACY Wang, Lee, and Wang (1998) propose a taxonomy for privacy concems in Internet marketing including A review of database marketing in a communications improper information acquisition, use, storage, and context would not be complete without a discussion of transfer. The consolidation of customer information privacy issues relating to the discipline. Privacy con- from different sources further escalated privacy con- cerns are not new and certainly pre-date the Intemet cems. The public outcry over DoubleClick's plan to era (e.g., Horowitz 1995; Miller 1991). However, merge its online clickstream data with the information technological advances have provided database mar- in the Abacus database of consumer purchasing data, keters with more detailed individual-level data and primarily from catalog and other direct mail retailers, have enabled data from disparate sources to be linked led DoubleClick to reverse course (Tedeschi 2000). together. The rise of the Internet fueled concerns as Today the prospect of using RFID technology for mar- the public became aware of the ways that their online keting is again fueling privacy advocates. Although behavior could be tracked and used. RFID's use to date is primarily for inventory tracking

24 International Journal of Integrated Marketing Communications and control, there are potential marketing applications One important research area is the development and that could allow marketers to track products and con- evaluation of new statistical techniques and modeling sumers' use of them post-purchase. The fact that con- methods. As new approaches are developed and refined, sumers may be unaware of RFID tags or unable to database marketing provides an excellent arena to test remove or disable the tag without harming the product and deploy advanced modeling and data mining. is particularly ttoublesome (Handler 2005). The other component that affects the performance Compared with the U.S., many countries in Europe, of modeling and segmentation is the data side of the Australia, and elsewhere have much stricter regula- equation. Research pertaining to types of data that re- tions on the collection, use and sale of consumers' per- sults in the most effective and efficient modeling and sonal information. With relatively few exceptions segtnentation is needed. Building on the teseatch like outside of medical and financial information, U.S. that of Malthouse and Elsnor (2006), Peltier et al. consumers have little control over how their informa- (2002a; 2002b; 2003), and Zahay et al. (2004), re- tion is used. Much research in this area has suggested search is needed to separate the relevant from the ir- fair information practices such as notice, choice, accu- relevant information. As the amount of information in racy, control, and security to guide ethical marketing customer databases increases, this issue of which in- decisions. Other research has focused on consumer formation to focus on as a marketer seeking to provide perceptions of different practices, willingness to pro- relevant communication becomes more important. In vide information, dimensions of privacy concerns, and addition, as more and more data from disparate comparisons of privacy perceptions across different sources becomes available, research on the best ways consumer groups. As new technologies such as RFID to consolidate, scale, and include it in modeling and offer marketers new ways to collect customer data, segmentation is needed. In addition, database research there will be new research opportunities to understand is needed to identify ways to deal with limited or consumers' perceptions and behaviors and also to ex- missing data. For example, the availability of attitudi- plore new ethical and legal issues. nal data is generally limited to, at best, a subset of the database. More approaches like those developed by Peltier et al. (2002a; 2002b; 2003) to reliably impute FUTURE RESEARCH missing values to other customers need to be devel- As customer databases become prevalent, it will allow oped and tested. more and more marketers to move from broadcast Probably the most important area for future research media to narrowcast communication vehicles. This regarding database marketing is the use of integrated will in tum increase the need for database marketing- marketing communications to build customer relation- related research. The purpose of this article was to ships. The big question is how database marketers can provide an overview of database marketing, highlight- best use their data to build strong relationships with ing some important research areas relating to inte- their best customers. Specific research is needed on the grated marketing communications and suggest new impact of the relevancy, timing, media channel, se- areas of future research. It became apparent in writing quencing, and what types of messages work. this article that database marketing applications could Another important challenge, especially when pur- not be limited to a small number of topics but that suing strategies to maximize the value of customer re- database marketing is pervasive in almost all aspects lationships, is the trade-off between short-term and of interactive marketing communications. It was a long-term results. Customers are a critical resource for challenge to identify the most important areas for fu- firms and firms should exercise caution when mining ture research and yet heartening to realize that mar- customer data for the best-next-offer for cross-selling keters do not need to be convinced about the or up-selling. Only limited research has explicitly con- importance of individual level data. sidered the longer-term dynamic implications of offers In addition to the research areas mentioned through- associated with database marketing and future re- out the article, there are several other areas that deserve search should give this issue more consideration. particular mention as opportunities for future work. Another area of research that shows significant First is in the area of modeling and segmenting using promise is the development of interactive integrated customer data. There will continue to be a need for re- marketing communication programs that are truly de- search that advances the sophistication and perform- signed to get smarter over time, in the same they do in ance of the empirical modeling using customer data. typical person-to-person relationships. Just as the con-

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Fall 2009 29 Marketing: Issues and Research Directions." Journal of nal of Database Marketing 10, no.3 (2003): 255-271- Interactive Marketing 23, no.2 (2009): 108-17. 326. Zahay, Debra L. (2008). "Successful B2B Customer Data- Zahay, Debra L., Holly Haines and Richard Irwin. The base Management." Journal of Business & Industrial Multi-Channel Shopper, ROI, And Other Marketing Met- Marketing, 23, no.4 (2008): 264-72. rics, (June 26, 2006). http://www.retailsolutionsonline Zahay, Debra L. and James Peltier. "Interactive Strategy .com/article.mvc/The-Multi-Channel-Shopper-ROI-And- Formation: Organizational and Entrepreneurial Factors Related to Effective Customer Information Systems Other-Marke-OOOl, retrieved September 12, 2009). Practices in B2B Firms." Industrial Marketing Manage- ment 37, no.2 (2008): 191-205. DEBRA ZAHAY is Acxiom Corporation Professor of Zahay, Debra L., James Peltier, Abbie Griffin and Don Interactive Marketing at Northern Illinois University. Schultz. "The Role of Transactional versus Relational Data in IMC Programs: Bringing Sales and Marketing CHARLOTTE H. MASON is head of the Department of Data Together." Journal of Advertising Research 44, Marketing & Distribution in the Terry College of no.l (2004): 3-18. Business of the University of Georgia and Director of Zahay, Debra L. and Abbie Griffin. "Customer Learning the Coca Cola Center for Marketing Studies at the Processes, Strategy Selection, and Performance in Busi- University of Georgia.. ness-to-Business Service Firms." Journal of Decision Sciences 35, no.2 (2004): 169-203. JOHN A. SCHIBROWSKY is Professor of Marketing in Zahay, Debra L. and Abbie Griffin. "Antecedents and Con- the Department of Marketing in the College of Busi- sequences of Personalization and Customization." Jour- ness of The University of Nevada, Las Vegas.

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