EVALUATING DIRECT MARKETING CAMPAIGNS; RECENT FINDINGS AND FUTURE RESEARCH TOPICS

Transcription

1 EVALUATING DIRECT MARKETING CAMPAIGNS; RECENT FINDINGS AND FUTURE RESEARCH TOPICS Jedid-Jah JONKER* Tinbergen Institute, Erasmus University Rotterdam Philip Hans FRANSES Econometric Institute, Erasmus University Rotterdam Nanda PIERSMA Econometric Institute, Erasmus University Rotterdam Econometric Institute Report 9851/A This paper contains a survey of the recent literature on the evaluation of direct marketing campaigns. We give an outline of the various stages included in such a campaign. Next, we review the statistical methods most frequently used and we review the general findings from using these methods. Finally, and most importantly, we indicate that comprehensive additional research is needed in at least four directions, that is (i) the explicit incorporation of competition (which is now seldom done), (ii) optimal selection of targeted individuals, (iii) an exploration of the maximum levels of responses and profits, and (iv) determining the long term effects of direct marketing. This version: November Comments are welcome. * Corresponding author: Tinbergen Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. Phone: , fax: , [email protected] 1

2 1. Introduction Many firms are aware of the fact that to acquire and keep customers requires some form of personal attention. Oftentimes products are targeted at only a specific subset of the entire customer population. Also, it appears more expensive to obtain new customers than to maintain current customers (Peppers and Rogers, 1993). So, firms understand the importance of developing a good relationship with current and new customers. Direct marketing is an important tool in this process, because its goal is to develop and maintain a long-term relationship with (individual) customers. Direct marketing has become an important business. In 1995, 12% of total sales on business-tobusiness markets and 5% of total sales on consumer markets in the United States were generated by direct marketing (DMA,1995). There is no consensus in the literature on the definition of direct marketing. In this paper we will define direct marketing as a form of marketing that is aimed at obtaining and maintaining direct relations between suppliers and buyers within one or more product/market combinations. The marketing activities are usually based on (partial or full) knowledge of the individual (potential) customers. The use of the marketing instruments can thus be tuned to the individual buyer. An important characteristic of direct marketing is the use of direct communication and/or direct delivery (Hoekstra, 1998). Being part of the marketing activities of a firm, direct marketing has become a serious research topic in the last 5 to 10 years in the marketing research literature. In this paper, we attempt to provide a noverview of the literature, in order to describe the theoretical and empirical insights that have been obtained so far, and to determine areas for possible future research. Our main interest lies in the quantitative aspects of direct marketing, because we believe that this is an area with numerous opportunities for future research. Also, improved data collection and processing techniques, caused by advanced Information and Communication Technology (ICT), facilitates the quantitative evaluation of direct marketing. In this paper, we will limit the focus by analyzing direct marketing only by looking at direct marketing campaigns. A direct marketing campaign consists of a number of sequential steps or stages. Almost all of the major issues concerning direct marketing can be addressed by analyzing these different stages. As noted before, the objective of direct marketing is to obtain a long term 2

3 relationship with customers, and this goal can not be reached by a single campaign. Therefore, it is necessary to have multiple campaigns, aimed at current (and possibly new) customers. By communicating regularly with customers the company can try to build a relationship with the customer. This can be done by monitoring the satisfaction with current (offers of) products and by trying to determine future demand for these and other products. The outline of our paper is as follows. In Section 2, we present a general (theoretical) framework for direct marketing campaigns. The campaigns can be described by a number of stages (including determining the product and selecting the customers), and we discuss each of these stages. In Section 3 we review the recent literature on direct marketing. Section 4 contains a number of directions for future research, and in Section 5 we conclude our paper with some generalizing remarks. 2. Framework A direct marketing campaign consists of a number of stages. These stages are encountered sequentially, although some can occur simultaneously. The stages are represented in figure 1. A campaign is often motivated by the product (or service) that is to be offered. The product can be developed or adjusted, when customers tend to indicate so. The company then decides on the targetgroup for the campaign. The campaign can be targeted at new customers (in general) and/or at current customers. The company needs to obtain information about the target audience. For current customers, this information is usually internally available, but for new customers this information has to be obtained from external sources. The information has to be analyzed, in order to determine whether relevant segments of customers can be differentiated. Customers within a segment are expected to respond to the offer in a similar fashion. The company can choose the segments it wants to target. Usually the segments that promise to be most profitable are chosen. The next step is to determine the media that will be used for the campaign. This decision usually depends on the size and characteristics of the target segments. The company can decide to have a test campaign before having the actual campaign. With the results of a test campaign, different versions of the product or different ways of presenting the product can be evaluated. Also, the response to the test campaign can be analyzed to determine whether the individuals who 3

4 did respond possess distinguishing characteristics. When the campaign has been executed, and its impact has been analyzed, the campaign is not over. The, it is important to provide good fulfilment, that is all actions after a consumer has decided to buy the product. Such fulfilment consists for instance of distributing the product quickly and in a good condition. It also deals with handling complaints and answering questions. Providing service and quality above expectations can be a useful tool for building a (long-term) relationship with the customers. By communicating regularly with the customers, a company may be able to anticipate future demand. Monitoring customer satisfaction is also important. By registrating and handling dissatisfaction in an early stage, the company can prevent or reduce customer withdrawal. This can lead to a deeper relationship with the customer, and stimulate repeat purchases and cross-selling. Finally, the company needs to determine the profitability of its direct marketing campaign. For example, do the returns justify the costs? All of these aspects of the direct marketing campaign will be discussed in main detail in the sequel of this section. To focus the discussion, we use a running example to clarify the different aspects of the direct marketing campaign. 2.1 Goal of the Campaign A company first has to decide on the goal of the campaign. This goal can be (Roberts and Berger, 1989): - sale of a product or a service; - lead generation (obtaining response from potential customers); - lead qualification (determining whether the respondents who first ask for information can be qualified as having a genuine intention to buy); - creating brand knowledge; - maintenance of customer relationships. The first three are directly measurable. The audience targeted with the campaign is usually known, so the sales can be related to the total number of people targeted. The last two have to be measured indirectly. 4

5 A direct marketing campaign can be used to offer certain products to the customer. The campaign can also be used to obtain the names and addresses of potential customers (leads), for example when the product is too complex to be sold by mail. In this case, most respondents will first ask for more information. During this first round, the company obtains the leads, and then needs to determine which of these respondents will be targeted in the second round, that is which of these respondents have a high probability of becoming customers (lead qualification). By using the campaign to sell a new product to current customers, or by offering special services or advice to current customers, the campaign can also be used to maintain, and possibly deepen, customer relationships. It should be mentioned, though, that most direct marketing campaigns, especially in consumer markets, are concerned with the sale of a product or service. The use of direct marketing to generate and qualify leads and maintain customer relationships is more applicable to business-to-business markets. 2.2 Target Audience A decision has to be made on the target audience, that is, who will be approached by the campaign? Should the campaign be targeted at current customers and/or at potentially new customers? The decision on who to approach is usually related to the goal of the campaign. Lead generation is targeted at potential new customers, whereas the maintenance of customer relationships is aimed at current customers. When a company decides to approach either current and/or new customers, a quantitative analysis is usually performed to determine which segments of the customers may be profitable. The company can decide to aim the campaign at a few or all segments. To determine the segments the company needs information about the existing and/or potential new customers. Information about existing customers may be internally available from the customer database. The development and maintenance of a customer database is an important part of direct marketing. Some of the information about customers that the database could contain is name, address, telephone, date of entry in database, gender, date of birth, mailings send, response behavior, satisfaction, complaints and problems, dates of purchases, products/services 5

6 bought, amount spent and bank account-number. Although more and more companies are becoming aware of the importance of keeping an accurate and up-to-date database, information is often not recorded accurately. For instance, information about customers is recorded by different departments, and it may be difficult to link these different databases. On the other hand, different departments may record the same information, whereas other data is not recorded at all. Because direct marketing focuses on developing a long-term relationship between the customer and the company, the information in the database needs to be complete, accurate and up-to-date. To obtain information about potential new customers, the company usually has to rely on information from external sources. There are a number of companies that can provide information about potential customers at a low level of aggregation. A distinction should be made between zipcode segmentation systems (providing information at the level of the zipcode) and individual segmentation systems (providing information at the individual level). These systems use demographic and socio-economic information to distinguish a number of customer profiles. An example of such a profile would be young couples without children with an income above average, who live in the suburbs of larger cities and have an education above average. Other elements that can be a part of such a profile are the magazines that are read, and the products and services in which individuals have an interest. Companies can use this information to determine potentially new customers, for example by comparing this external information with the profile of current customers. In the Netherlands, the two most important developers of zipcode segmentation systems are GEO-MARKTPROFIEL and MOSAIC, and the three individual level segmentation systems are the LARGE CONSUMER SURVEY (GROTE CONSUMENTEN ENQUÊTE), OMNIDATA and CLARITAS. It is expected that individuals with a profile that matches the profile of current customers will be more interested in the products of the company. At this stage (before the campaign actually has started) it is difficult to analyse the information about (new) customers, that is, to determine the people that should be addressed in the campaign. In fact, there are no data available on the relationship between (the characteristics of) the respondents and their willingness to respond to this offer. One can now decide to analyze previous campaigns in order to obtain some insight into this relationship, by determining the look- 6

7 alikes of the current customers, that is, the individuals with similar characteristics as those of the current customers. This is done using internal data instead of external data. It is then often assumed that individuals who have bought products in the past are reasonable proxies for individuals who intend to buy in the future. With a test campaign, it is possible to examine the presence of identifiable segments. A test is a campaign performed to a selection of the total group of potential customers. The individuals included in the test can be selected randomly from the total population, but one can also decide to consider individuals belonging to certain segments. If a company wants to analyze whether there is a difference in response between one or double income households, both with a top level income, the company can send a mailing to individuals in both groups and compare the responses. If there is a difference in response in that double incomes respond more, and as this difference can be attributed mainly to the difference in the size of the household (because the other characteristics are comparable), the company can decide to approach mainly double income households. 2.3 Media Selection The next step is to determine the media that will be used in the campaign. The choice between the different media is influenced by the goal(s) of the campaign (creating product knowledge or selling the product), the budget, the target audience, available time, nature of the product (service) and position in the market (Hoekstra, 1998). A distinction should be made between the direct media, like direct mail, direct non-mail (door-to-door advertising), telephone and catalogs, and the more traditional mass-media, like television, radio, newspapers and magazines. The direct media are mainly used to sell a product or service, whereas the mass-media are primarly used to create awareness and knowledge, besides generating sales. Direct mail is the most well-known and most used form of the different direct media. Direct mail is an addressed written commercial message. Direct non-mail is a non-addressed mailbox advertisement (with the possibility to response directly by mail, telephone or electronically). It is mostly door-to-door advertising, and the customer s ability to respond directly to the supplier makes it a form of direct media. 7

8 Direct mail has a number of advantages: - It is possible to target with substantial precision. - It is personal and confidential. - It is more competitively secret. - The message can be highly specific. - A variety of formats and materials can be used - There are many opportunities to introduce novelties. Direct mail is however not appropiate for all markets, or for all objectives, and it cannot be used in isolation to build a brand (image). Additionally, some customers are very sceptical of direct mail (Stone et al., 1995). A company should not only rely on direct mail to communicate with potential customers. Using for instance mass-media like billboards to increase awareness (and generate sales) can enhance the positive effect of the direct mail campaign. Also the telephone can be used to interact directly with consumers. This medium provides many insights into the satisfaction of the customers. Direct non-mail has some of the advantages of direct mail, but its main advantage is that the cost per contact are lower. This is because one abstains from a detailed analysis of previous responses or alternative campaigns. However, the distribution of direct non-mail is less specific and the message is not personal. Direct non-mail is most useful when there are no addresses of possible prospects (= leads) available or when direct mail is too expensive. Some of the advantages of the use of the telephone are that it provides immediate feedback (on the quality of the marketing program), it is a flexible medium (it is possible to respond immediately to questions of the customer), it provides incremental effectiveness when used in conjunction with other media, it provides a method of building and maintaining customer goodwill between sales and it provides opportunities for increased levels of customer service (Roberts and Berger, 1989). The disadvantages are that if it is to be effective, it has to be applied systematically, and this requires substantial investments. Also, the costs per contact are relatively high and it is perceived to be a very intruisive medium. 8

9 The mass media sources allow for less selection of potential customers than direct media do. There is information available on the audiences of magazines, newspapers, television programs, radio, internet and billboard advertising, and these can be used to select the outlets that will approximately reach the target audience. The costs per contact are relatively low, but the effectiveness of the advertisements is also expected to be lower than direct (non-) mail. When used in a direct marketing context, mass media sources are mainly used to create awareness or are used in support of the direct media. For example: a company can develop a direct mail campaign, that is preceded by an announcement in a newspaper. During the campaign, television commercials can be used to increase awareness about the campaign and the product. 2.4 Type of Campaign The company has subsequently to decide on the type of campaign: will it be a test (aimed at part of the target audience) or will the campaign be addressed to the whole target audience? Testing in direct marketing campaigns is mainly done in direct mail campaigns. For example, the effect of different versions of the direct mail package on responses can be analyzed in a test. Here there are a number of elements that can be tested, that is, the physical elements of the product or service (the most attractive composition), and the additional elements of the product (like positioning, price, length of commitment and terms of payment). Other aspects that can be tested are the physical characteristics of the mailing, timing of the mailing and frequency of the mailing, see Roberts and Berger (1989) and Hoekstra (1998). After analyzing the impact of the different versions, it can be determined which version is most suitable to use in the real campaign. The test mailing may also indicate that it is more profitable to send different versions to different segments. Finally, the results of the test mailing may also suggest that one is better of not sending a mailing at all or to only one or a few segments. Kobs (1992) indicates that a direct marketing campaign can be used to test or expand into new product and service concepts. When there are doubts about the potential of a new product, it can be offered in a test to a limited group of individuals. By analyzing the response, the company can determine whether it is profitable to bring the product into the entire marketplace. 9

10 2.5 Response Once the mailing has been sent, the company has to keep track of the response. This requires an accurate database which contents must be updated when a response comes in. It is important to record if the response is informative or sales-oriented. This defines the quality of the response. A high quality response is characterized by a high percentage of sales. The incoming order should be processed quickly, and the reply to response should be accurate. The time between an incoming order and an outgoing shipment should be as short as possible. 2.6 Evaluating and Measurement The very important next step is to evaluate the campaign. The campaign should be monitored constantly during its execution, and when necessary, correcting steps should be taken. Usually the firm has a contingency plan to indicate what action should be taken when the results are not as expected. When for instance the intermediate response is very low, mass-media sources can be used to create extra awareness for the campaign. When the campaign is over, the results should be analyzed with substantial precision. Here the following questions should be addressed: What was the quantity of the response? In other words: how many people responded? What was the quality of the response (were the respondents asking for information or did they buy the product)? It is also important to know who bought the product: do these individuals have distinguishing characteristics? How much did the respondents buy? Who did not buy the product? How do the results compare with any prior expectations? When the campaign was a test, the relationship between response and characteristics of the respondents and characteristics of the mailing should be analyzed. This analysis will provide the basis for segmentation of potential customers. The segmentation is performed well when the response to marketing instruments is as homogeneous as possible within segments and as heterogeneous as possible between segments (Wedel and Kamakura, 1998). 2.7 Fulfilment Fulfilment is defined as all the activities that occur once an order has been made or information 10

11 has been requested. It deals with order-form issues, receiving orders, processing of orders, inventory policy, warehousing issues, customer service and planning and control (Roberts and Berger, 1989). Fulfilment is an essential element of direct marketing because it is an important aspect in creating a relationship with the customer. Although good fulfilment is no guarantee for a good relationship, bad fulfilment will almost surely lead to a bad relationship. Good fulfilment can become a competitive advantage. One of the important aspects of fulfilment is after-sales service, including handling of complaints and dealing with questions. It is found that there is a high correlation between the degree of success of a direct marketing campaigns and the quality of the after-sales service (Katzenstein and Sachs, 1992). Good fulfilment is one way to deepen the relationship with the customer. Stone et al (1995) indicate that consumers seek: - convenience and easy access to products and services - appropiate contact and communication with the company - special priviliged status as a known customer - recognition of their history with the company - effective and fast problem-solving - appropriate anticipation of their needs - a professional and friendly two way dialogue To address these issues, the company needs to understand its customers. From a database, the company can obtain information on the characteristics and the sales history of the customers. The company also wants to know if the customers are satisfied with the products they have bought, and what products they want to buy in the future. This information can be obtained by approaching individual customers, either through mail or by telephone. By showing interest in the satisfaction and future needs of the customers, and by offering them new products, the company can try to deepen the relationship with the customers. Retailers can issue customer cards that give discounts on certain products. Producers can provide membership cards, giving the holders for instance the right to buy their products at lower than average retail prices. The advantages of these cards for the providers are that they constitute 11

12 a relationship with the customer (an indication of commitment of the customers to buy products from the provider), and that it is possible to keep track of the purchases of individual customers (which is often difficult for retailers and producers who do not deliver their goods directy to the customer). It is also important to monitor the profitability of the direct marketing campaign. First of all, a test can provide insight into the expected performance (and profitability) of a campaign. Next, a front-end analysis and a back-end analysis can help in monitoring the profitability. A frontend analysis measures the initial costs of and response to a direct marketing activity, and deals primarily with the costs of gathering new customers. Back-end analysis starts after the initial response, and deals with the potential revenues of a group of respondents after their personal data have been added to the database (Hoekstra, 1998). Criterions in front-end analysis can be: costs (per thousand pieces of mailing), response per thousand, orders per thousand, costs per response and costs per order. Back-end analysis deals with sales, contribution-margin and profit. Finally, the profitability of the individual customer is of relevance. Which customers will provide profit for the company in the future? This is often measured as the term lifetime value (LTV) of a customer, which is defined as the discounted value of the expected future sales of a customer. This value can be used to determine whether it is profitable to keep sending offers to a particular customer. In a sense, the LTV is a proxy of the value of this customer to the company. 3. Review of the Literature In this section we review the recent literature on direct marketing corresponding to the different stages discussed in the previous section. Some of these stages have been investigated more intensively than others in recent years. In the following paragraphs, each of the stages from figure 1 will be discussed. 3.1 Target Audience It appears that companies usually do not decide at this stage who they will approach, that is, what their target audience will be. Exceptions are fundraisers. They have to decide who they want to approach in a campaign: Should they ask for a contribution from current donators or try to 12

13 approach potential new donators? The first group will be more profitable in the short run, whereas the second group will have more pay-off in the long term. A linear programming method is developed by Stanford, Martin and Myers (1996) to determine these trade-offs between approaching current or new customers. They find that a trade-off has to be made when the mailing budget is not high enough to mail both current and customers. The selection is not stable: small changes in the mailing budget can lead to significantly different optimal mailing strategies. The authors not that the model can only applied in this situation when a detailed sensitivity analysis is performed. 3.2 Information on Characteristics Information on specific characteristics of customers is often internally available (name, address and purchase history), but this information often has to be enriched by using external data (supplied by specialized companies). A number of issues concerning the use of external data are discussed by Lix, Berger and Magliozzi (1995). They mainly look at the use of external data to acquire new customers. The first issue they discuss is the development of a scoring equation for linking the external and internal lists. The scoring equation is used to determine the probability of response. It is usually based on the purchase-related measures Recency of the last purchase, Frequency of purchases and Monetary value of the purchases (RFM). These measures are obviously not available for individuals on the external list who are not yet customers. The authors therefore propose to use information from the internal list to predict a score for the individuals on the external list. They use regression analysis and log-linear analysis to obtain a prediction of the scores. Interestingly, the treatment of missing values is a second issue that is investigated in their study. Missing values are not an unusual phenomenon in commercially available databases. They can not be handled using convential techniques because these amount to the elimination of a substantial amount of the information. The authors suggest to handle missing values by using individualbased data whenever possible, and to proceed with a higher level of aggregation if that is needed to impute missing individual-based values. By an empirical analysis the author show that this is a superior way to cope with missing values. 13

14 The potential pay-off of using information (especially purchase history) is examined by Rossi, McCulloch and Allenby (1996). They assess the information content of various information sets available for direct marketing purposes. They find that there is a tremendous potential for improving the profitability of direct marketing efforts by more fully utilizing household purchase histories. Even rather short purchase histories can produce a substantial net gain in revenue. This result implies that even modestly targeted marketing strategies are already profitable, and that these strategies will probably become more prevalent in the future. 3.3 Analysis of Information and Selection of Target Segments The information on customers has to be analyzed, and the results of the analysis should be used to determine who will be targeted in the campaign. Schmittlein and Peterson (1994) perform a customer base analysis. Past purchase behavior is analyzed in order to predict future behavior. A statistical model using transaction rates and retention/dropout rates (whether or not the customer does a repeat purchase) is developed and estimated. Both rates are assumed to be heterogeneous across customers. The results show that the model is useful for aggregate customer forecasts, customer segmentation, individual customer valuation, and inferences about the reorder / dropout process actually taking place among customers. An important aspect of the commonly used segmentation and selection models concerns modelling the response of customers to a direct mail campaign. There are a number of techniques available to estimate the relationship between response and characteristics of the mailing and of the respondents. These techniques can be divided into cluster scoring methods and individual scoring methods. The cluster scoring methods provide prediction of response for clusters of individuals. Some of these methods are: Cluster Analysis, Automatic Interaction Detection (AID), Chi square Automatic Interaction Detection (CHAID) and Classification and Regression Trees (CART). Individual scoring methods predict the probability of response per individual (although these predictions are usually again combined into clusters). Some of these techniques are the linear model, discriminant analysis, logit and probit models, neural networks, Multiple Regression Analysis (MRA), Multiple Discriminant Analysis (MDA) and Log Linear Modeling (LLM), see Hoekstra (1998) and Roberts and Berger (1989). Franses (1997) provides an overview on a few 14

15 of the econometric models for direct marketing response. Most recent literature on direct marketing deals with selection and segmentation questions. The company has a list with names and has to decide who should be confronted with the campaign. The information that is available to make this decision comes from the internal database (sometimes combined with external databases). If a test-campaign has been carried out the results of this test can also be used. The list can first be segmented to obtain a number of homogeneous segments. This usually means homogeneous with respect to response to elements of the marketing mix. Next, a selection has to be made between the segments that will be included in the real campaign. It is also possible to use a selection technique that does not require segmentation first, see Bult (1993). Segmentation Bauer (1988) describes an elementary model for segmenting direct marketing customers. The segmentation is based on (traditional) RFM criteria, that is, a three-dimensional classification based on Recency, Frequency, Monetary Value of the purchases. Although the RFM variables are often used for segmentation in practice, researchers advocate using these criteria in a more sophisticated segmenting procedure, thereby not only relying on the RFM criteria. Some of these methods are compared by Magidson (1988). He finds that the CHAID (Chi-squared Automatic Interaction Detection), logit and log-linear models for segmentation are more appropiate than multiple regression, discriminant analysis and AID. The latter techniques are shown to provide erroneous and misleading results, because they are based on the normal distribution whereas the other techniques are justified under the binomial distribution. The binomial distribution is more appropriate when modelling the probability of response than the normal distribution, because the probablity of response is usually very small. Trasher (1991) recommends the CART method. It is a segmentation tool that is similar to CHAID, but has some additional advantages. For example, CHAID examines every variable only once. In contrast, CART is able to capture a much wider set of interactions than CHAID. Haughton and Oulabi (1993) compare CART with CHAID by evaluating whether individuals are classified correctly as being either respondents or nonrespondents. They find that the performance is comparable. Based on practical and theoretical 15

16 considerations, CART or CHAID are found to be equally useful, although CART is preferable when there are a large number of (continuous) variables, and CHAID is more preferable when there are many categorical variables. The previous methods all consider the calibration of a single response model for the entire population. DeSarbo and Ramaswamy (1994) propose a method called CRISP (Customer Response-based Interative Segmentation Procedures) that allows for heterogeneity in the database with respect to the magnitude and direction of the response parameters across customers. CRISP simultaneously constructs market segments and estimates models of consumer response in each of these segments. The authors show that the method leads to better results (in terms of correctly classifying a customer to the appropriate segment) than methods that do not take the heterogeneity into account when the population is indeed not homogeneous. Bayus and Mehta (1995) develop a method that simultaneously determines segments and the (household) characteristics that are significant predictors of segment membership. The segments are determined using household characteristics and product ages. In fact, the method deals with replacements of durable consumer items. Using empirical data they show that the method outperforms the one-segment method, still often used in practice. Zahavi and Levin (1995) show that neural networks can be a useful tool for determining who to mail, and for predicting how many orders can be expected from the mailing. However, application of neural networks to database marketing (or direct marketing) is non-trivial. In a subsequent article, the authors find some discouraging results for the performance of neural networks (Zahavi and Levin, 1997). They investigate the use of neural networks in targeting audiences in solo mailings (mailings offering only one product). They compare the performance of the neural network with a logistic regression method by evaluating the percentage of buyers captured by the models relative to the total number of buyers. The neural networks appear to be outperformed by the logistic regression method. Not all the information that is needed is usually available. Managers often want to have an estimate of the response rate per segment. This is one of the criteria used to decide if the segment should be included in the campaign. Often this estimate is obtained from a test on a random sample from the population. The goal can also be to test the effect of different versions of the 16

17 mailing. Such a test is often constructed using a fractional factorial design. A fractional factorial design is an experimental design ensuring that the effects and interaction effects of different factors can be determined without having to examine every possible combination. However, tests are costly, and are not always performed. Levin and Zahavi (1996) propose a nonparametric approach for segmentation analysis, that is, determining the segments that should be targeted, that does not require a priori knowledge about the (true) response rate of the segments in the list (other than a classification into good, marginal or bad). Based on the empirical studies so far, we should mention one important issue here. Suppose a test mailing is performed. The problem often is that a test mailing is usually performed on a small sample of the entire list. Because the response to direct mail campaigns can be very low, the number of respondents (in absolute terms) is small. This greatly influences the quality of the segmentation procedure, which is based on the response rate of the test mailing. We believe that more research is needed into the small sample properties of currently applied segmentation techniques. Selection There are several methods for selecting individual customers from the mailing list. Bult (1993) describes a maximum score method that provides an optimal selection for a promotional direct mail campaign. Given an asymmetric loss function, the maximum score method performs better than the more convential logit model, see also Bult and Hoekstra (1991). Bult and Wittink (1996) expand the asymmetric loss function by incorporating heterogeneity, and show empirically that its enhances the (expected) net returns of the mailing campaign. Another more sophisticated method for selecting individual customers is presented by Bult and Wansbeek (1995). They introduce a comprehensive methodology for the selection of targets (customers) from a mailing list for direct mail. The method explicitly takes the profit function into account, and equates marginal costs to marginal returns. The households that should receive a mailing are determined such that expected profit is maximized. The methodology shows to have great predictive accuracy and generates higher net returns than traditional (aggregate) approaches. The previous two methods contain a separate estimation and selection stage. First, the 17

18 parameters of the model describing consumers reaction to a mailing have to be estimated before addresses for a future mailing are selected. Because these methods consider these two stages separately and neglect estimation uncertainty, they lead to a suboptimal decision rule, and hence will not lead to optimal profits. Therefore, Muus, van der Scheer and Wansbeek (1996) derive an optimal Bayesian decision rule that follows from the firm s profit function and explicitly takes estimation uncertainty into account. Indeed, empirical results show that the rule does lead to higher profits. Rao and Steckel (1995) propose a method that not only looks at the current mailing, but also takes subsequent mailings into account. The expected response by a given prospect (potential customer) is modelled, based on a set of descriptor variables. For each mailing, a submodel is proposed that predicts the probability of a positive response to initial and subsequent mailings. The initial probability is updated after a negative or nonresponse. An extension of this idea is the concept of lifetime value, where the decision to include a customer in the campaign is based on the expected revenues that this customer will generate for the firm during his/her lifetime (as a customer). Berger and Nasr (1998) describe (but do not compare) a number of mathematical methods for determining customer lifetime value. Dwyer (1997) also discusses the importance of lifetime value and Hoekstra and Huizingh (1997) illustrate the use of lifetime value in developing relationships with customers, and also develop a model to measure lifetime value. Another issue is how often should the company contact potential customers. Hansotia (1995) argues that the decision to initiate a contact or not can be based on a break-even analysis, using the lifetime value of the customer. Catalog sales companies face with similair decisions as companies that want to send a direct mail advertising. The main difference is that the costs of catalogs are usually higher than the cost of one piece of direct mail. Especially in catalog sales it is important to look beyond the current offer and to estimate the future revenues that can be earned from an individual customer. Bitran and Mondschein (1996) develop a model that determines optimal mailing policies in the catalog sales industry when there is limited access to capital. A Markov Chain representation is used, where customers make transitions to different states. The states are based on the three dimensional RFM (Recency, Frequency, Monetary Value) classification. The optimal solution is obtained with 18

19 a heuristic, and comparison with an upper bound shows that the heuristic gives satisfactory results. Gonul and Zhi (1997) extend the model by Bitran and Mondschein (1996) by assuming a dynamic environment for both the firm and the customer. In brief, customers maximize their utility functions over a specific time horizon and the direct mailer maximizes profits. 3.4 Media There are many different media that can be exploited for direct marketing. Direct mail is most commonly used, but most recent research on the use of media in direct marketing focuses on the internet and on home shopping networks. Mehta and Sivadas (1995) look at internet as a direct marketing medium. They find that respondents react negatively to untargeted (electronic) mail, but are more favorable to targeted communication efforts. Parsons, Zeisser and Waitman (1998) find that most efforts in using online services and the World Wide Web have mixed success. They provide an integrated perspective on leveraging interactive media for marketing. Much research is also done into the effectiveness of home shopping networks. Eastlick and Liu (1997) find a positive relationship between overall attitude towards retail stores and television shopping. Danaher and Green (1997) consider the advertising effectiveness of direct response television. The most effective advertisements are those placed in interruptable program types and/or during morning and afternoon dayparts. Hughes and Wang (1995) relate the use of different media to retention rates of customers. Using a customer database, a marketer is able to capture long-term purchasing behavior and can measure the retention rates based on the kind of media used. The result can be a radical shift in the acquisition of media and a significant improvement in retention rates. Verhoef, Hoekstra and van Aalst (1998) consider another media vehicle, that is, they examine the effectiveness of direct response radio commercials. The use of these commercials is growing, but the authors note that little research has been done into their effectiveness. They analyze the timing of direct response radio commercials, and find that the most effective ones are those broadcasted during the first half of the week in the afternoon. 19

20 3.5 Sales During the sale it is important to convert as many people as possible from prospects (potential customers) to customers. Seaver and Simpson (1995) investigate how response rates can be improved through catalog design. Two other approaches to improve the effectiveness of direct mail are proposed by Vriens, van der Scheer, Hoekstra and Bult (1998). The first is a traditional conjoint experiment, where a respondent has to judge several mailings. The second is a conjoint field-experiment, where only the response is observed and not the judgement. Either one of these approaches can be applied, based upon the specific requirements of the application of the mailing. 3.6 Evaluating and Measurement If a test mailing has been performed, the response of the test mailing can be used to predict the response to the real mailing (or roll out). Wang and Baker (1996) argue that taking a random sample from the available database may lead to inaccurate forecasts, and propose different methods of drawing a sample from the houselist. Their results show that the proposed procedures improve the accuracy of the forecasts. Basu, Basu and Batra (1995) propose a method that uses early returns to forecast the total number of orders. This procedure is useful when keeping track of the results of the real campaign. The response curves obtained from direct marketing campaigns are analyzed and used to determine the total number of orders. The response curves are allowed to be heterogeneous across customers. The method is compared with four commonly used methods. It outperforms three of them, and performs as well as the fourth one. Therefore, it offers a promising alternative to other methods of mail survey response patterns. Besides predicting the response of the roll out campaign, the outcomes of the campaign can also be analyzed for other reasons. Bawa and Shoemaker (1987) analyze the effects of a direct mail coupon promotion and determine the characteristics of the households that use these coupons. They find that the coupons are mostly used by regular buyers and that most consumers revert to their precoupon choice behavior immediately after their redemption purchase. 3.7 Fulfilment 20

21 As noted earlier, fulfilment is an important aspect of direct marketing. Good fulfilment is necessary to build a relationship with a customer. Morrow and Tankersley (1994) investigate the usage of telephone service numbers and Tom, Burns and Zeng (1997) look at the perception of the waiting times customers face when telephoning to the company. Another way to get information from the customers about their satisfaction with the company is through interviews. Woodside and Wilson (1995) discuss the use of long interviews in direct marketing to obtain feedback from consumers, and show that it can be a valuable tool for providing insights into the purchase processes of customers. Gengler and Leszczyc (1997) show that customer satisfaction can be used as an instrument to build relationships with customers. Kestnbaum, Kestnbaum and Ames (1998) develop an Longitudinal Contact Strategy for firms, indicating when a customer should be contacted, what should be said to the customer and how much can be spent on each customer. 3.8 Other topics Figure 1 does not seem to exhaust all the direct marketing topics that are being discussed in the recent literature. Two examples of other topics that have been investigated are privacy and international direct marketing. With regard to privacy, research has been done into the confidentiality of personal data, see for example Wang and Petrison (1993), Nowak and Phelps (1995) and Campbell (1997). In international direct marketing, Mehta, Grewal and Sivadas (1996) and Desmet and Xardel (1996) have looked at developing campaigns for different countries. Spiller and Campbell (1994), Rosenfield (1994) and Maynard and Taylor (1996) have compared direct marketing in different countries. To summarize, in this section we reviewed the recent literature on direct marketing. We conclude that most research has focused on a few (important) topics, such as segmentation and selection. In the next section, we will present a number of directions for future research, that may be of interest for increasing our theoretical and empirical knowledge concerning direct marketing campaigns. 4. Further Research Topics 21

22 It seems that the opportunities for future research in direct marketing are extensive. Based on the literature review in the previous section, some of the research questions that can be formulated are: - Why is the percentage of response still low in many direct marketing campaigns, although there are numerous techniques available to help companies with the selection of customers? Can these techniques be further improved? - Is non-response treated correctly when analyzing the effectiveness of direct marketing campaigns? Why do individuals not respond? - How can the new (interactive) media, such as internet and cd-roms, be used in direct marketing? - What are the consequences of the use of the concept of Lifetime Value (LTV) in direct marketing, and how can it be used efficiently (and beneficially) in direct marketing? Additional to these, we aim to focus on four research topics in this section, where the selection of the topics is based on our conjecture that advances can be made using methods in econometrics and operations research. Of course, this does not exclude other interesting areas! We consider the development of a benchmark for a direct marketing campaign, improving the selection of customers, incorporating competition when modelling the probability of response, and determining the long run effect of direct marketing in the subsequent four subsections. 4.1 Benchmark for a Direct Marketing Campaign A difficult task in evaluating a direct marketing campaign amounts to determining whether the response rate is satisfactory or not. The response to direct marketing (and more specifically direct mail) campaigns, is usually low, that is often not higher as 5%. The response rate is often compared with response to previous campaigns. Companies often do not analyse the response, in order to determine the factors that influenced the response. More importantly, companies do not know what response the campaign could have been generated, based on the characteristics of the offer and of the selected prospects. Such a benchmark is very helpful in determining afterwards whether the campaign has been successful or not. Suppose a company has a long tradition in direct mail. All their campaigns have generated a response of around 5%. So if a new campaign has a 22

23 response of 5%, it is considered a successful campaign. If the maximum response of this campaign was 8%, then 5% is a reasonable result. This maximum response can perhaps be determined through a careful analysis of other campaigns. This analysis shows that the campaign could have generated a maximum response of 20%, then 5% turns out to be a rather poor result. An important aspect of direct marketing is the development of relationships with the customers. The more campaigns a company has done, the more knowledge the company has about its customers. Every previous campaign can be viewed as a test, and the results can perhaps be used to improve the effectiveness of future campaigns. Updating techniques can be designed, which lead to a stepwise improvement of the response rate. The idea behind determining the maximum response that a campaign can generate is that each individual has a certain probability to respond. This probability can be determined using for example a logit or a probit model. If we assume that this probability is the midpoint of a certain density, then it is possible to determine the maximum probability of response with some degree of confidence. Using the maximum value for each individual, it is possible to determine the overall maximum response to the campaign. 4.2 Selection of Customers In practice, the selection of customers is usually based on segmentation. The segments are determined via some form of statistical analysis. Associated with every segment is an (average) probability of response and/or an (average) profit. Usually, the most profitable segments are chosen to target with the campaign. However, experience shows out that individuals in these topsegments often end up in top-segments, independent of the product that is being sold. Therefore these individuals often receive many direct mailings, as opposed to individuals in the segments just under the most profitable ones. It is not unreasonable to assume that the probability of responding to an individual mailing is negatively correlated with the total number of mailings that is received. This can be due to an information overload or because of increased irritation. Targeting the segments just below the most profitable ones, could turn out to be more profitable than targeting the most profitable ones, because of the irritation factor mentioned above. Another issue concerns the customers that do not belong to the top-segments, but who resemble 23

24 the members of the top-segment very closely. First, CHAID-analysis could be applied to determine segments, and secondly the resemblence could be determined by an alternative technique using the characteristics of the respondents that are used in the segmentation. Multidimensional Scaling may be applied to a segment to determine its average profile. Individuals not belonging to the segment have to be compared with this average profile. Finally, a topic of interest is to compare alternative segmentation methods in an extensive simulation experiment. In contrast to the results for case studies, documented in the literature, a simulation experiment can lead to more generalizing statements. 4.3 Competition Experience shows that the observed response to direct mailing campaigns is frequently less than expected, even in case the company supposedly has access to a database containing detailed information on their customers, including their past behavior. This empirical fact can perhaps be explained by introducing competitive effects into a theoretical model of direct mailing response, where it is assumed that the same customers are also approached by other firms, who obtained their addresses from alternative sources. A theoretical model can contain two main features. The first is a so-called mailing generating process [MGP]. Given the individual characteristics, one may postulate the probability that an individual receives zero, one or k mailings, all offering a similar type of product. As an example, one may think of life insurance or charity donations. The second feature is the notion that more direct mailings can increase irritation, and increase the probability that an individual becomes inclined not to respond at all to any mailing. To simplify matters one can assume that the MGP can be described by an ordered regression model, where the number of mailings (0 to k) one is likely to receive is a function of explanatory variables although other descriptive models can also be considered. For example, income and age can be the latter variables. An individual may now decide to respond and make a choice between 1 to k alternative offers, but he or she may also decide not to respond. If one postulates that the latter decision depends again on age and income but also on the number of mailings received (as a proxy for irritation), simulation techniques can be used to generate artificial responses to direct 24

25 mailing campaigns. 4.4 Long-Run Impact of Direct Marketing on Relationships An important question that has not been examined in the literature is the long term effect of direct marketing. Does direct marketing lead to a stronger relationship with customers? DeKimpe and Hanssens (1995) examine the long term effect of marketing effects (advertising and promotion) on sales, where they use an approach that can determine whether the effects of marketing efforts are persistent (permanent). They use econometric techniques to determine if this persistence is present, and next they analyze to what degree. It seems interesting to use this approach to direct marketing, in order to determine whether and if so, to what degree, direct marketing effects are permanent. 5. Conclusion When developing a direct marketing campaign, a company has to address a number of questions. These questions can be assigned to the different stages of the direct marketing campaign. In this paper, we have discussed these stages sequentially, and we have looked at empirical and theoretical research that has been done into these different stages. From our review of the literature, we can derive the following generalizing statements concerning direct marketing campaigns: - The use of information on household purchase histories (or more generally: behavior) improves profitabilty considerably. - There are a number of methods available for the segmentation and selection of customers, but no method consistently outperforms all other methods. - Lifetime value is a promising tool for determining the future value of individual customers. - The effectiveness of the campaign can be improved by catalog design and by the design of the direct mail package. - Early returns can be used to predict the total response. - Fulfilment (all the activities after a consumer has decided to buy a product) is a crucial 25

26 element of the direct marketing campaign. It is important that the company monitors satisfaction, and tries to anticipate future demand, because trying to retain current customers is more profitable than trying to obtain new customers. As regards to further research topics, we gan give the following summary. Most quantitative research has focused on developing techniques to segment and select customers. A number of practically applicable methods have been developed, but we could fiond no method which incorporates competition or irritation. Therefore, we consider this as an interesting area for future research. Another area is the development of a benchmark for direct marketing (or more specifically direct mail) campaigns. This would give a tool to determine whether a campaign was successful or not. Finally, we consider it important to estimate the long term effects of direct marketing. In fact, does the pursuit of a (long term) relationship also result in more commitment by consumers, and therefore, more sales in the long-run? 26

27 Figure 1 Stages of a Direct Marketing Campaign Product Target Audience Information on Characteristics Analysis of Information Selection of Target Segments Media Update Campaign Real Campaign Test Campaign Sale Sale Evaluating and Measurement Evaluating and Measurement After Sales / Fulfilment Service and Quality above Expectations Communicating Regularly Deepening Relationship Monitoring Profitability 27

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