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Formulating direct marketing offers with conjoint analysis
RAJ ARORA
Formulating Direct Marketing Offers with Conjoint Analysis RAJ ARORA i s Schutte Professor of Direct Marketing at the University of Missouri-Kansas City He received his PhD from Claremont Graduate School Raj Arora’s research interests are in consumer behavior and marketing management He has published articles in the Journal of Marketing Research, the Journal of the Academy of Marketing Science. the Journal ofAdverfning Research and PIychology and Marketing The author gratefully acknowledges the helpful commenrs of Don E Schultz and two anonymous reviewPrs on this article The support of Joe Curry and Sawtooth Software, Inc , Ketchurn, Idaho, for providing the Adaptive Conjoint Analysrs program, and the assistance of Melinda Andre in data collection are also appreciated
RAJ ARORA
ABSTRACT Conjoint analysis i s described as an important method for designing direct marketing offers prior to a market test. The method i s demonstrated with an application in designing direct marketing offers which focus on the price of the product, the channels through which the product may be purchased, and the exchange/refund policy. The results of conjoint analysis provide the utilities of each variable at each level. This information i s then used to construct alternative direct marketing offers. The simulated willingness to purchase is also discussed.
0 1991 J o h n Wiley & Sons, Inc. a n d Direct Marketing Educational Foundation, Inc. CCC 0892-0591/91/01048-09$04.00
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INTRODUCTION It is common practice in direct marketing to test direct marketing programs before the rollout ( 1 , Z ) . Although the objectives of testing may vary from one mailing to another, testing can be used to assess the impact of: the product or service; the media or the method of accessing the defined target; the message or communication; seasonality; and/or the promise of some incentive. Furthermore, the offer is considered to be critical to achieving the firm’s objectives (22). Kobs (17) identifies 99 different proven components of a direct marketing offer. These include: 1) right price, 2 ) free trial, 3 ) money-back guarantee, 4 ) “bill me later,” 5) installment terms, 6) rush shipping service . . . .99) competitive offer. Given the vast number of proven components of an offer available, selecting the best response offers is not an easy task. I t is possible to eliminate or alter the direct marketing program on the basis of the test results. Although relatively inexpensive compared to the cost of a national rollout, program testing can be quite expensive and inefficient if not carefully planned. O n e approach that can be helpful in designing a direct marketing program is the use of conjoint analysis or “tradeoff” (16). For example, consider price as an element of the offer. Choosing the “right price” from a range of “acceptable prices” is a difficult and involving exercise. Furthermore, price is but one of the critical components of a direct marketing offer. Perhaps customers are willing to pay a higher price for an offer backed by a money-back guarantee, compared to one without such an assurance. Similarly, a buyer may be willing to pay a higher price if the order could be shipped instantly. These situations raise the question: “Is the customer willing to ‘tradeoff’ money-back guarantee for an earlier delivery date, or ‘tradeoff’ either one for a lower price?” Conjoint analysis helps answer these questions. A brief description of the technique and an application illustrating its use in direct marketing follows.
CONJOINT ANALYSIS
In designing new marketing programs, it is in the interest of direct marketers to determine what the
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customers value most and reflect these in the offerings. A simple strategy would be the self-reported importance scores or preferences of various features (attributes) of the total offer. One is most likely to discover that customers prefer to gain the most possible benefits; for example, the lowest price, fastest delivery, instant credit, best warranty, and so on. Through use of conjoint analysis, “tradeoffs,” or the extent to which customers are willing to forego a specific level of one attribute in order to receive a specific level of another attribute, can be determined. Customers are presented with statistically designed offers (or products). The offers may be various combinations of features representing existing or proposed products and services.’ Consider, for exampIe, two features of the offer-product’s delivery and price-each at two levels (variations of the feature). A quicker delivery implies having goods in the warehouse ready for instant shipment. On the other hand, a longer delivery means that a large supply of merchandise is not critical, thus resulting in inventory (back end) savings. From a customer perspective, some may be willing to pay a slightly higher price for quicker delivery. Together these two features of the product, each at two levels, form four possible combinations. Of course in this example the two extreme cases (low price, quick delivery and high price, late delivery) are not particularly important. The critical tradeoff is between low price with late delivery and high price with quick delivery. In conjoint analysis, customers are asked to state the overall preferences (ranked or rated) for each of the four possible combinations of price and delivery. From these overall evaluations the specific utilities (preferences or benefits) of each level of the attributes are inferred. In this hypothetical example, the four options considered and their corresponding utilities are shown below: Utility -
Variable Price
S 12.77 S 15.99
141
Delivery
24 hours 3 weeks
57
0 0
’ In typical conjoint applications, researchers choose attributes for a single product. Thus the variables are the product’sattributes,and not the product. In direct marketing, however, it is possible that some products are more likely to be purchased through direct channels, whereas others through traditional retail channels. In such situations the researcher may wish to include products as a variable in the experiment.
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These utility values indicate that this particular customer is price-conscious (higher utility for lower price). If the costs associated with the change in deliveryservice are approximately equal to the price difference above, the direct marketing manager is better off to lower the price, than to provide a quicker delivery. Design Options in Conjoint Analysis
The above example illustrates evaluation of only two features at two levels. Thus, it is easy to collect the customer’s reaction to all possible combinations of the design features. This approach, where a respondent is asked to rank all possible combinations, is referred to as the “full profile.” However, a more realistic setting may include several variables with two or more levels each. As an example, consider the following hypothetical situation. A direct marketer is considering the choice of three levels of “price,” three levels of “exchange policy,” five levels of “delivery dates,” and a choice between two types of “products.” In this case the choice between all possible combinations is 3 X 3 X 5 X 2 or a total of 90. The task on the respondent to rank all 90 possibilities can be quite burdensome. Green (12) illustrates this problem in data collection vividly. For example, if a marketer is faced with five variables at three levels each, the full profile design requires a total of 243 combinations. In situations like this Green illustrates a set of procedures and alternative designs which significantly reduce the number of combinations. These designs fall under the general category of “fractional factorial” designs. In these situations the respondents are shown only a subset of all possible combinations. One such design is referred as “orthogonal array.” Here the test combinations are selected in such a manner as to permit the estimation of importance of each independent variable. Conjoint Analysis Procedures
A variety of procedures are available for conducting
conjoint analysis, collectively described as “any decompositional method that estimates the structure of a consumer’s preferences (e.g., part worths, importance weights, ideal points) given his/her overall evaluations of a set of alternatives that are pre-
SO
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specified in terms of levels of different attributes” ( 1 4 ) . Green and Srinivasan ( 1 4 ) discuss the methodological issues of each procedure. However, recent developments make it easier to use conjoint analysis with the aid of a personal computer. Two of the prominent software packages are: “Adaptive Conjoint Analysis” (ACA) from Sawtooth Software, Ketchum, Idaho, and “Conjoint Analyzer” from Bretton Clark in New York City. A recent addition to these programs is OLS Conjoint Analyzer. This is part of other multivariate statistics programs “PCMDS 5.1” from Scott Smith at Brigham Young University, Provo, UT 84602. The use of Adaptive Conjoint Analysis is illustrated in the problem setting that follows this section. The Conjoint Analyzer’ software is described briefly. The program comprises two modules. The first module helps in designing the experiment (statistically choosing various combinations of features to collect and analyze the data). In view of the number of combinations in full profile approach, this program helps to select the fractional designs to collect data efficiently. The data however, are collected by a standard paper and pencil approach. The second module of Conjoint designer is used in analyzing the data. The ACA, in contrast, helps in designing the experiment, data collection, and analysis using the personal computer.
PROBLEM SETTING
Direct marketing is known for its convenience, providing a high degree of “place utility” to customers as an alternative to traditional retail stores. It is often known as nonstore and in-home shopping. The popular forms of direct marketing are by direct mail, catalogs, and cable TV. Consider time utility-ability to obtain the product at the desired time. There is considerable room for increasing the time utility to the customers and the effectiveness of direct marketing operations. Although several direct marketing telemarketing firms have emerged that fill the order within the same or next day (e.g., firms
* Interested readers are referred to a complete review of Conjoint Designer in the Journal ofMarketing Research, (August) 1986, 31 1-312.
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situationally defined strengths/niches, it does not delineate the attributes which make this possible. Competitiveness in team patronage situations, for example, may be enhanced by retailer attributes such as 1) quantity discounts, 2 ) monogramming/ silkscreening, and 3) guaranteed availability of necessary quantities, sizes, colors, and quantities. Competitiveness in gift-giving situations may be enhanced by attributes such as the availability of 1) gift-wrapping service, 2 ) notecards enclosed with the gift, and 3) gift-shipping service. Although the aforementioned attributes are largely within the retailer's control, other attributes that affect situational competitiveness may not be. In many cases, however, apparently uncontrollable attributes may b e indirectly manageable. For instance, the reason that nonstore retailers scored poorly in footwear patronage situations was probably because nonstore shoppers are unable to try o n the footwear. Many nonstore retailers have indirectly managed this problem with liberal return policies. Future research should empirically assess the impact of retailer attributes o n situational competitiveness. Such analysis will greatly enhance the managerial implications of situational research by delineating the causative measures that can be taken to appeal to various situational segments. This study examines a limited array of situational factors. Future research should examine additional factors. If additional situational factors are investigated, however, they should be derived from consumer-based elicitation techniques (6,15) to ensure that the situational treatment is valid. It is also important to note that had this study examined an additional two-level situational factor, a full-factorial representation would have doubled the number of cel Idquestionnaire pages from eight to sixteen. The study has limited generalizability due to the retail product-market which is investigated. The methodologic framework utilized by this research should be tested in other product markets. Sporting goods represents a narrow range of retailing. The profile of a sporting goods shopper may differ from shoppers in other retail product-markets. The nature of situations pertinent to sporting goods shoppers may also differ from other product markets. The sample also limits the research. Because the sample consists of individuals who at least visited and who in some cases patronized a specialty sporting goods store, they are generally predisposed to
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this type of store. If subjects had been drawn from a grdup not closely affiliated with a specific retailer, responses may have differed. Traditionally, segmentation efforts have been based o n demographically defined consumer markets. The opportunity to more fully develop segmentation strategies which are based on situationally defined markets, as illustrated in this study, also appears to be fruitful.
CONCLUSIONS
This research demonstrates that patronage perceptions of various store and nonstore retailers are significantly affected by the nature of the patronage situation. Past nonstore research has been primarily concerned with identifying individual and retailer characteristics which are related to nonstore patronage. Previous research has not investigated perceptual or behavioral variations attributable to factors outside of the individual or retailer. The study strongly suggests that an explanation of perceptual differences couched solely in terms of individual and retailer characteristics will overlook other important sources of variation. The research represents the first effort to assess the competitive structure of a nonstore retail market. Previous research has generally compared a single store and nonstore retailer. The study delineates the competitive market structure of a group of store and nonstore retailers in terms of their situational similarity/substitutability and provides a methodological framework that can b e useful for future investigations of direct marketing. The study demonstrates that the competitive market does not necessarily consist of store retailers competing against nonstore retailers. The store versus nonstore conceptualization of the marketplace does not reflect the true nature of competition. The research demonstrates that inter-retailer competition is conceived by consumers and can be quantitatively delineated in terms of pertinent situational factors. REFERENCES 1. Akaah, Ismael P. and Korgaonkar, Pradeep K. (1989), "The Influence of Product, Manufacturer, and Distributor Characteristics on Consumer Interest in Direct Marketing Offerings," Journal of Direct Marketing, 3;3 (Summer), 27-33.
VOLUME 5 NUMBER 2 SPRING 1991 I 1
example, a person might have a higher degree of preference for cassette player. This will then be represented by a higher estimated utility for cassette player. It is possible to treat each of these as a stimulus object and not as an attribute; however, this approach increases the data collection requirements in that two separate experiments are required, one for each product. Since the products are used as levels, it is essential that the selected price be realistic for both products. The price levels chosen are consistent with the prevailing advertised prices of these products. Furthermore, recently appearing advertisements of these products (with brand names removed) were used in exhibits to show the products to the respondents. The procedure of ACA involves constructing profiles of various possible product offerings by combining these attributes with different levels. For example, one offering may involve an AM/FM cassette player, priced at $19.95, offered through the mail with 1-week delivery, and n o exchange or refund options. The respondents are asked to rate these various offerings in terms of desirability. In selecting the various attributes and levels, care must be taken to ensure that respondents are not asked to rate unrealistic product offerings. In this time and study, two of the variables-delivery choice of channels-are likely to form unrealistic combinations. For example, in purchasing a product from a department store, one normally does not expect the delivery time to be 1 week or 3 weeks. To avoid such artificiality, the two variables were combined to one variable, channel/delivery with five levels. The levels of price were: regular price of $19.95, a discount of 15 percent resulting in $16.95, and a 25 percent discount resulting in $14.95. These prices were selected from a preliminary survey of prices of comparable merchandise advertised in the local market. The levels for the channel were: shop at a store-immediate delivery; shop at home via cable TV-1 week delivery; shop at home via cable-3 week delivery; shop at home via mail-1 week delivery; and shop at home via mail-3 week delivery. The levels for exchange/refund were: no exchange or refund, exchange only (no refund), or choice of complete refund or exchange.
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Sample
The respondents for this study were undergraduate students at a large Midwestern university. A total of 134 students participated in this experiment and form the basis of analysis. The students, who represented various academic disciplines on the campus, were predominantly in the 20-to-34 year age group. Respondents varied in their levels of past experience with various direct marketing purchase media. For example, 67 percent had made a purchase by direct mail in the past 12 months, and 22 percent had made a purchase by cable TV in the same period. This compares with 22 percent of adults who described themselves as having purchased through one or more direct marketing solicitations (10). Within this latter overall sample, those with at least a high school diploma and an income of over $30,000 had a much higher incidence of purchase through direct marketing. The collegiate respondents were informed that this survey would be conducted using the computer. They were also shown in separate exhibits the two products which were the focus of the study. Appropriateness of the products for the student sample was determined from a separate convenience sample of 60 students. None of the students indicated that they would not purchase either of these products. Instrument
Data for this study were collected by using the Adaptive Conjoint Analysis (ACA) program. ACA is designed to collect data (and simultaneously estimate the respondent’s utilities) for conjoint analysis by self-administered computer interviewing. Respondents were asked a series of questions. The first set of questions asked the respondents to indicate if any level of an attribute was unacceptable. Next, the respondents were asked to state their preferred levels of each attribute and the importance of differences among all combinations of levels within an attribute. Finally, they were asked to rate their preferences for various profiles of the product. Direct marketing managers may also be interested in obtaining other demographic or lifestyle information,which can be used for segmenting their target markets. In this study respondents were asked whether they had purchased any item through catalogs, department stores, mail order, and cable TV
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in the past 12 months. They were also asked if they would consider buying these items (AM/FM cassette player or sunglasses) as a gift.
TABLE 2 Attribute Levels and Their Utilities Attribute/Levels
RESULTS
The program computes the correlation between the respondents’ predicted and actual answers to various product concepts. This correlation serves as the goodness-of-fit index. The average value of the correlations in the sample is 0.91, indicating a high degree of fit between the actual and predicted values. The average utilities for each level of the concept and their relative importance are shown in Figure 1 and Table 2. The first column (Table 2) shows the major variables and their corresponding levels. The second column shows the average utilities’ for each level of an attribute. The third column shows the relative importance of each attribute, and the last column shows the ranking of these relative importance scores for the attributes. The most important attribute in the table is the channel/delivery time combination, followed by the price of the product. Ability to exchange or to receive a refund is the third most important attribute, followed by the least important-which product is being purchased. Considering the channel/delivery attribute, the highest utility (94.26) is from purchasing from the retail store. The next highest utility (30.69) is for purchasing by direct mail with delivery promised within 1 week. Thus, there is a sharp drop in utility in purchasing by direct mail as compared to a retail store. The utility for direct mail with a longer delivery promise date of three weeks drops from 30.69 to 3.34. The utility for purchasing through TV is 7.5 for 1-week delivery and drops to 1 . 8 for the longer delivery. The utilities for other attributes, such as price and exchange, both have considerable range between the levels. For the product, however, utilities range from 22.38 (sunglasses) to 5 4 . 5 1 (cassette ’Conjoint analysis provides utilities at the individual level. The utilities shown in TaMe 2 are the average values for all the respondents. The individual level utiZities can be used in segmentation.
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Utilities
Relative Importance
Importance Ranking
Price Regular Discount 15% Discount 25%
0.28 38.50 78.26
0.30
2
Exchange No Exchange/refund Exchange only Refund
0.58 6.92 60.94
0.23
3
Channel/Delivery Time Store TV/one week TV/three weeks Mail/one week Mail/three weeks
94.26 7.52 1.81 30.69 3.34
0.35
I
Product Cassette Player Sunglasses
54.5 I 22.38
0. I 2
4
player). The utilities do not differ due to previous use of direct marketing in the past 12 months, or d u e to the respondent’s attitude toward these items as appropriate for gift giving. DISCUSSION
The results of this study provide answers to questions of enhancing the response rates to direct marketing strategies (various product offerings). The values of the utilities can be used in designing new direct marketing strategies such that the overall utility is maximized. For example, considering the channel/delivery option, the utility is substantially higher for mail than for TV, especially if the promised delivery time is within one week. Another important factor is the exchange or refund option. Study findings reveal that complete refund is the only reasonable option to consider. These findings are consistent with the findings of Akaah and Korgaonkar ( 5 ) . Money-back guarantee was found to be the most important attribute in their study. Some of the newer conjoint programs (ACA and Conjoint Designer) offer the researcher the ability to simulate and ask “what if” questions for config-
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Thus, o n e can simply decide to market a product offer with the highest overall utility. Alternatively, o n e may wish to perform additional analysis using ACA. The ACA program provides the options for simulations in which “what i f ” questions can be ex-
uring new direct marketing offers. Once the utilities have been estimated for the features and their corresponding levels, it is possible to design new offers from among the features considered in the study. The utilities for these new offers are simply the sum of the individual utilities for each level of a feature.
utility
Utility
EXCHANGE
PRICE
100
-
100
-
50
-
50
-
0 -
1 L 1. Regular 2 15%Discount 3. 25% Discount
5
1. No Exchange 2 Exchange only 3. Refund Utsitv
Utility
PRODUCT
CHANNEL
100
-
50
-
loo--
50 - -
0-
0,-
1 L 1. Store 2 TV 1 week
5
4
3
4. Mail 1 week 5. Mail3weekr
Cassette
Sun Glasses
3. TV3weelcs FIGURE 1
Attribute Levels and Their Utilities
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plored. One of the options in ACA is to estimate the respondent’s “First Choice”* model. This is the simplest of the models, wherein a respondent is classified as likely to purchase the product with attributes adding to the highest overall estimated utility. An illustration of the use of this option with the estimates obtained from the model is described. Suppose a direct marketing manager wishes to know the response for the following three options. A cassette player (product), offered with full exchange or money-back guarantee, sold at regular price through 1) direct mail, and 2) through TV, each with promised delivery of less than a week. Furthermore the manager also wishes to know his (her) relative performance (response) in each of these two options, especially with respect to the same product’s availability through traditional retail stores. Thus, in this illustration, the comparison is with three modes of channel/delivery options: the retail store, mail order, and TV. The other variables common to all strategies are: cassette player, offered at the regular price of $19.95, with full exchange or refund. The results of the model for the above strategies are shown below: Percent Preferring Channel/Del. Option
Standard Error
93.1
2.2
Direct mail- 1 w e e k
5.3
2.0
W-I
1.5
1.1
Channel/Delrvery Retail store week
The above results closely approximate the response rates likely to be found in the marketplace. One can now ask the question: What would be the response if the same product is offered at a 15 percent discount through the direct response channels-direct mail and TV? These results are shown below: Percent Preferring Channel/Del. Option
Standard Error
Retail store @ S 19.95
59.5
4.3
Direct mail-I w e e k @ S 16.95
35.9
4.2
4.6
I .8
Channel/Delivery
TV-1
w e e k @ $16.95
‘Other major simulation options available in ACA are: ( 1 ) Share of Preference-a respondent’s preference is allocated fractionally to products in proportion to h i d h e r predicted preference; and (2) Purchase Likelihood-a respondent’s purchase probability is estimated for each product.
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These results reveal the impact of the change in pricing on the product sold through direct channels. With this information, the direct marketer can calculate the costs and benefits of reducing prices as an incentive for buyers to switch to direct mail channels. In this case, it may be to the interest of the direct marketer to offer the product at the discounted price, a practice not uncommon in direct marketing. A couple of comments on the utilities are desirable. Unlike response rates, which are measured in ratio scale, and thus have an absolute interpretation about their magnitude, the utilities are measured in interval scale. Consistent with the properties of interval scale, we can infer that the difference in price utilities between regular price and a 15 percent discounted price (38 units) is approximately equal to the difference between 15 percent discount and 25 percent discount (40 units). But we can not conclude that the utility of a 25 percent discount is approximately twice as much as the utility of a 15 percent discount. Secondly, while we expect the utilities to be related to the demand for the product and thus to the response rate, this relationship of utilities to a response (demand) function needs to be established. Finally, in the context of designing new product offerings, it is customary to refer to attributes or features of products. For some attributes, the link between the attribute and the benefits is quite obvious. However, in technically complex attributes of products-such as decibels of sound distortion in a stereo-the marketer may be advised to convert the attributes into benefits in conjoint studies.
FUTURE RESEARCH
The purpose of this research was to illustrate the application of ACA in direct marketing. This study shows how conjoint analysis can be applied in direct marketing to design offers that have a higher overall utility which, in turn, affects the response to these offers. Several important implications emerge for direct marketing managers. There is ample evidence to suggest that consumers do compare the features of one product (including the intangibles) with another, and make tradeoffs among the available options (8,13,14,16). Therefore, it is important for di-
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rect marketing managers to be cognizant of the tradeoffs consumers make and the utilities associated with each attribute level involved in the tradeoff decision. This study reveals the utilities associated with purchasing a given product from a retail store or from o n e of the direct marketing channels. Furthermore, the results reveal the relative price-elastic nature of the market. Although the findings of this study are important and interesting, some caveats need to be noted. Although care was taken to select products which are used by the student population, caution is advisable in generalizing the findings to the larger population and for other products. Further research of this nature should be conducted to include more product categories and to determine which, if any, products are associated with specific direct marketing channels. One of the direct marketing channels not explored in this study is telemarketing. The role of telemarketing is gaining more prominence due to the growth of direct marketing and the high costs associated with personal sales calls. The impact of telemarketing also warrants exploration in future research. In this study, price is used as one of the attributes. I t may be of interest to direct marketers to segment their market on the basis of price. Further research using this approach may suggest how to benefit most in targeting the product and in developing price and service combinations to reach relevant target markets effectively. rn REFERENCES 1. “Jersey Bank Targets Co-op Owners for its First Equity Loan Mailing,” DM News, 9 (19) (October l ) , 30. 2. (1987). “Joe Weider to Launch Mail Report for Men’s Fitness, Renamed Magazine,” DMNews, 9 (19) (October I ) , 17. 3. ____ (1988), “Austad Not Impressed with Results of Catalogs Sent by Federal Express,” DM News, 10 (2) (January 151, 2. ~
~
4. (1988), “Discovery Channel Mailer Yields Suburban Cable 52% Sales Gain,” Cable Marketing, 8 (1) (February), 16. ~
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5. Akaah, Ishmael P., and Korgdonkar, P. K. (1988), “A Conjoint Investigation of the Relative Importance of Risk Relievers in Direct Marketing,” Journal ofAdvertising Research, (August/Sep[ember), 38-44. 6. Akaah, Blamires, Chris (1987), ‘Trade-off’Pricing Research: A Discussion of Historical and Inventory Applications,” Journal ofMarket Kesearch Society, 29(2), 133-152. 7. Brezen, T. S . , Block, M . P., and Schultz, Don E. (1987), con^ sumer’s Perceptions of Direct Marketing Techniques,” Journal ofDirect Marketing, 1 (1) (Winter), 38-49. 8. Cattin, Phillip and Wittink, D. R. (1989), “Commercial Use o f Conjoint Analysis: An Update,”Journal ofMarketing, 51 (July), “
91-96. 9. Cunningham, Isabella and Cunningham, W. (19731, “The Urban I n - H o m e Shopper: Socioeconomic and Attitudinal Characteristics,”Journaf of Refailing, 49 (Fall), 42-50, 88. 10. Gallup Survey (1988), “Consumers in 1988: The Year in R e view,” 7arget Marketing, (February), 48. 1 1 . Gould, James (1987), “Why Recipients of Direct Mail Do and Don’t Respond,”./ournal of Direct Marketing, 1 (3) (Summer), 47-56. 12. Green, Paul (1974), “On the Design of Choice Experiments Involving Multifactor Alternatives,” The Journal of Consumer Research, 1, 61-68. 13. -and Yoram Wind (1975), “New Ways to Measure Consumers’Judgments,” Harvard Business Review, 53 (4),107115. 14. -and V. Srinivasan (1978), “Conjoint Analysis in Consumer Research: Issues and Outlook,” Journal of Consumer Research, 5 ( 3 ) , 103-123. 15. James, E. Lincoln and Cunningham, Isabella (19871, “A Profile of Direct Marketing Television Shoppers,”Journal of Direct Marketing, 1 (4) (Autumn), 12-23. 16. Johnson, Richard M . (1974), “Tradeoff Analysis of Consumer Values,” Journal of Marketing Research, 8 (May), 121-127. 17. Kobs, Jim (1979), Profitable Direct Marketing, Chicago, 11.: Crain Books. 18. Korte, J. Marc D e (1977), “Mail and Telephone Shopping a s a Function of Consumer Self-Confidence,” Journal of the Academy of Markefing Science, 5 (Fall), 295-306. 19. McCorkle, Denny E., Planchon, J . M., and James, W. L. (1987), “In-Home Shopping: A Critical Review and Research Agenda,” Journal of Direct Marketing, 1 (2) (Spring), 5-21. 20. Stanton, William J. and Futrell, C. (1987), Fundamentalsof Marketing, New York, NY: McGraw-Hill. 21. Reynolds, Fred D. (1974), “An Analysis of Catalog Buying Behavior,”Journal ofMarketing, (July), 47-51. 22. Stone, Bob (1988), Successjiul Direct Marketing Methods, Lincolnwood, IL: NTC Business Books.
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Formulating Direct Marketing Offers with Conjoint Analysis RAJ ARORA i s Schutte Professor of Direct Marketing at the University of Missouri-Kansas City He received his PhD from Claremont Graduate School Raj Arora’s research interests are in consumer behavior and marketing management He has published articles in the Journal of Marketing Research, the Journal of the Academy of Marketing Science. the Journal ofAdverfning Research and PIychology and Marketing The author gratefully acknowledges the helpful commenrs of Don E Schultz and two anonymous reviewPrs on this article The support of Joe Curry and Sawtooth Software, Inc , Ketchurn, Idaho, for providing the Adaptive Conjoint Analysrs program, and the assistance of Melinda Andre in data collection are also appreciated
RAJ ARORA
ABSTRACT Conjoint analysis i s described as an important method for designing direct marketing offers prior to a market test. The method i s demonstrated with an application in designing direct marketing offers which focus on the price of the product, the channels through which the product may be purchased, and the exchange/refund policy. The results of conjoint analysis provide the utilities of each variable at each level. This information i s then used to construct alternative direct marketing offers. The simulated willingness to purchase is also discussed.
0 1991 J o h n Wiley & Sons, Inc. a n d Direct Marketing Educational Foundation, Inc. CCC 0892-0591/91/01048-09$04.00
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INTRODUCTION It is common practice in direct marketing to test direct marketing programs before the rollout ( 1 , Z ) . Although the objectives of testing may vary from one mailing to another, testing can be used to assess the impact of: the product or service; the media or the method of accessing the defined target; the message or communication; seasonality; and/or the promise of some incentive. Furthermore, the offer is considered to be critical to achieving the firm’s objectives (22). Kobs (17) identifies 99 different proven components of a direct marketing offer. These include: 1) right price, 2 ) free trial, 3 ) money-back guarantee, 4 ) “bill me later,” 5) installment terms, 6) rush shipping service . . . .99) competitive offer. Given the vast number of proven components of an offer available, selecting the best response offers is not an easy task. I t is possible to eliminate or alter the direct marketing program on the basis of the test results. Although relatively inexpensive compared to the cost of a national rollout, program testing can be quite expensive and inefficient if not carefully planned. O n e approach that can be helpful in designing a direct marketing program is the use of conjoint analysis or “tradeoff” (16). For example, consider price as an element of the offer. Choosing the “right price” from a range of “acceptable prices” is a difficult and involving exercise. Furthermore, price is but one of the critical components of a direct marketing offer. Perhaps customers are willing to pay a higher price for an offer backed by a money-back guarantee, compared to one without such an assurance. Similarly, a buyer may be willing to pay a higher price if the order could be shipped instantly. These situations raise the question: “Is the customer willing to ‘tradeoff’ money-back guarantee for an earlier delivery date, or ‘tradeoff’ either one for a lower price?” Conjoint analysis helps answer these questions. A brief description of the technique and an application illustrating its use in direct marketing follows.
CONJOINT ANALYSIS
In designing new marketing programs, it is in the interest of direct marketers to determine what the
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customers value most and reflect these in the offerings. A simple strategy would be the self-reported importance scores or preferences of various features (attributes) of the total offer. One is most likely to discover that customers prefer to gain the most possible benefits; for example, the lowest price, fastest delivery, instant credit, best warranty, and so on. Through use of conjoint analysis, “tradeoffs,” or the extent to which customers are willing to forego a specific level of one attribute in order to receive a specific level of another attribute, can be determined. Customers are presented with statistically designed offers (or products). The offers may be various combinations of features representing existing or proposed products and services.’ Consider, for exampIe, two features of the offer-product’s delivery and price-each at two levels (variations of the feature). A quicker delivery implies having goods in the warehouse ready for instant shipment. On the other hand, a longer delivery means that a large supply of merchandise is not critical, thus resulting in inventory (back end) savings. From a customer perspective, some may be willing to pay a slightly higher price for quicker delivery. Together these two features of the product, each at two levels, form four possible combinations. Of course in this example the two extreme cases (low price, quick delivery and high price, late delivery) are not particularly important. The critical tradeoff is between low price with late delivery and high price with quick delivery. In conjoint analysis, customers are asked to state the overall preferences (ranked or rated) for each of the four possible combinations of price and delivery. From these overall evaluations the specific utilities (preferences or benefits) of each level of the attributes are inferred. In this hypothetical example, the four options considered and their corresponding utilities are shown below: Utility -
Variable Price
S 12.77 S 15.99
141
Delivery
24 hours 3 weeks
57
0 0
’ In typical conjoint applications, researchers choose attributes for a single product. Thus the variables are the product’sattributes,and not the product. In direct marketing, however, it is possible that some products are more likely to be purchased through direct channels, whereas others through traditional retail channels. In such situations the researcher may wish to include products as a variable in the experiment.
VOLUME 5 NUMBER I WINTER 1991 49
These utility values indicate that this particular customer is price-conscious (higher utility for lower price). If the costs associated with the change in deliveryservice are approximately equal to the price difference above, the direct marketing manager is better off to lower the price, than to provide a quicker delivery. Design Options in Conjoint Analysis
The above example illustrates evaluation of only two features at two levels. Thus, it is easy to collect the customer’s reaction to all possible combinations of the design features. This approach, where a respondent is asked to rank all possible combinations, is referred to as the “full profile.” However, a more realistic setting may include several variables with two or more levels each. As an example, consider the following hypothetical situation. A direct marketer is considering the choice of three levels of “price,” three levels of “exchange policy,” five levels of “delivery dates,” and a choice between two types of “products.” In this case the choice between all possible combinations is 3 X 3 X 5 X 2 or a total of 90. The task on the respondent to rank all 90 possibilities can be quite burdensome. Green (12) illustrates this problem in data collection vividly. For example, if a marketer is faced with five variables at three levels each, the full profile design requires a total of 243 combinations. In situations like this Green illustrates a set of procedures and alternative designs which significantly reduce the number of combinations. These designs fall under the general category of “fractional factorial” designs. In these situations the respondents are shown only a subset of all possible combinations. One such design is referred as “orthogonal array.” Here the test combinations are selected in such a manner as to permit the estimation of importance of each independent variable. Conjoint Analysis Procedures
A variety of procedures are available for conducting
conjoint analysis, collectively described as “any decompositional method that estimates the structure of a consumer’s preferences (e.g., part worths, importance weights, ideal points) given his/her overall evaluations of a set of alternatives that are pre-
SO
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specified in terms of levels of different attributes” ( 1 4 ) . Green and Srinivasan ( 1 4 ) discuss the methodological issues of each procedure. However, recent developments make it easier to use conjoint analysis with the aid of a personal computer. Two of the prominent software packages are: “Adaptive Conjoint Analysis” (ACA) from Sawtooth Software, Ketchum, Idaho, and “Conjoint Analyzer” from Bretton Clark in New York City. A recent addition to these programs is OLS Conjoint Analyzer. This is part of other multivariate statistics programs “PCMDS 5.1” from Scott Smith at Brigham Young University, Provo, UT 84602. The use of Adaptive Conjoint Analysis is illustrated in the problem setting that follows this section. The Conjoint Analyzer’ software is described briefly. The program comprises two modules. The first module helps in designing the experiment (statistically choosing various combinations of features to collect and analyze the data). In view of the number of combinations in full profile approach, this program helps to select the fractional designs to collect data efficiently. The data however, are collected by a standard paper and pencil approach. The second module of Conjoint designer is used in analyzing the data. The ACA, in contrast, helps in designing the experiment, data collection, and analysis using the personal computer.
PROBLEM SETTING
Direct marketing is known for its convenience, providing a high degree of “place utility” to customers as an alternative to traditional retail stores. It is often known as nonstore and in-home shopping. The popular forms of direct marketing are by direct mail, catalogs, and cable TV. Consider time utility-ability to obtain the product at the desired time. There is considerable room for increasing the time utility to the customers and the effectiveness of direct marketing operations. Although several direct marketing telemarketing firms have emerged that fill the order within the same or next day (e.g., firms
* Interested readers are referred to a complete review of Conjoint Designer in the Journal ofMarketing Research, (August) 1986, 31 1-312.
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situationally defined strengths/niches, it does not delineate the attributes which make this possible. Competitiveness in team patronage situations, for example, may be enhanced by retailer attributes such as 1) quantity discounts, 2 ) monogramming/ silkscreening, and 3) guaranteed availability of necessary quantities, sizes, colors, and quantities. Competitiveness in gift-giving situations may be enhanced by attributes such as the availability of 1) gift-wrapping service, 2 ) notecards enclosed with the gift, and 3) gift-shipping service. Although the aforementioned attributes are largely within the retailer's control, other attributes that affect situational competitiveness may not be. In many cases, however, apparently uncontrollable attributes may b e indirectly manageable. For instance, the reason that nonstore retailers scored poorly in footwear patronage situations was probably because nonstore shoppers are unable to try o n the footwear. Many nonstore retailers have indirectly managed this problem with liberal return policies. Future research should empirically assess the impact of retailer attributes o n situational competitiveness. Such analysis will greatly enhance the managerial implications of situational research by delineating the causative measures that can be taken to appeal to various situational segments. This study examines a limited array of situational factors. Future research should examine additional factors. If additional situational factors are investigated, however, they should be derived from consumer-based elicitation techniques (6,15) to ensure that the situational treatment is valid. It is also important to note that had this study examined an additional two-level situational factor, a full-factorial representation would have doubled the number of cel Idquestionnaire pages from eight to sixteen. The study has limited generalizability due to the retail product-market which is investigated. The methodologic framework utilized by this research should be tested in other product markets. Sporting goods represents a narrow range of retailing. The profile of a sporting goods shopper may differ from shoppers in other retail product-markets. The nature of situations pertinent to sporting goods shoppers may also differ from other product markets. The sample also limits the research. Because the sample consists of individuals who at least visited and who in some cases patronized a specialty sporting goods store, they are generally predisposed to
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this type of store. If subjects had been drawn from a grdup not closely affiliated with a specific retailer, responses may have differed. Traditionally, segmentation efforts have been based o n demographically defined consumer markets. The opportunity to more fully develop segmentation strategies which are based on situationally defined markets, as illustrated in this study, also appears to be fruitful.
CONCLUSIONS
This research demonstrates that patronage perceptions of various store and nonstore retailers are significantly affected by the nature of the patronage situation. Past nonstore research has been primarily concerned with identifying individual and retailer characteristics which are related to nonstore patronage. Previous research has not investigated perceptual or behavioral variations attributable to factors outside of the individual or retailer. The study strongly suggests that an explanation of perceptual differences couched solely in terms of individual and retailer characteristics will overlook other important sources of variation. The research represents the first effort to assess the competitive structure of a nonstore retail market. Previous research has generally compared a single store and nonstore retailer. The study delineates the competitive market structure of a group of store and nonstore retailers in terms of their situational similarity/substitutability and provides a methodological framework that can b e useful for future investigations of direct marketing. The study demonstrates that the competitive market does not necessarily consist of store retailers competing against nonstore retailers. The store versus nonstore conceptualization of the marketplace does not reflect the true nature of competition. The research demonstrates that inter-retailer competition is conceived by consumers and can be quantitatively delineated in terms of pertinent situational factors. REFERENCES 1. Akaah, Ismael P. and Korgaonkar, Pradeep K. (1989), "The Influence of Product, Manufacturer, and Distributor Characteristics on Consumer Interest in Direct Marketing Offerings," Journal of Direct Marketing, 3;3 (Summer), 27-33.
VOLUME 5 NUMBER 2 SPRING 1991 I 1
example, a person might have a higher degree of preference for cassette player. This will then be represented by a higher estimated utility for cassette player. It is possible to treat each of these as a stimulus object and not as an attribute; however, this approach increases the data collection requirements in that two separate experiments are required, one for each product. Since the products are used as levels, it is essential that the selected price be realistic for both products. The price levels chosen are consistent with the prevailing advertised prices of these products. Furthermore, recently appearing advertisements of these products (with brand names removed) were used in exhibits to show the products to the respondents. The procedure of ACA involves constructing profiles of various possible product offerings by combining these attributes with different levels. For example, one offering may involve an AM/FM cassette player, priced at $19.95, offered through the mail with 1-week delivery, and n o exchange or refund options. The respondents are asked to rate these various offerings in terms of desirability. In selecting the various attributes and levels, care must be taken to ensure that respondents are not asked to rate unrealistic product offerings. In this time and study, two of the variables-delivery choice of channels-are likely to form unrealistic combinations. For example, in purchasing a product from a department store, one normally does not expect the delivery time to be 1 week or 3 weeks. To avoid such artificiality, the two variables were combined to one variable, channel/delivery with five levels. The levels of price were: regular price of $19.95, a discount of 15 percent resulting in $16.95, and a 25 percent discount resulting in $14.95. These prices were selected from a preliminary survey of prices of comparable merchandise advertised in the local market. The levels for the channel were: shop at a store-immediate delivery; shop at home via cable TV-1 week delivery; shop at home via cable-3 week delivery; shop at home via mail-1 week delivery; and shop at home via mail-3 week delivery. The levels for exchange/refund were: no exchange or refund, exchange only (no refund), or choice of complete refund or exchange.
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Sample
The respondents for this study were undergraduate students at a large Midwestern university. A total of 134 students participated in this experiment and form the basis of analysis. The students, who represented various academic disciplines on the campus, were predominantly in the 20-to-34 year age group. Respondents varied in their levels of past experience with various direct marketing purchase media. For example, 67 percent had made a purchase by direct mail in the past 12 months, and 22 percent had made a purchase by cable TV in the same period. This compares with 22 percent of adults who described themselves as having purchased through one or more direct marketing solicitations (10). Within this latter overall sample, those with at least a high school diploma and an income of over $30,000 had a much higher incidence of purchase through direct marketing. The collegiate respondents were informed that this survey would be conducted using the computer. They were also shown in separate exhibits the two products which were the focus of the study. Appropriateness of the products for the student sample was determined from a separate convenience sample of 60 students. None of the students indicated that they would not purchase either of these products. Instrument
Data for this study were collected by using the Adaptive Conjoint Analysis (ACA) program. ACA is designed to collect data (and simultaneously estimate the respondent’s utilities) for conjoint analysis by self-administered computer interviewing. Respondents were asked a series of questions. The first set of questions asked the respondents to indicate if any level of an attribute was unacceptable. Next, the respondents were asked to state their preferred levels of each attribute and the importance of differences among all combinations of levels within an attribute. Finally, they were asked to rate their preferences for various profiles of the product. Direct marketing managers may also be interested in obtaining other demographic or lifestyle information,which can be used for segmenting their target markets. In this study respondents were asked whether they had purchased any item through catalogs, department stores, mail order, and cable TV
VOLUME 5 NUMBER I WINTER 1991
in the past 12 months. They were also asked if they would consider buying these items (AM/FM cassette player or sunglasses) as a gift.
TABLE 2 Attribute Levels and Their Utilities Attribute/Levels
RESULTS
The program computes the correlation between the respondents’ predicted and actual answers to various product concepts. This correlation serves as the goodness-of-fit index. The average value of the correlations in the sample is 0.91, indicating a high degree of fit between the actual and predicted values. The average utilities for each level of the concept and their relative importance are shown in Figure 1 and Table 2. The first column (Table 2) shows the major variables and their corresponding levels. The second column shows the average utilities’ for each level of an attribute. The third column shows the relative importance of each attribute, and the last column shows the ranking of these relative importance scores for the attributes. The most important attribute in the table is the channel/delivery time combination, followed by the price of the product. Ability to exchange or to receive a refund is the third most important attribute, followed by the least important-which product is being purchased. Considering the channel/delivery attribute, the highest utility (94.26) is from purchasing from the retail store. The next highest utility (30.69) is for purchasing by direct mail with delivery promised within 1 week. Thus, there is a sharp drop in utility in purchasing by direct mail as compared to a retail store. The utility for direct mail with a longer delivery promise date of three weeks drops from 30.69 to 3.34. The utility for purchasing through TV is 7.5 for 1-week delivery and drops to 1 . 8 for the longer delivery. The utilities for other attributes, such as price and exchange, both have considerable range between the levels. For the product, however, utilities range from 22.38 (sunglasses) to 5 4 . 5 1 (cassette ’Conjoint analysis provides utilities at the individual level. The utilities shown in TaMe 2 are the average values for all the respondents. The individual level utiZities can be used in segmentation.
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Utilities
Relative Importance
Importance Ranking
Price Regular Discount 15% Discount 25%
0.28 38.50 78.26
0.30
2
Exchange No Exchange/refund Exchange only Refund
0.58 6.92 60.94
0.23
3
Channel/Delivery Time Store TV/one week TV/three weeks Mail/one week Mail/three weeks
94.26 7.52 1.81 30.69 3.34
0.35
I
Product Cassette Player Sunglasses
54.5 I 22.38
0. I 2
4
player). The utilities do not differ due to previous use of direct marketing in the past 12 months, or d u e to the respondent’s attitude toward these items as appropriate for gift giving. DISCUSSION
The results of this study provide answers to questions of enhancing the response rates to direct marketing strategies (various product offerings). The values of the utilities can be used in designing new direct marketing strategies such that the overall utility is maximized. For example, considering the channel/delivery option, the utility is substantially higher for mail than for TV, especially if the promised delivery time is within one week. Another important factor is the exchange or refund option. Study findings reveal that complete refund is the only reasonable option to consider. These findings are consistent with the findings of Akaah and Korgaonkar ( 5 ) . Money-back guarantee was found to be the most important attribute in their study. Some of the newer conjoint programs (ACA and Conjoint Designer) offer the researcher the ability to simulate and ask “what if” questions for config-
VOLUME 5 NUMBER I WINTER 1991 53
Thus, o n e can simply decide to market a product offer with the highest overall utility. Alternatively, o n e may wish to perform additional analysis using ACA. The ACA program provides the options for simulations in which “what i f ” questions can be ex-
uring new direct marketing offers. Once the utilities have been estimated for the features and their corresponding levels, it is possible to design new offers from among the features considered in the study. The utilities for these new offers are simply the sum of the individual utilities for each level of a feature.
utility
Utility
EXCHANGE
PRICE
100
-
100
-
50
-
50
-
0 -
1 L 1. Regular 2 15%Discount 3. 25% Discount
5
1. No Exchange 2 Exchange only 3. Refund Utsitv
Utility
PRODUCT
CHANNEL
100
-
50
-
loo--
50 - -
0-
0,-
1 L 1. Store 2 TV 1 week
5
4
3
4. Mail 1 week 5. Mail3weekr
Cassette
Sun Glasses
3. TV3weelcs FIGURE 1
Attribute Levels and Their Utilities
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V O L U M E 5 N U M B E R I WINTER 1991
plored. One of the options in ACA is to estimate the respondent’s “First Choice”* model. This is the simplest of the models, wherein a respondent is classified as likely to purchase the product with attributes adding to the highest overall estimated utility. An illustration of the use of this option with the estimates obtained from the model is described. Suppose a direct marketing manager wishes to know the response for the following three options. A cassette player (product), offered with full exchange or money-back guarantee, sold at regular price through 1) direct mail, and 2) through TV, each with promised delivery of less than a week. Furthermore the manager also wishes to know his (her) relative performance (response) in each of these two options, especially with respect to the same product’s availability through traditional retail stores. Thus, in this illustration, the comparison is with three modes of channel/delivery options: the retail store, mail order, and TV. The other variables common to all strategies are: cassette player, offered at the regular price of $19.95, with full exchange or refund. The results of the model for the above strategies are shown below: Percent Preferring Channel/Del. Option
Standard Error
93.1
2.2
Direct mail- 1 w e e k
5.3
2.0
W-I
1.5
1.1
Channel/Delrvery Retail store week
The above results closely approximate the response rates likely to be found in the marketplace. One can now ask the question: What would be the response if the same product is offered at a 15 percent discount through the direct response channels-direct mail and TV? These results are shown below: Percent Preferring Channel/Del. Option
Standard Error
Retail store @ S 19.95
59.5
4.3
Direct mail-I w e e k @ S 16.95
35.9
4.2
4.6
I .8
Channel/Delivery
TV-1
w e e k @ $16.95
‘Other major simulation options available in ACA are: ( 1 ) Share of Preference-a respondent’s preference is allocated fractionally to products in proportion to h i d h e r predicted preference; and (2) Purchase Likelihood-a respondent’s purchase probability is estimated for each product.
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These results reveal the impact of the change in pricing on the product sold through direct channels. With this information, the direct marketer can calculate the costs and benefits of reducing prices as an incentive for buyers to switch to direct mail channels. In this case, it may be to the interest of the direct marketer to offer the product at the discounted price, a practice not uncommon in direct marketing. A couple of comments on the utilities are desirable. Unlike response rates, which are measured in ratio scale, and thus have an absolute interpretation about their magnitude, the utilities are measured in interval scale. Consistent with the properties of interval scale, we can infer that the difference in price utilities between regular price and a 15 percent discounted price (38 units) is approximately equal to the difference between 15 percent discount and 25 percent discount (40 units). But we can not conclude that the utility of a 25 percent discount is approximately twice as much as the utility of a 15 percent discount. Secondly, while we expect the utilities to be related to the demand for the product and thus to the response rate, this relationship of utilities to a response (demand) function needs to be established. Finally, in the context of designing new product offerings, it is customary to refer to attributes or features of products. For some attributes, the link between the attribute and the benefits is quite obvious. However, in technically complex attributes of products-such as decibels of sound distortion in a stereo-the marketer may be advised to convert the attributes into benefits in conjoint studies.
FUTURE RESEARCH
The purpose of this research was to illustrate the application of ACA in direct marketing. This study shows how conjoint analysis can be applied in direct marketing to design offers that have a higher overall utility which, in turn, affects the response to these offers. Several important implications emerge for direct marketing managers. There is ample evidence to suggest that consumers do compare the features of one product (including the intangibles) with another, and make tradeoffs among the available options (8,13,14,16). Therefore, it is important for di-
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rect marketing managers to be cognizant of the tradeoffs consumers make and the utilities associated with each attribute level involved in the tradeoff decision. This study reveals the utilities associated with purchasing a given product from a retail store or from o n e of the direct marketing channels. Furthermore, the results reveal the relative price-elastic nature of the market. Although the findings of this study are important and interesting, some caveats need to be noted. Although care was taken to select products which are used by the student population, caution is advisable in generalizing the findings to the larger population and for other products. Further research of this nature should be conducted to include more product categories and to determine which, if any, products are associated with specific direct marketing channels. One of the direct marketing channels not explored in this study is telemarketing. The role of telemarketing is gaining more prominence due to the growth of direct marketing and the high costs associated with personal sales calls. The impact of telemarketing also warrants exploration in future research. In this study, price is used as one of the attributes. I t may be of interest to direct marketers to segment their market on the basis of price. Further research using this approach may suggest how to benefit most in targeting the product and in developing price and service combinations to reach relevant target markets effectively. rn REFERENCES 1. “Jersey Bank Targets Co-op Owners for its First Equity Loan Mailing,” DM News, 9 (19) (October l ) , 30. 2. (1987). “Joe Weider to Launch Mail Report for Men’s Fitness, Renamed Magazine,” DMNews, 9 (19) (October I ) , 17. 3. ____ (1988), “Austad Not Impressed with Results of Catalogs Sent by Federal Express,” DM News, 10 (2) (January 151, 2. ~
~
4. (1988), “Discovery Channel Mailer Yields Suburban Cable 52% Sales Gain,” Cable Marketing, 8 (1) (February), 16. ~
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5. Akaah, Ishmael P., and Korgdonkar, P. K. (1988), “A Conjoint Investigation of the Relative Importance of Risk Relievers in Direct Marketing,” Journal ofAdvertising Research, (August/Sep[ember), 38-44. 6. Akaah, Blamires, Chris (1987), ‘Trade-off’Pricing Research: A Discussion of Historical and Inventory Applications,” Journal ofMarket Kesearch Society, 29(2), 133-152. 7. Brezen, T. S . , Block, M . P., and Schultz, Don E. (1987), con^ sumer’s Perceptions of Direct Marketing Techniques,” Journal ofDirect Marketing, 1 (1) (Winter), 38-49. 8. Cattin, Phillip and Wittink, D. R. (1989), “Commercial Use o f Conjoint Analysis: An Update,”Journal ofMarketing, 51 (July), “
91-96. 9. Cunningham, Isabella and Cunningham, W. (19731, “The Urban I n - H o m e Shopper: Socioeconomic and Attitudinal Characteristics,”Journaf of Refailing, 49 (Fall), 42-50, 88. 10. Gallup Survey (1988), “Consumers in 1988: The Year in R e view,” 7arget Marketing, (February), 48. 1 1 . Gould, James (1987), “Why Recipients of Direct Mail Do and Don’t Respond,”./ournal of Direct Marketing, 1 (3) (Summer), 47-56. 12. Green, Paul (1974), “On the Design of Choice Experiments Involving Multifactor Alternatives,” The Journal of Consumer Research, 1, 61-68. 13. -and Yoram Wind (1975), “New Ways to Measure Consumers’Judgments,” Harvard Business Review, 53 (4),107115. 14. -and V. Srinivasan (1978), “Conjoint Analysis in Consumer Research: Issues and Outlook,” Journal of Consumer Research, 5 ( 3 ) , 103-123. 15. James, E. Lincoln and Cunningham, Isabella (19871, “A Profile of Direct Marketing Television Shoppers,”Journal of Direct Marketing, 1 (4) (Autumn), 12-23. 16. Johnson, Richard M . (1974), “Tradeoff Analysis of Consumer Values,” Journal of Marketing Research, 8 (May), 121-127. 17. Kobs, Jim (1979), Profitable Direct Marketing, Chicago, 11.: Crain Books. 18. Korte, J. Marc D e (1977), “Mail and Telephone Shopping a s a Function of Consumer Self-Confidence,” Journal of the Academy of Markefing Science, 5 (Fall), 295-306. 19. McCorkle, Denny E., Planchon, J . M., and James, W. L. (1987), “In-Home Shopping: A Critical Review and Research Agenda,” Journal of Direct Marketing, 1 (2) (Spring), 5-21. 20. Stanton, William J. and Futrell, C. (1987), Fundamentalsof Marketing, New York, NY: McGraw-Hill. 21. Reynolds, Fred D. (1974), “An Analysis of Catalog Buying Behavior,”Journal ofMarketing, (July), 47-51. 22. Stone, Bob (1988), Successjiul Direct Marketing Methods, Lincolnwood, IL: NTC Business Books.
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