- Email: [email protected]
The targeted marketing of consumer durables
BARRY L. BAYUS
The Targeted Marketing of Consumer Durables BARRY L BAYUS i s an associate professor of marketing at the Kenan-Flagler Business School, University of North Caro iina He received a Phi3 and MS from the Wharton School Prior tojoining the faculty at UNC. he was on the business school faculties at Cornell University and the Wharton School, and a senior staff member of the Corporate Operations Research Group at RCA His research interests deal with product life cycle issues. the marketing of consumer durables and product replacement behavior. and technological change and product management Financial support from the Marketing Science Institute is gratefully acknowledged Special thanks to M/A/R/C for providing access to their databases, and to Dennis Gonier for valuable drscussions at various points during this research Thanks are also extended to Ambar Rao for comments on an earlier draft
BARRY L. BAYUS
ABSTRACT In this article, an approach for identifying prime prospects and developing targeted marketing strategies for durables is presented and empirically illustrated using a set of major home appliances. The proposed approach combines probability models for identifying segments (as a function of household characteristics)and determining replacement potentials (as a function of the age of a current unit). Importantly, these models use data that are generally available to manufacturers and retailers of durable products 1e.g.. syndicated survey data, warranty card information, data overlays from commercial firms).
0 1993 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc CCC 0892-0591/93/0404-10
4
JOURNAL OF DIRECT MARKETING
VOLUME 7 NUMBER 4 AUTUMN 1993
The importance of communicating with, and listening to, customers has received an increased amount of attention in recent years (e.g., Peters and Waterman [18] and subsequent studies by others). Packaged goods and services firms have embraced the ideas of targeted direct marketing and database marketing as a way to communicate with consumers and to halt the erosion of brand loyalty. Various “club” programs have been implemented by manufacturers, service firms, and retailers to strengthen brand loyalty and to develop leads for future prospects (15). Airlines and hotels, for example, have had frequent flyer/stayer award programs for several years. Packaged goods firms and supermarket chains are beginning to offer frequent buyer/shopper programs. The Quaker Oats Company has recently tried a massive targeted coupon delivery system aimed at developing a direct link to individual households (19), and Kraft General Foods has tested a similar system ( 1 4 ) . Although not as widely used, database marketing principles have also found their way into the marketing of consumer durables. Actdirect, a joint venture between National Demographics and Lifestyles and Actmedia, has marketed a direct-mail appliance rebate program called “Express Certificates” (21,271. According to a spokesman of Actdirect, “Actdirect is not merely discounting. It is an advertising message targeted to the right person” (21). Sustaining brand loyalty for replacements is particularly important for manufacturers and retailers of durable products, since a consumer is out of the market for several years once a purchase is completed. In addition, manufacturer and retailer concern for maintaining brand loyalty extends to product lines (e.g., kitchen appliances such as stoves, refrigerators, and dishwashers; and stereo components such as receivers, turntables, tape decks, and compact disc players) and trade-up buyers who buy new appliances with more features and thus higher price tags. As a result, industry interest in direct marketing approaches has increased (8). Interest in influencing durable replacement demand is also expected to increase among industry associations and government groups due to.the growing concern for energy efficient products and products that do not harm the environment. Despite this importance however, very little research has addressed dur-
JOURNAL OF DIRECT MARKETING
able brand loyalty or the marketing of established consumer durables (a recent exception is Bayus [4]). This article presents an approach that can be used to identify prime segments of consumer durable owners for which targeted marketing strategies can be developed. This approach takes into account the timing of replacement purchases (i.e., the age of the current unit). Importantly, this approach uses data that are generally available to manufacturers and retailers of durable products (e.g., syndicated survey data, warranty card information, and data overlays from commercial firms). In the next section, a conceptual overview of the targeted marketing of durables is discussed. A model framework that considers replacement buyer segments and the associated replacement probability distributions is proposed in Section 3. Section 4 outlines an available data set and presents an application of this approach for major home appliances. Conclusions and further research directions are discussed in the final section. 2. THE TARGETED MARKETING OF CONSUMER DURABLES
Two fundamental components are at the heart of targeted direct marketing: 1) identifying consumer segments to target, and 2) developing relevant communication strategies to these segments (8). For mature durable products, the age and condition of the units in use will also be an important factor in the household decision of whether or not to make a replacement purchase ( 5 ) . Prior research suggests that durable replacement buyers can be divided based on the timing of replacements; for example, the author, in another study, defines early, average, and late replacers based on the age distribution of products being replaced ( 3 ) . Other efforts further indicate that these replacer segments have distinct demographic and attitudinal profiles. For example, Tippett, et al. (23) suggest that appliances are held for a shorter period of time if the household is of higher income, head of household is younger in age, children are present in the family, and the family has moved recently. “Early” automobile replacers are found to generally have high incomes and are concerned with brand styling and image, whereas “late” replacers have
VOLUME 7 NUMBER 4 AUTUMN 1993
5
high educational and occupational status and are concerned with cost-related product attributes. In addition, search activity and usage of mass media sources differ across automobile replacer segments (3). These key conceptual elements are combined in Figure 1. Given an a priori definition of replacer segments based on the timing of replacement purchases, households are assigned to a segment using demographic and attitudinal data. Households in each replacer segment are then considered for targeting depending on the age of a currently owned unit (e.g., households in a particular segment owning an appliance that is older than the mean lifetime of units in that segment are expected to have higher replacement probabilities than households owning a relatively newer appliance). Relevant communications for these targeted households are next developed, and the effectiveness of the communication program is tracked and evaluated in terms of the response generated (e.g., sales, leads, improvement in brand consideration). The approach outlined in Figure 1 is appealing for several reasons. First, it is conceptually straight-
1 MASS MARKET
forward. More importantly, this approach makes us,e of data that are usually available to durable manufacturers and retailers. For example, syndicated sucvey data can be used to develop replacer segment definitions and to determine the demographic and attitudinal profile of each segment. Information on household characteristics (e.g., demographics andl/ or attitudinal data) and on the ages of units currently owned can be assembled from warranty cards (e.g., National Demographics and Lifestyles markets lists of durable buyers; see also [22]). Other data elements not available in-house can be merged with existing household information using data overlays from several commercial firms (e.g., consider t h e rise and fall of Lotus Marketplace, a relatively iiiexpensive PC package that would have provided access to information on millions of households and businesses [20,26]). A database constructed with such information at the household level (i.e., by specific address and phone number) can be used to track potential buyers and reinforce replacement brand loyalty. Over time, it may be possible to s u p plement this database with information on households owning competing brands (through brand switching by current owners and merging data from other surveys). Finally, as discussed in the next secztion, the conceptual approach outlined in Figure 1 can be translated into a quantitative framework using probability models.
3. A MODEL FRAMEWORK REPLACER SEGMENTS
Ic-7
E I TARGETED HOUSEHOLDS
RESPONSE
I
Household Characteristics
Age O f Current Unit
Targeted Communications
FIGURE 1
The Targeted Marketing of Consumer Durables
6 JOURNAL OF DIRECT MARKETING
In order to develop a model of purchase behavior at the household level, the random utility framework of discrete choice models can be used (e.g,., 7,lO). Several studies of consumer packaged goocls have taken this approach (e.g., 6,9,13). Consumer durables however, present several challenges not faced in the study of frequently purchased item.s. Most important is that the time between purchases is generally several years. This implies that the tracking of individual households for several years is needed to estimate these choice models. Unfortunately, panel data are not usually used by manufacturers and retailers of consumer durables and, consequently, these kind of data are not available for research purposes. (A data collection system like a supermarket scanner data is not yet available.) The type of data that is regularly used by firms
VOLUME 7 NUMBER 4 AUTUMN 19'73
involves large, nationally representative surveys of recent buyers (2,3,4). As already discussed, data on individual households at a particular point in time is also available from customer lists and commercial sources. These practical concerns of data availability are taken into account in the modeling approach developed. Once segments have been defined based on the timing of replacements, identifying specific households in each segment to target requires some measure of response potential. A natural measure to use is the probability of replacement (given ownership of a unit of a specific age). This household replacement probability is modeled as being conditional on segment membership. Letting P(t) be the cumulative probability' of a household replacing a unit of age t, P(tln) the cumulative probability of a household replacing a unit of age t given that it is a member of replacer segment n, and Q(n) the probability that the household is a member of replacer segment n, the following equation is obtained: P(t) = -WtIn)Q(n) n
(1)
Specific functional forms for fit1 n) and Q(n)are discussed next. 3.1 Modeling Segment Probabilities
From Figure 1 and the associated discussion, households can be assigned to replacer segments using household demographic and attitudinal information. Since the replacer segments defined based on the timing of replacements represent various ordered categories, the familiar logistic model can be' used to estimate a relationship between household characteristics and segment membership. Walker and Duncan (25) extend the basic dichotomous dependent variable model to one applicable to multiple, ordinal categories. Letting & denote segment membership for the ith household, the model is: P ( Y , 2 j ) = 1/(1 + exp(-q - x$)) ( j = 1, . . . , k;with k
+ 1 = n segments)
(2)
' Other functions such as the hazard rate Mt) = f ( f ) / [ l - F ( r ) ] could also be considered. Since the hazard rate can be any positive number however, managerswould probably havedi5cultycomparingand interpretinghazard values. Thus, for practical reasons, the cumulative distribution (i.e., the fraction of households that have replaced a unit by the time it is of age r) is used because of its managerial familiarity.
JOURNAL OF DIRECT MARKETING
where X, is the vector of explanatory variables for the ith household, 0 is the coefficient vector, and cy/ is the intercept term for categoryj. Maximum likelihood methods are used to estimate the parameters (e.g., through the LOGIST package as implemented in SAS; see [ 111). Importantly, the logistic regression model can be used to determine the probability of segment membership for any household, given a set of explanatory variables (i.e., household characteristics). The resulting segment probabilities are thus used as weights (which sum to one) in equation (1). 3.2 Modeling Replacement Probabllltles
The probability of replacement depends on the age of a currently owned unit. Several approaches have been suggested for analyzing aggregate replacement purchases for the purpose of sales forecasting. These methods use probabilistic distributions for the age distribution of units replaced such as the Rayleigh (17), truncated normal (12), and Weibull (2). A two-parameter Weibull distribution (Rt) = 1 - exp(-(t/X)'); p = hr(1 l/@)is used here because of its flexibility and previous use for consumer durables (1,3,4). For the Weibull, X is the location parameter (i.e,, has the greatest influence on the mean since the gamma function value is between 0 and 1) and 0 is the shape parameter. Other research reports that 2 < 8 < 3 for home appliances (1). The shape parameter 19 is modeled as being product category specific, while X will vary by replacer segment. Thus, P(tl n) in equation (1) becomes:
+
f i t l n ) = 1 - exp(--(t/Xn)')
(3)
3.3 The Model
For the purpose of illustrating how the probabilities of segment membership and replacement are combined, three segments are considered: early replacers ( n = l), average replacers ( n = 2), and late replacers ( n = 3 ) . Equation (1) now becomes: P(t) = P(tl1)[1 - P(q 2
01 + P(tl2)[P(Y,r
- P ( & 2 211 + P ( t J 3 ) P ( x2 2)
1)
(4)
where P( & 2 j ) and P(tl n) are defined in (2) and (3) respectively. Large values of P(t) are associated with households that have high potential.
VOLUME 7 NUMBER 4 AUTUMN 1993
7
TABLE 1
Weibull’ Replacement Curve Fits (f-Statistics in Parentheses) Refrigerator
e
x
2.15 (8.8Jb
Color TV
2.36 (10.4Jb
2.13 (10.9Jb
14.41 (14.3Jb
13.55 (18.5Jb
11.76 (18.9Jb
( I 7.7Jb
0.42
0.58
0.65
0.70
12.8
2.39
Dishwasher
(10.l)b
R’
Replacement mean (years)
Washer
12.0
i 0.4
10.83
9.6
Defined as f(rJ= (B/h’Jf-’ exp(-(r/X)’]
’Significant at 0.01 level.
If estimates of the p(tl n) and p(Y;:r j ) functions are known, then households in an available database (e.g., constructed from warranty card information) can be ranked in terms of replacement potential using ( 4 ) . It is then an economic decision which balances costs (e.g., printing, postage) and expected response (e.g., revenue) as to how many households to select for targeting (e.g., 2 2 ) . Alternatively, management can choose some critical replacement probability level, P,; households with a replacement probability greater than P, (in other words, owning a unit of at least age t,) might then be targeted.
4. A N APPLICATION TO HOME APPLIANCES
In this section, the probability models presented in the previous section are empirically illustrated. For this application, a large database of major home appliance buyers was made available by M/A/R/C. These data are part of a syndicated consumer survey that contacted 50,000 to 70,000 households on a quarterly basis. The information from this survey was widely used by major appliance and consumer electronics manufacturers and retailers. Detailed information was collected from respondents who had purchased at least one household appliance in the previous three months. Interviews were conducted within the four weeks before the quarterly reporting period, and were conducted between 9:OO A.M. and 1O:OO P.M. so as to include working and nonworking households. Telephone numbers were selected by standard randomization procedures from the current editions of local telephone directories, as well as from a sample of random
8 JOURNAL OF DIRECT MARKETING
numbers generated from the M/A/R/C in-house system. The sample is composed of buyers of refrigerators, clothes washers, dishwashers, or color televisions during 1985. The collected data include information on the type of purchase (i.e., whether this was a first-time purchase, a replacement, or an additional unit for the household), the purchase transaction (e.g., price paid, method of payment, whether the product was purchased on sale or at a discount), the product purchased (e.g., brand, features), reasons for purchasing the brand, and several demographic variables. In addition, respondents who indicated that their purchase was a replacement were asked for information on the previous brand owned and the year in which they bought their old unit.2 After screening for missing values and including only respondents making replacement purchases who also owned their homes, sample sizes available for analysis ranged from over 2,800 respondents who bought color TVs to over 450 respondents who bought dishwashers. 4.1 Estfmatlng Replacement Distrlbutfons
The age of a product being replaced is calculated as the difference between the survey year (i.e., 1985) and the reported year of purchase of the original unit. The reported replacement distributions are well represented by Weibull distributions as shown in Table 1. On average, refrigerators are reOther research using this syndicated survey (2) indicates that there is some potential error associated with the reported year of purchase for a
previous unit (responses tend to cluster around years ending in five). However, experience suggests these errors are not large, and d o not greatly influence the fits of statistical replacement distributions.
VOLUME 7 NUMBER 4 AUTUMN 1993
TABLE 2
Characteristics of Appliance Replacement Segments (Chi Square in Parentheses] Dishwasher
Color l V
( I 20.1 4Ja
1.18 (36.29)”
0.96 ( 170.99)’
-1.21 (97.24) a
-0.72 (44.80ja
-0.73 (14.52)’
( I 12.71)’
Remodeling done in past six months Moved within past year
-0.27 (3.74)b
-0.07 (0.26)
0.10 (0.201
-0.12 (1.44j
-0.24 (2.36)
-0.62 ( 13.65)’
- 1.43 (29.97)a
-0.5 1 (13.22)’
Age of household head e 35 years
-1.01 (44.98)a
- 1.08 (59.46Ja
-1.16 (20.00)’
-0.68 (54.77)’
Age of household head > 50 years
0.86 (44.80)‘
0.38 (10.97)a
(7.40ja
0.19 (6.02Jb
Household income < S25K
-0.36 (9.3 7)a
-0.09 (0.71)
0.07 (0.09)
0.02 (0.06)
0.26 (5.59)b
-0.29 (2.54)
-0.0 1 (0.03)
-0.72 (28.24)’
0.05 (0.05)
-0.00
90.86
142.03
a2
At least four people in household Children < 6 years household
Refrigerator
Washer
1.14 (88.79)’
0.0 I (0.0 1) -0.26 (2.81)‘
jn
Chi square
222.92
1.21
230.46
0.53
-0.77
fO.001
d.f.
7
7
7
7
pValue
0.00
0.00
0.00
0.00
..
Significantat 0.01 level.
’Significant at 0.05 level.
‘Significant at 0.10 level.
placed after almost 13 years, clothes washers after 12 years, dishwashers after a little over 10 years, and color TVs after almost 10 years. Although these mean replacement times are potentially biased downward (since the sample only contains buyers, and is not necessarily representative of owners) ,3 these values closely correspond to the average life expectancy values reported in Appliance magazine. 4.2 Estimating Segment Probabilities
In order to define segments on the basis of replacement timing, the replacement distributions in Table 1 were discretized into three categories representing early, average, and late replacers (see also 1,2). To accomplish this, the marginal distributions of each product were divided into approximately equal
’This should not be of too much concern since it is probably better to anticipate replacement in the timing of a direct mail piece. Mailing too early is much better than mailing too late and may even prompt earlier replacement.
JOURNAL OF DIRECT MARKETING
sections. (These probabilities are not equal because calculated ages can only be integers.) This resulted in the following categories: refrigerator (early replacers: <9 years; average replacers: 9-15 years; late replacers: >1S years), washer (early replacers: <9 years; average replacers: 9-14 years; late replacers: >14 years), dishwasher (early replacers: <8 years; average replacers: 8-12 years; late replacers: >12 years), and color TV (early replacers: <7 years; average replacers: 7-1 1 years; late replacers: >11 years). For this application, the following demographic variables were considered: household income (425,000; r$25,000), age of household head (<35 years; 35-50 years; >SO years), number of household members ( ~ 4 r4), ; whether there ‘are any young children (<6 years) in the household, whether the household had moved within the past year, and whether any home remodeling was done in the past six months. Although more detail is
VOLUME 7 NUMBER 4 AUTUMN 1993 9
available for several variables, these definitions are easily interpretable. More importantly, the categories used are representative of the type of information that can be obtained from external data suppliers or that might be available in an internal database. Logistic regression results for the four appliances with replacer segment (three categories) as the dependent variable are shown in Table 2. For all four products the model chi square is highly significant, indicating very good explanatory power of the independent variables. Across the set of appliances considered, age of household head and the coefficient for whether a household has recently moved (significant at the 0.12 level for refrigerator) are significant. Consistent with prior research, the coefficient signs indicate that younger households and households recently moving tend to be early appliance replacers. The other demographic variables do not show a consistent pattern across appliances. Early refrigerator and washer replacers tend to have young children. Although it is only significant at the 0.11 level, the negative dishwasher coefficient for household size implies that larger households tend to be early dishwasher replacers. When combined with the coefficient for presence of young children, the positive washer coefficient for household size suggests that early washer replacers probably have one young child. Early refrigerator replacers also tend to have recently remodeled some part of their home. Consistent with other research examining the replacement process for appliances (51,income does not play a major role in the timing of replacement~.~ 4.3 Ettlmatlng Replacement Potentlal The empirical results in the previous two sections can be combined to develop a distribution for Pt. The results in Table 2 are used to estimate P( yi 2 j ) in equation (2). Given 8 and A, (calculated from the observed mean unit replacement age for replacer segment n) from Table 1 for each appliance, equation (3) can be constructed. For example, Figure 2 shows Rt) = f i t ! n) as defined in (3) for three The negative income coefficient for refrigerator suggests that early refrigerator replacers are of lower income. A possible explanation may be that these households originally owned a low quality refrigerator ( e g , a low priced brand or a used product) and thus had to replace it sooner than the average lifetime of 12 years.
10 JOURNAL
OF
DIRECT MARKETING
refrigerator replacer segments (0 = 2.15, harly = 5.48, Xaverage = 13.75, Alate = 22.41). Finally, equation ( 4 ) is used to generate a distribution of replacement probabilities. As an illustration, refrigerator replacement probability distributions for two household profiles (no remodeling, did not recently move, income > $25,000, <4 household members) are shown in Figure 3: 1) young households with children, and 2) older households without young children. Given the distributions of replacement potential and some P, level, it is a straightforward matter to identify specific households (with key characteristics and owning a unit of at least age t,) in an available database for targeting. Assuming P, = 0.6, the distributions in Figure 3 indicate that young households with children who own a refrigerator that is at least nine years old and older households without young children who own a refrigerator that is at least 16 years old should be targeted. 4.4 Evaluatlng the Level of Response
Unlike much of mass media advertising, an appealing aspect of targeted database marketing is that the effectiveness of a particular communication program can be evaluated by the response generated. A straightforward method of evaluation is through a two-group experimental design. For example, the response to a particular communication program by a group of targeted households can be compared against a matched group of households who do not receive any message. Several communication programs can also be tested in a similar fashion. Even if a formal experimental design is not used, the effectiveness of a targeted marketing program can be evaluated over time by tracking the behavior of current owners and matching it with any communications to these households.
5. CONCLUSIONS AND AREAS FOR FURTHER RESEARCH The goal of this article is to describe an approach that can be used to develop targeted marketing strategies for consumer durables. The approach presented here takes into account the timing of replacement purchases and, importantly, uses data which are generally available to manufacturers and retailers of durable products. An illustration of how
VOLUME 7 NUMBER 4 AUTUMN 1993
1 0.9 -
0.8 -
EARLY REPUCERS
0.7 0.6
-zLr
VERAGE REPLA
0.5 0.4
-
0.3
-
LATE REPLACERS
0.2 0.1 0 0
4
12
8
16
20
24
UNIT AGE
FIGURE 2
Refrigerator Replacement Probabilities by Replacer Segment
this approach can be applied using syndicated survey data for a set of four major appliances was also presented. As discussed, a key component of a targeted direct marketing program is determining appropriate segments. The empirical findings presented in this article indicate that households can be assigned to replacement buyer segments using demographic information. In terms of the significant explanatory variables, age of household head, and whether the household had recently moved were significant for 811 four of the appliances considered; presence of young children, household size, recent home remodeling, and income did not show a consistent pattern across the appliances. From a practitioner’s perspective, the approach described in this article, and empirical results for major appliances, show the importance of actively managing internal data such as warranty card information on current owners. When enhanced with information from syndicated and custom surveys,
JOURNAL OF DIRECT MARKETING
and data overlays from commercial sources, such a database can be used to: 1) identify segments to target and 2) develop communication programs for these consumer segments. A database for consumer durable products constructed with this type of generally available data managed over time will provide an enduring competitive advantage for firms in mature product categories. The approach presented in this article, however, is just the first step toward developing a state-ofthe-art model for marketing consumer durables. Several areas still need further research. The three replacer segments used in the application were developed simply by dividing the overall replacement distribution into thirds based on marginal probabilities.-Although it adds a level of statistical complexity to the problem, more precise segment definitions might be achieved using finite mixture distributions (e.g., 16;24). Further research is needed to examine the validity of resulting replacer segments and the generalizability of segment bound-
VOLUME 7 NUMBER 4 AUTUMN 1993
11
09 0.8 -
sI-
0.7 -
5
0.6 -
a
z
I-
0.5 -
8
0.4 -
ea
0.3 -
4
3
0.2
-
0.1
0
4
8
12
16
20
24
UNIT AGE
FIGURE 3
Refrigerator Replacement Potential by Market Segment aries across durables. A related topic that can be studied is the degree of replacer segment overlap across product categories. For example, an important question is whether a household is an early replacer across durable categories. If it turns out that there is some consistency in household characteristics, multi-product manufacturers should be very interested because it will mean that at any given point in time a household might be in the market for some type of durable product that the firm sells. Finally, the probability model proposed in this article and associated decision regarding which households to target is static. The market, however, is dynamic. Household characteristics (e.g., age of household head, presence of children, residential move) change over time so that the replacement segment to which a particular household is classified can shift. Additionally, the age of a currently owned unit increases with time. The empirical findings in this article indicate that the probability of being an early (late) replacer decreases (increases) over time, and at the same time the probability of replacement increases since the unit is older. Further,
12
JOURNAL OF DIRECT MARKETING
marketing variables (e.g., price and promotion) may be used to accelerate replacements. Effects of these marketing mix elements may also differ across replacer segments. Modeling these dynamic elements will help pinpoint the replacer segments to target and will allow an investigation of whether it is optimal to accelerate the replacement decision €or certain households.
REFERENCES 1. Bayus, B. (1988), “Accelerating the Durable Replacement Cycle with Marketing Mix Variables,” Journal of Product Innovation Management, 5 , 216-226. 2 . -, Hong, S., and Labe, R. (1989), “Developing and Using Forecasting Models of Consumer Durables: The Case of Color Television,”Journal ofProductInnovation Management,
6, 5-19. 3. ____ (19911, “The Consumer Durable Replacement Buyer,” Journal of Marketing, 55 oanuary), 42-51. 4 . ___ (1992), “Brand Loyalty and Marketing Strategy: An Application to Home Appliances,” Markefing Science, 11 (Winter), 21-38.
VOLUME 7 NUMBER 4 AUTUMN 1993
5. Bayus, B. and Gupta, S. (1992), “An Empirical Analysis of Consumer Durable Replacement Intentions,” International Journal of Research in Marketing 9,257-267. 6 . Bucklin, R. and Gupta, S. (1992), “Brand Choice, Purchase Incidence, and Segmentation: An Integrated Approach,” Journal ofhfarkefingResearch, 29 (May), 201-215. 7. Deaton, A. and Muellbauer, J. (1980), Economics and Consumer Behaoior, (Cambridge: Cambridge University Press). 8. Gonier, D. (1990), “How to Direct Market Durables,” Target Marketing, May.
9. Gupta, S. and Chintagunta, P. (1992), “On Using Demographic Variables to Determine Segment Membership in Logit Mixture Models,” Cornell University working paper. 10. Hanemann, W. (1984), “Discrete/Continuous Choice Models of Consumer Demand,” Econometrica, 52 (May), 541-562. 11. Harrell, F. (1986), “The LOGIST Procedure,“ in SAS User’s
Guide, (Cary, NC: SAS Institute). 12. Kamakura, W. and Balasubramanian, S. (1987), “Long-term Forecasting with Innovation Diffusion Models: The Impact of Replacement Purchases,” Journal of Forecasting 6, 1-19,
13. Kamakura, W. and Russell, G. (1989), “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure,”Journal ofMurketing Research, 26 (November), 379-390. 14. Liesse,J. (1990), “KGFTaps Data Base toTarget Consumers,” Advertising Age, (October 8), 3.
15. Liesse, J. and Teinowitz, I. (1990), “Data Bases Uncover Brands’ Biggest Fans,” Advertising Age, (February 19), 3. 16. Mehta, R. and Moore, W. (1991), “Studying Diffusion of In-
JOURNAL OF DIRECT MARKETING
novation Within Segments Using Finite Mixture Distribution Techniques,” University of Cincinnati working paper. 17. Olson, J . and Choi, S. (1985), “A Product Diffusion Model Incorporating Repeat Purchases,” TechnologicalForecastingand Social Change, 27,385-397. 18. Peters, T. and Waterman, R. (1982), In Searcb ofkcellence, (New York: Harper & Row). 19. Peterson, L. (1990), “Quaker Bets Direct Promotion is the Right Thing to Do,” Adweek, oanuary 8), 4-5. 20. Radding, A. (1991). “Consumer Worry Halts Data Bases,” AduertisingAge, (February 11),28. 21. Schwartz, J. (1987), “The Goods Life,” American Demographics, (December), 32-35. 22. Sheppard Associates (1990), The New Direct Marketing, (Homewood, IL: Business One Irwin). 23. Tippett, K., Magrabi, F., and Gray, B. (1978), “Service Life of Appliances: Variations by Selected Characteristics of Owner Households,” Home Economics ResearchJournal, 6 (March), 182-191. 24. Titterington, D., Smith, A., and Makov, U. (1985), Statistical Analysis of Finite Mixture Dbtributions, (Chichester, U.K.: John Wiley) . 25. Walker, S. H. and Duncan, D. (1967), “Estimation of the Probability of an Event as a Function of Several Independent Variables,” Eiometrika, 54, 167-178. 26. Wilkie, J. (19901, “Lotus Product Spurs Fears About Privacy,” Wall StreetJournal, (November 13), B1. 27. Youman, N. (1987), “Actdirect Unveils Hard-Goods Coupons,” Adweek, (ruly 20), 1.
VOLUME 7 NUMBER 4 AUTUMN 1993 13
The Targeted Marketing of Consumer Durables BARRY L BAYUS i s an associate professor of marketing at the Kenan-Flagler Business School, University of North Caro iina He received a Phi3 and MS from the Wharton School Prior tojoining the faculty at UNC. he was on the business school faculties at Cornell University and the Wharton School, and a senior staff member of the Corporate Operations Research Group at RCA His research interests deal with product life cycle issues. the marketing of consumer durables and product replacement behavior. and technological change and product management Financial support from the Marketing Science Institute is gratefully acknowledged Special thanks to M/A/R/C for providing access to their databases, and to Dennis Gonier for valuable drscussions at various points during this research Thanks are also extended to Ambar Rao for comments on an earlier draft
BARRY L. BAYUS
ABSTRACT In this article, an approach for identifying prime prospects and developing targeted marketing strategies for durables is presented and empirically illustrated using a set of major home appliances. The proposed approach combines probability models for identifying segments (as a function of household characteristics)and determining replacement potentials (as a function of the age of a current unit). Importantly, these models use data that are generally available to manufacturers and retailers of durable products 1e.g.. syndicated survey data, warranty card information, data overlays from commercial firms).
0 1993 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc CCC 0892-0591/93/0404-10
4
JOURNAL OF DIRECT MARKETING
VOLUME 7 NUMBER 4 AUTUMN 1993
The importance of communicating with, and listening to, customers has received an increased amount of attention in recent years (e.g., Peters and Waterman [18] and subsequent studies by others). Packaged goods and services firms have embraced the ideas of targeted direct marketing and database marketing as a way to communicate with consumers and to halt the erosion of brand loyalty. Various “club” programs have been implemented by manufacturers, service firms, and retailers to strengthen brand loyalty and to develop leads for future prospects (15). Airlines and hotels, for example, have had frequent flyer/stayer award programs for several years. Packaged goods firms and supermarket chains are beginning to offer frequent buyer/shopper programs. The Quaker Oats Company has recently tried a massive targeted coupon delivery system aimed at developing a direct link to individual households (19), and Kraft General Foods has tested a similar system ( 1 4 ) . Although not as widely used, database marketing principles have also found their way into the marketing of consumer durables. Actdirect, a joint venture between National Demographics and Lifestyles and Actmedia, has marketed a direct-mail appliance rebate program called “Express Certificates” (21,271. According to a spokesman of Actdirect, “Actdirect is not merely discounting. It is an advertising message targeted to the right person” (21). Sustaining brand loyalty for replacements is particularly important for manufacturers and retailers of durable products, since a consumer is out of the market for several years once a purchase is completed. In addition, manufacturer and retailer concern for maintaining brand loyalty extends to product lines (e.g., kitchen appliances such as stoves, refrigerators, and dishwashers; and stereo components such as receivers, turntables, tape decks, and compact disc players) and trade-up buyers who buy new appliances with more features and thus higher price tags. As a result, industry interest in direct marketing approaches has increased (8). Interest in influencing durable replacement demand is also expected to increase among industry associations and government groups due to.the growing concern for energy efficient products and products that do not harm the environment. Despite this importance however, very little research has addressed dur-
JOURNAL OF DIRECT MARKETING
able brand loyalty or the marketing of established consumer durables (a recent exception is Bayus [4]). This article presents an approach that can be used to identify prime segments of consumer durable owners for which targeted marketing strategies can be developed. This approach takes into account the timing of replacement purchases (i.e., the age of the current unit). Importantly, this approach uses data that are generally available to manufacturers and retailers of durable products (e.g., syndicated survey data, warranty card information, and data overlays from commercial firms). In the next section, a conceptual overview of the targeted marketing of durables is discussed. A model framework that considers replacement buyer segments and the associated replacement probability distributions is proposed in Section 3. Section 4 outlines an available data set and presents an application of this approach for major home appliances. Conclusions and further research directions are discussed in the final section. 2. THE TARGETED MARKETING OF CONSUMER DURABLES
Two fundamental components are at the heart of targeted direct marketing: 1) identifying consumer segments to target, and 2) developing relevant communication strategies to these segments (8). For mature durable products, the age and condition of the units in use will also be an important factor in the household decision of whether or not to make a replacement purchase ( 5 ) . Prior research suggests that durable replacement buyers can be divided based on the timing of replacements; for example, the author, in another study, defines early, average, and late replacers based on the age distribution of products being replaced ( 3 ) . Other efforts further indicate that these replacer segments have distinct demographic and attitudinal profiles. For example, Tippett, et al. (23) suggest that appliances are held for a shorter period of time if the household is of higher income, head of household is younger in age, children are present in the family, and the family has moved recently. “Early” automobile replacers are found to generally have high incomes and are concerned with brand styling and image, whereas “late” replacers have
VOLUME 7 NUMBER 4 AUTUMN 1993
5
high educational and occupational status and are concerned with cost-related product attributes. In addition, search activity and usage of mass media sources differ across automobile replacer segments (3). These key conceptual elements are combined in Figure 1. Given an a priori definition of replacer segments based on the timing of replacement purchases, households are assigned to a segment using demographic and attitudinal data. Households in each replacer segment are then considered for targeting depending on the age of a currently owned unit (e.g., households in a particular segment owning an appliance that is older than the mean lifetime of units in that segment are expected to have higher replacement probabilities than households owning a relatively newer appliance). Relevant communications for these targeted households are next developed, and the effectiveness of the communication program is tracked and evaluated in terms of the response generated (e.g., sales, leads, improvement in brand consideration). The approach outlined in Figure 1 is appealing for several reasons. First, it is conceptually straight-
1 MASS MARKET
forward. More importantly, this approach makes us,e of data that are usually available to durable manufacturers and retailers. For example, syndicated sucvey data can be used to develop replacer segment definitions and to determine the demographic and attitudinal profile of each segment. Information on household characteristics (e.g., demographics andl/ or attitudinal data) and on the ages of units currently owned can be assembled from warranty cards (e.g., National Demographics and Lifestyles markets lists of durable buyers; see also [22]). Other data elements not available in-house can be merged with existing household information using data overlays from several commercial firms (e.g., consider t h e rise and fall of Lotus Marketplace, a relatively iiiexpensive PC package that would have provided access to information on millions of households and businesses [20,26]). A database constructed with such information at the household level (i.e., by specific address and phone number) can be used to track potential buyers and reinforce replacement brand loyalty. Over time, it may be possible to s u p plement this database with information on households owning competing brands (through brand switching by current owners and merging data from other surveys). Finally, as discussed in the next secztion, the conceptual approach outlined in Figure 1 can be translated into a quantitative framework using probability models.
3. A MODEL FRAMEWORK REPLACER SEGMENTS
Ic-7
E I TARGETED HOUSEHOLDS
RESPONSE
I
Household Characteristics
Age O f Current Unit
Targeted Communications
FIGURE 1
The Targeted Marketing of Consumer Durables
6 JOURNAL OF DIRECT MARKETING
In order to develop a model of purchase behavior at the household level, the random utility framework of discrete choice models can be used (e.g,., 7,lO). Several studies of consumer packaged goocls have taken this approach (e.g., 6,9,13). Consumer durables however, present several challenges not faced in the study of frequently purchased item.s. Most important is that the time between purchases is generally several years. This implies that the tracking of individual households for several years is needed to estimate these choice models. Unfortunately, panel data are not usually used by manufacturers and retailers of consumer durables and, consequently, these kind of data are not available for research purposes. (A data collection system like a supermarket scanner data is not yet available.) The type of data that is regularly used by firms
VOLUME 7 NUMBER 4 AUTUMN 19'73
involves large, nationally representative surveys of recent buyers (2,3,4). As already discussed, data on individual households at a particular point in time is also available from customer lists and commercial sources. These practical concerns of data availability are taken into account in the modeling approach developed. Once segments have been defined based on the timing of replacements, identifying specific households in each segment to target requires some measure of response potential. A natural measure to use is the probability of replacement (given ownership of a unit of a specific age). This household replacement probability is modeled as being conditional on segment membership. Letting P(t) be the cumulative probability' of a household replacing a unit of age t, P(tln) the cumulative probability of a household replacing a unit of age t given that it is a member of replacer segment n, and Q(n) the probability that the household is a member of replacer segment n, the following equation is obtained: P(t) = -WtIn)Q(n) n
(1)
Specific functional forms for fit1 n) and Q(n)are discussed next. 3.1 Modeling Segment Probabilities
From Figure 1 and the associated discussion, households can be assigned to replacer segments using household demographic and attitudinal information. Since the replacer segments defined based on the timing of replacements represent various ordered categories, the familiar logistic model can be' used to estimate a relationship between household characteristics and segment membership. Walker and Duncan (25) extend the basic dichotomous dependent variable model to one applicable to multiple, ordinal categories. Letting & denote segment membership for the ith household, the model is: P ( Y , 2 j ) = 1/(1 + exp(-q - x$)) ( j = 1, . . . , k;with k
+ 1 = n segments)
(2)
' Other functions such as the hazard rate Mt) = f ( f ) / [ l - F ( r ) ] could also be considered. Since the hazard rate can be any positive number however, managerswould probably havedi5cultycomparingand interpretinghazard values. Thus, for practical reasons, the cumulative distribution (i.e., the fraction of households that have replaced a unit by the time it is of age r) is used because of its managerial familiarity.
JOURNAL OF DIRECT MARKETING
where X, is the vector of explanatory variables for the ith household, 0 is the coefficient vector, and cy/ is the intercept term for categoryj. Maximum likelihood methods are used to estimate the parameters (e.g., through the LOGIST package as implemented in SAS; see [ 111). Importantly, the logistic regression model can be used to determine the probability of segment membership for any household, given a set of explanatory variables (i.e., household characteristics). The resulting segment probabilities are thus used as weights (which sum to one) in equation (1). 3.2 Modeling Replacement Probabllltles
The probability of replacement depends on the age of a currently owned unit. Several approaches have been suggested for analyzing aggregate replacement purchases for the purpose of sales forecasting. These methods use probabilistic distributions for the age distribution of units replaced such as the Rayleigh (17), truncated normal (12), and Weibull (2). A two-parameter Weibull distribution (Rt) = 1 - exp(-(t/X)'); p = hr(1 l/@)is used here because of its flexibility and previous use for consumer durables (1,3,4). For the Weibull, X is the location parameter (i.e,, has the greatest influence on the mean since the gamma function value is between 0 and 1) and 0 is the shape parameter. Other research reports that 2 < 8 < 3 for home appliances (1). The shape parameter 19 is modeled as being product category specific, while X will vary by replacer segment. Thus, P(tl n) in equation (1) becomes:
+
f i t l n ) = 1 - exp(--(t/Xn)')
(3)
3.3 The Model
For the purpose of illustrating how the probabilities of segment membership and replacement are combined, three segments are considered: early replacers ( n = l), average replacers ( n = 2), and late replacers ( n = 3 ) . Equation (1) now becomes: P(t) = P(tl1)[1 - P(q 2
01 + P(tl2)[P(Y,r
- P ( & 2 211 + P ( t J 3 ) P ( x2 2)
1)
(4)
where P( & 2 j ) and P(tl n) are defined in (2) and (3) respectively. Large values of P(t) are associated with households that have high potential.
VOLUME 7 NUMBER 4 AUTUMN 1993
7
TABLE 1
Weibull’ Replacement Curve Fits (f-Statistics in Parentheses) Refrigerator
e
x
2.15 (8.8Jb
Color TV
2.36 (10.4Jb
2.13 (10.9Jb
14.41 (14.3Jb
13.55 (18.5Jb
11.76 (18.9Jb
( I 7.7Jb
0.42
0.58
0.65
0.70
12.8
2.39
Dishwasher
(10.l)b
R’
Replacement mean (years)
Washer
12.0
i 0.4
10.83
9.6
Defined as f(rJ= (B/h’Jf-’ exp(-(r/X)’]
’Significant at 0.01 level.
If estimates of the p(tl n) and p(Y;:r j ) functions are known, then households in an available database (e.g., constructed from warranty card information) can be ranked in terms of replacement potential using ( 4 ) . It is then an economic decision which balances costs (e.g., printing, postage) and expected response (e.g., revenue) as to how many households to select for targeting (e.g., 2 2 ) . Alternatively, management can choose some critical replacement probability level, P,; households with a replacement probability greater than P, (in other words, owning a unit of at least age t,) might then be targeted.
4. A N APPLICATION TO HOME APPLIANCES
In this section, the probability models presented in the previous section are empirically illustrated. For this application, a large database of major home appliance buyers was made available by M/A/R/C. These data are part of a syndicated consumer survey that contacted 50,000 to 70,000 households on a quarterly basis. The information from this survey was widely used by major appliance and consumer electronics manufacturers and retailers. Detailed information was collected from respondents who had purchased at least one household appliance in the previous three months. Interviews were conducted within the four weeks before the quarterly reporting period, and were conducted between 9:OO A.M. and 1O:OO P.M. so as to include working and nonworking households. Telephone numbers were selected by standard randomization procedures from the current editions of local telephone directories, as well as from a sample of random
8 JOURNAL OF DIRECT MARKETING
numbers generated from the M/A/R/C in-house system. The sample is composed of buyers of refrigerators, clothes washers, dishwashers, or color televisions during 1985. The collected data include information on the type of purchase (i.e., whether this was a first-time purchase, a replacement, or an additional unit for the household), the purchase transaction (e.g., price paid, method of payment, whether the product was purchased on sale or at a discount), the product purchased (e.g., brand, features), reasons for purchasing the brand, and several demographic variables. In addition, respondents who indicated that their purchase was a replacement were asked for information on the previous brand owned and the year in which they bought their old unit.2 After screening for missing values and including only respondents making replacement purchases who also owned their homes, sample sizes available for analysis ranged from over 2,800 respondents who bought color TVs to over 450 respondents who bought dishwashers. 4.1 Estfmatlng Replacement Distrlbutfons
The age of a product being replaced is calculated as the difference between the survey year (i.e., 1985) and the reported year of purchase of the original unit. The reported replacement distributions are well represented by Weibull distributions as shown in Table 1. On average, refrigerators are reOther research using this syndicated survey (2) indicates that there is some potential error associated with the reported year of purchase for a
previous unit (responses tend to cluster around years ending in five). However, experience suggests these errors are not large, and d o not greatly influence the fits of statistical replacement distributions.
VOLUME 7 NUMBER 4 AUTUMN 1993
TABLE 2
Characteristics of Appliance Replacement Segments (Chi Square in Parentheses] Dishwasher
Color l V
( I 20.1 4Ja
1.18 (36.29)”
0.96 ( 170.99)’
-1.21 (97.24) a
-0.72 (44.80ja
-0.73 (14.52)’
( I 12.71)’
Remodeling done in past six months Moved within past year
-0.27 (3.74)b
-0.07 (0.26)
0.10 (0.201
-0.12 (1.44j
-0.24 (2.36)
-0.62 ( 13.65)’
- 1.43 (29.97)a
-0.5 1 (13.22)’
Age of household head e 35 years
-1.01 (44.98)a
- 1.08 (59.46Ja
-1.16 (20.00)’
-0.68 (54.77)’
Age of household head > 50 years
0.86 (44.80)‘
0.38 (10.97)a
(7.40ja
0.19 (6.02Jb
Household income < S25K
-0.36 (9.3 7)a
-0.09 (0.71)
0.07 (0.09)
0.02 (0.06)
0.26 (5.59)b
-0.29 (2.54)
-0.0 1 (0.03)
-0.72 (28.24)’
0.05 (0.05)
-0.00
90.86
142.03
a2
At least four people in household Children < 6 years household
Refrigerator
Washer
1.14 (88.79)’
0.0 I (0.0 1) -0.26 (2.81)‘
jn
Chi square
222.92
1.21
230.46
0.53
-0.77
fO.001
d.f.
7
7
7
7
pValue
0.00
0.00
0.00
0.00
..
Significantat 0.01 level.
’Significant at 0.05 level.
‘Significant at 0.10 level.
placed after almost 13 years, clothes washers after 12 years, dishwashers after a little over 10 years, and color TVs after almost 10 years. Although these mean replacement times are potentially biased downward (since the sample only contains buyers, and is not necessarily representative of owners) ,3 these values closely correspond to the average life expectancy values reported in Appliance magazine. 4.2 Estimating Segment Probabilities
In order to define segments on the basis of replacement timing, the replacement distributions in Table 1 were discretized into three categories representing early, average, and late replacers (see also 1,2). To accomplish this, the marginal distributions of each product were divided into approximately equal
’This should not be of too much concern since it is probably better to anticipate replacement in the timing of a direct mail piece. Mailing too early is much better than mailing too late and may even prompt earlier replacement.
JOURNAL OF DIRECT MARKETING
sections. (These probabilities are not equal because calculated ages can only be integers.) This resulted in the following categories: refrigerator (early replacers: <9 years; average replacers: 9-15 years; late replacers: >1S years), washer (early replacers: <9 years; average replacers: 9-14 years; late replacers: >14 years), dishwasher (early replacers: <8 years; average replacers: 8-12 years; late replacers: >12 years), and color TV (early replacers: <7 years; average replacers: 7-1 1 years; late replacers: >11 years). For this application, the following demographic variables were considered: household income (425,000; r$25,000), age of household head (<35 years; 35-50 years; >SO years), number of household members ( ~ 4 r4), ; whether there ‘are any young children (<6 years) in the household, whether the household had moved within the past year, and whether any home remodeling was done in the past six months. Although more detail is
VOLUME 7 NUMBER 4 AUTUMN 1993 9
available for several variables, these definitions are easily interpretable. More importantly, the categories used are representative of the type of information that can be obtained from external data suppliers or that might be available in an internal database. Logistic regression results for the four appliances with replacer segment (three categories) as the dependent variable are shown in Table 2. For all four products the model chi square is highly significant, indicating very good explanatory power of the independent variables. Across the set of appliances considered, age of household head and the coefficient for whether a household has recently moved (significant at the 0.12 level for refrigerator) are significant. Consistent with prior research, the coefficient signs indicate that younger households and households recently moving tend to be early appliance replacers. The other demographic variables do not show a consistent pattern across appliances. Early refrigerator and washer replacers tend to have young children. Although it is only significant at the 0.11 level, the negative dishwasher coefficient for household size implies that larger households tend to be early dishwasher replacers. When combined with the coefficient for presence of young children, the positive washer coefficient for household size suggests that early washer replacers probably have one young child. Early refrigerator replacers also tend to have recently remodeled some part of their home. Consistent with other research examining the replacement process for appliances (51,income does not play a major role in the timing of replacement~.~ 4.3 Ettlmatlng Replacement Potentlal The empirical results in the previous two sections can be combined to develop a distribution for Pt. The results in Table 2 are used to estimate P( yi 2 j ) in equation (2). Given 8 and A, (calculated from the observed mean unit replacement age for replacer segment n) from Table 1 for each appliance, equation (3) can be constructed. For example, Figure 2 shows Rt) = f i t ! n) as defined in (3) for three The negative income coefficient for refrigerator suggests that early refrigerator replacers are of lower income. A possible explanation may be that these households originally owned a low quality refrigerator ( e g , a low priced brand or a used product) and thus had to replace it sooner than the average lifetime of 12 years.
10 JOURNAL
OF
DIRECT MARKETING
refrigerator replacer segments (0 = 2.15, harly = 5.48, Xaverage = 13.75, Alate = 22.41). Finally, equation ( 4 ) is used to generate a distribution of replacement probabilities. As an illustration, refrigerator replacement probability distributions for two household profiles (no remodeling, did not recently move, income > $25,000, <4 household members) are shown in Figure 3: 1) young households with children, and 2) older households without young children. Given the distributions of replacement potential and some P, level, it is a straightforward matter to identify specific households (with key characteristics and owning a unit of at least age t,) in an available database for targeting. Assuming P, = 0.6, the distributions in Figure 3 indicate that young households with children who own a refrigerator that is at least nine years old and older households without young children who own a refrigerator that is at least 16 years old should be targeted. 4.4 Evaluatlng the Level of Response
Unlike much of mass media advertising, an appealing aspect of targeted database marketing is that the effectiveness of a particular communication program can be evaluated by the response generated. A straightforward method of evaluation is through a two-group experimental design. For example, the response to a particular communication program by a group of targeted households can be compared against a matched group of households who do not receive any message. Several communication programs can also be tested in a similar fashion. Even if a formal experimental design is not used, the effectiveness of a targeted marketing program can be evaluated over time by tracking the behavior of current owners and matching it with any communications to these households.
5. CONCLUSIONS AND AREAS FOR FURTHER RESEARCH The goal of this article is to describe an approach that can be used to develop targeted marketing strategies for consumer durables. The approach presented here takes into account the timing of replacement purchases and, importantly, uses data which are generally available to manufacturers and retailers of durable products. An illustration of how
VOLUME 7 NUMBER 4 AUTUMN 1993
1 0.9 -
0.8 -
EARLY REPUCERS
0.7 0.6
-zLr
VERAGE REPLA
0.5 0.4
-
0.3
-
LATE REPLACERS
0.2 0.1 0 0
4
12
8
16
20
24
UNIT AGE
FIGURE 2
Refrigerator Replacement Probabilities by Replacer Segment
this approach can be applied using syndicated survey data for a set of four major appliances was also presented. As discussed, a key component of a targeted direct marketing program is determining appropriate segments. The empirical findings presented in this article indicate that households can be assigned to replacement buyer segments using demographic information. In terms of the significant explanatory variables, age of household head, and whether the household had recently moved were significant for 811 four of the appliances considered; presence of young children, household size, recent home remodeling, and income did not show a consistent pattern across the appliances. From a practitioner’s perspective, the approach described in this article, and empirical results for major appliances, show the importance of actively managing internal data such as warranty card information on current owners. When enhanced with information from syndicated and custom surveys,
JOURNAL OF DIRECT MARKETING
and data overlays from commercial sources, such a database can be used to: 1) identify segments to target and 2) develop communication programs for these consumer segments. A database for consumer durable products constructed with this type of generally available data managed over time will provide an enduring competitive advantage for firms in mature product categories. The approach presented in this article, however, is just the first step toward developing a state-ofthe-art model for marketing consumer durables. Several areas still need further research. The three replacer segments used in the application were developed simply by dividing the overall replacement distribution into thirds based on marginal probabilities.-Although it adds a level of statistical complexity to the problem, more precise segment definitions might be achieved using finite mixture distributions (e.g., 16;24). Further research is needed to examine the validity of resulting replacer segments and the generalizability of segment bound-
VOLUME 7 NUMBER 4 AUTUMN 1993
11
09 0.8 -
sI-
0.7 -
5
0.6 -
a
z
I-
0.5 -
8
0.4 -
ea
0.3 -
4
3
0.2
-
0.1
0
4
8
12
16
20
24
UNIT AGE
FIGURE 3
Refrigerator Replacement Potential by Market Segment aries across durables. A related topic that can be studied is the degree of replacer segment overlap across product categories. For example, an important question is whether a household is an early replacer across durable categories. If it turns out that there is some consistency in household characteristics, multi-product manufacturers should be very interested because it will mean that at any given point in time a household might be in the market for some type of durable product that the firm sells. Finally, the probability model proposed in this article and associated decision regarding which households to target is static. The market, however, is dynamic. Household characteristics (e.g., age of household head, presence of children, residential move) change over time so that the replacement segment to which a particular household is classified can shift. Additionally, the age of a currently owned unit increases with time. The empirical findings in this article indicate that the probability of being an early (late) replacer decreases (increases) over time, and at the same time the probability of replacement increases since the unit is older. Further,
12
JOURNAL OF DIRECT MARKETING
marketing variables (e.g., price and promotion) may be used to accelerate replacements. Effects of these marketing mix elements may also differ across replacer segments. Modeling these dynamic elements will help pinpoint the replacer segments to target and will allow an investigation of whether it is optimal to accelerate the replacement decision €or certain households.
REFERENCES 1. Bayus, B. (1988), “Accelerating the Durable Replacement Cycle with Marketing Mix Variables,” Journal of Product Innovation Management, 5 , 216-226. 2 . -, Hong, S., and Labe, R. (1989), “Developing and Using Forecasting Models of Consumer Durables: The Case of Color Television,”Journal ofProductInnovation Management,
6, 5-19. 3. ____ (19911, “The Consumer Durable Replacement Buyer,” Journal of Marketing, 55 oanuary), 42-51. 4 . ___ (1992), “Brand Loyalty and Marketing Strategy: An Application to Home Appliances,” Markefing Science, 11 (Winter), 21-38.
VOLUME 7 NUMBER 4 AUTUMN 1993
5. Bayus, B. and Gupta, S. (1992), “An Empirical Analysis of Consumer Durable Replacement Intentions,” International Journal of Research in Marketing 9,257-267. 6 . Bucklin, R. and Gupta, S. (1992), “Brand Choice, Purchase Incidence, and Segmentation: An Integrated Approach,” Journal ofhfarkefingResearch, 29 (May), 201-215. 7. Deaton, A. and Muellbauer, J. (1980), Economics and Consumer Behaoior, (Cambridge: Cambridge University Press). 8. Gonier, D. (1990), “How to Direct Market Durables,” Target Marketing, May.
9. Gupta, S. and Chintagunta, P. (1992), “On Using Demographic Variables to Determine Segment Membership in Logit Mixture Models,” Cornell University working paper. 10. Hanemann, W. (1984), “Discrete/Continuous Choice Models of Consumer Demand,” Econometrica, 52 (May), 541-562. 11. Harrell, F. (1986), “The LOGIST Procedure,“ in SAS User’s
Guide, (Cary, NC: SAS Institute). 12. Kamakura, W. and Balasubramanian, S. (1987), “Long-term Forecasting with Innovation Diffusion Models: The Impact of Replacement Purchases,” Journal of Forecasting 6, 1-19,
13. Kamakura, W. and Russell, G. (1989), “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure,”Journal ofMurketing Research, 26 (November), 379-390. 14. Liesse,J. (1990), “KGFTaps Data Base toTarget Consumers,” Advertising Age, (October 8), 3.
15. Liesse, J. and Teinowitz, I. (1990), “Data Bases Uncover Brands’ Biggest Fans,” Advertising Age, (February 19), 3. 16. Mehta, R. and Moore, W. (1991), “Studying Diffusion of In-
JOURNAL OF DIRECT MARKETING
novation Within Segments Using Finite Mixture Distribution Techniques,” University of Cincinnati working paper. 17. Olson, J . and Choi, S. (1985), “A Product Diffusion Model Incorporating Repeat Purchases,” TechnologicalForecastingand Social Change, 27,385-397. 18. Peters, T. and Waterman, R. (1982), In Searcb ofkcellence, (New York: Harper & Row). 19. Peterson, L. (1990), “Quaker Bets Direct Promotion is the Right Thing to Do,” Adweek, oanuary 8), 4-5. 20. Radding, A. (1991). “Consumer Worry Halts Data Bases,” AduertisingAge, (February 11),28. 21. Schwartz, J. (1987), “The Goods Life,” American Demographics, (December), 32-35. 22. Sheppard Associates (1990), The New Direct Marketing, (Homewood, IL: Business One Irwin). 23. Tippett, K., Magrabi, F., and Gray, B. (1978), “Service Life of Appliances: Variations by Selected Characteristics of Owner Households,” Home Economics ResearchJournal, 6 (March), 182-191. 24. Titterington, D., Smith, A., and Makov, U. (1985), Statistical Analysis of Finite Mixture Dbtributions, (Chichester, U.K.: John Wiley) . 25. Walker, S. H. and Duncan, D. (1967), “Estimation of the Probability of an Event as a Function of Several Independent Variables,” Eiometrika, 54, 167-178. 26. Wilkie, J. (19901, “Lotus Product Spurs Fears About Privacy,” Wall StreetJournal, (November 13), B1. 27. Youman, N. (1987), “Actdirect Unveils Hard-Goods Coupons,” Adweek, (ruly 20), 1.
VOLUME 7 NUMBER 4 AUTUMN 1993 13