Customer portfolio analysis for strategy development in direct marketing

ARCH C. WOODSIDE PRAVEEN K. SON1

Customer Portfolio Analysis for Strategy Development in Direct Marketing ARCH G WOODSIDE is the Malcolm S Woldenberg Professor of Marketing of the A B Freeman School of Business, Tulane University. New Orleans, LA He i s the editor of Advance5 in Business Markefing,an annual series of original essays and research on business-to-business marketing He completed his PhD in business administration at Penn State in I968 He serves as president of ARC Consultants. Inc , a firm specializing in assisting companies in preparing marketing strategies and business plans PRAVEEN K SON1 is an associate professor of marketing at California State University at Long Beach His research interests include advertising, business marketing, new product development, and marketing strategy He has previously been published in academic and professionaljournals. and has completed his PhD in business administration at Penn State This article was presented at the Second Annual Robert B Clarke DMEF Educators' Conference, held in San Francisco last October It was one of two presentations which were each awarded a S I .ooOcash prize (the other. by Peter Curasone. Herb Brown. and Gordon Wise appeared in JDM 5. I J The conference is named for the late Robert B Clarke, w h o provided an endowment fund for direct marketing education

ABSTRACT Segmenting existing customers into distinct groups based on their dollar purchases and the product categories which they buy can reveal which accounts are contributing substantially to net profits and which should receive more or less attention relative to others. Detailed profile analyses may be developed on the demographics, product-related psychographics, and buying behavior of competitors' products for each customer segment. Insights for refining existing and creating new productmarket positioning strategies are likely to result from such profile analyses. In the present article a framework for performing customer portfolio analysis is described and applied, using the customer database for a large, retail-direct marketing firm. The article concludes with recommendations for additional empirical research and strategic use of customer portfolio analysis.

0 1991John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc. CCC 0892-0591/ 9 1/0206- 14 $04.00

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VOLUME 5 NUMBER 2 SPRING 1991

INTRODUCTION All customers are not alike. Research reports in the

industrial and consumer marketing literature offer consistent support for the proposition that a few customers contribute the lion’s share of the purchases and profits for an enterprise. The industrywide 20/80 rule-of-thumb is supported at the individual firm level: 20 percent of the customers may account for 80 percent of the volume (1,5,9, 10,11,12,14,15,20). A life-cycle classification of customer relationships has been proposed by Campbell and Cunningham (4) to measure the degree of similarities and differences in demographics, psychographics, and buying and usage behaviors of customer segments. Campbell and Cunningham (4) adapt Drucker’s (8) four-stage product typology to offer four customer categories: tomorrow’s, today’s regular, today’s special, and yesterday’s customers. Campbell and Cunningham ( 4 ) define “tomorrow’s customers” as new buyers of an enterprise’s products and services. Such customers are characterized as having low average sales volume, producing low profit contribution, and requiring high technical, marketing, and production (strategic) resources from the enterprise to help develop a longterm relationship. “Today’s regular customers” have average sales volume and require average use of the firm’s strategic resources. “Today’s special customers” purchase large orders and are likely to require high use of a firm’s strategic resources. ‘‘Yesterday’s customers” are often numerous, but, although the relationships are old and established, each contributes only small sales volume and they receive little or no technical development work. Campbell and Cunningham predict that only for today’s special customers will one supplier’s share of a customer’s purchases be high-that is, the customer awards 50 percent or more of his or her purchases to o n e supplier ( 4 ) . In the present article, Drucker’s (8) and Campbell and Cunningham’s (4)life-cycle classification is modified further for use in customer portfolio analysis in direct marketing. The proposed classification is presented in the next section of the article. In the third section one research method used to apply the classification in direct marketing is illustrated. The results of a national field research study are presented in the fourth section. The final

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section includes conclusions and recommendations for using customer portfolio analysis for strategy development in direct marketing. The present article complements previous studies in direct marketing (11,14,15) on the need for financial and marketing analysis of new, existing, and dormant accounts. These studies are demonStrations of Bursk’s (391) wisdom that “a company’s investment in customers can be just as real as its investment in plant and equipment, inventory, and working capital. And it can be even more valuable in dollars and cents . . . But, equally important, it may also be more valuable in the sense rhat it is more closely related to the company’s existence, growth, and profitability over time.” Thus, insights into the allocation (investment) of resources among possible target markets is a rationale for customer portfolio analysis. Should a marketer attempt to gain a larger share of the purchases among customers now placing small or big orders? For a given marketer’s customers, are smallorder customers placing big orders with competitors moreso than are big-order customers? Do new, small- and big-order customers buy for distinctly different reasons? Do they have different productuse lifestyles? Different demographics? In answering such questions, marketers may find they have to accept a small share of available business among some customer segments and concentrate on producing, for such customers, an acceptable product with a minimum of service at as low a cost as possible. Special product offers and new product developments offering higher margins may have to seek more responsive customers elsewhere. Learning about the reasons for buying, competitive buying behavior, product-use lifestyles, and demographics of different customer portfolio segments is likely to be useful for improving productmarket positioning strategies. For example, a mail order garden seed company may find that a positioning message focused on “fresh, better-tasting vegetables,” matches best with the reason why most new, young families buy seeds via mail order, but that a message focused on flowering bulbs best matches with the reason why older, retired customers buy via mail order. A few additional points should be emphasized before proceeding to the life-cycle approach of customer portfolio analysis. Multiple approaches should be considered for analyzing customer port-

VOLUME 5 NUMBER 2 SPRING 1991 7

folio segments. The method selected should be contingent o n the strategic issues raised in corporate, marketing, and financial planning. Some methods will be easier to apply than others. For example, Dubinsky and Ingram (10) propose a twoby-two cell customer portfolio analysis by crosssegmenting customers into high versus lowpresent profit contribution and high versus low potential profit contribution. While potentially useful for deciding how to allocate resources of the firm, this approach may be difficult to implement because the method requires estimating future sales to a given customer as well as cost of goods sold and direct selling expenses incurred to generate those sales. Instead of starting with a division of customers into high and low sales, profits, or groupings on o n e or more other marketing control variables, the use of cluster analysis of customers based o n multiple customer and relationship dimensions has been recommended by Rost and Salle (19) as an additional method for portfolio analysis. In o n e application using this approach for a manufacturing of industrial components in France, Rost and Salle (19) describe seven well-defined distributor customer segments which include, for example, “Rotten Customers” (distributors who buy only for occasional needs for low-profitability products) and “Strategic Customers” (very large customers having “partnership” type relationships with the manufacturer). Such cluster analysis may be particularly useful for gaining a deeper understanding into how multiple dimensions influence the formation of multiple customer segments. The approach complements the less complex and easier-to-use approach of starting with customer groupings based o n two or three easy-to-access relationship dimensions-for example, length and type of the relationship (new, previous, and current, repeat customers) and annual purchase volume. Cluster analysis for forming customer portfolio groupings is a forward market segmentation and target market approach for strategy development. This approach starts with multiple customer characteristics and customer-supplier relationships and works forward to find customer segments with maximum differentiation between the segments in buying behavior and relationships with the marketer. In contrast to cluster analysis, starting with a 2 by-2 matrix of high-low sales (or profit) and new-

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old groupings of customers is o n e type of backward market segmentation (22) and target market approach for strategy development. While both forward and backward segmentation approaches are useful for strategy development, working backward may be more appropriate initially for customer portfolio analysis, because managers are likely to prefer working with naturally occurring customer groups (e.g., n e w versus light-buyer and heavybuyer, repeat customers). Forward segmentation using cluster analysis may not result in easy-to-interpret customer segments, and requires management judgment o n the number and naming of relevant clusters (17). Portfolio Analysis of Customer Relationships in Direct Marketing

For application in direct marketing, we expanded and revised Campbell and Cunningham’s ( 4 ) four life-cycle customer portfolio groupings into seven categories. The seven categories are summarized in Figure 1. We worked with the senior managers of a national mail order catalog company in the United States o n developing the customer portfolio approach these managers believed would be useful for strategy development in direct marketing. The previous studies o n customer portfolio analysis were not described to these managers. The CEO of the company agreed to participate in an in-depth portfolio analysis of the firm’s customers based on the portfolio approach developed from preliminary discussions.’ While the company’s principal business is consumer mail order seeds, and the firm is more than 100 years old, the company had never done an in-depth customer portfolio analysis prior to the mid-1970s. The senior managers preferred the use of the term, “customer/prospect portfolio analysis” for direct marketing. “Yesterday’s” and “tomorrow’s customers” were labels used by these senior managers to identify portfolio segments but, unlike Campbell and Cunningham ( 4 ) , these segments were identified as prospective, and not current, customers of the firm. Thus, the definitions of customer/prospect portfolio categories are different somewhat than the categories proposed by Camp-

’ The company is one of the five largest companies in the mail order catalog seed and plant industry in the U.S., and is not identified by name for competitive reasons.

VOLUME 5 NUMBER 2 SPRING 1991

Today‘s Special Customers sales

Target Market Catemries

Small

Kepeat Customers

Dollar Order Size Medium Large

Customers

Special

New Customers

Former Customers

Yesterday’s Customers

New Prospects

Tomorrow’s Customers

A Few of Tomorrow’s Customers

Second Attempt Prospects

17

FIGURE 1

Custorner/Prospect Portfolio

bell and Cunningham. While the customer portfolio analysis presented here may not fit all direct marketing situations, the approach may be a useful reference point for additional applications.

These customers are a direct marketer’s customers who have purchased large dollar orders in two, or all three, of the most recent three years from the company. Do such customers buy mostly from one supplier? Is this the reason they become today’s special customers? The findings in the results section of the study reported here for o n e direct marketing firm indicate that most of today’s special customers d o notbuy most of their purchases from one supplier; they split their purchases among mail order and retail store marketers as often as do today’s regular customers. Thus, the strategy to create more of today’s special customers by increasing the share of business awarded to the company by today’s regular customers may be unworkable. At least for buying seeds and plants using mail order, very few customers in any part of o n e company’s portfolio single-source their purchase requirements or buy more than 50 percent of their purchases from one vendor. Based o n findings of the present study, strategies that may work to increase the relative and absolute size of a direct marketer’sspecial customer segment are considered in the results section. New Customers

Customers buying for the first-time this year from a given marketing firm are defined as new customers. Given that few of today’s regular customers migrate to become today’s special customers, some new customers start their relationship with the direct marketer as new special customers-that is, customers placing very large dollar orders from the beginning of the relationship. Nevertheless, for purposes of parsimony the new customer segment is shown to include only o n e category in Figure 1. Yesterday’s Customers

Today‘s Regular Customers

O n e useful definition of repeat customers for direct marketing is: customers who purchased from the company during two or all three years during the most recently completed three years. The repeat customers are further divided into three subsegments as shown in Figure 1, including two segments of today’s regular customers. Today’s regular customers include small and medium sales order size customers. Most of a company’s customers would be expected to be included in o n e of these two categories.

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Former customers no longer buying from the firm are defined as yesterday’s customers. Such customers may be defined as previous customers who have not purchased any of a company’s products during the past three-year buying seasons. They may be further divided into one-time-only customers and previous repeat customers. Tomorrow’s Customers

Prospects not yet buying from the company and mailing in advertising coupons requesting catalogs; prospects requesting information from reader-ser-

VOLUME 5 NUMBER 2 SPRING 1991 9

vice cards; and prospect names acquired from buying mailing lists are examples of sources of tomorrow’s customers. Tomorrow’s customers may be labeled more appropriately as new prospects, as shown for cell 6 in Figure 1.

The data provided in Table 1 are useful for asking appropriate managerial questions in comparing customer portfolio segments. For example, should even more resources be assigned to new prospect marketing and less to attract former customer and second attempt prospects? Note that the ratio of assignable expense share to percent of total customers is far less for new prospects (19/51) compared to former customers and second attempt prospects (7/11 and 4/5, respectively). What are the relative payoffs for allocating resources to each of the customer portfolio segments? Today’s special customers represent only 6 percent of the total share of customers, 2 percent of the customers and prospects, and produce 17 percent of the sales dollars. Additional analyses revealed that these customers also produce more than one-third of the contribution margin for the firm (gross margin minus cost of goods sold and minus marketing expenses). Thus, the direct marketer emphasizes marketing to new prospects in hopes of producing tomorrow’s customers. Sales are driven by purchases by today’s regular customers, but a disproportionate high share of profits are generated by the relatively small share of today’s special customers. Such analyses are useful for understanding how different categories of customers contribute specifically to different types of necessary objectives for the direct marketing enterprise: present and future sales and present and future profits. The empirical study reported in the present study was designed to profile the buying behavior, life-

Second Attempt Prospects

These prospects have received the company’s catalogs for last year’s buying season. Some have requested copies of this year’s catalog without placing an order last year; some have been selected by the direct marketer to receive catalogs two years in a row based on additional information (from five or nine digit zip-code analysis, for example), even if they had not requested a catalog a second time. The Shares of Customers, Sales, and Assignable Expenses within the Portfolio

For the mail order seed catalog company, the shares

of customers and prospects among the seven categories included in Figure 1 are listed in Table 1. Also shown are the percent of current sales to each category and percent of assignable expenses. The assignable expenses include assignable product costs and marketing costs. Slightly more than half the households in this direct marketer’s database are new prospects. The company focuses more than one-third of its marketing resources and 19 percent of its total assignable expenses on new prospects. Thus, an analysis of the data in Table 1 suggests that senior management orients marketing substantially to attracting new customers.

TABLE 1

A Mail-Order Seed Company’s Customer Analysis Category of Customer/Prospect

Percent of Total

Percent of Sales S

Percent of Assiqnable Expenses ~ _ _ _ ~

I . Today’s regular customers-small

18

48

37 11

2. Today’s regular customers-medium

6

21

3. Today‘s special customers

2

17

5

4. Today’s n e w customers

8

14

17

11

51

0 0

19

5

0

4

100

100

100

5. Former customers

6 . N e w prospects 7. Second attempt prospects

Total

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VOLUME 5 NUMBER 2 SPRING 1991

styles, demographics, and reasons for product use of the customers and prospects in the first five cells of Figure 1 and first five rows of Table 1. For each of these customer/prospect portfolio segments, data were collected on who did what, how, where, and why with respect to products purchased from the direct marketing firm and its competitors.

METHOD Data were collected from representative, national samples (every nth household) from each of the five customer/prospect categories. To reduce memory recall problems, the sampled households were surveyed in August 1987 and asked about their winter and spring 1987, and their fall 1986, purchasing and planting behavior. Instrument

An eight-page mail survey was used to collect the data for the study. The same survey was used to collect data from each of the five customer/prospect categories. The survey included five parts. Part A included questions o n purchasing and planting flowering and vegetable seeds; type of retail sources used in buying; time spent each week gardening; and purchases of spring flowering bulbs. Demographic questions were included in Part B. In Part B, sample members were asked to identify their occupations, and 15 occupational categories were listed with examples of specific occupations given for each. Information o n marital status and occupation of spouse was also collected. Information was requested on total annual household income using seven categories ranging from under $15,000 to over $100,000. In Part B, the sample households were also asked to identify whether o r not they had purchased from each of 4 1 mail order catalogs-for example, from Abbey Press, L.1,. Bean, Sear’s By Mail, and Yield House. The sampled households were also requested to rate their ability at gardening, using five possible ability levels, from professional to novice. Part C covered 25 product-specificpsychographic questions to measure the activities, interests, and opinions (AIO’s) of members of each customer/ prospect category. “I usually plant some roses each year,” is an example of o n e question. The respon-

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dent was requested to select a number response which best described his or her belief: very unlikely, unlikely, not sure, likely, and very likely (scored 1 to 5 , respectively). Such psychographic questions measure productusage situations (2,7,16). Substantial evidence exists that consumers experiencing different usage situations also have unique demographic and buying behavior profiles (16,18,21). Parts D and E were questions on the reasons for vegetable gardening and planting flowers, respectively. A total of 12 possible reasons for vegetable gardening and 7 possible reasons for planting flower seeds or flowering bedding plants were included. The reasons listed were developed based on previous motivational research on gardening (6,23). For both Parts D and E, a constant-sum question approach was used: the sampled households were asked, “how many votes would you give to each reason if you had ten votes to divide among them? You may give all votes to one reason if you wish or split the votes among several reasons, but the votes should add u p to ten. Your voting should express why you really garden.” Such constant-sum scales provide relative measures of importance with interval scale properties and avoid “yea-saying’’ responses to all possible reasons (13). Part F included detailed questions about buying garden products using mail order and other sources. Data were collected about buying specific gardening products, for example: annual flower seeds, vegetable seeds, fall planted bulbs, and rose bushes from competing mail order firms and from competing retail source categories, such as nursery/garden centers, supermarkets, and discount stores. Procedure

The study was done as a university research project. The cooperating mail order catalog firm provided a grant to cover data collection costs. To control for source bias in responding to the survey, no mail order firm was mentioned in the cover letter accompanying the questionnaire. The research project was identified to sampled households as a study on gardening by families in the United States. The total mailing included 1,473 households, 292 to 296 households per customer/prospect category. To encourage response, the sampled individual’s name and address were included for each cover letter. Each cover letter was hand signed. First-class

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stamps were used o n the return envelopes in the mailing. Regular, small sales order, customers (cell 1 in Figure 1) were defined as customers placing orders valued under $15.00 in each of two of the most recent three-year buying seasons. Regular, medium sales order (cell 2) were defined as customers placing orders valued between $15.00 and $30.00 in each of two of the most recent three-year buying seasons. Today’s special customers (cell 3) included customers placing sales orders valued above $30 in each of two of the most recent three-year buying seasons. New customers (cell 4 ) included customers placing orders for the first time in 1987 (any retail value) from the mail order seed catalog company. Yesterday’s customers (cell 5) included prospects who had been customers in o n e or more years during 1982 through 1984, but who had not purchased from the company in the most recent three annual buying seasons. The sampled households were offered a summary of the answers to the study. Respondents provided their names and addresses to receive a copy of the summary. (In February 1988, a two-page summary of the results was sent to each respondent requesting the results.) To further encourage response, a drawing was mentioned in the cover letter: 10 persons responding to the survey would receive a gift certificate in appreciation for participating in the study. At the end of the survey the respondent was

asked to check if s/he would like to participate in the drawing of 10 respondents to receive a $50.00 gift certificate for gardening products. (A total of 74 percent of the respondents participated and 10 gift certificates were mailed in February 1988.) T o increase the response rates, in early September 1987 a second cover letter, copy of the survey, and response envelope were mailed. Individual salutations and hand signatures were used in the second mailing. First-class stamps were placed on the return envelopes. In the second cover letter the sampled households were asked to: “Please complete and mail the enclosed survey (if you have not mailed the copy which I sent before).” Mailings were limited to three or fewer households per fivedigit zip code. For more than 95 percent of the cases, nonrespondents to the first mailing could be identified, so that receiving two completed surveys from the same household could be avoided. We believe no duplicated responses occurred in the study.

Response Rates The response rates for each of the customer/prospect categories are summarized in Table 2. The overall response rate was 59 percent. For each question the distributions of the responses of the 650 respondents to the first mailing were compared to the responses of the 218 second mailing. The distributions of the responses were not different statistically in nearly all comparisons.

TABLE 2

Response Rates Category of Customer/Prospect

I . Today‘s regular small-order customers

First Mailing

Second Mailing

Returned to Sender

Useable

Total

148 50

45 15.20

0 0

193 65.20

193/296 65.20

2. Today‘s regular medium-order customers Percent

127 43.05

45 15.25

0 0

172 58.30

172/295 58.30

3. Today‘s special customers

I28 43.54

45 15.3 I

1

0.34

173 58.85

174/294 59.19

4. Today’s n e w customers Percent

145 48.99

43 14.53

0 0

I88 63.52

188/296 63.52

5. Yesterday’s customers Percent

I02 34.93

40 13.70

5 1.71

I42 48.63

147/292 50.34

Total all categories Percent

650 44. I3

218 14.80

6 0.4 1

868 58.34

874/ 1473 59.34

Percent

Percent

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VOLUME 5 NUMBER 2 SPRING 1991

Consequently, the responses to both mailings were combined for analyzing the results of the study. Given that yesterday’s customers is the only category that may include some households who are not active product users-that is, n o longer gardening-a lower response rate might be hypothesized for this customer/prospect category compared to the others. Such a finding did occur. The response rate (50 percent) among yesterday’s customers was significantly statistically lower compared to any other customer category. Analyses

Multiple discriminant analysis (MDA) , analysis of variance (ANOVA),and cross-tabulations were performed to examine how the five-group life cycle classification is associated with buying behavior, demographics, psychographics, and reasons for gardening. Only the ANOVA and cross-tabulation findings are reported in the results section, because these results provided more depth about how the customer/prospects groups differed across the independent variables.‘

RESULTS

Applying backward segmentation indicates that one or more of the comparisons between the five customer/prospect portfolio respondent groups were significant statistically for most of the buying behavior, demographic, and lifestyle questions. The responses differed among the five categories for only a few reasons for vegetable and flower gardening. Buying Behavior A summary of the results for the key buying behavior variables appears in Figure 2. Each of the columns of cells in Figure 2 includes one or more compar-

isons that are significant statistically. For example, while the majority of all five customer/prospect categories reported buying vegetable and flowering plant seeds in 1987, note in the second column that 92 percent of today’s regular, small customers (I) versus 72 percent of yesterday’s customers reported buying vegetable seeds (X2 > 6.00, 1 d.f.,p 4 001). The M D A results are available by request from either author

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This analysis was a planned comparison: all of the senior managers at the mail order seed catalog had the prior expectation that today’s small, regular, customers were most likely to be oriented to vegetable gardening. Yesterday’s customers were expected to include the lowest shares of vegetable and flowering seed gardeners. Note also in the second column of Figure 2 that a greater share of today’s special customers only buy flowering seeds versus vegetable seeds. This stronger orientation toward flowers among today’s special customers carries over to bulb buying behavior (shown in the third column of Figure 2 ) . The highest share of customers/prospects buying bulbs is among today’s special customers (79 percent) and the lowest share is among today’s, regular, small-order customers (43 percent). A finding that surprised several senior managers at the mail order seed catalog firm was the high share of bulb buying at garden centers by today’s special customers (29 percent), a share of customers similar to the shares buying bulbs in the other four customer/prospect categories. Thus, offering a high-quality product line in bulbs is associated strongly with the buying behavior of the firm’s most profitable customers. Offering a strong vegetable product line is associated strongly with the firm’s largest segment of customers, today’s regular, small-order customers. Offering a catalog that includes an equal balance of vegetable and flowering seeds may appeal in particular to today’s regular, medium-order customers and new cus tomers. Even though today’s regular, small-order customers are oriented more often toward vegetable than flowering seeds, only 14 percent of them reported buying more than $25.00 in vegetable seeds from all retail sources. These small-order customers, as well as yesterday’s customers more often have small total orders from all sources compared to the other three customer/prospect categories (see the last two columns in Figure 2 ) . Today’s special customers dominate the large orders of flowering seed purchases: 32 percent reported buying more than $25.00 in flowering seeds from all sources, a purchase share more than double the shares of any other customer/prospect category. Offering a special catalog of high-qualityand unique bulbs and flowering seeds for today’s special customers may be a viable strategy.

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Target Market Categories

s-ay

Categones

-

Today's Regular I

Today's Regular I1

-

Today's Special

-

%Buying Bulbs at aW Mail order/ Gardencenter

products

Purchased Vegetable/ = 92%/77%

%Buying %Buying %Bueg >$25 >$w h m MaJor Vegetable Flower Competitor Seeds seeds +

-

43/21/22

- 5 4 -

14

-

26

- 11

Vegetable/

-

~~~~~~~

- Vegetable/ Flowers =

-

55/33/23

75/49/29

75%/84%

-46-

-

51

26

32

Vegetable/

New

- Flowers = 84%/84%

Vegetable/ Yesterday's ~l~~~~~= Customers - 72%/59%

Noneustomers

7

1

-

58/25/26

-43-

18

- 15

51/21/30

-46-

9

-

-

5

-

-

FIGURE 2 Backward Segmentation: Buying Behavior

Demographics

Demographic comparisons for the five customer/ prospect categories appear in Figure 3. All the column comparisons include o n e or more significant statistical differences among the categories. The relative youth of the new customers (55 percent under 45 years old), their recency in living in the present home (53 percent less than 6 years), and their relatively low incomes (only 10 percent with incomes greater than $60,000), may suggest a viable market for a catalog for the middle-income, recent house buyer who may want information on house and garden design layouts, and substantial product purchase and care information. The shares of high-income earners and college graduates are highest among today's special customers. A lower share of these customers is retired from the labor force compared to the two categories

14

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of today's regular customers. Given the upscale demographic pattern associated with today's special customers, they are likely to have different media reading and television viewing behaviors compared to customers/prospects in the other four categories. Consequently, a unique media vehicle schedule may be necessary for effectively reaching today's special customers. Product-Specific Psychographics

The key responses to the psychographic questions among the five respondent groups are summarized in Figure 4 . Each of the column comparisons in Figure 4 includes o n e or more significant statistical differences. In Figure 4 , all mean differences greater than 0.3 are significant statistically ( p < .05). The psychographic results summarized in Figure 4 complement and extend the buying behavior re-

VOLUME 5 NUMBER 2 SPRING 1991

Target Market Categories

summary

Categories

Today‘s Regular I

% Age

% Income

<45

>$so,ooO

32

% Years fibgin

Home<6

%with %College % Young Education PrOfessionalS, Children completed &timd

- 13 - 19 - 19 - 3 4 -

14,35 -i

Customers

Today‘s Regular I1

25-

-

16

-

21

I

Today’s Special

New

Yesterday’s Customers

-

-

- 16 -

- 39 -14,37 - 47 20,29

- - - - . . 31

24

21

55-

10

- 53-

38-

14

- 24-

13

28-

34

23,23

29-

43

18,32

i

Noncustomem Prospects

FIGURE 3 Backward Segmentation: Demographics

sults presented in Figure 2. For example, planting flowering buIbs in the fall is widespread among today’s special customers in both absolute and relative terms. Note that the average is 4.4 for this activity among today’s special customers in Figure 4 , substantially above each of the average responses for the other four customer/prospect categories. While bulb planting in the fall is reportedly done by more than two-thirds of today’s special customers, the majorities of each of the five customer/ prospect categories also reported that they were very likely to plant bulbs in the fall. Details on the distributions, means, and standard deviations of the responses for each of the five categories for planting flowering bulbs in the fall are included in Table 3 . Note in Figure 4 that only for yesterday’s customers is the average reported rating of buying many

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products from mail order lower than the average rating for buying many products from garden centers or nurseries. Thus, yesterday’s customers are not likely to have switched heavily toward buying from other mail order catalog retailers, but rather, to be more oriented to buying from retail stores. Reasons for Gardening The significant differences in average votes assigned to reasons for vegetable gardening are summarized in Figure 5. All column differences in the means in Figure 5 which are greater than 0.3 are significant statistically ( p < 05). ’Thus, the average votes assigned to have “fresher vegetables” was significantly higher among today’s regular, small-order, customers compared to n e w customers, and yesterday’s customers (see the second column in Figure 5 ) .

VOLUME 5 NUMBER 2 SPRING 1991

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Buy Mainly by Mail

summary Categories

Plant Flowering

Order

Bulbe

Regular I J

-

Today‘s Regular I1

-

Today’s Special

New

Yesterday‘s Customers

- 4.5,4.4 -

4.3,4.4

- 4.3,4.1 -

4.1**, 3.6**

-

3.5

3.7

-

- 3.6,2.6 -

4.3

-

3.8

1

3.0 **,3.5

-

4.3

-

4.4 **

3.9

- 3.4,2.9 -

4.0

-

3.6

4.2 **

- 3.5, 2.9 -

3.1 **

-

3.7

Noncustomers ** Difference between means significant ( p < ,051among one or more customer prospect categories

Prospects

including this mean versus others

FIGURE 4

Backward Segmentation: Psychographics,Very Unlikely

11 ) to

“For the fun/joy of working in a vegetable garden” received a lower average number of votes among yesterday’s customers compared to each of the other customer/prospect categories. However, the fun/joy reason was still o n e of the three reasons receiving more than an average of 1.0 among yesterday’s customers. Saving money is not a reported key motivator among any of the five categories, even though the difference in the average responses of 5 versus .2 among today’s regular, small-order, customers and today’s special customers is significant statistically ( p < .05). These results lead to the conclusion that “better tasting and freshness” is a positioning association that is necessary to build and maintain with customers for a vegetable seed direct marketer. For planting flower seeds o r flowering bedding

16 JOURNAL OF DIRECT MARKETING

Very Likely (5) Scale

plants (results not shown in a figure), one reason dominated among all five respondent categories: to beautify and decorate around my home and landscape. This reason received an average of 4.3 votes among all the respondents; the averages did not differ among the customer/prospect categories. The only differences in the average number of votes awarded was for yesterday’s customers in comparison to all other groups. “Exercise, to help me keep fit” and as a “hobby, to relax, reduce my anxiety or job pressures,” received low average votes among yesterday’s customers as reasons for planting flower seeds or flowering bedding plants (0.3 and 0.9, respectively, compared to 0.7 and 1.4 average responses for these two reasons, respectively, among respondents in the other customer/prospect categories).

VOLUME 5 NUMBER 2 SPRING 1991

TABLE 3

I Usually Plant Flowering Bulbs in the Spring ~~

Very Unlikely

Unlikely

Not Sure

1. Today’s regular small-order customers

18%

9%

6%

2. Today’s regular medium-order customers

15

4

7

6

2

1

22

68

148

4. Today’s new customers

23

4

4

32

37

164

5. Yesterday’s customers

14

8

4

39

34

iI 2

Total

16%

5 Yo

5%

29%

46%

719

Category of Customer/Prospect

3. Today’s special customers

X z = 62.92, d.f. = 16, p

Category of Customer/Prospect

Mean

Very Likely

Total

22%

45%

152

31

43

143

< .ooO

S

F

d.f.

P<

9.31

4‘7 14

.ooo1

1. Today’s regular small-order customers

3.7

1.55

2. Today’s regular medium-order customers

3.8

1.41

3. Today’s special customers

4.4

1.06

4. Today’s new customers

3.6

1.57

5. Yesterday’s customers

3.7

1.39

Total

3.8

1.45

CONCLUSIONS AND RECOMMENDATIONS

Examining the usefulness of a life-cycle classification and analysis of customer relationships in direct marketing has been the primary focus of this article. The classification and analysis of customers categorized into new, regular, yesterday, special, and tomorrow segments is likely to result in substantial differences not only in buying behavior, but also in demographic and psychographic profiles. Such analysis is likely to lead to important new product development, positioning, and refinements in target marketing. For the customer/prospect database examined, the value of backward segmentation classifications of new-regular-special customers and yesterday’s customers is supported empirically. Based o n the results described, the decisions made by the senior managers for the direct marketing seed catalog company included a reduction in the marketing resources invested in attempting to re-attract yesterday’s customers; the decision to continue to price very competitively to retain today’s regular, smallorder customers; and the decision to expand new

JOURNAL OF DIRECT MARKETING

Likely

product offerings and catalog mailings of both fall and spring bulbs to today’s special customers. The results of the study respective to psychographics and reasons for gardening were used for planning catalog and magazine illustrations and writing advertising copy. The empirical results presented here are limited to o n e direct marketing enterprise. The results are likely to vary substantially due to industry, economic and competitive conditions, and specific firm factors (product, pricing, and marketing strategies). The key strategic implications include the following points. Small-order customers are likely to have different buying and use behavior compared with large-order, special customers. Small-order customers are likely to be more price-sensitive, while large-order customers are likely to be more product-sensitive. Yesterday’s customers are most likely to be insensitive to all marketing actions compared to current customers. Senior management should actively consider planning and implementing separate marketing strategies to effectively influence the buying behavior of new, small-order, and large-order customers. The impact of product and marketing resource al-

VOLUME 5 NUMBER 2 SPRING 1991

17

S-arY Categories

Target Market Cat.’4ZO*es

-

Today’s Regular I

-

-her Vegetables

1.7** -

Better Tasting,

Buality

Fun/Joy 1.6

Vegetables

-

Activity my Save Money FamilyCan onFood D o T o ~ ~ & e r Exwnses

1.5

-

.1

-

.5 **

1.9 **

-

.1

-

.4

4

-

Today’s Regular I1

-

Today’s Special

-

New

-

-

1.5

1.6

1.2

-

-

1.6

1.4

1.6

-

-

1.6

1.3

-

-

.4

**

-

.2

.3

-

.3

.1

-

.4

2

Yesterday‘s Customers

-

1.2

-

1.1 **

-

1.1

-

Noncustomers ** Difference between means significant [p < 05) among one or more customer prospect Categories includmg this mean versus others.

FIGURE 5

Backward Segmentation: Reasons for Vegetable Garden Using a 10-Point Constant-Sum Scale locations o n the responses to each category of custorners/prospects needs to be assessed. Unique segments of an enterprise’s customer portfolio may be expected to contribute at substantially different rates to sales, market share, and profit objectives of the firm. W REFERENCES

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18 J O U R N A L OF DIRECT M A R K E T I N G

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V O L U M E 5 N U M B E R 2 SPRING 199 I

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JOURNAL OF DIRECT MARKETING

Buying Behavior, ed. Arch G. NIoodside, Jagdish N. Sheth, and Peter D. Bennett, NY: North-Holland, 67-76. 19. Rost, Cecile and Salle, Robert (19891, “Customer Portfolio Analysis as an Opportunity to Improve Marketing Strategy: A Case Study,” Ecully Cedex, France: Lyon Graduate School of Business, working paper, unnumbered. 20. Sevin, Charles H. (1965), Marketing Profitability Analysis, NY: McGraw-Hill. 2 1 . Weiss, Michael J. (1988), The Clustering ofAmerica, NY:

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19