benefit segmentation in industrial markets

J BUSN RES 1986:14:463-486

463

Benefit Segmentation in Industrial Markets

Rowland T. Moriarty David J. Reibstein Harvard University The Wharton School, University of Pennsylvania

This article investigates whether or not traditional bases of industrial segmentation, such as SIC codes and company size, produce segments that are homogeneous within and heterogeneous between with respect to benefits sought. The study is applied to the acquisition of nonintelligent data terminals. We discovered that the traditional bases do not yield segments that seek significantly different dimensions. Alternatively, a benefit segmentation approach is demonstrated that results in segments substantially different from the traditional approach.

Introduction

Ever since the pioneering work of Wendell Smith [20], increasing attention has been given to the concept of market segmentation. The need for segmentation as a strategy is rarely questioned. The process for attaining segments, however, remains less straightforward. Over the past 20 years, the focus of the majority of segmentation studies has been on determining the appropriate bases for forming market segments.

Earlier studies used demographics as bases for segmentation, whereas later ones used benefits sought. Most of the studies and applications reported in academic journals have been in consumer goods markets, although the concept of market segmentation is equally relevant for industrial goods [16, 22, 231.

Although the literature reflects wide acceptance of the concept of benefits sought as a viable and perhaps as the superior basis for segmentation, practitioners, particularly in industrial goods markets, have not overwhelmingly adopted this approach. The primary reasons they have not are 1) they have become accustomed to using other criteria, such as SIC codes and customer size; 2) they can readily identify which segment a customer (organization) belongs to by using these traditional criteria; 3) data are easily available on traditional criteria; and 4) traditional criteria may serve as reasonable bases for forming near or quasi-benefit segments. This article focuses on the latter point.

Segments formed based on traditional criteria do result in segments whose mem- bers seek the same benefits or have the same marketing mix needs. (By traditional criteria we mean non-benefit-based variables, as reviewed in the next section.) For

Address correspondence to Rowland T. Moriarty, Graduate School of Business Administration, Harvard University, Boston, Massachusetts 02163.

Journal of Business Rcscarch 14, 463-4X6 6) Elscvicr Science Publishing Co.. Inc. 19X6 52 Vaderhilt Ave.. New York. NY l(M)17

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464 J BUSN RES 19X6:14:463-4X6

R. T. Moriarty and D. J. Reibstein

example, trucking firms can expect firms selling perishables (SIC code is the segmentation basis) to be most concerned about the attributes of speed of delivery and refrigeration capability. Customer firms shipping large quantities (size as segmentation basis) are presumably most interested in price and reliability, whereas small shippers may be concerned about willingness to ship less than a truckload of goods.

The preceding example illustrates the ways in which traditional segmentation bases may serve as surrogate means for achieving a benefit segmentation. But not all cases are as obvious or as straightforward as the example. The purpose of this article is to test whether or not traditional segmentation approaches yield benefit or quasi-benefit segments. The problem is tested in an industrial setting, an often neglected area for segmentation research and one in which the traditional approaches prevail.

Traditional Methods of Segmentation

According to Kotler Ill], “Market segmentation is the subdividing of a market into distinct subsets of customers, where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix” (p. 195). The largest problem is how to subdivide the market. The criterion for appropriate segmenting most often cited is that the segments be “homogeneous within and heterogeneous between” [7, 61.

We presume that the homogeneity within and heterogeneity between is with respect to responsiveness to marketing variables or benefits sought, although the result is rarely tested.

In consumer markets, researchers began with demographic and socioeconomic variables, such as age, income, and education, as the basis for segmentation. Seg- mentation bases now include personality and lifestyle, attitude, behavior, product usage, and purchase pattern variables.

In industrial markets, similar variables have been applied to organizations rather than to individuals to serve as segmentation bases. Rogers and Shoemaker [18] provide a list including variables relating to the size and the prestige of the organization, the degree of specialization, the financial stability, the decision-making process, and several variables relating to the specific decision makers in the organization. (For a complete review of the literature as well as a new conceptual approach to this topic see Bonoma and Shapiro [4]). In reviewing the literature, Wind [23] contends, “In building an organizational segmentation mode, the variables included should be not only the characteristics of the relevant organizational decision-making units (DMUs), but also organizational characteristics such as size and SIC” (p. 319). Excluded from these lists of traditional segmentation bases are direct measures of needs or responsiveness, which would include work on responses to specific marketing variables, like that done by McCann [ 121, and Green, Carroll, and Carmone’s [8] componential segmentation, which is considered a combined form of benefit and traditional bases of segmentation. The traditional variables, however, indirectly lead to segmentation by needs or responsiveness. The assumption in using any one or a combination of these variables is, again, that when the market is divided into subsets based on these variables, the subsets are homogeneous within and heterogeneous between with respect to benefits sought from the product category.

Benefit Segmentation in Industrial Markets J BUSN KES 19X6: 14:463-4X6

Benefit Segmentation

Because the ultimate objective of segmentation is to produce segments that are homogeneous within and heterogeneous between with respect to benefits sought, a logical alternative is to begin the segmentation by grouping consumers or organizations based directly on measures of benefits sought. This approach was appro- priately named “benefit segmentation” by Russell Haley, who in 1968 described it thus:

An approach to market segmentation whereby it is possible to identify market segments by causal factors rather than descriptive factors might be called “benefit segmentation.” The beiief underlying this segmentation strategy is that the benefits which people are seeking in consuming a given product are the basic reasons for the existence of true market segments. [9]

Since then, benefit segmentation has become widely adopted by consumer mar- keters. Little explicit evidence in the literature, however, shows that this approach has been used in industrial marketing.

Although the benefit segmentation technique is quite straightforward, some barriers may inhibit its use. First is the data requirement. Data for the traditional segmentation approaches are usually readily available. Frequently, the marketer already has such data or can obtain the needed information through the Bureau of Census or syndicated data sources. This is not true for such specialized segmentation approaches as product-specific attitudes, product usage, or benefit segmentation. In these cases, special data must be collected in order to develop the segmentation strategy.

A second problem relates to the criterion that the segments must be identifiable and accessible. Once the market has been divided into benefit segments, to identify to which segment a potential customer belongs without measuring his or her sought benefits is difficult. Further, although the segments may be homogeneous with respect to their desired benefits, they may not be with regard to their media exposure. To try to target a message to any one segment might then prove inefficient.

Research Question

If benefit segments are isomorphic with traditional segments, then the resulting segments could be easily identified and reached and a distinct offering devised to satisfy the needs of a large share of a segment. Unfortunately, whether or not the traditional segments yield subsets that are homogeneous within and heterogeneous between with respect to desired benefits remains unknown. The objective of this article is to test this issue.

Hence, the central hypothesis of this study is:

H,, :&=I?, ,

that is, the set of benefits sought by segment i is the same as that sought for segment j for all i # j, where the segments are formed based on traditional bases for segmentation. The hypothesis will be rejected if the desired benefits are heterogeneous between segments and homogeneous within. If the hypothesis is not rejected, we must conclude that traditional forms of segmentation do not yield appropriate segments based on the aforestated criterion.

466 J BUSN KES lwi: 14:463-4X6


Research Methodology

Information was obtained from approximately 300 companies that had recently (within the last 24 months) made a major acquisition of “dumb” or “nonintelligent” data terminals. The choice of this particular product/market resulted from a series of related decisions. The first decision dealt with whether or not the research should investigate a prospective buying decision or one that had already taken place [S]. The implicit assumption of research based on prospective decisions is that complex organizations will behave the way people think they will behave. Because the purpose of this research was to investigate actual organizational buying behavior rather than perceived or potential organizational buying behavior, the use of a prospective buying decision was rejected in favor of a retrospective approach.

The next decision involved the specification of a particular product or product category. In 1979, Johnston [lo] investigated the decision-making units in 32 companies by asking each company to select a recent purchase of an industrial product and an industrial service. Johnston’s research design is useful for comparing buying behavior across broad product categories (i.e., industrial services vs. industrial products). For our research, however, we felt that the purchasing decision should be held constant so that variations in the buying criteria could more reliably be attributed to environmental, organizational, individual, and situational variables independent of the product being purchased. Given this decision to investigate a specified product procurement, the following criteria were established for choosing a product/market that would maximize the generalizability of the study:

The product exists in a competitive market;

The purchase of the product is relatively important to the operation of the acquiring company;

In the buying companies an active purchase decision process exists that could potentially involve a number of functional areas and a number of levels of management; and

The product has a broad target market; it is purchased by different types of businesses and different sizes of businesses.

Major acquisitions of data terminals met all of the above criteria. Initially, defining the term “major” proved difficult, because what constitutes a major acquisition for a small firm might be a simple add-on acquisition for a larger firm. Because the addition of one or two terminals to a large system usually does not involve a search or vendor evaluation procedure, this type of “automatic rebuy” situation needed to be avoided. Qualitative research identified four different types of buying situations for data terminals:

1. Pilot; 2. Implementation; 3. Replacement (Swap Out); and 4. Expansion.

As a direct result of the qualitative research, potential respondents were asked to categorize their recent acquisitions. All expansions were considered ineligible and the minimum purchase for inclusion in the sample was set at three terminals.

Benefit Segmentation in Industrial Markets J BUSN KES lYXh:l4:46,3-4X6

Although the market is migrating toward more intelligent terminals, nonintelligent terminals were selected because they account for the vast majority of terminals being sold (SO-90%). The result of this product/market selection process is a purchase decision that involves:

Decision-making units that vary considerably in size and complexity from company to company;

A capital good that is neither a low-technology nor a high-technology product; and

An amount of risk that can vary considerably from company to company depending on the perceived importance of the information system.

The focus on the “dumb” data terminal market provides the research with a broad target market while holding constant many of the extraneous variables that would be introduced if the purchase were unspecified.

Data Collection

The first objective was to create a sampling frame through which approximately 300 decision-making units involved in a data terminal acquisition could be identified. Five industry segments, based on two-digit standard industrial classification codes, were selected for inclusion in the universe: Manufacturing, Transportation, Whole- sale/Retail, Finance, and Business Services. Corporations belonging to these segments were stratified into three size categories depending on the number of employees (as listed in the Dun and Bradstreet file) in the company.

The data collection process, which began in May 1979, lasted six weeks and consisted of three phases:

Phase I: Contacting companies from the Dun and Bradstreet listings to determine their eligibility and their cooperativeness;

Phase II: Identifying and screening the decision participants in each of the eligible decision-making units by telephone; and

Phase III: Surveying by mail each of the decision participants.

Some 6122 Dun and Bradstreet company listings were required to produce 319 eligible decision-making units (DMUs) for the study. An eligible DMU was defined as one that met the following criteria:

1. The company had made a major procurement of nonintelligent data terminals within the past 24 months. The minimum number of terminals purchased was three and the purchase was not an expansion of an existing system of data terminals.

2. More than one person had to be involved in the DMU. For DMUs consisting of fewer than five people, all of the people had to have been contacted by telephone. For DMUs consisting of five or more people, at least 51% of the people had to be screened by telephone (Phase II) to insure DMU representation.

468 J BUSN RES 19X6: 14:463-4X6

Table 1. DMU Distribution


Company Size By Employees

Industry Segment 1 W-249 250- 1000 Over 1000 Total

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Wholesale/Retail

Finance

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12 25

4% 8%

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44

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7

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76

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92

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65

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319

100%

“The upper number in each cell is the number of responding DMUs and the lower number is the percentage of

responses relative to the total sample.

Of the 6122 Dun and Bradstreet listings, 859 (14%) were noncontactable (gen- erally due to an out-of-date listing or an erroneous listing) and 240 (4%) refused to participate. Both figures are relatively low for a study of this nature [25].

Of the 5023 companies interviewed, 1601 (32%) had not made a recent major purchase of nonintelligent data terminals. The largest problem, however, was that a suitable primary respondent could not be identified in 2528 (50%) of the companies screened. Although no additional information was collected from these companies, no noticeable differences were found on objective dimensions from those in which primary respondents were found. Whether or not their noninclusion introduces a potential bias into the sample is unclear. Those companies dropped, because the purchase was fewer than three terminals or an expansion, made up less than 8% (388) of the companies screened. Finally, 97 companies (2%) were dropped because they were single-person DMUs.

Of the remaining DMUs, 319 met all the criteria listed above. Their distribution by employee size and industry is shown in Table 1. Large companies (over 1000 employees) represent over 50% of the sample. The Business Services, Whole- sale/Retail, Finance, and Manufacturing sectors each represent between 20% and 30% of the sample. Finance is the largest group (28.8%). The Transportation sector is significantly underrepresented (2.2%) due to the small number of transportation companies listed by Dun and Bradstreet (For more details on the sampling plan, see [14]).

The 319 eligible DMUs represented a total of 1670 individual participants. Each of these individuals was mailed a questionnaire within five days of the telephone interview; 663 people responded. Those questionnaires from which significant data were missing were excluded from further analysis. (For the cases included with slight missing data, average values were assumed for the absent variables.) This step reduced the number of observations from 663 to 489 individuals. All subsequent discussion deals strictly with these 489 respondents.

Benefit Segmentation in Industrial Markets J BUSN RES 1986: 14:463-486

469

Development of Measures

Before the data-collection effort began, two focus group interviews were conducted by Booz, Allen and Hamilton in the Philadelphia area. One of the purposes of these interviews was to develop a set of attributes commonly used to evaluate product offerings in the nonintelligent data terminal market. These interviews led to the development of the final questionnaire.

A number of company and individual descriptors, product-usage characteristics, and product-attribute importances were gathered from each respondent. A necessary step was to develop a measure of importance of attributes/benefits sought. Importance can be measured in a variety of ways. One would be to measure importance directly. In this study, however, a measure was desired that would identify the factors that play a role in the choice among suppliers. Direct measures of importance may not capture this aspect, because the role a factor plays in the decision process also depends on the perceived variability of the factor across suppliers. That is, an individual may rate highly the importance of price, but if all companies in the industry charge the same price, then this factor will not play an important role in determining which vendor is selected.

A second way to measure importance would be to have each respondent rate all of the suppliers across each attribute and provide a measure of likelihood of supplier choice. To regress the likelihood of choice on the attribute perceptions would then be possible; the resulting coefficients would serve as measures of discriminating importance. For full details of this approach, see McCann, Reibstein, and Wittink [13]. But this approach has extensive data demands. An alternative approach, less demanding of data, is to develop determinancy scores based on the direct importance rating and a rating of attribute variability across suppliers. This was the approach used.

As shown in Figure 1, each respondent was asked to rate 33 different selection criteria in terms of their importance to him or her and their variability across suppliers in the industry. Responses were given on a scale from one to six, the higher end of the scale representing high importance or high variability. A final set of determinancy scores was derived by multiplying, for each attribute, the importance rating by the industry variability rating. Alternatively, the two scores could be added together, but the choice of addition implies different things about how importance and variability combine. As mentioned above, the determinancy or saliency of an attribute in the decision process is a function of two things: perceived importance and perceived industry variability. High determinancy must be characterized by both high personal importance and high industry variability.

We realized that different people will employ different intrinsic importance scales, such that a rating of four represents high importance to one individual and low importance to another. To eliminate this potential bias, each individual’s ratings were normalized about a common mean of six prior to the creation of the determinancy score [l]. Normalization was done by subtracting from each rating the mean of all 33 ratings of that person and then adding the constant of six so that the normalized ratings had a positive value. No information loss is associated with this normalization procedure, because the focus of the research is to understand the perceived relative determinancy of the product attributes as opposed to their absolute determinancy.

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472 .l BUSN RES 1986:14:463-486


Table 2. Ranking of Attributes by Determinancy (N = 489)

Mean Determinancy

Rank Score Attribute

1 46 2 46 3 45 4 44 5 44 6 43 7 40 8 40 9 40

10 40 11 39 12 39 13 38 14 37 15 37 16 36

17 36 18 36 19 36 20 35 21 35 22 35 23 35 24 34 25 32 26 31 27 31 28 29 29 29 30 29 31 28 32 26 33 26

Service response time Service available at point of need Overall quality of service Quality of software support Competence of service representatives Reliability of product (up time) Ease of installation into your system Compatibility with your present system Compatibility with future systems Financial stability of the manufacturer Price/performance Provision of mainframe software support Ease of maintenance designed into product Ability to keep delivery promises Delivery (lead time) Compatibility with other makes of terminals (for replacement or addition) Ease of operation Throughput speed Cost of mainframe software support Cost of service contract Speed of output Salesperson’s competence Type and level of language available Offers a broad line of hardware Amount of operator training required Visibility, size, and color of screen Terminals are the lowest priced Number and position of characters on keyboard Vendor’s willingness to negotiate price Offers savings in operator costs Vendor visibility among your top management people Vendor offers large volume discounts Aesthetics of product (style, design, color size)

Because both the importance and variability measures were centered about the value of six, the resultant determinancy scores are centered about the value of 36. Thus, a determinancy value greater than 36 indicates that a factor plays a relatively more important role in the decision-making process. Likewise, a rating lower than 36 indicates a less important factor. Table 2 ranks the 33 attributes/benefits sought according to their average score across all 489 respondents. From this ranking one can see that service is the single most important area in the decision-making process of the 489 individuals. The top three selection criteria are all service-related attributes. Another important buying criterion is compatibility, whereas the aesthetics of the terminal are ranked as the least important factor overall. Note that the values in Table 2 represent averages across the entire group of observations (certain subgroups may vary significantly from the averages).

Benefit Segmentation in Industrial Markets J BUSN RES IWh: I4:4h3486

Reduction of Attribute Set

Attribute evaluations may be collected at different levels of specificity. For example, a respondent could be asked to evaluate the importance of either delivery in general or more specific components of delivery, such as delivery lead time and the vendor’s ability to keep delivery promises. Delivery can be considered a “macroattribute,” whereas the more specific attributes of delivery lead time and the vendor’s ability to keep delivery promises can be termed “microattributes.” Although collecting data on the 33 microattributes shown in Table 2 created a more tedious task for the respondent, this approach offers two important advantages over collecting data on a few macroattributes:

1. If specific microattributes are highly correlated, then they can then be ag- gregated into a macroattribute. To collect macroattribute data and then dis- aggregate it, on the other hand, is not possible;

2. Future research on this costly data base might require highly specific product attributes.

Although the data were collected on 33 attributes, these attributes may in fact be representing a smaller and more manageable set of more global underlying constructs used in the evaluation process. To produce a more workable number of product attributes, we decided to explore the potential for aggregating some of the product attributes based on:

0 A correlation matrix; and

0 The results of various clustering programs.

Cluster analysis was applied to the correlations between each attribute. The clustering algorithm was the AQD agglomerative method, using the minimum squared error as the criterion for clustering. This resulted in a reduced set of 14 variables formed as equally weighted composites of the clustered variables. Three different clustering algorithms in the AQD package were used, all resulting in the same general pattern, as shown in Figure 2. Factor analysis yielded similar results, which are not presented here because of space limitations.

Four variables were not used from the original set of 33 due to a lack of stability. Follow-up interviews with respondents indicated a lack of clarity on the interpre- tation of these attributes. Five other variables were left in their original form, because they exhibited a high degree of independence. Table 3 shows the ranking of the reduced set of 14 attributes across all respondents.

As a check of the face validity of our measures of the importance of the attributes in the choice process, or benefits sought, the measures were compared between respondents who bought their terminals from IBM and those who bought from other suppliers. In Table 4, the respondents who chose IBM were significantly more interested in software (c~ < .05), a broad line (a < .OS), and manufacturer visibility among top management (cx < .Ol), and significantly less interested in absolute price (0~ < .Ol) and price flexibility (IX < .Ol). These results are consistent with the differences we, and others familiar with the industry, would expect to find between those likely to buy IBM versus those not likely to buy IBM.

474 J BUSN RES 19X6: 14:463-486


ORIGINAL 33 PRODUCT ATTRIBUTES REDUCED SET OF 14

FROM THE QUESTIONNAIRE PRODUCT ATTRIBUTES

USED FOR ANALYSIS

Speed of output

Throughput speed speed

Amount of operator training required

Visibility, size and color of screen

Ease of operation

Operator

Aesthetics of product (style, design, color, size)

Number and positions of characters on keyboard

Ease of installation into your system

Compatibility with other makes of terminals (for replacement or add-on)

Compatibility with future systems 5 Compatibility with your present systems

Competence of service representative -

Service response time

Aesthetics

Service available at point of need Overall quality of service

Service

Delivery (lead time)

Ability to keep delivery promises

Terminals are the lowest price

Price/performance

Vendor’s willingness to negotiate price

Vendor offers large volume discounts

Delivery

3 Absolute Price

l Price Flexibility

Provision of mainframe software support

Cost of mainframe software support

Quality of software support

Software

UNCHANGED PRODUCT ATTRIBUTES

Offers a broad line of hardware

Vendor visibility among your

top management people

F

t

Financial stability of the manufacturer -

Salesperson’s competence W

Reliability of product (“up-time”) -

NOT USED IN THE ANALYSIS

a Type and level of language available

a Cost of service contract a Ease of maintenance designed into product a Offers savings in operator cost

Broad line

Manufacturer visibility

Manufacturer stability

Sales competence

Reliability

Figure 2. Original 33 product attributes compared with reduced set of product attributes.

Benefit Segmentation in Industrial Markets J BUSN RES 1986:14:463-486

475

Table 3. Ranking of Reduced Set of Attributes by Determinancy (N = 489)

Company Size By Employees

Rank Mean Min Max S.D. Range

1. Service 45 20 72 7 52 2. Reliability 45 19 71 9 52 3. Manufacturer Stability 40 4 67 10 63 4. Software 40 11 68 9 57 5. Compatibility 39 13 91 9 78 6. Delivery 37 7 75 9 69 7 Speed 35 11 71 9 60 8. Absolute Price 35 13 76 8 63 9. Broad Line 34 12 75 11 64

10. Operator 33 9 60 7 51 11. Sales Competence 32 9 77 10 67 12. Manufacturer Visibility 28 4 60 11 56 13. Price Flexibility 28 4 69 10 65 14. Aesthetics 28 6 51 8 45

Segmentation Results

Traditional Segmentation Schemes

The most popular method for segmenting industrial markets has been to divide them according to industry sector and company size [cf. 41. For example, all manufacturing firms with more than 1000 employees might be considered a segment. These firms are readily identifiable from lists developed and updated by the Dun and Bradstreet Corporation and other commercial information services. Most of

Table 4. Comparison of IBM and Non-IBM Buyers in Terms of Benefits Sought (bv Mean Determinancy Scores)

Benefits Sought IBM Non-IBM

Buyers Buyers (N = 204) (N = 285)

Total Sample

(N = 489) Rank

Speed Operator Aesthetics Compatibility Service Delivery Absolute Price Price Flexibility Software Broad Line Manufacturer Visibility Manufacturer Stability Sales Competence Reliability

“Significant at the 99% level. hSigniticant at the 95% level.

35 36 35 (7) 33 33 33 (10) 28 27 27 (14) 39 39 39 (5) 45 45 45 (1) 37 37 37 (6) 33 36 35 (8) 26 29” 28 (13) 41 39h 40 (4) 36 33h 34 (9) 31 26” 28 (12) 40 40 40 (3) 32 32 32 (11) 43 43 43 (2)

476 J BUSN RES lYXh:14:463-486

R. T. Moriarty and D. J. Reihstein

Table 5. Comparison of Industry Segments in Terms of Benefits Sought (by Mean Determinancy Scores and Standard Deviations)

SIC Sector

Benefits Sought Business Wholesale/ Services Transportation Retail Finance Manufacturing

(N = 101) (N = 13) (N = 105) (N = 194) (N = 76)

Speed

Operator

Acsthctics

Compatibility

Service

Delivery

Absolute Price

Price Flexibility

Software

Broad line

Manufacturer Visibility

Manufacturer Stability

Sales Competence

Reliability

38” (9.35)h

34 (6.28)

(&

$2)

$3)

$3)

$9)

$5)

(x”‘nl,

(l&)

(lzi5)

(I&

(I&,

$6)

37 (X.62)

$6)

$6) 39

(9.39)

(Z 1)

(l&)

$2)

$5)

(!&)

(lG3) 30

(12.24)

(Kz9)

(zl2)

$9)

33 37 (8.37) (9.89)

33 (6.73) $9)

$9) (6?3) 40

(8.03) (l&

(h45WI) (Z9) 37

(9.10) (;.;I,

(& &7)

$4) (*~~.~~5) 40

(9.33) (lZ7)

(l& 34

(11.17)

(lt%) (lZ5)

$4) 40

(11.35)

(86) 33

(11.41)

(‘z4)

“Average score on this attribute (Speed) for this SIC Sector (Business Services).

‘Standard deviation of this attribute (Speed) within this SIC Sector (Business Service).

these information services classify businesses according to the Standard Industrial Classification Code [ 171.

To illustrate how well this traditional approach segments the market, the decision participants were first segmented by industry sector. The mean determinancy scores of the benefits sought (and the standard deviations) are shown for each industry sector in Table 5. If this were a sufficient method of segmenting the market, it would yield segments that have small standard deviations (homogeneous within) relative to the differences in means (heterogeneous between). By comparison of the columns in Table 5, no statistically significant differences can be found. Thus, the central null hypothesis of this study,

H,, :fi, = I?, for all i#j,

cannot be rejected based on segmenting by industry sector. Similarly, testing the differences based on segmentation by company size (num-

Benefit Segmentation in Industrial Markets J BUSN RES 1986: 14:463-486

477

Table 6. Comparison of Company Size Segments in Terms of Benefits Sought (by Mean Determinancy Scores and Standard Deviations)

Number of Employees

Benefits Sought < 250 250-1000 >I000 (N = 63) (N = 151) (N = 275)

Speed II

(Z4)” Operator

$7) (;k) Aesthetics

&O) (Z9) Compatibility

$922) (Z) Service

(I%) $I*) Delivery

$6) (1:.:6) Absolute Price

(s3.353) $4) Price Flexibility

$6) (Z3) Software

(ZO) (!%I) Broad Line

(I&) (1 Es, Manufacturer Visibility

(46) (IZl) Manufacturer Stability

(l&9) (I&) Sales Competence

(1%) (lZ7) Reliability

(7?3) (;.:I)

“Average score on this attribute (Speed) for this SIC Sector (Business Services). *Standard deviation of this attribute (Speed) within this SIC Sector (Business Service).

ber of employees) cannot cause rejection of the null hypothesis. These results are shown in Table 6.

The conclusion is that traditional forms of segmentation (at least SIC code and company size) may be operationally useful but do not provide an adequate basis of segmentation. These traditional bases, at least for the product category of nonintelligent data terminals, certainly do not serve as surrogates for benefit segments.

Benefit Segments

Benefit segmentation seeks to isolate homogeneous groups of buyers based on the importance of various product selection criteria. In this study, cluster analysis was used to group the respondents based on their determinancy scores for the 14 product attributes. An agglomerative clustering method in the AQD analytic package, with a minimum squared-error criterion, was used. The respondents were neatly grouped into four segments, as shown in Table 7. The names selected for each segment are

478 J BUSN RES 1986:14:463-486 R. T. Moriarty and D. J. Reibstein

Table 7. Comparison of Benefit Segments (by Determinancy Index”)

Benefits Sought

Segment 1: Segment 2: Hardware Brand

Buyers Buyers (N = 79) (N = 75)

Segment 3: People Buyers

(N = 171)

Segment 4: One-Stop Shoppers

(N = 159)

Speed

Operator

Aesthetics

Compatibility

Service

Delivery

Absolute Price

Price Flexibility

0.88

0.86

0.97

0.99

1.02

[--K--J

0.93 1.03 1.06

0.89 r-K-j 0.98

0.86 1.10 0.98

1.00 1.00 1.00

1.07 0.95 1.01

0.95 1.03 0.92

0.94 0.97 0.98

0.94 1.00 1 0.86 )

Software 0.90 1.10 0.90 1 1.11 1

Broad line 0.86 1.09 0.91 1 1.12 1

Manufacturer Visibility

Manufacturer Stability

Sales Competence

Reliability

0.89

1.03

[XT--l

0.97

1 1.52 1 1.00 0.81

1.10 1.02 0.92

0.90 1 ] 1.21 0.94

1.05 0.97 1.02

“Index = the segment mean determinancy score divided by the sample mean determinancy score

arbitrary and merely chosen to reflect the most salient or discriminating dimension for that cluster. The segments are quite clearly distinct from each other. Segment 1 is particularly interested in the price of the terminal and the supplier’s delivery capability and is uninterested in the firm’s sales competence. We presume that this segment is unlikely to buy from IBM, in contrast to Segment 2, which seems to focus on manufacmrer visibility among top management people.

Segments 3 and 4 are larger segments, with the former interested in the supplier’s sales competence and features related to the operator’s interaction with the terminal. The latter segment tends to be slightly more interested in the support the supplier can provide in its products, including broad product line and software, and less interested in price flexibility.

Benefit Segmentation in Industrial Markets J BUSN RES 1%6:14:463-486

Commonality Between Segmentation Approaches

The central problem of this article is whether or not the traditional segmentation approaches, particularly in the field of industrial marketing, do in fact produce segments that are homogeneous in regards to the benefits sought from the product category. As stated earlier, this is an underlying assumption of traditional segmentation approaches.

Table 8 presents the five industry segments and their distribution across the four benefit segments. If the industry sector segmentation approach were an isomorphic representation of benefit segments, 100% of each industry segment would be contained within one benefit segment. This is obviously not the case. In fact, the x- square statistic of x2 = 10.8 indicates that the two approaches are independent of each other.

Similarly, Table 9 shows the three company-size segments and their distribution across the benefit segments. The resulting x2 is 5.75, which again indicates that this segmentation approach is independent of the benefit segments. In other words, neither company size nor industry sector as bases for segmentation yield segments that are homogeneous within or heterogeneous between with respect to the benefits sought from the product category.

Discriminant Model

In addition to showing that it is possible to develop benefit segments for an industrial market, this study demonstrates that traditional methods of segmentation are not a surrogate scheme for the development of benefit segments. Instead, such traditional segments are quite heterogeneous in terms of benefits being sought from the product category. The implication is that a firm selecting a target market segment based on traditional methods will meet with only limited success if it has a single marketing-mix offer.

The obvious solution is thus to segment the market according to desired benefits, given that this result is the ultimate objective of the segmentation strategy. The drawbacks of doing so are that it is neither easy to identify to which segment a potential customer belongs (identifiability) nor easy to reach potential customers (communicability).

Using the traditional approaches, identifying and communicating with target customers is easy. Identifying to which industry a firm belongs and generating a list of firms in an industry via SIC codes is simple. Similarly, communications can be directed to identified segments via trade journals and industry associations.

Ideally, identifying surrogate measures of “benefit segment” membership would be possible. Otherwise, conducting a survey of all potential customers would be necessary in order to classify them into benefit segments, select appropriate target segments (based on a variety of criteria), and develop a mix to approach the target(s). Given that traditional criteria do not yield benefit segments in this case does not mean that other measures might not. Hence, the next step in this study was to attempt to identify variables that could adequately predict benefit segment membership.

First, a list was made of “logical” correlates of benefits sought, which could be used in a discriminant model. These variables fall into four categories:

Tab

le

8.

Dis

trib

utio

n of

In

dust

ry

Segm

ents

A

cros

s B

enef

it Se

gmen

ts

Bus

ines

s

SIC

Sec

tor

Who

lesa

le/

Num

ber

of

Serv

ices

T

rans

port

atio

n R

etai

l Fi

nanc

e M

anuf

actu

ring

Ben

efit

Segm

ents

O

bser

vatio

n”

N

%

N

%

N

%

N

%

N

%

Segm

ent

1 79

10

13

3

4 16

20

38

48

12

15

Se

gmen

t 2

75

19

2.5

0 0

19

2.5

26

35

11

15

Segm

ent

3 17

1 34

20

5

3 41

24

66

39

25

15

Se

gmen

t 4

159

37

23

5 3

28

18

62

39

27

17

Tot

al S

ampl

e 48

4 10

0 21

13

3

104

21

192

40

75

15

“No

te

that

th

e to

tal

nu

mb

er

of

ob

serv

atio

ns

acro

ss

row

s d

oes

n

ot

alw

ays

equ

al

the

tota

l fo

r th

e ro

w

du

e to

m

issi

ng

va

lues

.

Tab

le

9.

Dis

trib

utio

n of

Com

pany

Si

ze S

egm

ents

A

cros

s B

enef

it Se

gmen

ts

Num

ber

of E

mpl

oyee

s

Num

ber

of

< 25

0 25

0-14

00

> 1,

000

Ben

efit

Segm

ents

O

bser

vatio

n”

N

%

N

Segm

ent

1: H

ardw

are

Buy

ers

79

9 11

20

Se

gmen

t 2:

Bra

nd

Buy

ers

75

8 11

20

Se

gmen

t 3:

Peo

ple

Buy

ers

171

20

12

57

Segm

ent

4: O

ne-S

top

Shop

pers

15

9 26

16

52

T

otal

Sa

mpl

e 48

4 63

13

14

9

“Not

e th

at t

he t

otal

nu

mbe

r of

obs

erva

tions

ac

ross

ro

ws

does

not

alw

ays

equa

l th

e to

tal

for

the

row

due

to

mis

sing

va

lues

.

%

N

%

25

50

63

27

47

63

33

94

55

33

81

51

31

272

56

482 J BUSN RES lY86:14:463-486 R. T. Moriarty and D. J. Reibstein

1. Sector of business and size (in terms of number of employees) of company; 2. Individual respondent’s level within the organization and functional area of

responsibility; 3. Characteristics of purchase, such as size of purchase (number of units ac-

quired), type of purchase (pilot or full scale), and choice of supplier; and 4. Individual characteristics, such as years of experience, educational level, age,

and income.

Even though segments based on sector and size were heterogeneous with respect to benefits sought, sector and size were chosen as potential correlates with benefits sought because they have been the most common bases for industrial segmentation [24]. Alternatively, Johnston [lo] found that characterizing a decision participant by vertical and horizontal dimensions is instrumental: the vertical dimension is the level in the organization or steps up the organizational hierarchy, and the horizontal represents the location or function within the level. Choffray and Lillien [5] also found useful segmenting individuals in industrial organizations by level and function within the organization. They explicitly assume that “decision participants who belong to the same category share the same set of product evaluation criteria. . . . “(p. 22). Hence, in our case we might find, for instance, that financial managers may be more interested in the price dimensions, and data processors may be more concerned about some of the operating features.

The third category of variables consists of characteristics of the specific purchase. Both the size of the purchase (number of terminals acquired) and the type of purchase (whether the purchase was intended to be a “pilot acquisition” or a “full- field implementation”) can represent level of risk involved and some of the needs of that specific purchase; i.e., situational factors. We can draw from consumer behavior research on the need for including such variables [2,3]. Sheth [ 191 includes these variables in his model of industrial buying behavior.

Sheth [19] also proposes individual characteristics as potential influences on the buying process. The presumption is that a decision participant’s education, income, and years of experience might all contribute to the risk he or she is willing to take on the job or might influence the benefits sought.

To test whether or not these variables are good indicators of benefit segment membership, a discriminant analysis was employed. The basis for judging the performance of the discriminant models is how well they predict group or segment membership relative to a naive model. The “no-information” naive model would classify everyone into the largest benefit group, Segment 3, the People Buyers. This would yield a correct classification of 35.3% (171 / 484).

Initially, to validate the cluster analysis that led to our four benefit segments, how well the original 14 product attributes would discriminate between the groups was tested. We would be surprised if they did not discriminate well, because they were used as the basis for clustering. The possibility, however, that considerable heterogeneity remains with the benefit segments with respect to the measures of benefits sought is conceivable. Alternatively, the possibility that the benefit segments are, in fact, relatively homogeneous between groups is also possible. If either of these situations is the case (i.e., heterogeneous between or homogeneous within segments), we would not expect the other dimensions listed above to discriminate between groupings.

Benefit Segmentation in Industrial Markets J BUSN RES 1986:14:463-486

Table 10. Discriminant Model Classification Based on 14 Benefits Sought

483

Actual Benefit Group Membership

1

Predicted Group Membership

2 3 4 Total

Segment 1 61 Segment 2 1 Segment 3 3 Segment 4 9 Total 71

Percent accurate prediction = 77.07%.

6 7 5 79 51 17 6 7.5 7 139 22 171 7 21 122 1.59

71 194 155 489

Hence, the validity test was the following equation:

I4 Y = d, + c d, Benefit, ,

,= I

where

Y = benefit segment membership; d,, = centroid of data; d, = the discriminant function; and

Benefit, = the 14 product attributes/benefits described in Figure 2.

(1)

The result of applying Equation (1) is a correct classification capability of 77.07%, as shown in Table 10. Although this predictive ability falls short of perfect classification, thereby indicating some of the heterogeneity within the segments or homogeneity between, overall this result tends to indicate that the groups are distinct, at least with respect to benefits sought. Thus, it corroborates our cluster analysis. All 14 of the benefit factors significantly (0~ < .05) discriminated between the four benefit segments. We would be surprised if Equation (1) did not distinguish as well as it does between the groups.

The next problem is whether or not other variables can be used to distinguish segment membership. The first test of this notion is the following discriminant Equation:

Y = d, + 2 ;; SIC, f 2 hd,EMP, , (2) i=

where

Y=

d,, = d,andd, =

SIC =

1 j=S

EMP =

benefit segment membership; centroid of data for level of deleted dummy variable; discriminant coefficients; dummy variables for four of the SIC codes (the last SIC code is deleted and contained in the d, term); and dummy variables for less than 250 employees and 250 to 1000 employees (the more-than-lOOO-employees level is contained in the d,, term).

As can be expected from the results shown in Tables 8 and 9, discriminant

484 J BUSN RES 1986: 14:463-4X6


Equation (2), based on industry sector and size, does not do a good job of segment classification. The model correctly classifies only 36.8% of the respondents. This provides an improvement over the “naive” model of only 1.50/o, and no variables are statistically significant in discriminating between the groups. This finding is consistent with the conclusions of the preceding section.

The next step was to add the decision participants’ level and function within the organization to the discriminant model. This step improved the discriminant model’s classification capabilities, but only to 39.3%, a mere improvement over the “naive” model of 4%. This was quite surprising, because we expected that, for example, most data processing managers (function) would be seeking the same benefits from the product category. Even within functional responsibilities or level within the organization, the benefits sought apparently varied substantially.

Similarly, a discriminant model with independent variables based on individual respondents’ characteristics-such as years of work experience, educational level, age, and income-did not provide a significant classification improvement over a naive model. In this case again, no variables were significant discriminators.

The last equation attempted was based on some characteristics related to the specific purchase. Included as independent disciminators were 1) the size of the purchase, measured in terms of the number of terminals acquired; 2) whether the purchase was made outright or on a rental basis; and 3) the choice of supplier. This model did the best of our four models in discriminating between the four benefit segments but still was only able to yield a correct classification level of 44%. Further, its variables are the most difficult, of our four models, to identify a priori.

None of the four discriminant models does an adequate job of identifying benefit segment membership.

Conclusions and Suggestions for Future Research

Our intent in this article was to test whether or not traditional forms of segmentation in industrial markets yielded benefit segments. Specifically, did the division of the market by industry (as indicated by SIC codes) or company size (as measured by number of employees) yield benefit segments? If this were the case, it would suggest that a marketing mix can be tailored to a particular industry or company size. What we clearly found in this study, however, was that this presumption does not hold, at least with respect to the product category of nonintelligent data terminals. In other words, traditional bases of segmentation do not act as surrogate measures for benefits sought.

For this article to become a call for benefit segmentation in industrial markets would require the ability to easily identify, on an a priori basis, benefit segment membership. Unfortunately, we were unable to do so with the measures we had available. This is simply to state that for the measures used, extensive heterogeneity existed with respect to benefits sought.

The results provide a practitioner’s dilemma and perhaps a researcher’s delight. The dilemma is how to segment the market in a way that yields customers who will respond favorably to the same marketing mix. This result would allow the development of a product and service level, price, distribution method, and communication message that would have appeal to the target market. As demonstrated in this article, however, finding such a segmentation method is not an easy task.

Benefit Segmentation in Industrial Markets J BUSN RES 19X6:14:463-486

These difficulties almost suggest surveying all of a firm’s potential customers and applying a benefit segmentation approach as was done in this article. However, this is a very costly and time-consuming process. Ideally, surrogate measures for benefits sought would exist.

The researcher’s delight is the failure to identify such surrogate measures. Hence, our research question remains. We have thus done the easy task, that of identifying measures that do not serve as good surrogates, and leave the challenge of identifying measures that do serve as good surrogates to our future work and that of our fellow researchers.

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486 J BUSN RES 19X6:14:463-4X6 R. T. Moriarty and D. J. Reibstein

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benefit segmentation in industrial markets

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