12. Market segmentation in 828 markets

Page 182 - 186, 191 - 207 in

Thomas, R. J. (2012). Business-to-business market segmentation. In G. L. Lilien, & R. Grewal (Eds.), Handbook of business-to­business marketing (pp. 182-207). Cheltenham, UK: Edward Elgar Publishing, Inc.

71/1 *

11 Business-to-business market segmentationRobert J. Thomas

x

Market segmentation is a dynamic business decision process driven by a theory abou

how markets function. The theory is based primarily on economics literature pertainm

to price discrimination developed during the 19205 and 19305 (Chamberlin 1965, Pigo1920; Robinson 1954) but also has been well received in marketing literature (DeSarband DeSarbo 2007; Dickson and Ginter 1987; Frank et al. 1972; Moorthy 1984, Smlt

1956). The theory suggests that customer heterogeneity supports the ex1stence 0demand-based segments from which firms can generate greater responses by Shaping dif

ferent offerings for those various segments, rather than by providing thc same offermgto

the whole market. As a dynamic decision process, it resides in the domain of managena

decision-making and thus can be improved by concepts, methods and tools developeand tested by academics and practitioners.

As a decision process, market segmentation holds the promise of being used in practi 3by managers of a single firm facing competition to pursue business objectlves throug

a more eflicient and effective allocation of resources. This promise appears to have pro\

gressed in BZC marketing, where it has become an imperative to know target consumerwell and build highly focused marketing programs to meet their needs (Yankelov1ch an

Meer 2006). However, managers in B2B markets have been slower to adopt market seg

mentation, beyond just traditional industry or application segments.

Consider the case of Thomson Corporation, as reported by Harrington and T](2008). A provider of information services to organizational customers, Thomson tra

ditionally marketed to its direct customers (corporate information se1v1ces managers

with little consideration for end users who might also influence the purchase of the]

services. The authors contrasted the benefits of going from an industry-based segme

tation involving direct customers to a more comprehensive one based on the needs an

problems of end users. They also studied the work flow of end users, measured thel

trade—OHS for product features using conjoint analysis and clustered the resultant utIIiw

ties to obtain need-based user segments. This information then enabled them to develoinnovative products and marketing plans for target segments, thereby rev1talizi

Thomson’s strategy.What is notable about the Thomson approach is that it is almost identical to th

methodology recommended by Wind et al. (1978) for scientific and technical infonn

tion services — albeit some 30 years earlier! Although there is almost always a lag 1n th

adoption of academic approaches among practitioners, it appears that B2B manager

have been lax in taking advantage of this process. There may be several reasons, ne

the least of which may be difficulties in implementation, as reported by Robertson an

Barich (1992), Dibb and Simkin (1994) and Berg et al. (2009). In this chapter, I revre

B2B market segmentation from both academic and business perspectives to clarify it

potential effectiveness. In the next section, I consider major challenges of usmg B2

market segmentation, followed by a consideration of several activities required to co

182 ,

4

BZB market segmentation 183

te a successful segmentation process. This analysis sets the stage for a discussion ofactions for research that should be considered to improve the process and practice offB market segmentation.

31%”

HALLENGES TO B2B MARKET SEGMENTATIONQ

“s derived from economic theory and marketing conceptualizations, B2B market seg-entation can be summarized as the following belief typically held by marketing aca—nucs and practitioners:

Vt

? A market can be segmented into groups of business customers whose needs (or other bases ofA market response)‘ are similar within-group and different between-groups, and all firms compet—

ing ln this market can extract more profit with offerings that meet the needs of the differentgroups than the same offering to meet total market needs.

(This belief is generally supported by observations that multiple brands competed profit in the same market application. Despite the apparent conviction that B2B

arket segmentation works, there are as of yet no broad—based empirical studies that

ort this belief. A review of prior literature reveals at least three major challenges to

potenual effectiveness: (1) mixed reports on B2B segmentation success in practice;B2B market structure complexities; and (3) variation in defining B2B segmentation

ncesses

; ed Reports on B2B Segmentation Success in Practice

w published comprehensive studies document the effectiveness of business- marketentation in practice.! These studies tend to support the notion that segmentation

work m practice, but not without deeper Q0nsideration of how it might work best in

även Sltuation (Doyle and Saunders 1985). In a classic case study reporting a chance

& experiment for ABB Electric, Gensch et al. (1990) provide compelling evidence ofelfectlveness of B2B market segmentation. In a one-year test, they use a segmenta—approach based on choice modeling applied to two of three geographic districts

nattamed sales increases of 18 per cent and 12 per cent, even as sales declined by 15

cent for the industry overall and 10 per cent in the district without segmentation.

eésales manager in the third district, who declined to participate in the segmentationK each, thus created an opportunity to demonstrate the value of segmentation whileultaneously revealing a key challenge: the importance of cooperation.«fiaser et al. (2004) report a segmentation of hospitals by Hill-Rom, a health care

pment manufacturer and service provider. They segment the market and identify

target segments. Following a redesign of the organization to better meet the needs of

segment, they reallocated the sales force to the two target segments. The results over”o—year period, attributable to the new segmentation scheme as implemented by man-

Tment, included an 11 per cent increase in sales per employee, a customer satisfactionase of 6 per cent and a gross margin increase of 6.7 per cent, yielding more than $70

ion Apparently the firm also anticipated and coped with sales force implementation33/15ms, which indicates that such issues are not insurmountable.

184 Handbook of business-to-business marketing

An even more dramatic segmentation success story is Dow Corning’s segmentation of;Eits silicone market (Gary 2004). As a perennial market leader, it admittedly had become;

complacent. HOWever, the threat of aggressive competitors and increasing costs for inno—vation and service compelled Dow Corning to consider segmentation as an option. In its

segmentation, it learned that one segment no longer needed the added value of service,innovation or a broad product assortment of the silicone products; instead these custom-ers just wanted the best price. Rather than lose them to aggressive price competitors,Dow Coming chose to launch a new brand, Xiameter“), with an Internet-based business

model that could meet the segment’s needs. The needs—based segmentation approach,was so successful that it reportedly paid for itself in three months. The reasons givenfor the success are notable, including the role of the team responsible for implement-ing the XiametercD business model and the way the entire organization prepared for and

managed the new segmentation approach (Gary 2004).Despite these successes, there also have been reports of problems with B2B market

segmentation (Abratt 1993; Berg et al. 2009; Plank 1985). In an ISBM Segmentation

Consortium, Jan Ekonomy (2000) of Sprint described a case study about how segmenta-l

tion can fail to deliver promised benefits. She reported that some segmentation effortsfwere effective but not sufficiently actionable; others were actionable but not effectivef

After analyzing Sprint’s past approaches and segmentation literature, she concluded thatå(1) none of the segmentation approaches used adequately modeled the complexity of the;decision-making process within large business customers; (2) none of them provided aa

holistic view of the business market; and (3) the resources, time and leadership required

for success were significantly underestimated. ,Robertson and Barich (1992) lend credibility to this Sprint example, noting that the%

iresults of well-designed segmentations often include resounding rejections by the salesfforce. Dibb and Simkin (1994) express similar concerns about implementation and

note that it is possible to develop a segmentation that managers and salespeople cannot—implement. As a case in point, they describe a seven-segment solution based on factori

analysis and clustering of 40 supplier requirements in the car parts after-market. Because;no descriptor variables in the study could sufficiently discriminate across the segments,

the segmentation was eventually abandoned 1n favor of a more traditional one, based on»customer types.

What emerges from these examples is that B2B segmentation has worked bettersome cases than in others, perhaps for a variety of reasons. A partial explanation m

be the way managers and salespeople become involved 1n and understand the segmentet,-ågtion process. For example, Dolnicar and Leisch (2010) study 198 Australian marketingämanagers and find some 65 per cent had difficulties interpreting segmentation solutions,;

while many others lacked an understanding of how the data collected could result 1n theidentification of segments. These difliculties may be due in part to the inherent complex

ties of business markets.

B2B Market Structure Complexities

The allure of using segmentation in business markets is promulgated by its apparent:successes in consumer markets. The siren sounded by Robertson and Barich (1992, p.

is clear: ‘Surprisingly, the sophisticated market segmentation methods that power dec å

%%%

BZB market segmentation 185

n making for the consumer products businesses are often strikingly ineffectual in the=idustrial arena’. It is not just that the sales force has difficulty implementing a segmenta-gön approach; it is that business markets are inherently different and far more complex.:structure than consumer markets, which makes them more difficult to understand and

;search. 3Business firms are part of commercial networks, value chains and inter- -organizational

:fld interpersonal relationships that complicate transactions. For example in a value

T? ”ain, business firms as customers’ participate in transactions upstream and down-

seam, whereas consumers as customers are the end of a value chain. Business firms—äten exist to serve other customers that have their own business model to generateffoiit or maintain a budget, whereas consumers seldom resell their purchases for a profit.{fonsumer markets are typically characterized by large numbers of individuals who can

fake numerous purchase decisions in a week or a day and who pay with cash or a credit

” rd. Business markets typically contain fewer customers that make less frequent but;ften larger purchase decisions. They tend to include organizations with complex buying

s enters several decision- makers, influencers inside and outside the buying organization

"d a professional purchasing agent trained to obtain the best price or value for theganization.

;Por example a manufacturer of automotive paint has some 50 potential customers in

ie world, of which the top 15 customers make more than 80 per cent of the 60 millionntomobiles sold globally (International Organization of Motor Vehicle Manufacturers

Q09). The challenge for the paint supplier — segmenting large, complex automotive

anufacturers with multiple product lines offered to millions of potential consumersobally will be very different from that for an automobile manufacturer that must

figment several million consumers who might purchase vehicles. The paint supplier has

& ompound task: to consider the needs and problems of not only its direct customers but

o of its customers, customers (Tang and Mantrala 2010). For the paint manufacturer

be Competitive, it must understand the segmented structure of both the automotivenufacturer market and the consumer market; thus, clearly, the multilevel B2B seg-ntation challenge is a complicated one.

Brown et al. (2007) provide a compelling set of differences between business and con-

gner markets on a continuum of ten dimensions. They propose that B2B markets, more

» n BZC markets, exhibit several general tendencies. Although the focus of their study

é ion branding, the differences are relevant for B2B segmentation too. Specifically B2B,@mpared with B2C, tends to be

higher in buying situation risk;focused more on technological and utilitarian product drivers;more likely to engage in group rather than individual purchase decision processes;

focused on economic and performance risk rather than social risk;more rational than impulsive in purchasing;

more responsive to different types of reference groups;focused more on a corporate brand than a product brand;focused more on services that support the product;

. more responsive to personal and interactive selling rather than mass communication;

%). more responsive to technical message content than to image-based content.

186 Handbook of business-to-business marketing 4

A final key difference is the accessibility of large amounts of scanning and other data 111B2C markets relative to B2B markets; transaction data can be critical for segmentation

research. {/3

In addition to such difl‘erences, there are also complications in business markets w1th

respect to the networks of firms within and adjacent to the value chain. These largerconstellations of organizational purchasing reflect networks of activity that increasrnglyinfluence how organizations behave in general and in their purchases (Henneberg et al2009). The emergence of the Internet, telecommunication and other powerful drivers has

created a much more interdependent world that affects organizational buying behavwrin ways that are not yet fully understood (Wind and Thomas 2010). These kinds of differ

ences have an impact on the activities involved in the process of segmentation and porntto areas that academic research should pursue to better understand the theoretical andxconceptual underpinnings of segmentation in B2B markets.

Variation in Defining B2B Segmentation Processes

Faced with business markets that are complex and highly situational, it is helpful fotmanagers to have processes that provide some guidelines so they can capitalize on anyopportunities from the use of market segmentation. A review of published segmentationprocesses over the past 50 years reveals many different formulations. Table 11.1 sum-zmarizes eight selected segmentation processes defined in prior literature. Each is briefly,

reviewed here, but interested readers should consult the original references for more»

comprehensive explanations., Hummel (1960) proposes what might be labeled an ‘industry-focused’ segment

tion process using standard industrial classification (SIC) codes4 to classify markets b

industry, then estimating size and value using company sales data, judgment and marke

surveys. Customer prospects can be identified and targeted using one or more of fou

suggested information sources: (l) industrial directories; (2) surveys; (3) trade sho

attendance lists; and (4) advertising and promotional inquiries.Wind and Cardozo’s (1974) survey of 25 industrial firms reveals that the most use

approaches to industrial segmentation at that time were similar to Hummel’s (1960)They propose a more formal, two—step, macro- and micro-segmentation approach thabegins by identifying macro-segments of organizations with similar characteristics sucas size, SIC code and geographic location. Each macro-segment then can be evaluatewith regard to the extent to which it provides an ‘attractive’ opportunity (e.g. salepotential). The most attractive macro-segments become the focus of micro-segmen1 s, 0small groups of firms defined by similarities and differences among their decision-makmunits (DMUs). A variety of bases and descriptor variables and methods then can be useX

to develop a more refined understanding of micro-segments, which can be evaluated an

selected according to their profitability and distinct responses to the firm’s marken (program. Choffray and Lilien (1978) extend the macro— and micro-segmentation proces“with a ‘decision matrix’ approach applied to the heating and air conditioning equipmerämarket. After identifying macro-segments, their decision matrix structures stages int

buying decision process according to the people in the DMU involved in the vanou

stages.

As Table ll.l shows, Thomas and Wind (1982) introduced a more comprehensrv

18!

Tab

le

11.1

E

ight

32B

mar

ket

segm

enta

tion

pr

oces

ses

Indu

stry

- M

acro

- an

dFo

cuse

d M

icro

-

Proc

ess

Segm

enta

tion

Hum

mel

Pr

oces

s

( 196

0)

Win

d an

d

Car

dozo

(1

974)

Use

SI

C

code

M

acro

—

to c

lass

ify

Segm

en-

indu

stri

al

tatio

n:

mar

ket

Iden

tify

mac

ro-

segm

ents

base

d on

orga

niza

—

tiona

l

char

ac-

teri

stic

s

Gen

erat

e hi

gh

Sele

ct

set

of

prob

abili

ty

mac

ro-

pros

pect

s se

gmen

ts

thro

ugh:

co

nsis

tent

with

corp

orat

e

obje

ctiv

es

and

reso

urce

s

Mac

ro-M

icro

Man

ager

ial

Dec

isio

n-M

atri

x D

ecis

ion

Proc

ess

Proc

ess

.C

hoff

ray

and

Lili

en

(197

8)

Phas

e 1:

Mac

ro-

Segm

enta

tion

Phas

e 2:

Mic

ro-

Segm

enta

tion

Tho

mas

an

d W

ind

(198

2)

Segm

enta

tion

deci

sion

(S

houl

d

1 se

gmen

t th

is

mar

ket?

)

Segm

ent

iden

tific

atio

n

deci

sion

(I

f so

,

how

?)

Nes

ting

Proc

ess

Bon

oma

and

Shap

iro

(198

3)

Dem

ogra

phic

s

nest

Ope

ratin

g

vari

able

s

nest

Con

join

t-B

ased

Ph

ased

Pr

oces

s

Proc

ess

McD

onal

d an

d

Gre

en

and

Dun

bar

(200

4)

Kri

eger

(1

991)

Def

ine

initi

al

Stag

e 1:

You

r

rese

arch

er

Mar

ket

and

focu

s H

ow

it W

orks

(cus

tom

er

or

prod

uct)

Segm

enta

tion

Step

1:

Def

inin

g

appr

oach

th

e ‘m

arke

t’

(a p

rior

i, (s

cope

of

the

post

ho

c,

proj

ect)

step

wis

e)

Impl

emen

tatio

n

Proc

ess

Cla

rke

(200

9)

1. 2.

Iden

ti-

fica

tion

of

purp

ose

Iden

tific

atio

n

of

mar

ket

to

segm

ent

SSI

Tab

le

II.]

(con

tinue

d)

Indu

stry

—

Mac

ro—

and

Focu

sed

Mic

ro-

Proc

ess

Segm

enta

tion

Hum

mel

Pr

oces

s(1

960)

W

ind

and

Car

dozo

(1

974)

1.

Indu

stry

E

valu

ate

each

dire

ctor

ies

mac

ro—

segm

ent

acco

rdin

g to

resp

onse

to

mar

ketin

gst

imul

i; if

yes

,

sele

ct

targ

etan

d st

op

2.

Surv

eys

of

Mic

ro-

pros

pect

s Se

gmen

-in

SI

C

code

ta

tion:

Iden

tify

with

in

each

acce

ptab

lem

acro

-se

gmen

t,

mic

ro-

segm

ents

base

d on

DM

U

char

ac—

teri

stic

s

Mac

ro-M

icro

Dec

isio

n-M

atri

xPr

oces

s

Cho

ffra

y an

dL

ilian

(1

978)

Step

1:

Mea

sure

the

patte

rn

ofin

volv

emen

tin

the

purc

hasi

ng

proc

ess

Step

2:

Def

ine

an

inde

xof

int

er-

orga

niza

tiona

lsi

mila

rity

Man

ager

ial

Dec

isio

n Pr

oces

s

Tho

mas

an

d W

ind

(198

2)

Mar

ketin

g

reso

urce

allo

catio

n

deci

sion

(W

hat

reso

urce

s sh

ould

be

allo

cate

d to

each

se

gmen

t?)

Segm

ent

sele

ctio

nde

cisi

on

(Whi

chse

gmen

ts

shou

ld

be s

elec

ted?

)

Nes

ting

Proc

ess

Bon

oma

and

Shap

iro

(198

3)

Purc

hasi

ngap

proa

chne

st

Situ

atio

nal

fact

ors

nest

Con

join

t-B

ased

Ph

ased

Pr

oces

s Im

plem

enta

tion

Proc

ess

Gre

en

and

Kri

eger

(1

991)

Opt

imal

prod

uct

desi

gn

mod

elfi

nds

best

prod

uct

for

each

of

kse

gmen

ts(o

r be

st k

prod

ucts

for

step

wis

ese

gmen

ts)

Tot

al cont

ribu

tion

to

over

head

prof

its

isco

mpu

ted

McD

onal

d an

d Pr

oces

sD

unba

r (2

004)

C

lark

e (2

009)

Step

2:

Mar

ket

3.

Iden

tific

atio

nm

appi

ng

of v

aria

bles

(str

uctu

re

and

mod

elan

d de

cisi

on—

mak

ers)

Stag

e 2:

4.

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gmen

ting

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isio

n-

and

anal

ysis

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ers

and

Tra

nsac

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681

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de S

how

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lect

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cost

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of t

each

ing

1t

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Adv

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tify

and

com

plet

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prom

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prof

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l of

ta

rget

inqu

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s se

gmen

t

Step

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Use

clus

ter

anal

ysns

to 1

dent

1fy

grou

ps

of

orga

niza

tions

hom

ogen

eous

in b

uyin

g

cent

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com

posi

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of t

he

patte

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of

invo

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ent

in t

he

purc

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each

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ro-s

egm

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onal

varl

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nest

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s w

hat?

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(dec

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lect

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mak

ers

and

thei

r of

seg

men

tspu

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omm

uni—

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e ca

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and

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ket

impl

emen

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Step

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? ta

tion

(nee

ds

of

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itori

ng

deci

sion

-mak

ers)

an

d up

datin

g

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5:

For

min

g

segm

ents

(com

bini

ng

like—

min

ded

deci

sion

mak

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Stag

e 4:

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tifyi

ng

You

r

Tar

get

Segm

ents

Step

6:

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men

t

attr

activ

enes

s

(seg

men

t po

tent

ial)

Step

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Com

pany

com

petit

ive

stre

ngth

s by

segm

ent

190 Handbook of business-to-business marketing

‘managerial decision process’ to segment business markets, in which the first (1601l årecognizes that managers should embrace the possibility that segmentation may or may

not have a pay—off for them. The segment identification decision then might includemacro— and micro—segmentation process. The third decision involves determining thextent to which the firm can offer the necessary marketing resources (e.g. products,price, advertising, sales force) to the identified segments to obtain a desirable responseéNotably, this stage precedes the segment selection or targeting decision, which requiremanagerial criteria to pick an optimal target segment. Finally, the segment implementation decision anticipates concerns mentioned more recently. In effect this segmentatronprocess takes a wide-ranging decision perspective but still remains sequential.

The decision and staged approaches thus tend to be sequential, but Bonoma an

Shapiro (1983) instead propose five sets of ‘nested’ variables, such that managers cabegin at any nest and work their Way through the others. The outermost nest begiwith demographics, within which are nested operating variables, purchasing approach?variables, situational factors and the micro-level personal characteristics of buyers Thauthors note that as the nests go from the outer to the inner ones, the variables Changin terms of Visibility, permanence and intimacy; the outermost variables are higher ()Visibility and permanence and the innermost ones are higher on intimacy. Although theprovide no specific decision rules to move from one nest to the next, the authors suggesmanagers should stop the process when they discover segments that appear economicz;and useful. The process dilTers from previous approaches in its nesting and the flexrbiht”for managers to begin at any nest.

The conjoint-based segmentation process defined by Green and Krieger (1991) als

affords considerable flexibility in identifying market segments because it uses ConJOI

analysis for data collection. The approach allows the manager/researcher to choosbetween defining segments by customers or by product attribute partworth utilrtiesCustomers can be put into segments a priori, according to predetermined partwortutility values, or their utilities can be clustered post hoc to identify segments for Whl

customers can be described. The process also includes aproduct optimizer model, Whlsets the stage for estimating segment profitability and the background characteristics ”*

customers for targeting. The value of such an approach is that it links customer responto product design and other marketing variables through a conjoint task to identifysegment with the most profitable outcomes. The process differs from the others 1n thit illustrates the centrality of a specific methodology that involves various assumptlon

which may be more appropriate for some situations than others.McDonald and Dunbar (2004) propose a process with two major phases: (l) develo

ing segments; and (2) prioritizing and selecting segments. The first phase involves ästeps, including understanding the overall market structure, identifying key decmiamakers and forming segments based on customer needs and other ‘why’ variables T

second phase involves estimating segment attractiveness to prioritize segments

selecting segments with respect to company strengths: in effect, a portfolio approac

The process is noteworthy because it recognizes market structures (”mapping”) andt

role of different decision-makers, and it is couched in a book that calls segmentationimportant part of the marketing process.

The last process in Table ll.1 has a clear emphasis on implementation. Clarke (20p. 344) sets out to construct ‘a market segmentation process and methodology that

%Ms””åx

* B2B market segmentation 191

åractlcal 1n nature, easily approachable for companies, operational within the company,

”Mnd that considers the eventual implementation ramifications throughout the segmenta—gfim process’ Working with a Danish company, she employs action research methodol-

” mvolvmg an aggregative rather than disaggregative process to identify segments.

begins With understanding the company’s purpose for the segmentation and clearlyéfimng the market. Next, the approach identifies variables to use in the segmentationhalysrs and a verification, evaluation and selection of segments. How the segmentation

communrcated throughout the organization to facilitate its implementation then sets

ghaa stage for its ongoing monitoring and updating.EM\VXWhat these eight selected segmentation processes make clear is that there are a variety

\ conceptualizations, variables, methods and targeting approaches to segmentation.

upled Wlth the complexity of business markets and the mixed results from using seg-

ntation m practice, the variations in process suggest the need for a different approach.Goller et al. (2002) note, it is difficult to generalize around segmentation variables and

“@thods Instead a focus on the activities involved in a process of segmentation may be

greater value.

ITICAL ACTIVITIES FOR B2B MARKET SEGMENTATION

”a,

n the challenges of developing effective B2B market segmentations and the variabil—

rn market situations encountered, the approach I recommend here is for managers toign their own segmentation process rather than adopt one that presumably works for

ry srtuation. On the basis of the processes in Table 11.1, I identify and present six corezentanon activities in Figure 11.1. Although each of these six activities is consistentextant recommendations, not all have to be used to design a segmentation process,

X3515 the order meant to imply a step-by-step approach. For example, a marketingger 111 the local subsidiary of a chemical firm may receive a well-defined positioning

%marketing strategy from the global organization and only need to find the best target\ent Thls segmentation process would place ‘positioning and marketing strategy’er to the beginning than the end of the task. Consequently managers who use or;are asked to use market segmentation must be prepared to design and implement

cess that best conforms to the situations they face. I consider each of the six core

entation activities briefly in the following sections.

de on the Use of Segmentation

dec1sron to use B2B market segmentation has three critical components: (1) the

et uncertainties faced from the outcome of a situation analysis; (2) the importance

marketing decisions contemplated; and (3) the organization’s readiness to embrace

sntatzon. The three aspects work together to provide a basis for defining the objec-

pf segmentation and ascertaining its value in terms of its benefits versus the invest—

x of time and resources required to carry out a segmentation process. Generally the

Net the uncertainties faced in a market situation (e.g. problems or opportunities), the& important the marketing decisions, and the greater the organization’s readiness

brace segmentation, the more likely a decision to use segmentation will produce

192 Handbook of business-to-business marketing

Decide On the Use of Segmentation

Build the Segmentation Database

Identify and Profile Segments

Select Target Segment(s)

Formulate Positioning and Marketing Strategy

Implement, Track, and Validate Segments

Figure 11.1 Core activities for BZB market segmentation

valuable results. Any other mixture of outcomes from these three possibilities requiresmore careful analysis of the decision to use segmentation.

A market situation analysis is quite standard yet critical to all marketing activme

including the decision to segment (Rao and Steckel 1998). lt involves the careful defi

tion of the market or category and the trends that influence it. Analyses of custom.

competitors, the value chain and the companies’ own strengths and weaknesses w1t

and across cultures in a global economy should lead to a clearly stated set of insrghts :1challenges that help define the central marketing uncertainties facing managers Usm

marketing situation analysis, managers then can consider the marketing de01s10ns t

will ameliorate and/or capitalize on market uncertainties and decide whether segmentthe market will improve the quality of these decisions. The decisions may involve nmarkets that are sources of business development and growth, new product devel

ment, pricing, distribution, communication, customer relationship management or ot

critical marketing decisions. The issue is whether implementing a market segmentatprocess will reduce the market uncertainties linked to marketing decisions.

Although the market situation analysis and marketing decision problems are Imptant, if the organization is not ready to embrace segmentation, the entire effort Will b

little value. Organization structure, operations, size, leadership, communication sty

decision-making processes, teamwork and other factors may lead to differences in

segmentation is Viewed and how it is implemented (Dibb and Simkin 2001). For exa

larger versus smaller organizations may have different resources that can influencethe segmentation process is staffed and funded. Furthermore the personnel 1nvolvsegmentation may not agree on the objectives or the resources required, or they

not have adequate analytical capabilities. As Jan Ekonomy (2000) noted, Spr mt 1m "underestimated the resources, time and leadership necessary to commit to a segmetion process that would be effective for the organization. '

BZB market segmentation 193

%» Firms using segmentation for the first time occasionally or routinely also need to

% assess their organization’s response. For example firms using it for the first time mayålconfront what is essentially an organizational innovation, which often requires leader—

?sship and support from top management, including the CEO. Leadership must ensure%?hat the rationale for segmentation links to the corporate vision, strategy and objectives.

grr addition to providing necessary resources, leaders can appoint a segmentation team1th the responsibility for developing and implementing the segmentation process forrgamzations. Simkin (2008) proposes cross-functional teams, including some with a

detailed appreciation of customers’ purchasing behavior and needs. Clarke (2009) oilerseveral characteristics of the people to include on the segmentation team, including

pertise with segmentation, implementation power, a vested interest in the segmenta-n results and participation in involved departments, especially the sales force or thoseo might resist a new segmentation approach.

Such guidelines are useful, but little empirical research addresses questions about the

<adership and teamwork responsibilities required for difierent kinds of segmentationrecesses or the extent to which these responsibilities vary over time while carrying out

,grnentation. As Clarke and Freytag (2008) propose, the extent and process of seg-

ntation may differ according to the purpose of segmentation and the intended mar—ing offering. Their idea of developing a matrix that suggests different segmentation

Fpreaches for difierent situations is indicative of the kind of thinking required to better<derstand the decision to use segmentation.

äd the Segmentation Database

emphasis in segmentation literature is typically on methods of analysis (Dibb and1995; Wright 1996), with less consideration given to the quality of segmentation

‘ , such as the variables, measurement and data collection (Wind 1978; Wind and

2008) All too often standard sets of questions are asked of single respondents fromnizations in busmess markets, with little thought given to truly understanding cus—

xer needs or other relevant variables. Building a quality database for segmentationuues a conSideration of (1) the unit of analysis; (2) the variables and measures used;the research approach; and (4) the integration of multiple databases.

of analysns data

few exceptions the bulk of published research on B2B market segmentation relies

ta from a sm gle ‘customer’. However, in practice, business customers mainly consist

uying centers (Robinson et al. 1967), with multiple participants who may have dif-tneeds and problems. A segmentation based on the needs of a purchasing manager

,diifer conSiderably from that based on responses from marketing or R&D manag-

espite numerous studies of buying centers (Johnston and Lewin 1996), few have

idered the implications for market segmentation (Choffray and Lilien 1978; Thomas

xNarayandas 2005). Consider a buying center with three key participants, A, B andheir needs or responses to marketing stimuli are similar, there appears to be highem; Within that organization, which then can be treated as a unified customer

However, if their needs are diHerent or if two have similar needs and one does not,

are different implications in terms of segmentation. In addition, buying centers can

194 Handbook of business-ta-business marketing

Table 11.2 Illustrative segmentation variables

Bases Variables Descriptor Variables Response Variables Marketing VariablesUsed to Define Market Used to Describe and Used to Develop Used to Formulate ;

Segments Target Segments Segment Positioning Marketing Strategy3

Needs (core reasons why Organizational Awareness of major Product design, écustomers are motivated characteristics (size, brands (top-of-mind, development,

to purchase) age, industry, etc.) unaided, aided, etc.) assortment, etc.

Value (benefits to meet Buying center Perceptions of major Perceived value for

needs in relation to price) characteristics (e.g., brands on needs and pricing

size, influence, benefits, comprehension

location) of brand meaning

Attitudes, interests, Individual factors Preference/likeability Channels ofbeliefs, and related (age, income, for major suppliers and distribution,

psychological variables occupation, gender, brands direct vs. indirect :3

family, education) purchasing options;

Intention—to-buy brands Social and cultural Intention to buy Media usage andor new product concepts factors brands or new product preferences, touc

concepts points, etc. 53

Purchasing processes (new Time based and Brand loyalty, usage Sales force

task, modified, straight other variables rates, etc. sensitivity, technic;

rebuy), product usage such as customer support, customer;

rates (heavy, medium, life cycle, purchase service, etc. '

light), etc. frequency, etc.

be extended to value chains and other market networks through interactive media thhelp form special interest communities, consortia or buying groups (Clarke and Freyf

2008; Henneberg et al. 2009; Wind and Thomas 2010). This situation makes it importto consider a more expanded definition of whom to sample 1n data collection. "!

Variables and measuresThe second major concern in building a database for segmentation is the neeestablish creative and insightful variables. Thomas and Wind ( l982) and Wind a:Thomas (1994) suggest three sets of variables to consider. marketing resource varia(e. g. product, price, sales force), market segmentation variables and market respo?”variables at the organizational, buying center and individual levels. An alternative;

similar view with four variables sets in Table 11.2 includes bases variables traditio

used to identify segments, descriptor variables to describe and target segments, respojvariables for positioning and targeting, and marketmg variables to formulate market?"strategy .

With respect to the bases variables, the entire focus of the field of marketing has f

to identify customer needs and develop offerings to meet them (Kotler and Keller 20Thus any effort to segment a business market should focus on customer need variafIn some ways managers intuitively select variables such as organizational size, usag;

geographic location as surrogate indicators of customer need. Such variables are u

«s . , BZB market segmentation 195

qdentifying macro-segments, but competitors often adopt the same approach. To131 a competitive edge through segmentation, firms thus increasingly turn to customer* 5 and value (Lilien et al. 2010) and customer behavior (Rangan et al. 1992) to iden-

micro-segments.

Qonsequently the purpose of Table 11.2 is to encourage the development of a compre-swe set of variables, beginning with exploratory in—depth customer research as input

the segmentation research process. In addition, alternative measurement approaches

and the ubiquitous importance rating scale should be considered. Along with con-, tanalysis (Green and Krieger 1991), the development of the maximum differences

” dlfl) or best—worst scaling approaches (Louviere and Woodworth 1990) hold con-

‘rable promise for B2B segmentation. From a list of relevant customer needs or ben-several sets of needs are provided to a respondent, who then indicates the most and

”Important ones. The resulting data, similar to paired comparisons, can be analyzeda multinomial choice or hierarchical Bayes model to produce a ranking of needs,

most to least important. Unlike traditional rating scales, the maxdiff approachfates comparisons between respondents, which is essential for more discriminatory

entations. When compared with five other approaches, maxdiff scaling oHered theaccuracy, though it took longer to administer (Chrzan and Golovashkina 2006).

s though the maxdifi approach holds promise, it requires both conceptual and prac-idevelopment (Marley 2009), as well as specific consideration of its applicability inWegmentation, especially in the context of multiperson buying centers.

emergence of the Internet, mobile communication and online marketing have been

"3 commercial and research opportunities for business marketing. When customers

Qvebsite’s landing page, Clickstream data can be captured on several variables: cus-

sduration on a specific page, topic or the entire site; the number of clicks throughpages or specific products; the number of times they return and when; and

äse outcomes. As Naik et al. (2008) note, such data are high—dimensional; imagine

% tabases of VariablesXAIternativesXSubjects><Time (VAST matrix arrays), whereæ e or more dimensions might be very large. The data can be derived from social

rim and are numeric or text—based, including blogs, customer comments and so on.%ata require new thinking about their collection and analysis for the purposes of

tation. The value of such data is that they represent B2B customer behaviors, notpauses to survey questions. Unfortunately, few published studies use such data inNgmentation.& ?

Jr approachcts traditional marketing research decisions (e.g. sample, research instrument,

fection method), the overall segmentation research approach can take two forms:

rve and disaggregative. In an aggregative approach, data collection begins one

at a time, to build an understanding of market needs and how they are seg-larke 2009) With deeper customer intimacy, a database can be developed

tales the groupmg of even a few customers into meaningful segments. In largerfirms might collect data on individual customer organizations, one at a time,

proceed to place those customers together that are most similar on the critical

of interest This process can be continued until adding new customers no longer

ew segments

196 Handbook Ofbusiness-to-business marketing

The disaggregative research approach borrows heavily from consumer market’mentation techniques. Representative samples are drawn from large populations,databases are assembled from questionnaires completed by various data colk

procedures (e.g. online, in-person, mail, telephone). There is no evidence to indx

that one approach is better than another (Rao and Wang 1995); though case S111

and research on the costs and benefits of each would be of value to both academicspractitioners.

Integrating multiple databases

All too often companies develop and maintain separate databases with the same cust

ers, albeit for different purposes. Yet integrating relevant aspects of these databases

a single-source database provides greater opportunities to identify and profile segmAt issue is locating the data available that also are relevant to customer segmenlaiacross different databases. For example, how should a database from a customer su&a sales call database (e.g. from sales force software reports), clickstream data and/oenterprise database (e.g. SAP, Oracle) be integrated into a single segmentation

base for analysis? How should the segmentation database be cleaned, maintainedupdated? Without a high quality database, the important tasks of identifying and pr

ing segments are at risk,z.

Identify and Profile Segments

The identification and profiling of target segments may be the most difficult challin market segmentation, as well as the reason published literature on this segmetion activity is so extensive relative to other topics (Wedel and Kamakura 2000)challenge requires several decisions that both analysts and managers should cons

including (1) which method(s) should be used to identify groups of customers whosimilar within groups and different between groups on selected variables; (2) how *segments should define the feasible set; and (3) how the segments should be proEach is considered briefly.

Segment identification method

Many methods can be used to identify market segments and various classiticaschemes to organize these methods. Here my emphasis is on four segment identifica

methods that appear in prior literature and have also been used in B2B practice: (1)sifying customers on preselected categorical variables; (2) grouping customers ontiple variables with cluster analysis; (3) classifying customers with latent class analyst(4) optimizing segments using predefined criteria.

Method 1 involves selecting one or a few bases variables with defined categoriescreating a priori classifications of organizational customers. For example, considefivariables and their categories: customer sensitivity to price (low or high) and similan

needs within the buying center (similar or dissimilar). They produce four segments

which a sample of customers can be grouped for profiling and additional comparaanalysis. ,

Method 2 involves the consideration of multiple bases variables that are typiconstructed from rating scales, such as customer importance ratings of needs or

BZB market segmentation 197

,91' customer agreement ratings on various attitude statements. Cluster analyses

øse variables are conducted to identify segments. Whereas Method I started with

etermined segments (a priori), this multivariate approach enables the identifica—

Bf numerous segments from post hoc analyses of the data (Green 1977). Due to

ehhood that these bases variables may be correlated, they are typically factor

yZed to identify a reduced set of variables that capture the underlying need,fit or attitudinal ‘factor’ structure of the market. Individual- level ‘factor scores or

wired variables from the factors are then submitted to a cluster analysis to identify

åra] segments.ie Method 2 is a popular approach practiced by many firms and research sup-

s, 1t also is problematic. Selecting the best set of bases variables and rating scaleures to include in a questionnaire, assuming respondents answer them properly andar1z1ng them into underlying factors (that may explain only 50—60 per cent of the

;tlon 111 the responses) introduces the potential for error in the data analyzed. ThisÅs compounded by concerns for selecting the best cluster analysis procedure (Arabic; ubert 1994). There are decisions about which clustering procedure to use: overlap-((customers may be assigned to more than one cluster), non-overlapping (custom-

an be assigned to only one cluster) or fuzzy (customers can be assigned partiallyifferent clusters). With non-overlapping clustering (the most common approach), a

Kion must be made to use either a hierarchical or non-hierarchical approach. Withgarchlcal approach, the analyst needs to choose a similarity measure (typically a

1an distance measure) and a linkage method (e.g. Ward’s method of minimum

3 ce) Everitt et al. (201 l) provide a comprehensive review of cluster analysis, includ-

e various methods and decisions to consider.ethod 3, latent class analysis (LCA), has an objective similar to that of ”cluster

YSIS but identifies customer segments with a different set of assumptions, variables

odels. Essentially LCA, often associated with finite mixture models, uses statisti-

rocedures to identify a set of unobservable classes or clusters from observed vari-.’For example, We cannot directly observe if an organization’s purchasing behavior

averse, but we can measure selected variables (e.g. how long it takes to make aon, how many people get involved in the purchase) that, when analyzed, uncover

xtent to which an organization can be classified as risk averse. The covariation in‘observed variables should explain each observed variable’s relationship to theserved one. The data for the observed variables in LCA can be based on a variety ofurement scales; they are not based on distance scores (as required for cluster analy-ut can be raw responses from customers. Despite these advantages, there are issuescon51dered in using LCA (Dillon et al. 1994).

ereas Methods 1—3 can be used to identify segments, they are unconstrained. That

Were are few limiting resources and criteria applied until the targeting phase of the&) utat1on process. Method 4 seeks to find a more ‘optimized’ set of segments by

faneously including variables such as reachability, profitability and feasibility, infon to customer needs and descr1ptors. DeSarbo and Grisaffe (1998) and DeSarbo

eSarbo (2007) describe this more flexible approach using a methodology calledCLUS, which contains combinatorial optimization algorithms. The methodol-

smore flexible, in that it accommodates user— specified objective functions, single

fit] criterion objective functions, a variety of user- specified constraints, diiTerent

198 Handbook of busz'ness—to-busz'ness marketing

forms or types of segments, multiple sets of data collected from the same customers angalternative models of market segmentation. Comparing this approach to more tradtional ones in an application to the electric utility industry, DeSarbo and Grisaffe (199

demonstrate its value by identifying managerially relevant segments. %Although there are other methods and procedures for identifying and profiling seä

ments, the numerous possibilities suggest the need for research into guidelines to dete”mine situations in which some methods are more relevant and valuable than others %one method is better than others, will it yield marginally different results than othe

and at what cost in terms of data collection and analysis? With few exceptions, themethods were also derived from B2C literature with the assumption of individual buyerénot buying centers.

Number of segments to define a feasible setIn a database of N customers it is theoretically possible to have l—N segments Ongsegment implies that all customers are quite similar in terms of relevant variable?

and the best marketing approach is to prepare one offering for the entire market Thacase of N segments indicates that every customer is different, and the marketing offåshould be customized to each. One of the challenges in B2B marketing practice IS th

the sales force often treats each customer as a separate segment, sensing each one

needs, recognizing the complexity of the customer’s buying situation and coordinatmgthe offering accordingly. The value of two or more segments in such cases is to malt?the sales call more efficient, that 15, to provide the sales force with information that can:enable salespeople to allocate their limited resources better to obtain greater customerresponse. ?

Consequently one of the key problems in segmentation is determining the appropriate number of segments, between 1 and N, to avoid under- or over-segmenting. Theare basically two approaches: statistical analysis and managerial involvement Th;core idea of segmentation is to identify segments that are similar within themselves amidifferent from others on critical variables, so the use of segment variance calculat1on

can help determine the number of segments. Milligan and Cooper (1985) test 30 5110

measures and find that the index by Calinski and Harabasz (1974) is the best ind1catof the number of segments. Boone and Roehm (2002) suggest another approach usmg

artificial neural networks to select the appropriate number of segments. Althoughthese methods should prove useful 1n practice, they are often based on artificial data;sets with a known segment structure or consumer applications. A second approach tgdetermine the number of segments generally follows managerial rules of thumb Fexample, some firms set the rule that no segment should be smaller than 10 per ceiäof the market; others indicate that the number of segments should be between 3 anti

10. Until the statistical methods are applied and tested m B2B markets, it is str ongigrecommended that managers review the profiles of alternative segment structuresand use their judgment and experience to decide on the appropriate number for the

«&

mm %

Profiling segmentsThe primary objectives of profiling segments are to define who the customers in eachsegment are and to assess the reachability of the customers in each segment. lenæ

3

i

k4.” n" M- + "IN

B2 B market segmentation 199

gble set of segments, there are three approaches to profiling: bivariate, multivariate,

‘modelbased. The classical bivariate approach is often used with Methods 1 and 2:

are presented to managers in cross-tabulations, often with segments as the column

ables and categorical descriptors of the segments (e.g. geographical location) as row

iables The column percentages then can be compared across the segments for each

*Variable to identify similarities and differences. Similarly, the mean or other statisti-

easures of interval and ratio-scaled variables (e.g. importance of a benefit on a five-t scale) can be compared across segments.

he typical multivariate approach employs a statistical model to identify the set ofependent variables that best separates the segments. Multiple discriminant analysis,”RT (classification and regression trees) and similar methods are useful to identifyse variables that best define each segment. For example, discriminant analysis seeksfind the linear combinations of segment descriptor variables (e. g. organizationalx, location, product application) that maximize between-segment variance relative\ 1th1n segment variance. If a discriminant analysis is effective, the variables that

’rxbethe segments can predict membership in a segment, which is useful for both

dating the segments derived from a database and for predicting segment member-from customers not in the database, which in turns helps determine the reachability

figment“ e model-based approach is linked more closely with Methods 3 and 4. In this case,

ltiple variables appear in the model (e.g. LCA), such that segments are simultane-

ly Identlfied and profiled (Kamakura et al. 1994). There is no need for a subsequentx rnmnant analysis to profile the segments. With the exception of case study applica-

ns, such as those by DeSarbo and Grisaffe (1998) and DeSarbo and DeSarbo (2007),

ere have been few published applications of these approaches for B2B market segmen-on, let alone of their validity and comparison with other procedures.

a

ect Target Segments

Xrirnary challenge of selecting a target segment is that the eventual response of any oneent can be influenced by the marketing offering to that segment. Even the defini-

nof a segment can change depending on the resources the firm can offer to it. Typicalreaches to segment selection tend to avoid this challenge by assuming that a segment

xased on customer needs and that firm resources are unlimited in being able to provide

appropr1ate offering. This assumption can lead to segment selection decisions that

tcustomer needs but do not generate profit, due to the costs of serving the segment.arly marketing theorists and managers tended to approach the targeting decision by?definlng a set of criteria that would characterize ideal segments, such as measurabil-fsnbstannality, accessibility and actionability (Frank et al. 1972). Each can be defined

more specific metrics, and other criteria can be added. Callaghan and Morley (2002)ducted a survey among 124 Australian marketing managers to rank 40 potential seg-

”segment (e.g. size, gro , a ;auu firing.value to segment, ability to deliver the offer).

Sing the selected criteria, managers can establish a rating scheme, weight the cri-<a 1f desrred and evaluate each segment according to how well it meets the criteria.

'i 31101.1s; j

riain the list incl, istics of both ,,

200 Handbook of business—ta-business marketing

A portfolio-based approach can provide even more structure to the targeting procesé(Wind and Thomas 1994). In this approach managers identify and weight criteria tha

define a dimension of market attractiveness (e. g. size, growth, profitability, reachablhty}

and a dimension of business competency to serve the segment (e. g. brand strength, ability

to develop an offer, financial resources). Each segment then can be rated and plotted 0

these two dimensions. The segments that are most attractive and for which the organrzation has the greatest competency are more likely to be chosen as targets. Sarabia (1996extends this model by including multiple detailed variables, especially those involvmthe organization.

Target segment selection should be approached as a management decision proceswith multiple alternatives evaluated on selected criteria, preferably incorporated mta decision model, yet it is not easy to integrate all these elements while considermdifferences among individual managers involved in the process. Montoya-Weiss anCalantone (1999) tackle this problem in a BZB setting by modeling the trade-offs amoevaluation criteria and organizational resource constraints. Essentially they employprocess that starts with structuring the problem and setting managerial criteria to ev alate the segments. Segments then form with a variety of well-known marketing researcprocedures. A two—year post hoc review of an application of the model for an automctive supplier enables the authors to conclude that the model enhances management’decision-making and business performance.

Hartmann (2010) proposes a targeting model based on game theoretic conceptgand Bayesian estimates to incorporate the social interaction of group members. H:shows that targeting an entire group generates a 1 per cent increase in profit, whereåtargeting within the group increases profit by 20 per cent. Although his applicatloinvolves golfers, it may be extended to buying centers and other B2B communities. BotMontoya-Weiss and Calantoneis (1999) and Hartmannis (2010) applications of targetuf

are ambitious and apparently effective. However, it is unclear if these models would hav‘been superior to a more traditional segment selection approach, That is, what remaimissing is research on which targeting approaches work in which conditions or measur

of managerial receptivity to more comprehensive versus simpler models. Montoya-Weisand Calantone (1999) suggest that simpler is better as a reminder of the importancegmanagerial involvement.

Formulate Positioning and Marketing Strategy

Once a target segment is selected, the next recommended step usually is to developpositioning statement and formulate a marketing strategy to achieve that positionmWhereas data are important to inform positioning and marketing strategy statemen

(Berrigan and Finkbeiner 1992), all too often creativity and biases take over at th;

point. The frequent result is that the positioning diverges from the needs of customers$the target segment and reflects instead managerial beliefs about how customers sho

perceive the offering. This scenario is further complicated by the challenge of reach:the target segment with an effective communication approach. In many B2B market}situations, the marketing plan and its implementation are handed off to the sales forDifferent approaches to sales force training and who is targeted in the buying centerlead to different outcomes for segmentation.

B2B market segmentation 201

” eallv, the potential positioning and marketing strategy gets incorporated as part

ähe segment identification and selection process. Among the segmentation modelsussed in the previous sections, those that involve latent class structures and optimiza-

algorithms hold the most promise. These models can incorporate various market-

sdec1sions through conjoint analysis and other procedures, but they do not include

torner brand perceptions. DeSarbo et al. (1991) creatively integrate cluster analysis;multidimensional scaling in a model to include customer perceptions and prefer-

and other segmentation-related variables simultaneously. Wedel and Kamakura0) define this class of models as STUNMIX (for STochastic UNfolding MIXture

welmg). Clearly a variety of data, distribution and other modeling assumptions are

fled to make such models Operational, but with further development, they hold thelse of creating a single modeling approach that can select an optimal segment using

ted criteria relevant for successful implementation. Unfortunately such models inzmarket segmentation have received little attention and application, let alone valida-o prove their value in use.

ement, Track and Validate Segments

arketing literature segmentation processes usually end with the selection of a posi—

ng and marketing strategy for the targeted segments, along with recommendations

plementation. Various implementation problems and approaches arise in the first$61 segmentation activities, as discussed previously, and Maier and Saunders (1990)

ide relevant advice for working with the sales force to enhance implementation.ever there has been very little concern for the recommended process of tracking

\‘vahdating segments (Wind 1978). As Blocker and Flint (2007) discuss, markets

Emarket segments can be unstable over time, and there must be at least a conceptualårstanding of this concern, if not methodological rigor in tracking the existence of a

ant structure of the market.

elfect, managers face a simple but critical question. did the marketing program reach

:Jdentified target segment? For some managers, if sales increased after the segmenta—was implemented, it worked! However, sales could increase due to other factors or

:uSe the firm reached customers that had not been targeted. Tracking and validation

mportant challenges, yet there are few guidelines. In smaller markets tracking can be

mplished by the sales force, who can report results by customer, which can then be

elated with targeted segments. Sales force software can be designed to accommodate‘Effort.

larger markets, one approach might be to identify early customers, obtain their: cteristics from the sales force or a questionnaire and determine their segment

he.rship A more comprehensive follow- -up segmentation study might be con—

ed, say one year after implementation, with the results compared with the original

entation. Reported purchase data from the follow- -up study could be correlatedegment membership. The possibility of B2B customer online panels and variousercial sources can also track and validate customer responses by segment,Ing such data are available in the relevant categories. Although there are pos-

acking and validation methods, few have been reported or tested for their value

202 Handbook of business-to-business marketing

CONCLUSIONS AND DIRECTIONS FOR FURTHER RESEAR «

B2B marketing managers strive to support the continuously profitable growth of org

zations through the efficient and effective allocation of resources. Market segmentatpromises to be a dynamic business decision process to support this objective, becaucan identify one or more segments of target customers on which to focus a markastrategy for long-run competitive advantage. The purpose of this chapter has beeconsider the current state of B2B market segmentation, identify some major challeif'to its effectiveness, and note the major activities involved in developing a segmentprocess in prior literature. —

The literature reviewed reveals success stories for B2B segmentation, as well as re

of it not working as well as expected. A comprehensive baseline survey of currentmentation practices and methods used by B2B organizations is missing and musconducted. Relying solely on occasional case studies of segmentation efiec‘uveneinsufficient to shed light on its true value to B2B firms. Several case studies bring tosurface the problems of implementing B2B segmentation; however, many methodol

cal problems remain as well:

. The continued emphasis on single—respondent studies — when buying centeralmost always involved in purchase decisions — can lead to murky segmentatresults.

. The types of variables, measures and data used in B2B segmentation rese‘have not been adequately addressed, whether by traditional market researchsurveys or newly emerging sources of data from the Internet and telecommuni

tions applications.

. The approaches to identify and profile segments have moved well beyond cr

tabulations and cluster analysis, yet few published B2B segmentation apphcattinvolve latent class or optimization models that validate their utility for dritesituations.

. The implementation problems noted in recent literature need to be addressedthe form of guidelines for managers that enable them to get the most valuefsegmentation.

Table 11.3 summarizes a list of questions to stimulate research on these topics asxas some guidelines for managers involved in segmenting B2B markets. To overcothese challenges, both academics and practicing managers must rethink the processtfirms use to conduct B2B segmentation. The variety of B2B segmentation procei

that have been proposed in the past 50 years provide the basis for identifying ati

six major activities that managers can use to develop a more integrated segmentat

process that fits their organization and markets (Figure 11.1). The six activities can

taken as a process, as presented, but a different logical order also may be more relevfor some situations than others. The challenges inherent in these six activities promany opportunities for academic researchers and managers to improve B2B mar

segmentation.

e

B2B market segmentation 203

J] 3 Directions for future 323 segmentation research and practice

% s for Future 32B Segmentationch

at 1s the track record and effectiveness of .

market segmentation across a variety of

fistrles, cultures, organizational types andM“er factors?

ga]; are the best ways to manage the

”Elation for market segmentation to .

”øve its internal acceptance, minimize

'ers and enhance its implementation?

\at 1s the best composition of

entation ‘teams’ for different kinds

pplxcations, and how efiective are such

5 In managing and accelerating the o

Cass?at are the determinants of the value ofductmg segmentation to make the proper

:éeauon of resources to a segmentation

66555(e.g., does the greater importance

gnarketing decision lead to a greater .

”ntial value from segmentation)?

should data be collected and analyzedcount for similarities and differences of

onses among participants in the buying

ter of 1customer organization? How are

gag centers defined within multinationalanizations that span a variety of .

N cs?Mat are the validity, reliability, costs

benefits of using importance ratings,stant sum scales, maxdiff or other scaling

cedures to measure customer needs orer variables? Which measures are best forerent cultures?

w can multiple data bases be linked to o‘prove the segmentation process (e.g. how

Kintegrate data from customer surveys,” call reports, enterprise data bases,

* met activity)?at are the conditions and circumstances

X er Which aggregative versus”ggregative segmentation research .reaches to collect data should be used?

at are the problems and prospects

rganlzing massive data bases usingekstream and other forms of electronic

ta for purposes of B2B segmentation?

t((

B2B Segmentation Guidelines for Managers

Learn as much as possible about market

segmentation concepts and language to

communicate effectively about it within

your organization and with potential marketresearch suppliers.

Do your marketing situation analysis

homework up front to be very clear about

the potential value of segmentation prior to

starting such a process. Write down specific

decisions and objectives you hope to achieve

with the segmentation.

Make sure you understand your

organization’s propensity to embrace

segmentation and recognize its potential

value. Leadership ‘buy-in’ is critical for

success. Set realistic expectations for the

outcome of the segmentation.Whatever the size and global extent of your

market, focus on an expanded view of thebuying center beyond a single individual.

Explore the entire value chain and relatedinterdependencies, especially your customer’s

customers, to develop new insights about the

market to be segmented.

Be creative in developing new variables and

measures that can be used for segmentation.Consider using conjoint analysis or the

maxdifi" scaling approaches rather than

traditional rating scales of importance.

Include positioning and marketing strategy

variables to better link these decisions to the

segments.

Use more than one analytical approach to

identify segments and compare the findings

of the approaches (e.g. cluster analysis andlatent class analysis). Managers should review

more than one segmentation solution to pick

the best feasible set of segments from which tochoose a target.

Profitability is a time-tested way to select a

target segment; other strategic criteria can

impact profitability and should be considered

as well, such as reachability or consistencywith strategic positioning.

204 Handbook of business—to-business marketing

Table 11.3 (continued)

Questions for Future B2B Segmentation BZB Segmentation Guidelines for Managers

Research

. Are there new variables and measures to . Once a marketing approach has been

identify and profile segments that can be identified for target segments, prepare an

used to expand the unit of analysis from internal marketing communication plan for

individuals to buying centers, networks and the sales force and other relevant playei 5

business systems or to consider new forms of inside and outside the organization who maymultilevel segmentation analysis? have an impact on the implementation of ya

. Can guidelines be developed, situational segmentation approach.

or otherwise, that will provide insight into . Track and validate the response of your

which variables are best to use for bases of marketing approach for your segment(5)segmentation? Use tracking tools to recognize the potential

. Can a classification of segmentation instability and dynamics in important target

decisions, data and methods be developed segments and make corrections accordingly 3for various B2B marketing situations? What . Recognize that because of potential error

are the conditions for which latent class in the variables selected, the data and the

analysis should be preferred over traditional segmentation methods, there will most llkel

cluster analysis, for example? not be a single best segmentation or target

. What are the best data, methods and tools market. Use your managerial experience an

to track and validate a segmentation that open-mindedness to let the segmentationhas been implemented? findings improve your resource allocations

«then learn from the outcomes.

NOTESå

1. The term ‘needs’ as used here refers to a summary class of variables that provide a basis for d1v1dJcustomers into groups. It can include a variety of operationalizations, such as customer value, altltudpreference or usage. l

2. Unfortunately studies from business practice about BZB segmentation effectiveness may be rare due toconstraints of confidentiality and because segmentation is often done for competitive reasons. In rn

published academic case studies, the firms are disguised and actual outcomes seldom known.3. Wind (2006) cogently argues that B2B and B2C markets are converging due to the Internet, inert asl

outsourced value chains, consumer co-creation with business, a blurred marketing function in flQ

due to widely available customer data and the move from an industrial to a knowledge-based someUndeniably, these market drivers are at work, yet their impact on B2B segmentation serves to make

challenge even more difficult.4. In 1997, the US government launched a six-digit North American Industry Classification System (NAl

code to replace the four-digit SIC code, though some firms and government agencies continue to usecodes. ,!

»

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