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Segmenting customer-brand relations: beyondthe personal relationship metaphor

John Story

Idaho State University, Pocatello, Idaho, USA, and

Jeff Hess

TNS, Inc., Coto De Caza, California, USA

AbstractPurpose – The purpose of this paper is to propose and test segmentation of multi-dimensional customer-brand relationships as a superior method of defining, understanding, and predicting customer loyalty behaviors.Design/methodology/approach – A method of segmenting customer-brand relationships is proposed, based on the development of personal andfunctions connections. The resulting groups are hypothesized to better define and predict customer loyalty behaviors. The model is tested with anempirical sample.Findings – Customers can be effectively segmented into relationship groups, based on the extent to which they have personal and functionalconnections with the brand. These relationship groups display different levels of commitment to the brand and engage in significantly different levels of loyalty behaviors. The resulting segments serve to define and measure levels of customer loyalty.Research limitations/implications – The primary limitation of this research is that behaviors were self-reported. However, the impact was limited by

the fact that the initial survey was conducted six months before the behavior questionnaire.Practical implications – These results have extensive implications for developing customer-brand relationships that promote, enhance, and expandloyalty behaviors.Originality/value – Measures of loyalty based on behavior in the market or customer satisfaction have proven ineffective at defining, measuring, andpredicting loyalty behaviors. Relationship segmentation not only better defines loyalty, but also provides insight into loyalty development, based onpersonal and functional connections.

Keywords Customer loyalty, Customer satisfaction, Brand management, Brand awareness

Paper type Research paper

Introduction

Marketers embraced the relationship metaphor to explain the

realms of customer behavior that lie beyond the bounds of

simple loyalty (Hess, 1995; Fournier, 1998). Relationships

have now become the dominant paradigm in marketing

strategy, eclipsing transactional perspectives (Gronroos, 1997;

Gummeson, 2002). Meanwhile, the basic relationship

m etaphor has been expanded to include m ultiple

dimensions (Hess and Story, 2005) and encompass diverse

classes of relationships. The impact of relationships extends

beyond choice decisions to encompass additional outcomes of

loyalty, such as testimonials, price insensitivity, willingness to

expend additional effort, and advocacy.

Yet, while we have embraced relationships as the ultimate

manifestation of customer-brand connections, customer-

brand relationship concepts, beyond euphemisms for CRM

or traditional loyalty ideas, have seen little direct applicationto marketing strategy. The application of relationships has

been much more descriptive, rather than prescriptive. In

many ways, relationships have been introduced as a parallel

research stream, along with loyalty and satisfaction, rather

than as an organizing framework. In fact much of the research

in customer relationship management (CRM) has focused ondata mining rather than developing personal or emotional

connections (Parvatiyar and Sheth, 2001) which impact

behavior by transforming functional connections into real

relationships. Now that we have adopted and adapted

relationships to enhance marketing strategy, the time has

come to expand their application. In this paper we propose a

series of applications that employ relationship segments and

test a subset of these.

Conceptual background

Personal relationships were introduced as a metaphor for the

interactions and bonds that form between customers and

brands in order to better explain consumer behavior. Loyal

behaviors in the market, such as repurchase, share of

purchase, and positive testimonials have long been

recognized as desirable, yet until recently, there has been

little consensus on defining, measuring, or promoting these

behaviors. A relationship framework also allows us to explain

other behaviors that have an apparently less direct, but equally

powerful impact on profitability such as price sensitivity and

willingness to forgive failure.

Satisfaction is a poor predictor of loyal behaviors (Oliver,

1999) and fails to capture the full breadth and depth of

consumers’ brand experiences. Not only is satisfaction not

synonymous with loyalty, current loyalty is not even a reliable

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0736-3761.htm

Journal of Consumer Marketing

23/7 (2006) 406–413

q Emerald Group Publishing Limited [ISSN 0736-3761]

[DOI 10.1108/07363760610712948]

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predictor of future loyalty. The literature is replete with

examples of satisfied customers switching brands and

seemingly loyal customers defecting (Oliver, 1999;

Reichheld, 2003). These occurrences are inherent in a

system where loyal customers are virtually always satisfied,

but not the converse. Although it has long been recognized

that satisfaction alone is not a reliable determinant of

consumer loyalty, it was not until recently that this gapbegan to be filled and the reasons for the impotence of

satisfaction have been elaborated. Relationships between

customers and brands perform much better as predictors of

loyal behaviors over time than satisfaction alone.

There have been many approaches to defining and

measuring customer loyalty, with varying levels of overlap

across studies. For example:. the loyalty index approach that includes satisfaction and

intention to behavior along with other randomly collected

“loyalty” indicators such as recommend;. the pseudo-attitude approach that asks your intention;

and. the actual behavior approach that relies on reported

behavior or, more rarely, tracks behavior.

Relationships provide a richer definition of loyalty than

conventional, behavior-based models. Much, if not all,

previous work on loyalty can be categorized into one of

three approaches to loyalty based on the methods of definition

and measurement. Loyalty behaviors easily fall into two

groups – purchase (primary) behaviors and non-purchase

(secondary) behaviors. Primary loyalty behaviors such as

frequency, volume, share, and retention, are relatively easily

measured and translate directly into revenues and profits, but

are not reliable predictors of future behavior. Secondary

loyalty behaviors, such as referrals, endorsements, advocacy,

and selective exposure to alternative brands, are somewhat

more difficult to measure and their impact on revenues and

profits is less direct, even if greater in magnitude.A third approach to defining loyalty is even less direct and

potentially more difficult to measure – attitudinal measures.

Oliver (1999) proposed adapting a tripartite loyalty model

that incorporates beliefs, feelings, and intentions toward the

brand to drive actions. While advancing our understanding of

the depth and breadth of loyalty, this model still leaves us

measuring loyalty based primarily on purchase behaviors or

intentions. In addition, given the mercurial preferences of

satisfied customers, there is little direct guidance on how firms

can effectively develop and nurture loyalty among customers.

We know what marketers want from their customers. They

want primary loyalty behaviors (share, frequency, retention,

etc.) and secondary loyalty behaviors (advocacy, referrals,

selective exposure, response to market influences, etc.).

However, in order to really understand these behaviors andcraft strategies that initiate and support these behaviors we

have to develop full definitions and measures of all

component constructs.

Loyalty and satisfaction, the broken link

The fact that some satisfied customers remain behaviorally

loyal while others defect provides strong evidence of variance

within groups of satisfied customers. The question is, whether

satisfaction and loyalty are unrelated or whether other factors

moderate the relationship. Hess and Story (2005) proposed

that the significant differentiator between groups of satisfied

customers who behave differently is trust in the brand. In the

Trust-Based Commitment Model, satisfaction primarily leads

to functional connections between customers and brands, but

it also contributes to trust. If brands behave appropriately,

trust builds into personal connections (see Figure 1 for

process model). The combination of functional and personal

connections results in committed relationships. Hence,

customers in committed relationships with a brand are asubset of satisfied customers. While merely satisfied

customers may be relatively likely to change purchase

patterns or even brand affiliation, those satisfied customers

who are also in a committed relationship with the brand are

much more likely to continue to exhibit loyal behaviors.

Though many, perhaps even most, satisfied customers may

exhibit loyal behaviors in the checkout lane, committed

customers have formed a deeper relationship.

There are two main differences between committed

customers and customers who are simply exhibiting loyal

behaviors. The first is the motivation, the underlying rationale

for the behaviors. Committed customers not only exhibit loyal

behaviors, they are also emotionally invested in a continuing

relationship. There is a strong personal connection between

the customer and the brand. Conversely, merely satisfied

customers may exhibit loyal behaviors, such as repurchase,

share of purchase, or exclusive purchase, merely because of

convenience, lack of alternatives, or inertia (Schulz, 2005).

Satisfaction removes the motivation to seek out other

solutions, but does not act as a barrier to brand switching

behaviors as commitment does.

Though satisfied, customers who lack multidimensional

relationships with brands may engage in variety seeking, or

may easily be lured away by competing brands. Satisfaction

may merely indicate that needs or requirements are fulfilled,

not that these needs or requirements must be fulfilled by the

target brand. While satisfaction is required for true

commitment, satisfaction alone cannot drive commitment,

as there may be many brands capable of delivering similarutility. Commitment requires satisfaction, but does not result

unless trust is also present.

Trust-based commitment supports at least two dimensions

of loyal behaviors. There is behavioral loyalty – commonly

measured in the marketplace. Yet, beyond behavior, there is

attitudinal loyalty – comprising beliefs, feelings, and

intentions toward a brand (e.g. Oliver, 1999). While

satisfaction may be sufficient for behavioral loyalty, those

customers who are “merely satisfied” are fair game for the

competition. However, when loyalty goes beyond observable

behaviors to include trust-endowed personal connections,

substitutability declines and committed relationships develop.

Perhaps the most significant contribution of extending the

concept of loyalty to committed relationships is the ability to

explain why, within a group of customers who exhibit

consistently loyal behaviors, there may be a significant

number who will easily switch brands. Where satisfaction

fails to predict continuing loyalty, commitment may succeed.

Relationships as explication of behaviors

Neither behavioral loyalty nor satisfaction adequately explain

or predict customer behaviors. Loyal customers often defect

(Schultz and Bailey, 2000) and satisfied customers are often

disloyal to begin with. One logical question is whether this is

because we have poorly defined loyalty or whether our

measures of loyal customers are flawed.

Segmenting customer-brand relations

John Story and Jeff Hess


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In the first case, poorly defined loyalty, we find that most

loyalty research focuses on behaviors in the market – repeat

purchase, share (of stomach, wallet, or other receptacle), or

exclusivity. If we consider a more holistic definition of loyalty,

that encompasses emotional attachments, we find that those

customers who have strong affective connections with a brand

often display loyal market behaviors. (Even historical

attitudinal loyalty is often merely propensity to behave, and

a weak one at that.) However, should we examine groups of

customers defined as loyal simply by their purchase behavior,we should almost certainly find a large number of consumers

who purchase repeatedly and/or exclusively based on

contextual constraints such as convenience, availability, or

lack of alternatives (Reichheld, 2003) yet do not reflexively

have personal attachments to the subject brands.

Based on the evidence, it seems clear that marketers’

definitions of loyalty may do well in encompassing behaviors

that result in outstanding current financial performance, but

perform poorly in predicting future behaviors. When we

question whether the loyalty definition is flawed or whether

the measures of loyal customers are inaccurate, the answer is

inherently confounded, since the measures derive from the

definition. Defining loyalty as measurable behaviors leads to

measures limited to purchase intentions, actions, or history.Relationship commitment, as a measure of multidimensional

loyalty avoids the natural confound between loyalty and

satisfaction, since trust is the primary differentiator.

Committed relationships encompass a broad range of loyal

behaviors, which result from satisfaction and commitment,

along with personal connections that go beyond satisfaction.

The difference between measuring customer loyalty strictly

by behaviors, which may result from chance or circumstance

and measuring loyalty based on commitment to a brand

relationship may differentiate between a short-term

phenomenon and a durable brand franchise.

Definition, measurement, and nurturing

Marketing research on customer loyalty should allow us to

effectively define, accurately measure, and ultimately

influence the relationships between customers and brands.

One potential problem encountered in discussions of loyalty is

the breadth of the construct. Loyalty can be used to mean

anything from repeat purchase behavior to emotional

commitment to a brand. As previously stated, simple

behavioral measures have limited value since behaviorally

loyal customers often defect. The solution to providing an

effective definition is not to constrain loyalty to only certain

m eanings, but to provide an expanded m odel that

encompasses multiple dimensions of customer loyalty. The

relationship metaphor can provide this expanded definition of

loyalty, by incorporating behaviors that extend beyond the

purchase environment supported by multiple dimensions.

Inherent in a multidimensional relationship model, as a

function of the different dimensions, are groupings of

customers who have a broad range of relationships and

exhibit a variety of loyalty behaviors. Valid measures of these

relationship dimensions result in groups of customers who

share, not only behaviors, but also propensities for continued

behavior and responses to new stimuli. These customer

groups better define loyalty and predict loyal behaviorsbecause they reflect measures of functional, affective, and

relational components of customer-brand interaction. Those

customers who score high on functional connections may

behave similarly to those who score high on personal

connections, as long as no external stimuli intervene.

However, when faced with new brands, product/service

failure, or other intervening stimuli, they may respond quite

differently.

Application: segmentation and behaviors

Though there may be many ways to segment customers based

on relationships, including relationship depth, scope (number

of products or services consumed), or exclusivity, theTrust-Based Commitment model provides a

multidimensional approach. Segmenting customers based on

the relative strengths of personal and functional connections

with the brand increases both the information content of

segment membership and the probability that members of

different segments will behave differently in the marketplace.

The Trust-Based Commitment model measures two

dimensions, functional and personal. The functional

dimension focuses on satisfaction and the basic utility of

consumption. Functional connections form when needs are

satisfied and products or services perform as expected.

Personal connections, on the other hand, result from beliefs

and feelings that go beyond basic product and service

functions. When customers believe that the brand has their

best interests at heart, that the brand will go above and

beyond the call of duty, personal connections may begin to

develop. In addition to brand activities, customers may

enhance personal connections by incorporating brands into

their self-concept and deriving pleasure from relational

experiences. Each of these dimensions could be scaled into

multiple levels, but for the purpose of this research we have

limited each dimension to two levels, resulting in four

segments (Figure 2).

The first segment, those consumers who rate low on both

functional and relational connections, are in a transactional

relationship, at best. They do not perceive the brand as

Figure 1 Commitment process model




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providing significant value, relative to other brands, and do

not experience personal connections with the brand. Demand

among this group will be highly price elastic, its members may

engage in frequent brand switching and, on the whole, they

will exhibit neither behavioral nor attitudinal loyalty.

Satisfaction levels within this group vary, but none are

significantly more satisfied with the target brand than with

others. Or else, satisfaction is not a significant driver of their

choice in this product/service category. Any perceived loyal

behaviors result from inertia or external constraints, rather

than any relationship component.

Those customers in a functional relationship with the brand

have a true relationship, but it is primarily based on

satisfaction. Value, convenience, performance, and other

functional attributes of brand encounters drive the behaviors

of this group. Though less price sensitive than those in

transactional relationships, customers in functional

relationships may switch brands if the value equation is

altered by price competition. The value of the relationship is

strictly a function of the price-utility tradeoff that is made to

consume the brand. Members of this group are behaviorally

loyal, but may make up a majority of those loyal customers

who often defect.

Those customers represented by the lower right-hand

quadrant, the personal relationships, are motivated by

attitudinal factors related to personal connections. Beliefs

about brand motives, the role of the brand in self-definition,

the importance of relationships may all be components of personal connections. These customers may also exhibit loyal

behaviors, but these behaviors result from attitudinal loyalty

rather than functional outcomes. Though personally

connected with the brand, the basic utility of consumption

is no greater, if not less than, other brands in the market.

These customers may be relatively price insensitive, but do

not have strong functional ties to the brand. While not

necessarily prone to switch, these customers are at risk to

brands that offer significantly more value. Customers with

only personal connections may appear loyal, yet switch if

offered higher functionality by a competitor in the market.

Finally, the customers represented by the upper-right

quadrant, committed customers, have both functional and

personal connections with the brand. These customers are

both behaviorally and attitudinally loyal to the brand. Their

behavior is influenced, and perhaps constrained, by both

functional satisfaction and relational components of

consumption. Though many customers in functional and

personal relationships with the brand may be deeply satisfiedand exhibit behavioral loyalty, those in committed

relationships are the customers who will continue their loyal

behaviors for long periods of time. It is the combination of

functional and personal connections that provides the

continuing impetus for loyal behaviors. The differences

between these segments provide an explanation for loyal

customers who defect and satisfied customers who are

disloyal.

While the framework was originally proposed to expand our

understanding of customer-brand relationships, segmenting

customers based on relationship groups derived from the

Trust-Based Commitment model may provide better

measurement, understanding, and prediction of both

behavioral and attitudinal loyalty.

Market behaviors

One problem inherent in defining or measuring loyalty based

on marketplace behavior is that there is a broad range of

behaviors associated with the construct. Purchase,

repurchase, purchase frequency, and share of purchase may

all be used to indicate loyalty. Recommending, advocacy,

resistance to competitive advertising, willingness to expend

additional effort may all result from a loyalty orientation.

Narrowing the behaviors used to define and measure loyalty

may artificially constrain the construct, yet not result in more

viable measurement. What we are seeking is a method that

will effectively measure an underlying construct that will

provide a means to predict and promote loyal attitudes and

behaviors.Moving beyond measures of loyalty and satisfaction,

segmentation by the nature and strength of relationships

with the brand may predict a broad range of market

behaviors. Both primary (purchase, share, etc.) and

secondary (recommending, advocacy, etc.) loyalty behaviors

are expected to vary with relationship category. Repurchase,

share of purchase, visit frequency, price insensitivity, recency

of purchase, and advocacy are all expected to correlate with

relationship type and strength. However, unlike with simple

behavioral measures, relationship segmentation predicts these

behaviors and provides an underlying rationale. Even more

important, relationship segmentation discriminates between

behaviors based on convenience, inertia, or external

constraints and behaviors resulting from commitment.

Committed customers have more at stake and are more

likely to continue loyal behaviors.

Empirical tests of relationship segmentation

We propose that segmentation based on relationship type and

strength provides a measure of what really matters to firms

that is superior to simply recording behaviors. In order to test

the efficacy of segmenting customers based on their

relationships with brands, consumers were queried

concerning a broad cross-section of brands and their

behaviors toward the brands were measured.

Figure 2 Relational groups




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Selected behaviors represent both primary and secondary

loyalty behaviors. Primary behaviors include willingness to

pay more for the brand, dollars spent on the brand in the last

seven days, self-reported share of purchases, and number of

visits within the last seven days. Secondary loyalty behaviors

included likelihood of recommending the brand, willingness

to go out of your way to purchase from the brand, and

willingness to purchase from the brand on the web.

Relationship segments

Respondents were categorized into four relationship segments

– none (dis connected), functional, per son al, and

commitment based on their responses to a battery of

relational items (see Hess and Story, 2005 for a detailed

background). The items were designed to clearly discriminate

between personal and functional connections in order to

differentiate between the two groups and identify those

respondents in strong, committed relationships with the

brands. Table I provides a sample of functional and personal

connection measures.

Those respondents rating lowest on both personal and

functional connections were classified as disconnected, with

no brand connection. Those with relatively strong personal

and functional connections were classified as committed to

the brand. T hose w ith only personal or f unctional

connections were classed as having personal or functional

relationships.

Behaviors or intentions were then measured at two different

points in time. Past behavior and intentions were measured

during the same session as relationship indicators –

willingness to pay more, likelihood of recommending, and

willingness to go out of the way to patronize are examples. Six

months later, respondents were contacted for a follow-up

study that recorded actual behaviors.

Based on previous studies, we hypothesized several basic

relationships between relationship category and behaviors.

Customers who rate low on both personal and functionalconnection engage strictly on a transactional basis. These

customers may be transients, who are in relationships with

competing brands, or may not engage in relationships with

brands in the product category. Regardless of the motives or

causes for their disconnection, they were expected to exhibit

fewer loyalty behaviors.

H1. Customers lacking both functional and personal

connections with the brand will display fewer loyalty

behaviors than those with personal, functional, or

committed relationships.

Customers in committed relationships are satisfied with the

brand, have functional connections with the brand, and are

personally connected to the brand. Based on the breadth and

depth of these relationships, these customers were expected toengage in significantly more loyalty behaviors, both primary

and secondary, than customers with only a functional or

personal connection to the brand.

H2. Customers in committed relationships engage in more

loyalty behaviors than those in personal or functional

relationships.

W hile predicting the behaviors of com mitted and

disconnected customers is relatively straightforward,

predicting the behaviors of customers in personal and

functional relationships is somewhat more problematic.

Customers in functional relationships resemble many of thecustomers previously identified as loyal, based on market-

place behaviors. Their relationship is based on the utility of

the product or service, and this may result in shared loyalties

or switching.

Customers in personal relationships, on the other hand,

behave as a result of personal connections with the brand. In

the short term, customers in either of these groups may prove

profitable for the firm. Customers perceiving high levels of

functionality may even appear more loyal, at the cash register,

than those in strictly personal relationships. Particularly in

terms of share, number of visits, and recommendations,

customers in functional relationships may score higher than

those in strictly personal relationships.

In general, we would not predict significant differencesbetween the loyalty behaviors of customers in personal

relationships and those in functional relationships. For

instance, share of purchase should be higher among those in

personal and functional relationships than for disconnected

customers, but there is little support for a difference between

the two groups. However, one measure represents a direct

trade-off between cost and value – willingness to pay more for

the brand. Since customers buying based on functionality are

trading off utility for price, they would be severely limited in

their willingness to pay more. In this case we would expect

customers in personal relationships to score higher than those

simply buying for functionality.

H3. Customers in personal relationships with a brand will

be more willing to pay higher prices for the brand than

those in functional relationships.

Their willingness to pay more for the brand may also translate

into customers in personal relationships spending more on the

brand than those in functional relationships. Much of the

effect of relationship value will be captured only in the

behavior of committed customers, since they score highest on

personal connections. Yet, we also predict that customers in

personal relationships spend more on the brand in any given

time period than those in functional relationships.

H4. Customers in personal relationships with a brand

spend more in a given time period than those in

functional relationships.

Procedure

For the baseline survey, 1,988 respondents were randomly

selected from a nationwide online panel. Panel participants

are screened to participate in no more than four surveys per

year. In the baseline survey, respondents’ attitudes toward two

retail brands and their expected behavior were assessed. They

rated brands with which they were at least somewhat familiar

and had visited at least once in the last 30 days. Most

respondents were very familiar and visited the store more than

once in 30 days.

Surveys were all conducted via e-mail invitation online and

required approximately 20 minutes to complete. All ratings

questions used a seven-point semi-anchored Likert scale. The

Table I Samples of personal and functional connection measures

Personal Functional

I have an emotional connection They carry a wide variety of products

I have a personal connection They carry products I’m looking for

I feel a sense of loyalty They meet my basic needs




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responses range from Completely Agree to Completely

Disagree with the following statement. Approximately 51

percent of respondents were female and there were no gender

effects on the attributes of interest. The panel from which the

sample was drawn is carefully designed to represent a broad

range of demographic profiles. Age, Income and Occupation

are all distributed among panel members to reflect the US

adult population. Additionally the data are weighted toaccount for demographic biases that may result from online

sampling.

Approximately six months after the baseline survey was

fielded, respondents were re-contacted to determine how they

actually behaved in the intervening time between the initial

attitude assessment and their most recent visit to the target

retail stores. Specifically, respondents were asked only about

their behavior at store brands they rated six months previous.

In the follow-up survey customers were asked how much

money they spent, what proportion of their spending was at a

given store and how frequently they visited the stores they

rated previously, among other behaviorally-oriented metrics.

Of the 1,988 baseline respondents 978 responded to the

follow-up survey.

Results

Several different analyses were performed in order to test the

hypothesized relationships between category of relationship

and behavior. Seven loyalty behaviors (see Table II) were

tested for differences among groups of consumers. In

addition, a separate set of commitment measures were used

to verify the efficacy of defining commitment as resulting from

a combination of personal and functional connections with a

brand.

Two general analyses were performed to validate the overall

framework of the hypothesized relationships. First,

commitment was regressed on personal connection,

functional connection, and their interaction. The overall

model was significant ( p , 0.001), as were the parameterestimates for personal connection ( p , 0.001), functional

connection ( p , 0.001), and their interaction ( p , 0.05).

Not only are customers with personal or functional

connections more likely to be committed to the brand, and

by extension – loyal, the influence of either type of

connection is enhanced by the presence of the other type of

connection (Figure 3).

In the second general analysis, each of the behaviors was

regressed on relationship classification. The levels for all seven

behaviors were significantly different across relationship

groups ( p , 0.01). This overall result suggests that personal

and functional dimensions of relationships correlate with the

incidence of loyal behaviors. In addition, behaviors were

examined for individual groups in order to determine whether

the behavioral differences supported the hypothesized

relationships. These results are organized by hypotheses.

As expected, strong support was found for H1. Customers

who had neither functional nor personal connections with a

brand generally did not exhibit loyal behaviors. While not

unexpected, this result does provide additional insight into

loyal behaviors. It is important to note that if a meaningful

number of customers were behaviorally loyal through simple

inertia, the contribution of this effect was insufficient to

match the behaviors of customers in functional, personal, or

committed relationships. Figures 4 and 5 provide a graphical

summary of the results. In both cases, data was rescaled so

that the result for committed customers equals one hundred

to facilitate the comparison across relationship categories.

Disconnected customers (“None” in the figures) rated

lower on all behaviors, including primary and secondary

loyalty behaviors, than any of the other groups. This result

serves to validate the relationship between the measures of

personal and functional connections and loyalty behaviors.

Further, this indicates that loyalty is not simply a matter of

satisfaction with performance. Even customers who have

relatively low functional connections display loyal behavior if they perceive a personal connection.

Our second hypothesis was also strongly supported by the

results. Customers who were classified in committed

relationships were more likely to engage in all loyalty

behaviors measured than those in the other groups. Figure 4

summarizes the results for primary behaviors – willing to pay

more for the brand, actual spending in the previous seven

days, brand share of total purchases in the category, and

number of visits. Figure 5 summarizes the results for

secondary loyalty behaviors – likelihood of recommending

the brand, willingness to travel out of the way to patronize the

brand, and willingness to purchase the brand online.

Taken in the context of previous loyalty research, these

results for committed customers are particularly interesting.

These results suggest that customers who have strong

functional relationships become more “loyal” if they also

develop personal connections with the brand. Loyal behaviors

and the resulting financial impacts cannot be optimized

through satisfaction alone.

Conversely, customers with strong personal relationships

become more “loyal” if they also develop functional

connections with the brand. In other words, augmenting

personal connections with strong functional connections

optimizes loyal behaviors and the resulting financial impacts.

Our third hypothesis proposed that customers in personal

relationships, rated high on personal connection, but low on

Table II Loyalty behaviors

Primary behaviors (purchase

related) Secondary behaviors

Willing to pay more for the brand Recommendations

Dollars spent during the last week Willing to go out of the way

Brand share of purchases in the

category

Willing to purchase on the

internet

Visits during the previous week

Figure 3 Functional connections, personal connections, andcommitment




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functional connection, would be willing to pay higher prices

for the brand than those in strictly functional relationships.

This was measured using a seven-point Likert scale.

Disconnected customers averaged 2.2, those in functional

relationships averaged 2.5, and those in personal relationships

averaged 3.6. Those customers in personal relationships were

significantly more willing to pay more for the brand. As

anticipated, price was significantly less important for those

customers with a personal connection with the brand.

Given that customers with personal connections to the

brand are less price sensitive, we hypothesized that, within a

given time period, they would spend more overall on the

brand than those in functional relationships. This hypothesis

was not supported. While both groups, personal and

functional, spent more than disconnected customers and

less than committed customers, there was no significant

difference in spending levels between the two groups.

Discussion

These results, both in the aggregate and separately, provide

strong support for our overall thesis that relationship

segmentation reliably defines, measures, and predicts loyalty

behaviors among consumers. Unlike methods that rely on

marketplace behaviors or direct affective measures,

relationship segmentation characterizes customers based on

the levels of personal and functional connection with the

brand. Customers who develop both types of connections

tend to develop strong commitment to the brand. Hence, by

measuring customer positions that are not directly related to

purchase behaviors, we can identify groups with similar

loyalty profiles and predict their future behaviors.

In addition, relationship segmentation based on personal

and functional connections provides guidance on developing

loyalty. Past experience has shown that even successful pursuit

of satisfaction does not guarantee continued customer loyalty.

However, we have found support for the proposition that

developing personal and functional connections between

brands and customers promotes a broad range of profitable

loyalty behaviors.

Finally, it is important to note that the behavioral measures

were collected six months after the relationship measures. Not

only did relationship category correlate well with loyalty

behaviors, the relationship persisted over the intervening six

months.

Managerial implications

There is no question that customer loyalty, regardless of the

definition employed, is valuable to firms. The current focus

on customer relationships is driven by the loyal behaviors that

derive from the relationships. However, previous attempts to

define and measure loyalty have not resulted in reliable

predictors of future behavior, nor have they provided viable

strategies for building loyalty.

As demonstrated time and again in the marketplace,

satisfied customers defect and loyal customers drift away.

Employing relationship segmentation, based on personal and

functional connections, has two major advantages over

previous loyalty programs. First, it explains the variance in

behaviors of satisfied and behaviorally loyal customers.

Understanding why satisfied customers defect is the first

step in retaining them. The second advantage is that

Figure 4 Primary loyalty behaviors and relationship segments

Figure 5 Secondary loyalty behaviors and relationship segments




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trust-based commitment provides a prescriptive model for

initiating loyal behaviors and transitioning satisfied customers

to committed customers.

Based on a series of simple multi-item scales, relationship

segmentation provides a process by which firms can identify

existing customer groups, predict the probability of future

behaviors, and transition customers from less profitable to

more profitable groups. This method promises the possibilityof increasing primary and secondary loyalty behaviors by a

firm’s customers, now and into the future.

References

Fournier, S. (1998), “Consumers and their brands:

developing relationship theory in consumer research”,

Journal of Consumer Research, Vol. 24 No. 4, pp. 343-73.

Gronroos, C. (1997), “From marketing mix to relationship

marketing – towards a paradigm shift in marketing”, Management Decision, Vol. 35 No. 4, pp. 322-6.

Gummeson, E. (2002), Total Relationship Marketing ,

Butterworth-Heinemann, Oxford.

Hess, J. (1995), “Construction and assessment of a scale to

measure consumer trust”, paper presented at the AmericanMarketing Association Summer Educators’ Conference.

Hess, J. and Story, J. (2005), “Trust-based commitment:

multidimensional consumer-brand relationships”, Journal

of Consumer Marketing , Vol. 22 No. 6, pp. 313-22.

Oliver, R. (1999), “Whence customer loyalty”, Journal of

Marketing , Vol. 63 No. 4, pp. 33-44.

Parvatiyar, A. and Sheth, J.N. (2001), “Customer relationship

management: emerging practice, process, and discipline”,

Journal of Economic and Social Research, Vol. 3 No. 2,

pp. 1-34.

Reichheld, F.F. (2003), “The one number you need to grow”,

Harvard Business Review, Vol. 82 No. 12, pp. 46-54.

Schultz, D.E. and Bailey, S. (2000), “Customer/brand loyalty

in an interactive marketplace”, Journal of Advertising

Research, Vol. 40 No. 3, pp. 41-52.

Schulz, D.E. (2005), “The loyalty paradox”, Marketing Management , Vol. 14 No. 5, pp. 10-11.

About the authors

John Stor y (PhD from the University of Colorado at Boulder)

is an Associate Professor Of Marketing at Idaho State

University. He has published work in the Journal of Consumer

Marketing , Journal of Product and Brand Management , Journal

of Business Research, and International Journal of Internet

Marketing and Advertising , as well as in various national

conference proceedings. Dr Story’s research interests include

customer-brand relationships, cross-cultural marketing, and

internet adoption and implementation. He is the

c or re sp on di ng a ut ho r a nd c an b e c on ta ct ed at :[email protected]

Jeff Hess (PhD from the University of Colorado at Boulder)

is Senior Vice President of TNS, Inc. He has published in the

Journa l of Consumer Marketing and marketing industry

publications, as well as award-winning papers in various

national conference proceedings. Dr Hess’ primary research

interest is the formation, management, and implementation of

customer-firm relationships.




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48R_4 Segmenting Customer Brand Relations

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