HELSINKI BUSINESS POLYTECHNIC

Degree Programme in International Business

Jukka Niiranen

MEASURING E-MAIL DIRECT MARKETING EFFECTIVENESS IN

CUSTOMER RELATIONSHIP BUILDING

Bachelor’s Thesis

2002

HELSINKI BUSINESS POLYTECHNIC

Degree Programme in International Business Author and Student Number: Jukka Niiranen, 9800847 Title of the Bachelor’s Thesis: Measuring E-mail Direct Marketing Effectiveness in

Relationship Building Year of Completion: 2003 Number of Pages: 97 ABSTRACT

The objective of the study was to examine how response data from direct marketing e-mail campaigns could be utilized in measuring the development of a customer relationship in the context of an end-user loyalty program. The case analysed in the study was Club Nokia’s e-mail campaigns targeted at its registered members. The primary research problem covered in the study was “how can e-mail direct marketing results be measured by using Club Nokia’s existing campaign response and customer data?” In order to gain understanding of the role of e-mail direct marketing campaigns in a loyalty program, a framework was created around the concept of customer relationship. As a form of interactive marketing, e-mail direct marketing contributes to relationship development by providing a channel for dialogue that consists of both communication as well as interaction. The empirical study was conducted by using a quantitative research method. Data analysis was performed in ways that could also be applied in ongoing monitoring of relationship development within the case company. The study focus was on the information that click-through data can provide when combined with variables found from the customer database. The study was conducted based on individual level secondary data available on recent Club Nokia e-mail campaigns, collected from campaign report files and Club Nokia’s member database. Click-through activity was found to have positive correlation with all the other behavioral variables available on the customer relationship. The youngest age group was found to have the lowest response rates. Repeat responses were rare within the studied time period. E-mail address invalidity was found to have significant effect on dialogue termination. Analysing interaction data from e-mail direct marketing campaigns clearly provides new insight into the development of a customer relationship. Due to the relatively high frequency, tracking click-through activities enables monitoring relationship development in between monetary transactions and reflects the level of interest towards the loyalty program in particular. However, clicks on links in direct marketing e-mails should be mainly considered as a confirmation of interest rather than a requirement.

Keywords: Customer relationship marketing, Direct marketing, Interactive marketing, Loyalty

program, Dialogue, E-mail

HELSINGIN LIIKETALOUDEN AMMATTIKORKEAKOULU

Degree Programme in International Business Tekijä ja opiskelijanumero: Jukka Niiranen, 9800847 Opinnäytetyön nimi: Measuring E-mail Direct Marketing Effectiveness in

Relationship Building Valmistumisvuosi: 2003 Sivumäärä: 97

TIIVISTELMÄ

Työn tavoitteena oli tutkia miten sähköpostin käytöstä suoramarkkinoinnissa kertyvää vastaustietoa voidaan hyödyntää asiakassuhteen kehittymisen mittaamisessa kanta-asiakasohjelman yhteydessä. Aiheen tutkimisessa käytettiin esimerkkitapauksena Club Nokian jäsenilleen kohdistamia sähköpostikampanjoita. Primäärinen tutkimusongelma oli seuraava: ”Miten sähköpostitse tapahtuvan suoramarkkinoinnin tuloksia voidaan mitata hyödyntäen Club Nokian nykyistä kampanja- ja asiakastietoja?” Tutkimuksen teoreettinen viitekehys rakennettiin asiakassuhteen käsitteen ympärille sähköpostitse tapahtuvan suoramarkkinoinnin roolin määrittämiseksi kanta-asiakasohjelman yhteydessä. Interaktiivisen markkinoinnin välineenä sähköposti tukee asiakassuhteen kehittymistä mahdollistamalla asikasdialogin, joka muodostuu viestinnän ja vuorovaikutuksen yhdistelmästä. Empiirinen tutkimus toteutettiin käyttämällä kvantitatiivista tutkimusmenetelmää. Aineiston analysoinnissa käytettiin menetelmiä, joita on mahdollista hyödyntää myös yrityksen jatkuvassa asiakassuhteiden kehittymisen mittauksessa. Tutkimus keskittyi klikkaustiedoista saatavan uuden tiedon tutkimiseen yhdistettäessä se yrityksen asiakastietokannasta löytyviin asiakastietoihin. Empiirisessä tutkimuksessa käytettiin yksilötason sekundääristä aineistoa, joka kerättiin Club Nokian viimeaikaisten sähköpostikampanjoiden seurantaraporteista ja Club Nokian asiakastietokannasta. Club Nokian jäsenten klikkausaktiviteetillä havaittiin olevan vahvistava vaikutus kaikkien muiden saatavilla olevien käytösmuuttujien frekvenssiin. Klikkaustodennäköisyys oli matalinta nuorimassa kontaktoidussa ikäryhmässä. Tutkitulla ajanjaksolla esiintyi toistuvaa klikkausaktiivisuutta vain vähäisessä määrin. Sähköpostiosoitteiden toimivuuden laskun todettiin olevan merkittävin syy dialogin katkeamiseen. Sähköpostikampanjoista saatavien vuorovaikutustietojen analysointi antaa selvästi uutta tietoa asiakassuhteen kehittymisestä. Korkean volyyminsa ansiosta klikkaustiedot mahdollistavat asiakassuhteen rahallisten transaktioiden välisten jaksojen tarkemman seuraamisen ja kanta-asiakasohjelmaa kohtaan koetun mielenkiinnon määrittämisen. Sähköpostiviestien hyperlinkkeihin reagointia tulisi kuitenkin käsitellä lähinnä mielenkiinnon ilmentymänä muttei sen edellytyksenä.

Avainsanat: asiakassuhdemarkkinointi, suoramarkkinointi, interaktiivinen markkinointi, kanta-

asiakasohjelmat, dialogi, sähköposti

Table of Contents

1 INTRODUCTION .......................................................................................................................................................... 1

1.1 Background ............................................................................................................................................................. 1

1.2 Subject of the Study ................................................................................................................................................ 2

1.3 Case Introduction .................................................................................................................................................... 2

1.4 Purpose of the Study ............................................................................................................................................... 2

1.5 Research Problem .................................................................................................................................................... 4

1.6 Limitations .............................................................................................................................................................. 4

1.7 Structure of the Report ............................................................................................................................................ 5

2 THEORETICAL FRAMEWORK .................................................................................................................................. 6

2.1 Relationship Marketing ........................................................................................................................................... 6

2.1.1 Customer Relationship ................................................................................................................................... 7

2.1.2 Customer Relationship Development ............................................................................................................. 9

2.1.3 Customer Loyalty.......................................................................................................................................... 11

2.1.4 Relationship Value........................................................................................................................................ 12

2.1.5 Customer Dialogue ....................................................................................................................................... 14

2.1.6 Customer Interaction .................................................................................................................................... 15

2.2 Loyalty Programs .................................................................................................................................................. 16

2.3 One To One Marketing ......................................................................................................................................... 18

2.4 Customer Relationship Management .................................................................................................................... 19

2.4.1 CRM Technology .......................................................................................................................................... 20

2.5 Direct Marketing ................................................................................................................................................... 21

2.6 Interactive Marketing ............................................................................................................................................ 22

2.6.1 E-mail marketing .......................................................................................................................................... 23

2.7 Framework Summary ............................................................................................................................................ 25

2.8 Relationship Measurement .................................................................................................................................... 26

2.8.1 RFM Analysis ............................................................................................................................................... 28

2.8.2 The Leaky Bucket Theory ............................................................................................................................. 29

2.9 E-mail Marketing Measurement ............................................................................................................................ 30

2.9.1 Bounces ........................................................................................................................................................ 30

2.9.2 HTML Open Rate ......................................................................................................................................... 30

2.9.3 Click-throughs .............................................................................................................................................. 31

3 CASE CLUB NOKIA ................................................................................................................................................... 33

3.1 Introduction to Club Nokia ................................................................................................................................... 33

3.2 E-mail Messaging as Customer Dialogue ............................................................................................................. 34

3.2.1 Clicking as Customer Interaction ................................................................................................................. 37

3.3 Available Data ....................................................................................................................................................... 39

3.3.1 Campaign Report Summaries ....................................................................................................................... 39

3.3.2 Campaign Click Data ................................................................................................................................... 40

3.3.3 CN Member Database .................................................................................................................................. 41

3.4 Data Interpretation ................................................................................................................................................ 42

3.4.1 Definition of a Member ................................................................................................................................ 42

3.4.2 Definition of Purchase .................................................................................................................................. 43

3.4.3 Repeat Purchases ......................................................................................................................................... 43

3.4.4 Dissolution .................................................................................................................................................... 44

3.4.5 Framework Summary for Club Nokia........................................................................................................... 45

4 DESCRIPTION OF THE RESEARCH METHODOLOGY ........................................................................................ 47

4.1 Approach And Methodology ................................................................................................................................. 47

4.2 Available Data ....................................................................................................................................................... 47

4.3 Population ............................................................................................................................................................. 48

4.4 Sample ................................................................................................................................................................... 48

4.5 Data Analysis ........................................................................................................................................................ 49

4.5.1 Calculation of Averages ............................................................................................................................... 50

4.5.2 Counting Clicks ............................................................................................................................................ 51

4.6 Research Reliability .............................................................................................................................................. 51

4.7 Research Validity .................................................................................................................................................. 52

4.7.1 Internal validity ............................................................................................................................................ 52

4.7.2 External Validity ........................................................................................................................................... 53

5 EMPIRICAL RESEARCH RESULTS ......................................................................................................................... 55

5.1 Overview of Campaign Response Data .................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

5.1.1 Average Click Rates ......................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.1.2 Campaign Level Click-through Rates (CTR) .................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.1.3 Number of Campaigns Clicked ......................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.1.4 Number of Links Clicked .................................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

5.2 Member Demographic Data ................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.2.1 Gender .............................................................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

5.2.2 Age .................................................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.2.3 Registration Media ........................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.2.4 Latest Phone Category ..................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.3 Member Behavior Data .......................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.3.1 Digital Content Downloads .............................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

5.3.2 Product Registrations ....................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.3.3 Inbound Contacts.............................................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

5.3.4 Phone Repairs and Swaps ................................................................ Virhe. Kirjanmerkkiä ei ole määritetty.

5.3.5 Behavior Data Volumes .................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.4 Membership Length ............................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.5 Dissolution ............................................................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

5.5.1 Unsubscribers ................................................................................... Virhe. Kirjanmerkkiä ei ole määritetty.

5.5.2 Invalid E-mail Addresses .................................................................. Virhe. Kirjanmerkkiä ei ole määritetty.

6 CONCLUSIONS ........................................................................................................................................................... 56

6.1 Summary of the Empirical Research Findings ...................................................................................................... 56

6.2 Recommendations ................................................................................................................................................. 59

6.2.1 Time-series Data .......................................................................................................................................... 60

6.2.2 Member Activity Level .................................................................................................................................. 61

6.2.3 Member Profile Changes .............................................................................................................................. 62

6.2.4 Response Categorization .............................................................................................................................. 63

6.3 Further Research ................................................................................................................................................... 63

LIST OF SOURCES ............................................................................................................................................................... 65

APPENDIX A: EXAMPLE OF CLUB NOKIA E-MAIL NEWSLETTER .......................................................................... 70

APPENDIX B: DESCRIPTION OF RESEARCH DATABASE STRUCTURE ................................................................... 71

1

1 INTRODUCTION

1.1 Background

The marketing landscape is going through a transition phase that is driven by both shifts

in marketing focus and developments in the available tools and technology. The subject

of this study encompasses three new trends:

• New focus: build and manage customer relationships

• New media: communicate directly with customers by e-mail

• New information: capture customer behavior data on individual level

Companies are no longer satisfied with just talking to their customers, they would much

rather have a dialogue with them in order to get to know them better and build lasting

customer relationships. This means an increasing amount of the communication will take

place through direct media. The high adoption rate of e-mail, the web’s “killer app”,

combined with its ability to track message viewings and hyperlink clicks to individual

recipients, is a highly attractive tool for relationship marketers.

Marketing is becoming less obsessed with winning over new customers and more

focused on retaining the existing customers. Loyalty programs are introduced to reward

customers who frequently purchase the same brand, with the intention of enforcing their

loyal behavior and maybe even turning them into advocates of the brand. Instead of

considering all buyers of a given product to be equally valuable, companies aim to

evaluate the potential value that could be acquired from an individual customer during

his or her lifetime and use this information for investing the marketing budget where it

will reap the highest returns – their best customers.

Making decisions on which customers will be profitable in ten years time requires much

more information than deciding how to maximize next quarter sales. While the amount of

data available on customers is growing rapidly, it will not automatically turn into

information that can be shared, let alone knowledge upon which to base the decisions.

Communication and interaction with customers is increasingly moving into electronic

media, which enables a whole new level of detail in measuring these activities. Equipped

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with the new measures, marketers can begin to tackle the question of which customers

should they hold on to and how to establish and maintain a relationship with them.

1.2 Subject of the Study

The subject of this study is the measurement of e-mail direct marketing effectiveness

when used for building business-to-consumer relationships. The target is to gain

understanding of the possible applications of e-mail campaign response data in

measuring the success of customer dialogue, specifically when integrated into customer

information found from internal company databases. The empirical research will test how

the theoretical framework can be used to interpret individual level data and transform it

into measures that surpass aggregate level indicators in providing insight into the

communication process of e-mail direct marketing in the context of a loyalty program.

1.3 Case Introduction

The case analysed in the study is Club Nokia, which is the end-user loyalty program of

Nokia Mobile Phones, the world’s largest mobile phone producer. The program can be

seen as a tool for building relationships between Nokia and its customers. Membership to

Club Nokia is free to all Nokia mobile phone owners. The benefits offered include

product and service information, customer support, digital content for mobile phones,

invitations to special events etc. Club Nokia regularly uses e-mail and SMS direct

marketing campaigns to keep in touch with its members. A more detailed description of

Club Nokia and its activities can be found in chapter 3 of this report.

1.4 Purpose of the Study

In order for direct marketing campaigns to be considered as customer dialogue in a

relationship marketing sense, it is not enough to just talk to the customer through a direct

media. One must also listen to what the customer has to say and adapt the outbound

communication according to the response data received. Just like relationship marketing

is shifting the product oriented thinking of mass marketing towards a customer oriented

view, similarly the tracking of direct marketing communications needs to move from

campaign level to relationship level analysis. Measurements of campaign response rates

3

on aggregate level cannot accurately reflect how the relationship between the company

and the customer is developing, especially in the context of one-to-one marketing where

individual treatment of customer relationships is the foundation of all marketing

activities.

Although the electronic media makes it possible to capture a wide variety of customer

specific data, thus enabling individual level analysis of the relationships, the increasing

volume of data also creates its own challenges. Monitoring single customer actions in a

population of millions is impossible, which is why it is crucial to develop methods of

analysis that take advantage of the customer dimension without drowning the user into a

sea of detail. The information needs to be mapped to a conceptual framework that gives

meaning to it; otherwise we face the danger of degenerating back to mass marketing

measurement practices and losing one of the most significant advantages of using

interactive media.

Documentation on the practical applications of theoretical frameworks in the area of

relationship marketing utilising electronic media is still scarce. One reason for this is

likely to be the uniqueness of various CRM systems that have been customised to the

needs of particular companies. Applying generic measurement models in an environment

where the availability and definition of data varies not only between each case but also

over time is not a straightforward task.

The objective of the research from Club Nokia’s perspective is to study how the

customer and campaign data currently available could be used in increasing

understanding of the e-mail communication process that takes place between Club Nokia

and its members on a regular basis. The information systems involved in the process are

being developed to include functionalities required for continuous tracking of campaign

results on customer level, but until now there has not been a practical way of combining

the data, which has meant its potential has been left mostly unexplored.

The research includes an empirical part in which a sample of the two separate data

sources, the Club Nokia Data Mart (a customer database used for analytical purposes)

and the e-mail campaign response reports, will be combined in a manner similar to which

will likely become possible in the customer database through the development of Club

4

Nokia’s campaign management platform. The objective is therefore to examine the ways

in which this data can be used for analysis purposes, so that the findings can be later

applied to the planning of regular reporting.

1.5 Research Problem

The primary research problem discussed in the study is “How can e-mail direct

marketing results be measured by using Club Nokia’s existing campaign response

and customer data?”

Sub-problems to be solved in the study are:

• “In which ways do e-mail campaigns contribute to the development of a relationship

between Nokia and its customers?” Defining the purpose of the activity under

investigation, examining Club Nokia’s e-mail direct marketing in a theoretical

context. (3.2)

• “What information is available regarding the communication process?” Assessment

of data availability and processing possibilities. (3.3)

• “How can this information be linked to the customer relationship?” Meaning of data

in terms of the activity’s purpose, identifying validity problems. (3.4)

• “What is the key information to be gained from the linkage concerning past mailings’

effect?” Findings from the analysis of sample data. (5 & 6.1)

• “How could this information be monitored on a long-term basis?” Recommendations

for future analysis practice. (6.2)

1.6 Limitations

The study focuses only on activities related to e-mail direct marketing as one element of

the loyalty program. It does not discuss measurement of the overall success of such

programs in managing customer relationships nor the return on investment derived from

the use of e-mail in establishing a customer dialogue. While the ultimate purpose of

relationship marketing activities is to drive up variables such as customer retention, share

5

of wallet and value per relationship, their measurement does not fall within the scope of

this study.

The study focuses on member activities that are directly traceable to the outbound

communication, which in the context of the empirical research is limited to clicks on the

hyperlinks provided in e-mails. The member in question is identified from the click

stream data and the information is linked to profile and behavior data held in the

customer database. Due to the long repurchase cycle of mobile phones and the limited

amount of available data, the study will not focus only on phone purchases or the value

acquired through them. The key phenomenon under investigation is the forming of an

active relationship between Nokia / Club Nokia and the members of the loyalty program,

and how the e-mail direct marketing activities contribute to the development of this

relationship.

1.7 Structure of the Report

The theoretical framework to be used in the study is discussed in chapter 2. The

framework is then viewed in the light of the Club Nokia case in chapter 3, which

discusses details specific to the case and identifies data available for the quantitative

research. Chapter 4 describes the methodology used in performing the empirical research.

The research results are presented in chapter 5 and conclusions made based on the

findings are discussed in the final chapter.

6

2 THEORETICAL FRAMEWORK

2.1 Relationship Marketing

Since 1970 the core phenomenon of marketing was considered to be the exchange of

products for money. In this form of marketing, often labeled in literature as transaction

marketing, the major focus has been to make customers buy, whether they are old or new

customers. Marketing became campaign oriented, as the customers needed to be won

over and over again. The marketing mix management approach and the 4P’s model were

the results of research on this exchange phenomenon. (Grönroos 2000a, 21.)

Today the focus of marketing discussion is shifting away from the mass media thinking

of the 4P model. In what has often been called a paradigm shift, the attention of

marketers is increasingly focusing on buyer-seller interactions. This shift is based on the

notion that it is not exchanges that are the core of marketing, but that exchanges take

place in ongoing relationships between parties in the marketplace. This relationship

perspective states that continuous purchases and cross-sales opportunities follow from

well-managed relationships, thus seeing relationships as the most important concept in

marketing. (Grönroos 2000a, 21-22.)

Gummesson (1997, 267) defines relationship marketing as a set of relationships,

networks and interaction, where the value for parties involved is created through an

interaction process between suppliers, customers, competitors and others. Companies

benefit from the relationship marketing approach through increased customer retention

and marketing productivity, and the customers receive added value through more

customized service and products.

Whereas the exchange perspective in marketing is focused on the value distribution

process, the relationship perspective focuses on the value creation process. Instead of

distributing ready made value to customers, “relationship marketing aims to facilitate and

support the customers’ consumption and usage processes throughout the relationship, in

which value for customers is created by the customers and in interactions with the

supplier or service provider.” (Grönroos 2000a, 25.)

7

It is sometimes argued that relationships between a company and its customers do not

necessarily always exists, and that the parties may not want to engage in a relationship.

According to Grönroos (1997, 408), “latent relationships do always exist, and either party

may choose to activate that relationship, depending on their strategies, needs, wishes and

expectations, or choose not to do it.” It would therefore in principle always be possible

for a company to adopt a relational marketing strategy, but in reality a transactional

strategy may be more profitable, depending on factors such as nature of the product,

customer wishes and competitive situation.

A company cannot single-handedly turn from a transaction marketing organization into a

relationship marketing organization, since relationships with customers are not

established only because the marketer says so. Grönroos (2000a, 32) stresses that even

though the customer may be benefiting from a membership in a loyalty club or improved

direct mail, this does not yet qualify as relationship marketing. All of the important

customer contacts have to be relationship-oriented and present a clear improvement in

service level that the customer is able to notice.

Relationship marketing should not be perceived as a set of tools, such as direct marketing

or loyalty programs. While these tools may be useful in implementing a relationship

marketing strategy, they alone cannot make a company relationship-oriented.

Relationship marketing is not merely a new or improved way of communicating with

customers. Although this is an essential element, relationship marketing requires

integration of interactions and communication, not communication alone. (Grönroos

2000a, 40-41.)

2.1.1 Customer Relationship

According to Grönroos (2000a, 33) a relationship has developed when the customer

perceives that a mutual way of thinking exists between customer and supplier or service

provider. This definition emphasizes the attitudinal nature of a relationship. A firm

should create interaction and communication processes that support this feeling, but it is

ultimately the customer who determines whether or not a relationship has developed.

8

Grönroos (2000a, 35) categorizes customers as being in three different “modes”

depending on their interest in a relationship with the firm in a given marketing situation:

transactional mode, passive relational mode and active relational mode. In transactional

mode the customer’s interest is focused on making a successful purchase and he or she

can perceive continuous communication between purchases, such as an e-mail

newsletter, simply annoying and time consuming. Customers in passive relational mode

are looking for the knowledge that they could contact the supplier or service provider, by

for example finding an e-mail address printed on a soft drink can, but they seldom

respond to invitations to interact. Active relational customers are looking for

opportunities for interaction in order to get additional value and they can get disappointed

due to a lack of contacts.

The categorization points out that one should not always expect a customer to show high

levels of interaction, even if he or she does perceive the relationship marketing activities

to deliver added value. As stated by Grönroos (2000a, 36) “for customers in a passive

relational mode, a relationship-oriented marketing strategy is important, although it may

not seem so.” Relationship marketing activities can increase the value of a brand in the

minds of a customer in either passive or active relational mode, regardless of interactions

(Grönroos 1997, 408).

Gwinner et al (1998, 101-114) conclude that customers entering a relational mode can

see three types of benefits from maintaining the relationship: confidence, social benefits

and special treatment. Confidence benefits include reduced anxiety and faith in the firm,

which help in minimizing cognitive dissonance or the feeling of having made a less

optimal choice of supplier or service provider. These types of benefits were considered

most important by all customers in a study by Gwinner et al, showing they are key results

of a well-functioning long-term relationship. Social benefits such as personal recognition

by employees and special treatment benefits like extra services or special prices also play

a part in creating relationship benefits over the company’s core offering. (Grönroos

2000a, 36-37.)

Feurst (1999, 163) brings out the inconsistent nature of actual customer behavior through

an example of a young customer’s typical preferences. If you give a youngster the chance

to choose between a supplier that wants to create a learning relationship by recording the

9

customer’s personal data and a company that simply hands out the ready made product in

exchange for payment, he or she is likely to choose the latter one. The choice is made

regardless of the fact that the first alternative would be more beneficial to both the

customer and the society in the long term. The example aptly demonstrates the need to

explicitly communicate the relationship benefits to a customer in order to persuade him

or her to switch from transactional to relational mode, as the behavior of an individual is

often driven by short-term benefits.

In examining consumer attitude towards forming relationships with suppliers, Peppers &

Rogers (1993, 327-328) describe the transactional marketing approach of using mass

media to communicate your message to consumers as an implicit bargain. The mass

marketers pay a portion of a magazine’s production cost and in return the readers view

their ads. Due to the implicit nature of this bargain, consumers are free to ignore the ads

and mass marketers may freely harass many consumers with their message in order to

enlist a few new customers. When using any addressable, two-way medium, the company

and the customer make an explicit bargain, in which the parties collaborate and give

something (service, information) to get something in return. The explicit bargain is based

on mutual trust between the parties, which is an essential ingredient in any relationship

and can therefore be seen as a requirement for converting customers from transactional to

relational mode.

2.1.2 Customer Relationship Development

Customer relationships development can be described as a life cycle with three basic

phases: initial phase, purchasing phase and consumption phase. The objective of

marketing targeted towards the (potential) customer differs between the phases, from

creating interest to making promises and finally keeping the promises. (Grönroos 2000a,

250-251.) This definition underlines the fact that a life cycle is not dependent on whether

the customer makes a repeat purchases or not, but it exists in all purchase situations and

is a continuous loop that repeats itself throughout the relationship.

An alternative, more data-driven view to utilizing the concept of a customer life cycle is

to study repeat purchases over time and try to both minimize the time between purchases

and maximize the length of the relationship (Novo 2000, 45). This approach is based on a

10

sequence of purchase transactions made by the customer, which is used for determining

the appropriate marketing activities at any given point of the life cycle. Focusing only on

purchase events extends the scope of the life cycle but ignores the phases before and after

the events. An accurate life cycle model should therefore consist of two levels: the

process related to each individual purchase and the relation of sequential purchase events.

Taking a more detailed look at how relationships between two parties are born, Dwyer et

al (1987, 15) present a model with five phases for the relationship development process:

(1) awareness, (2) exploration, (3) expansion, (4) commitment, and (5) dissolution. These

phases will now be briefly described.

Awareness refers to the potential customer’s recognition that the company is a feasible

exchange partner, but there have not yet been interactions between the parties.

Awareness may have been generated through marketing communications or it may result

from factors such as outlet vicinity. (Dwyer et al 1987, 15.)

Exploration refers to the parties’ consideration of obligations, benefits, burdens and the

possibility of exchange. This phase can include testing and evaluation, or in some cases

trial purchases. The exploration phase consists of five subprocesses: (1) attraction, (2)

communication and bargaining, (3) development and exercise of power, (4) norm

development, and (5) expectation development. (Dwyer et al 1987, 16.)

Expansion refers to the continual increase in benefits obtained by the exchange partners

and the increasing interdepence between them. The subprocesses from the exploration

phase also operate in the expansion phase, as the satisfaction from these subprocesses

increases the parties’ motivation to maintain the relationship. (Dwyer et al 1987, 18.)

Commitment is the most advanced phase of a relationship, in which the satisfaction

from the exchange process keeps the parties from actively seeking alternative exchange

partners. Customer (seller) loyalty is achieved. (Dwyer et al 1987, 18.)

Dissolution can occur after any of the four phases of the relationship development

process, but its consequences are more significant if high interdependencies have been

reached in the expansion and commitment phases prior to the termination of the

11

relationship. While the relationship development process is mainly bilateral, dissolution

is typically initiated unilaterally. Dissolution occurs when one party evaluates that the

costs of continuation or modification outweigh the benefits from the relationship. (Dwyer

et al 1987, 19-20.)

The model by Dwyer et al is based on exchange theory and it has been generalized to

cover both interfirm and consumer relationships. Dwyer et al (1987, 16-20) conclude that

while the behaviors described are typical in business-to-business exchange, they apply to

only a portion of consumer transactions, namely those involving high priced durable

goods and complex services.

Grönroos (1996, 8) argues that this approach of combining the concepts of exchange and

relationship is not sensible. This is because exchange is a concept with a short-term

notion where something is given to someone else, whereas relationship has a long-term

notion implying an association of two parties. O’Malley and Tynan (2000) find the

conceptualization of exchange in consumer markets as either transactional or relational to

be inadequate, since these fail to recognize the wide range of possible positions on the

continuum. They consider exchange in consumer markets to be characterized by both

transactional and relational elements, and suggest the two perspectives should be merged

to allow the development of more coherent and robust theories.

2.1.3 Customer Loyalty

Customer loyalty can be defined as the degree to which customers are predisposed to stay

with a company and resist competitive offers (Peppers & Rogers Group 2002). As a

measure, customer loyalty is the proportion of times a purchaser chooses the same

product or service in a specific category compared to the total number of purchases made

by the purchaser in that category (Neal 1999, 21). While relationships are a central part

of loyalty, they alone are not enough to make a customer loyal (Grönroos 2000a, 7).

The process of building customer loyalty is often described using a loyalty ladder with

five ascending steps: suspect, prospect, customer, client and advocate. The purpose of

relationship marketing activities is to move the customer through a series of steps that

12

culminate in the customer being an advocate for the company’s product. (Roberts &

Berger 1999, 10-11.)

Frederick F. Reichheld demonstrated the economics of loyalty in his 1996 publication

“The Loyalty Effect” through examples from various lines of business, all of which

showed a clear correlation between customer retention and company profitability. He

argued that while most managers understand the linkage which both customer and

employee loyalty has on revenues and costs, they are blinded by short-term financial

indicators which drive them towards maximizing shareholder value instead of value to

the customer. (Reichheld 2001, 12-16.)

Drawing comparison between the concept of loyalty and commitment in the context of

the development process of a relationship, Dwyer et al (1987, 19) suggest that

commitment should be measured by three criteria: inputs, durability and consistency. The

first criterion of commitment is that the parties in a relationship provide relatively high

levels of inputs to the association. This can mean significant exchange of economic,

communication, and/or emotional resources. Second, there should be some durability of

association over time. The parties need to have a common belief in the effectiveness of

future exchange, which will enable them to bond and encourage the continued investment

in the relation. The third aspect of commitment is the consistency with which the inputs

are made to the association. Inconsistency in input levels reflects low commitment and

leads to a reduced reliance by the other party on the outcomes of the exchange. Social

bonds tend to weaken and dissolve over time unless actively maintained. (Dwyer et al

1987, 19.)

2.1.4 Relationship Value

The importance of customer loyalty becomes evident through the study of customers’

value to the company depending on the length of relationship. A customer’s contribution

to net profit may vary substantially over the lifetime of the relationship. New customers

who may initially be unprofitable will increase their profit contribution as the

relationship continues, thus making it essential to study the life cycle of a customer when

determining the value of the relationship. (Grönroos 2000a, 150.)

13

The lifetime value of a customer relationship to the company can be calculated as the net

present value of the net profits that can be expected over the years. In order to estimate

the future behavior of a customer, existing or new, historical data is needed for

calculating actual repeat purchases that have taken place and projecting model for

expected future purchases. (Roberts & Berger 1999, 180-182.)

Although marketing literature mainly includes models of the value of a customer, in the

context of relationship marketing one should also pay attention to the value-to-customer

perspective. Grönroos (1997, 412) defines customer perceived value as the sum of core

solution and additional services divided by the sum of price paid and relationship costs.

Relationship costs follow from the customer’s decision to engage a relationship with

another party. These costs can be of monetary or psychological in nature, ranging from

membership fees to feeling of inconvenience. If the costs become greater than the

benefits from the additional services, the customer is likely to prefer a transactional mode

approach instead of relational mode.

The most loyal customers are commonly considered to be also among the most profitable

ones, but the evidence behind this assumption is not unambiguous. Reinartz and Kumar

(2002, 4-12) suggest that the relationship between loyalty and profitability is much

weaker and subtler than what has been claimed in loyalty literature. Their study found

little or no evidence that customers who purchased steadily from a company over time

would be cheaper to serve, less price sensitive, or particularly effective at bringing in

new business.

Reinartz and Kumar (2002, 4-12) found that loyal customers tend to be even more price

sensitive than an occasional customer. These customers are generally more

knowledgeable about product offerings and can better evaluate their quality.

Furthermore, consumers seem to strongly resent companies that try to profit from loyalty,

since they believe loyal customers deserve lower prices. With this in mind, it is clear that

one should not use measurement of customer loyalty as the single indicator of

relationship value, nor should one aim to make all existing customers loyal to the brand.

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2.1.5 Customer Dialogue

Dialogue is defined as an exchange of thought between two or more parties. According to

Peppers and Rogers (1993, 213-215), there are four criteria that any marketing

communication must meet before it can be considered to be dialogue with individual

customers:

• All parties to a dialogue must be able to participate in it

• All parties to a dialogue must want to participate in it

• Dialogues can be controlled by anyone in the exchange

• Your dialogue with an individual customer will change your behavior toward that

single individual, and change that individual’s behavior towards you.

A dialogue can be seen as an interactive process of reasoning together so that a common

knowledge platform is developing. The process aims to build shared meanings and create

new knowledge, so that the knowledge platform enables the supplier to create additional

value for the customer on top of the value of goods and services that are exchanged.

(Grönroos 2000b, 6.)

Successful development of a relationship requires the parties involved to share

information about needs that have to be fulfilled and solutions that can be offered. A

distinction should however be made between sharing information and persuasion or

manipulation. According to Grönroos (2000b, 6), “relationship marketing frequently fails

because marketers rely on relatioship-like, but nevertheless manipulative, one-way

communication, such as personally addressed and even personalized direct mail, to lure

customers into business with the firms they represent without listening to their wishes

and responding to the feedback they may give.”

In a dialogue there are no senders or receivers, only participants. Participation in a

dialogue takes place through interactions between the firm and its customers, but also

one-way messages, such as advertisements, brochures and direct mail, can be considered

a part of the dialogue process. When such messages and interactions between the

15

customer and the firm support each other, they are contributing to the development of

shared meanings and new knowledge in a dialogue. (Grönroos 2000b, 7.)

With this in mind, the relationship dialogue process can be divided into planned

communication process and interaction process. The first one includes communication

messages which are planned and where separate and distinct communication media are

used, ranging from TV commercials to exhibition stands. The latter one is a real process

where the customer’s interactions with physical products, service processes, systems and

technology, e-commerce processes etc. occur. When these two processes get linked into a

sequence, such as a customer calling a help desk based on a TV commercial, the

consistency of communication messages becomes critical to relationship development.

(Grönroos 2000b, 8-9.)

Direct marketing messages often include an invitation for the customer to respond to the

message. If a response is received, this may be considered the beginning of a dialogue or

perhaps even as the manifestation of a dialogue. However, not all contacts between the

parties having a dialogue need to include an invitation to respond, if the information

provided contributes to the development of a common knowledge platform. (Grönroos

2000b, 7.)

When analyzing the types of communication messages involved in a dialogue process, it

is important to remember that also the absence of communication send a distinct message

and therefore contributes to the process (Grönroos 2000b, 8).

Effective relationship marketing dialogue should not be focused only on trying to make a

sale with each contact. Having an interesting conversation consisting of more than pure

sales talk with the customer can deepen the relationship and reap benefits over time

through increased loyalty. (Peppers & Rogers 1993, 215.)

2.1.6 Customer Interaction

Interaction between two parties is a key concept in any commercial relationship. Once a

relationship is established, it proceeds in an interaction process where various types of

contacts between the company and the customer occur over time. These range from

16

person to person contacts to contacts between the customer’s and supplier’s information

systems. (Grönroos 2000a, 82.)

Holmlund (1997) has developed a framework for interaction levels in a relationship,

which consists of four levels: act, episode, sequence and relationship. The framework is

illustrated in figure 1.

Figure 1: Interaction levels in a relationship (Holmlund 1997, 96)

Acts are the smallest unit of analysis, consisting of a distinct activity such as a phone

call. Episodes include a series of interrelated acts, for example a shipment of goods

starting from an order placed by a phone call, followed by packing of products etc.

Interrelated episodes form a sequence that can be defined in terms of a time period, a

product package, a campaign, a project or any combination of these. An example of a

sequence could be a visit to a restaurant or a stay at a hotel. The most aggregate level of

analysis is the relationship, which consists of sequences that may follow each other

directly, overlap or follow with different intervals. (Holmlund 1997.)

2.2 Loyalty Programs

Loyalty programs are sometimes categorized under the concept of loyalty marketing as a

relatively independent entity, but they can also be used as a component of relationship

marketing in enabling customer identification. Loyalty programs are typically visible to

the customer in the form of a loyalty card that entitles them to price reductions and other

benefits through a points scheme. (Gordon 1998, 20.)

According to Gummesson (1998, 142-143), true membership is non-commercial and

does not aim primarily at reaping financial benefits. Memberships found from within the

realm of marketing are usually commercial or available to anyone, which is why

Act Act Act Act Act Act Act

Relationship

Episode Episode

Sequence

Episode

Sequence

17

Gummesson classifies them as fake memberships. These fake memberships can be

divided into the following categories:

• Complete freedom of choice (the customer can use the service also without a

membership)

• Price based membership (reductions in price)

• Earned membership (the customer needs to pay an entrance fee)

• Exclusive membership (only members can use the service).

(Gummesson 1998, 143.)

Frequent flyer programs used by airlines are among the most technically advanced and

widespread loyalty programs. Members are given points for each mile they fly with the

airline, which can be converted to free tickets or a variety of different benefits.

Membership in the loyalty program can provide access to private airport lounges or

special menus during the flight. Members are promoted to a higher membership status

(“gold member”) based on the volume of service usage. (Gummesson 1998, 145.)

Loyalty programs often provide their members benefits in the form of better service,

bonuses or price reductions. Companies benefit from improved customer retention and

share of customer as a direct result from the changes in customer behavior generated by

the benefits offered. Compared to offering the same benefits without an explicit loyalty

program, the marketers gain valuable information on their customers, which would

otherwise be difficult if not impossible to obtain. (Gummesson 1998, 146-147.)

Hanifin (2001, 22) suggests a successful loyalty program should have the following

characteristics:

• Visibility: A loyalty program needs to be highly visible in all channels.

• Simplicity: The program must to be easy to use in order to attract active

members.

• Value: Rewards and recognition must establish a value in the customer’s mind.

18

• Trust: Promises have to be kept, personal information should result in

personalized service.

(Hanifin 2001, 22.)

The nature of the loyalty programs commonly used in airline and retail industries has

been criticized as inadequate to build true loyalty towards the program owners. While

these frequent shopper/flyer programs reward the customer for spending more, they are

only momentarily buying his or her loyalty with the discounts provided, but it is

questionable whether these programs have an effect on the customer’s long term

behavior or preferences. Surveys conducted by GartnerG2 have found that 56% of

frequent shopper club members belong to two or more such groups, and that the members

perceive the programs’ most important feature to be the discounts provided at the

checkout by using the membership card. (Gartner Inc. 2002.)

2.3 One To One Marketing

Don Peppers and Martha Rogers introduced the concept of one to one marketing in 1993

in their book “The One To One Future”. It was based on a notion that recent

developments in technology had generated a point of discontinuity that allowed

companies to start competing for business opportunities one customer at a time. By using

interactive media to create dialogues with individual customers and mass customization

to tailor products and services to meet their expressed needs, it would become possible to

provide the customer superior value compared to the traditional way of doing business,

which is based on making products for large segments and promoting them through mass

media. (Peppers & Rogers 1993, 7, 10, 15.)

One to one marketing is a type of relationship marketing that focuses on the individual

customer. Its basic idea can be simplified to “treat different customers differently”. Being

a customer-oriented company has usually meant being oriented to the needs of the typical

customer in the market, meaning the average customer. The one to one ideology requires

tracking the interactions of individual customers and acting according to this exact

information instead of statistical averages. (Peppers & Rogers 1999, 1.)

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By recording information about every interaction with the customer and customizing the

product and service accordingly, a company can establish a learning relationship with the

customer. This type of a relationship becomes increasingly valuable to the customer as

time goes by, which also means competitors’ ability to offer the same level of

convenience decreases, thus locking the customer into the existing relationship by

offering superior value. (Peppers & Rogers 1999, 2.)

Individual level information on customers has been used in marketing long before the

adoption of IT systems, but marketing theory has focused on mean values and masses

(Gummesson 1998, 146). With the introduction of new tools that make it possible to

target marketing activities to individual customers instead of customer segments, it will

no longer be acceptable to rely solely on aggregate measures when managing customer

relationships.

2.4 Customer Relationship Management

Customer relationship management (CRM) is a subject that has been discussed in

management literature since the 1960’s, but its implementation has become a hot topic

only recently. CRM can be described as the discipline of identifying, attracting and

retaining a company’s most valuable customers. According to Curry et al (2000), “the

true business of every company is to make customers, keep customers and maximize

customer profitability.”

CRM is based on the notion that not all customers are equally valuable to a company.

Following the Pareto principle frequently cited in business literature, the top 20% of the

customers normally deliver 80% of revenues. More importantly, the top 20% of the

customers deliver more than 100% of profits, meaning the majority of customers are

actually unprofitable. It is therefore vital for a company to target its marketing efforts

towards retaining the top 20% of existing customers rather than spending it on

communicating with non-customers who are likely to be unprofitable. (Curry et al 2000,

17-21.)

Identifying the most profitable customers has been a difficult task, but the development

of analytical techniques such as data mining and the increasing amount of electronic

20

customer touchpoints like web sites has enabled companies to start pursuing this goal

with a whole new level of intensity (Curry et al 2000). Due to its heavy reliance on

technological advancements, many have started to consider CRM as a combination of

software and business processes rather than purely a marketing strategy. While it is not

reasonable to ignore the technical aspect when discussing CRM, one should remember

that technology only provides tactical tools for implementing a company’s customer

relationship strategy. (Greenberg 2001, 32.)

2.4.1 CRM Technology

Ronni T. Marshak divides CRM technology into two dimensions: customer facing

application and company facing applications. Customer facing applications are those that

customers actually experience, such as call center management or online help facilities.

Company facing applications are only visible within the organization, where they enable

data interpretation for marketing optimization. Marshak calls these back-end applications

customer intelligence and proposes that the term CRM should be reserved for the front-

end, customer facing applications. These customer intelligence applications are also

sometimes called analytical CRM. (Greenberg 2001, 29-30, 40.)

As quoted by Marshak, Lynne Harvey describes the customer intelligence process to

consist of four steps:

1. Gathering customer data.

2. Analyzing that data.

3. Formulating a strategy based on the analysis in order to recognize customer value.

4. Taking action based on the strategy.

(Greenberg 2001, 29.)

The market for CRM related software has expanded rapidly during the last few years, as

both the established providers and numerous new entrants have rushed to introduce

solutions that attempt to cover both the customer facing processes as well as analytical

back-end activities. This expansion has resulted in an explosion of provider specific

terminology used for describing the applications, making it difficult to identify their

21

generic purpose. Harvey proposes using the following categorization for customer

intelligence tools according to their role in the customer intelligence process:

• Gathering customer information

• Storing customer information

• Processing customer data

• Accessing customer information

• Organizing customer data

• Modeling and analyzing customer data.

(Greenberg 2001, 29.)

2.5 Direct Marketing

Tapp (2000, 10) defines the concept of direct marketing with the following statement:

“Direct marketing is a way of acquiring and keeping customers by providing a

framework for three activities: analysis of individual customer information, strategy

formation, and implementation such that customers respond directly.” Roberts and

Berger (1999, 3) add the distinction from junk mail in their definition stating “database

direct marketing is an information-driven, relational marketing process that takes place in

a context of concern for the privacy of customer data.”

According to Tapp (2000, 4), “the key to modern direct marketing is the capture of

individual customer details at the first sale, so that the marketer can begin a relationship

with that customer, subsequently treating them differently over time in order to generate

repeat business”. This demonstrates the natural linkage between direct marketing and

concepts such as relationship marketing and CRM, as they all revolve around the same

key principles.

The terms direct marketing and database marketing are often used interchangeably. The

general distinction made between the terms by Tapp (2000, 6) points database marketing

towards strategy formulation, whereas direct marketing is focused on direct

communication to customers.

22

As suggested by its name, engaging in database marketing requires the company to

maintain or have access to a marketing database. The definition of a marketing database

provided by Tapp (2000, 29) states it as “a list of customers’ and prospects’ records that

enables strategic analysis, and individual selections for communication and customer

service support. The data is organized around the customer”. Roberts and Berger (1999,

165) further add that the content of the database has been collected over a considerable

period of time.

A database holding records on individual customers is the basis of direct marketing

activities: marketing analysis, planning, implementation of programs and control of all

this activity. The advantage drawn from this database centric approach, compared to

general marketing, is that it forces a natural focus on customers rather than products,

again enforcing the natural linkage to relationship marketing ideology. (Tapp 2000, 3-4.)

2.6 Interactive Marketing

In the context of direct marketing, Tapp (2000, 175-176) describes interactive marketing

as” the firm and customer having multiple two-way dialogue in which both purchase and

non-purchase-related information are shared”. A broader definition would be to consider

interactive marketing to occur at the moment when the buyer and seller interact, thus

including each individual purchase event within the concept (Grönroos 2000, 248).

Tapp (2000, 175-176) considers the Internet’s ability to enable customers to participate in

communities to be the main strength behind interactive marketing, referring to examples

such as football club supporter chat lines and Amazon.com’s customer book reviews.

Roberts and Berger (1998, 417) also use Amazon.com as an example when

demonstrating the high impact that can be achieved through word of mouth marketing in

the interactive landscape of the Internet. The online communities increasingly enable

interaction between customers, not only the customer and the company.

Interactive marketing should by no means be considered to exist only in the online world.

Grönroos (2000a, 372-374) describes traditional marketing as the function of creating

expectations and giving promises, internal marketing as enabling the fulfillment of these

promises and interactive marketing as the process of finally keeping the promises. This

23

marketing-oriented strategy evolves around moments of truth, i.e. service encounters,

which define the overall service quality perceived by the customer. As marketing

activities are becoming increasingly interactive, customer relationship will include an

increasing amount of these moments of truth. Service quality is not only dependent on

interactions between the customer and the supplier’s employees but also the supplier’s

information systems.

Tapp (2000, 175-176) reminds us that not all examples of technology enabled interactive

marketing are paradigm changes. Most of the ways of interaction in shopping at an online

retailer’s site are exactly the same as in mail order shopping, whereas offering quotes for

insurances through e-mail dialogue is actually less interactive than a traditionally used

method of performing this through telephone direct marketing.

2.6.1 E-mail marketing

E-mail is one of the most interesting new channels enabling interactive marketing.

Kinnard (2000, xviii) defines e-mail marketing as ”the act of sending marketing

communications to recipients who first request it”. This definition makes a clear

distinction between permission based, targeted e-mail marketing and unsolicited,

untargeted mass mailings often referred to as spam, which has to some extent given e-

mail a bad reputation as a contact medium in commercial relationships.

The key advantages of e-mail compared to traditional direct media are the following:

• Cost-effective contacts

• Preferred method of communication for many people

• Seen as a personal medium

• Can be customized for each recipient

• Allows easy interaction

• Interaction can be tracked and the effects measured.

(Kinnard 2000, xviii.)

24

One of the underlying assumptions behind a permission based approach to marketing is

that a customer who has given permission to be contacted by the marketer is a better,

more loyal, and more profitable customer overall. Combined with the fact that most e-

mail users find unsolicited e-mail marketing messages as intrusive and many of them

have started to take measures in trying to protect themselves from receiving spam, there

is little reason for any e-mail marketer to attempt building a relationship with customers

without first acquiring their permission. (MacPherson 2001, 14-17.)

There are two main strategies for acquiring a contact permission from the customer: opt-

in and opt-out. The opt-in policy means that the customer must explicitly state he or she

gives the permission to be contacted by e-mail. This type of permission can be asked in,

for example, the registration process of signing up for a username to an online service.

The opt-out policy requires the customer to actively unsubscribe from the e-mail contacts

if he or she does not wish to receive them. A typical method of using opt-out is to send

the message to a list of e-mail addresses acquired from external sources and provide the

recipients with the option to remove themselves from the list by clicking on a link or

replying to the message. (MacPherson 2001, 15.)

The opt-out policy is not a recommendable approach if the target of e-mail direct

marketing is to build lasting customer relationships. It takes the power away from the

customer in favor of creating opportunities for the marketer. Because customers do not

ask for opt-out based communication, the messages are unanticipated and often

irrelevant, which makes them far less effective than opt-in e-mail messaging. (Godin

1999, 229-230.)

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2.7 Framework Summary

In order to summarize the theoretical framework introduced in this chapter so far and to

demonstrate the interrelationships of the concepts, figure 2 gives a graphical presentation

of the framework.

Figure 2: Summary of the framework

The central concept of the framework is the relationship between the customer and the

company. It consists of exchange of value (products, services, money) and dialogue

(communication and interactions) between the two parties. These activities alone do not

yet establish a relationship between the two parties, as the existence of a relationship

depends on the customer’s perception.

The company can use a loyalty program as a tool for implementing activities based on its

relationship marketing strategy. The use of direct marketing e-mail campaigns supports

the dialogue by providing a channel for communication and an incentive for customer

interactions. These interactions form a feedback loop that adjusts the company’s planned

communication process based on the common knowledge platform that is gradually built

between the customer and the company. This platform delivers added value to both

parties, thus increasing the total benefits gained from the relationship and supporting the

continuous exchange of value.

Communication

Interactions

Exchange of value

DIALOGUE

E-mail marketing

Direct

marketing

Interactive

marketing

CUSTOMER COMPANY

RELATIONSHIP

Loyalty

program

26

We now move the discussion to measuring the relationship development and introduce

concepts related to tracking e-mail marketing activities.

2.8 Relationship Measurement

Measures used in traditional accounting have taken several centuries to develop into the

status where they are today. Compared to this, measures of loyalty are relatively new and

untried, and more importantly, not even applied yet in most companies. Measures are the

business idiom that shapes the attitudes and behavior of a business organization.

(Reichheld 2001, 217-220.) It is therefore clear that customer relationship measures are a

prerequisite for shifting management focus from the income statement to the customers.

The development process of a relationship includes interactions and communication

between the supplier and the customer. Interactions are exchanges of goods, services,

information or any other contacts that occur between the two parties. While

communication takes place in all interactions, there is an additional series of planned

communication efforts, such as direct mail or advertising. These interactions and

communication need to become integrated into one value-creating relationship process

over time for a relationship to emerge. Marketers should try to monitor this development

in order to measure the existence of a relationship, even if creating ideal measurement

instruments for this process may not be possible. (Grönroos 2000a, 34.)

According to Grönroos (2000a, 34) “once a relationship has been established, customers

are customers on a continuous basis – and they should be treated as such regardless of

whether at any given point in time they are making a purchase or not.” This statement

emphasizes the need to move beyond transaction-based measurements in the analysis of

relationship building activity effectiveness. It is also an area that is highly visible to the

customers themselves. Unless a firm treats it customers as relational customers who

receive the same attention whether they are making a purchase or between purchases, it is

not showing genuine relational intent in its operations. (Grönroos 2000a, 35.)

The number of purchases a given customer has made from the same firm is one measure

that can be used in defining when a relationship has developed. It should however not be

used as the only measure since there are typically many reasons for a customer to

27

continue buying from one supplier. Low prices can keep up repeat sales, as can a

convenient location, technological barriers, limited knowledge of competing offers etc.

There can be several bonds that keep a customer attached to a firm, even though the

customer does not feel that there is any relationship with that firm. Removing these

bonds can result in losing the customer that was thought to have a relationship with the

firm. (Grönroos 2000a, 33.)

Payne and Frow (2001, 800) advice to treat with caution the kind of measurement

systems that provide aggregated measures of profit, customer satisfaction and employee

attitudes as they may overlook individual differences. For example, increasing the overall

retention rate of customers does not necessarily result in significant profitability

improvements, since increasing the retention of unprofitable customers can destroy

profits. This is why relationship marketing requires measurement systems that allow

companies to analyze their customer base and measure profitability on individual

customer level.

The customer data upon which the measurement is based can be divided into two

categories: demographic and behavior data. Demographic data can include information

about customer age and gender, social status, area of residence, magazines read etc.

Behavior data tells about customer activities, such as purchases, call center contacts or

web site visits. Of these two profile types, behavior data is likely to change more

frequently as new information is collected on interactions between the customer and the

company. This is one of the reasons why customer behavior is also a much stronger

predictor of the future relationship than demographic data. (Novo 2000, 1.)

Capturing the necessary data for measuring relationship marketing activities can

sometimes be challenging. Relationship marketing requires a two-way flow of

information, but according to Schultz (1993, 28), this does not mean that the customer

has to give the information knowingly. Behavioral data captured from various

interactions made through digital touchpoints can be just as valuable as information

gathered explicitly from the customer.

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2.8.1 RFM Analysis

Recency, Frequency, Monetary analysis (RFM) has been used in direct marketing for

almost 40 years as a method for increasing response rates. It is based on both a priori

reasoning and empirical evidence of customer behavior. A typical way of applying the

method on a database of customers is to assign an RFM score to each record in the

database and use these scores to target the marketing activities towards the most

responsive customers. (Hughes 1996.)

RFM analysis is founded on the discovery that people who have bought from you

recently are much more likely to respond to a new offer than someone who had made a

purchase in the distant past. The frequency of purchases made also has a positive impact

on rate of response, as does the total monetary value of purchases. However, out of these

three variables, recency has been found to be the strongest indicator of willingness to

respond to an offer, hence its ranking as the first variable in the term RFM. (Hughes

1996.)

Through the increasing number of information systems involved in customer interactions,

a wealth of non-purchase related data is also available in modern customer databases.

This has extended the potential applications of RFM analysis, as the same laws that

predict purchase behavior also apply to other types of customer behavior, such as web

site visits, sign-ups or surveys. (Novo 2000.)

Whereas the modeling of demographic and other profile variables result in a near static

picture of the customer, RFM measures actual behavior and can therefore provide a more

accurate prediction of a customer’s likelihood to respond (Hughes 1996). Customer

migration between different cells of an RFM value grid can give an up-to-date view of

how recent marketing activities have affected customer behavior.

In the durable goods business, where the typical re-purchase time of a particular product

is several years, recency alone is not a practical measure for predicting customer re-

purchase likelihood. In a single product perspective, one should look at recency of

purchase relative to the expected life of the product, not recency of purchase from the

actual purchase date. In a whole relationship perspective, recency increases the relative

29

likelihood of a customer engaging in purchase behavior with the business regarding other

types of products on offer. (Novo 2001.) Reinartz and Kumar (2002, 86-94) criticize

RFM models for their lack of proper distinction between frequently and infrequently

bought goods and suggest that an event-history model should be used instead when the

pacing of customer interactions is slow.

Many companies offering long purchase cycle products actively shorten the cycle by

employing an inter-purchase contact strategy. By building an active dialogue with the

customer, the company can “bridge” the purchase cycle and maintain recency of contact,

which can work in a similar manner as recency of purchase (Novo 2001). This approach

increases the value that can be derived from using an RFM model in the area of

infrequently bought goods.

2.8.2 The Leaky Bucket Theory

Measuring customer migration is a way of digging deeper into aggregate measures such

as total revenue or profit growth from one fiscal year to the next. By measuring the

buying behavior of the customer base over a period of time it is possible to see some of

the underlying patterns that cause the changes on the bottom line. One metaphor

commonly used for illustrating these changes is the “leaky bucket” theory. (Rongstad

2001.)

Growth in revenue or profits from one period to the next acts like a leaky bucket that is

being filled with water. The water level in the bucket rises as new customers are added

and revenue from existing customers increases. The bucket also has several leaks from

customers who stop purchasing or decrease purchase volume from a prior period. If you

only measure how much the water goes up or down from one period to the next, it is not

possible to make appropriate decisions on how to increase the total amount of water in

the bucket. (Rongstad 2001.)

Tracking transaction information at the customer level allows the analysis of customer

migration by identifying what happens to individual customers from any one period over

to the next. Although customer migration can be reported in an aggregate format, its

value is derived by measuring actions at the individual customer level. Customers can be

30

grouped according to lifetime value, RFM score or any other relevant variable for

analysis purpose, but the migration patterns are calculated based on detailed transaction

data that can be captured where customers identify themselves, for example by showing

their loyalty program membership card. (Rongstad 2001.)

2.9 E-mail Marketing Measurement

As mentioned in chapter 2.6.1, one of the key benefits of e-mail marketing over many

traditional direct media is the possibility to accurately monitor the sending process and

track interactions on individual level. The three most common items used for measuring

e-mail sending activities are bounces, HTML opens and click-throughs.

2.9.1 Bounces

Bounces refer to e-mails that come back to the sender as undelivered. Bounces are

divided into hard or soft bounces depending on the level of the delivery problem. Hard

bounces will not become deliverable over time since the address is no longer valid due

to, for example, account termination. A soft bounce means the message is only

temporarily undeliverable and the address may become valid again. Reasons for soft

bounces include server problems, Internet connection downtime and blocked addresses.

(Kinnard 2000, 205-206.)

2.9.2 HTML Open Rate

E-mail formats can be divided into graphic (HTML) or plain text messages. While the

use of HTML can enhance the visual outlook of an e-mail and make it more attractive

compared to plain text, the downfall is that all e-mail clients do not yet support HTML

messages. This is why HTML e-mails need to also include a plain text version that can be

viewed by any client.

Open rate is used for measuring the number of recipients who open the e-mail. The

measure to which the number of openings is compared is either the total number of e-

mails sent or the number of successfully delivered messages. (Weil 2002.) Due to the

lack of unified standards in the area of e-mail marketing, the method used has to be

31

verified case by case, but the comparison to successfully delivered e-mails seems more

practical in measuring recipient activity.

Open rate is tracked by embedding an image into a HTML message, which results in a

server request for this image when the HTML document is constructed in the e-mail

client. This method of tracking is not accurately representative of the actual number of

recipients opening an e-mail. The biggest shortcoming is that it can only track HTML

messages, which means that a multipart message containing both HTML and plain text

versions of the content will only collect open data for the HTML part. Problems also

arise if the recipient views the message in offline mode (without an active internet

connection), if a firewall strips all images from incoming e-mails or if a proxy server

“recycles” images that have been viewed by previous users. The concept of opening an e-

mail is subject to argumentation as well, since the speed at which a receiver views the

message may either not provide enough time for a server to call up the images or

alternatively a slow preview of incoming messages may generate open data, all of which

is dependent on the positioning of the tracked image file in the HTML document.

(Grossman 2002.)

2.9.3 Click-throughs

The most widely used success measure for e-mail campaigns is the click-through rate

(CTR), which measures the number of times a link provided in the e-mail message was

clicked on (MacPherson 2001, 9). The high popularity of using CTR in comparing the

outcomes of online marketing efforts has generated critique towards the measure, with

several professionals saying the value of e-mail campaigns should not and cannot be

measured by clicks alone (Graham 2002, Posman 2000).

Tagged URL’s (uniform resource locator, the internet address of a hyperlink) in e-mails

can be used for individual level tracking of recipients’ clicks on the links provided. This

is done by generating dynamic URL’s which have an identification number of the

receiver included in them. When the link is clicked, the user is first directed to an

intermediate server that captures the identifier and redirects the request to the server

hosting the actual target web page. Records of all clicks on the links can therefore be

32

traced back to addresses on the original e-mail distribution list. (Revent Systems, Inc.

2002.)

Since tagged URL’s allow the clicks to be tracked on an individual recipient level, this

means also the CTR can be calculated for unique recipients instead of just the total

volume of clicks on a particular link. Although this is a clear benefit, the existence of

various methods means there is no universal definition for CTR. In the case of a direct

marketing e-mail with more than one link, CTR can be measured by at least three

different ways:

• Total number of non-distinct clicks (highest volume)

• Total number of distinct clicks by unique recipients

• Total number of unique recipients having clicked on a link (lowest volume)

Each of these methods can be the most suitable one, depending on the purpose of the

measurement. Since the CTR is likely to be higher when using non-distinct clicks, it is

important to always ensure the definitions used are the same when comparing figures

from various sources.

33

3 CASE CLUB NOKIA

Based on the theoretical framework presented in the previous chapter of this document,

we can begin analyzing the focus area of the research, Club Nokia’s e-mail direct

marketing campaigns. This chapter provides an overview of Club Nokia, examines the

role of e-mail messaging in its activities, identifies the data available for analysis and

defines its meaning in the context of the loyalty program.

3.1 Introduction to Club Nokia

Club Nokia is a loyalty program targeted at the end-users of Nokia Mobile Phones

products (from hereon referred to as CN and Nokia). The purpose of CN is to add value

to the customer experience with Nokia and thereby increase customer loyalty. It also

provides a channel through which Nokia can talk directly to its customers and received

feedback and information on their behavior and future needs. In short, the business

concept of CN is “to identify the customers and open a response-seeking dialogue with

individuals aimed at building trust and loyalty so as to maximize customer long-term

value” (Nokia Oyj 2001). At the time of writing, the CN of Europe, Africa and Gulf

region operates in 28 countries and has 14 million registered members.

Joining CN is free to all customers who purchase a new Nokia handset. They can join CN

by either using an online form at their local CN web site or by filling out and returning

the CN registration form found in the product package. After registration the members

are given a membership card and a CN customer identification number (CID), which is

their key to the various CN value-added services. Members are encouraged to update the

product information into their member account when purchasing further Nokia products.

(Nokia Oyj 2001.)

The main benefits communicated to the (potential) CN members are:

• Quick and easy customer support

• Personalized information on the usage of registered Nokia products

• Digital content (ring tones, logos, picture messages, games etc.) that can be

purchased by using the CN credit system

34

• The latest information on Nokia products and mobile communication

• Information on local Nokia events and campaigns.

(Club Nokia 2002.)

Since CN services are only offered to registered members, the program can be considered

an exclusive membership as defined by Gummesson’s (1998, 43) categorization of

commercial memberships. The three main services offered to members are CN web site,

CN service points and CN careline. The localized CN web sites provide members with

product information and support, digital content that can be purchased using CN credits

and updates on the latest member benefits and Nokia events. CN service points provide

repair and maintenance service for Nokia products during the warranty period, with

special member services such as the possibility to swap a broken phone to a similar

working product. CN careline offers members exclusive, free support service through

phone and e-mail. (Nokia Oyj 2001.)

A key element of the loyalty program is CN’s direct communication to its members, done

mainly by using e-mail and SMS messaging. CN aims to provide its members relevant

and valuable information on a regular basis in order to make them feel like they are part

of an active membership program. (Nokia Oyj 2001) The majority of this dialogue takes

place through monthly e-mail newsletters that are usually sent to all CN members who

have opted in for e-mail direct marketing. These newsletters are a combination of country

specific event and service information and regional product content used across the

various CN countries. An example of these newsletters can be found in appendix A. This

study focuses on the analysis of member response data (click-throughs) from these

newsletters as well as other possible e-mail campaigns sent during the time period

selected for the research.

3.2 E-mail Messaging as Customer Dialogue

The starting point of the analysis is to discuss the first defined sub-problem: “In which

ways do e-mail campaigns contribute to the development of a relationship between Nokia

and its customers?”

35

In the context of relationship marketing, e-mail campaigns sent to CN members can be

considered as a vehicle for customer dialogue. E-mail messaging supports the CN

services by reminding members of the existence of CN web site and announcing new

benefits that become available to the members. As described by Godin (1999, 150), a

web site that has no other contact points with its visitors is like a magazine that has only

newsstand sales but no subscribers. Regular e-mail messaging with reference to the CN

web site enables converting registered members into “subscribing members” who

frequently use the services CN has to offer.

The process of sending a direct marketing message through an electronic channel to the

registered customer’s terminal differs substantially from the typical everyday meaning of

the word ‘dialogue’, a face-to-face discussion between two people. Therefore we must

first determine whether e-mail messaging should be considered as having a dialogue with

the customer.

The concept of e-mail marketing may at the first glance seem as if it would not differ

considerably from physical direct mail. Moreover, the ever-increasing and widely

acknowledged problem of junk e-mail, spam, tends to enforce the image of e-mail as an

unwelcome invasion of privacy rather than a way of developing a common knowledge

platform. While some marketers continue to use the media in this manner, this should

however not be considered as the default application for direct marketing e-mail.

The most important distinction between spam e-mailings and CN’s e-mail campaigns is

the contact permission. All the recipients of CN’s e-mails have at some stage of their

membership explicitly opted in to receiving direct marketing communications from

Nokia. They also have the possibility to cancel the contact permission at any given time

if they feel that the messages they receive are bothering them or not providing enough

value to them.

As mentioned in chapter 2.1.5, dialogue is a two-way process. Therefore, merely sending

out an e-mail message does not establish a dialogue between CN and its members. It can

however be the starting point of a dialogue, given that CN receives some form of a

response that is related to the e-mail sent. On the other hand, one could argue that the

dialogue has already been initiated when the customer who has bought a Nokia phone

36

has registered into CN and become a member. From the point of view of CN, this is

certainly one of the most important and information rich steps in the process. However,

from the member’s point of view, the registration to CN does not necessarily yet reflect

motivation for initiating a dialogue, since he or she may only be in the phase of

examining what the program is about and acquiring a membership mainly for this

purpose. A member who registers to CN but does not give the permission for any type of

direct marketing contact, does not visit the site or contact careline should not be

considered a participant in a dialogue. It is therefore more practical to assume that the

confirmation of an existing dialogue requires a response from the customer to a contact

initiated by CN.

Examining CN’s e-mail direct marketing in the light of the four criteria set by Peppers

and Rogers (1993, 213-215) for qualifying marketing communication as a dialogue, we

can find two requirements that are clearly met: no one is contacted against their will and

the communication can be terminated at any time. The requirement for the possibility of

participation in the dialogue is implemented by providing members with instructions for

contacting CN in each of the e-mail messages sent, thus the communication vehicle can

be considered to support this criterion.

The last requirement, which states that a dialogue should change the parties’ behavior

toward each other, cannot be met by CN’s e-mail communication alone. Although in

theory it could be possible to customize the message based on any piece of data collected

on the customer’s behavior, this has not normally been applied in practice to CN’s e-mail

campaigns. Nevertheless, it is possible to consider that e-mail direct marketing can

support the modification of behavior towards members if all available data is utilized.

Relationship marketing consists of an integrated set of communications and interactions

(Grönroos 2000a, 40-41). As a method of interactive marketing, e-mail direct marketing

does not fall exclusively into either category but rather contains elements from both. As a

media, e-mail can be used for emphasizing either category, ranging from passive delivery

of a marketing message to mediating personal interactions initiated by either party.

Keeping in mind Grönroos’ (2000b, 8-9) classification of dialogue into planned

communication process and interaction process, we can conclude that also regular e-mail

37

newsletters can be an integral part of a relationship dialogue as an element of the planned

communication process. It is more challenging to define how the interaction process is

visible in e-mail messaging. The fact that e-mail in itself is an interactive media does not

yet guarantee that interaction would necessarily take place.

As can be observed from chapter 2.9, the e-mail marketing process directly generates

three types of measurable outputs: bounces, HTML opens and click-throughs. Message

delivery failures resulting in bounces happen without any interaction between the e-mail

sender and receiver. HTML opens could be seen as the equivalent of viewing a message

through any indirect media, such as magazine advertisements, and should not therefore

be qualified as interactions, regardless of their (partial) trackability to the recipients.

Excluding the possibility of the recipient sending a written response (which cannot in

practice be a part of the formal process due to the difficulty of response processing), the

main element of interaction in e-mail messaging would appear to be the click-throughs.

3.2.1 Clicking as Customer Interaction

The interaction process of a relationship consists of activities ranging from face-to-face

conversations to interactions between the information systems of the customer and the

company (Grönroos 2000b). Following the model created by Holmlund (1997, 96) on

relationship interaction levels, one can consider the interaction of clicking on a hyperlink

provided in a direct marketing e-mail to be one act in an episode initiated by an e-mail

contact. Depending on the desired member activity, the episode could include a series of

acts that lead to, for example, participation in a prize draw or purchase of digital content.

Even a simple delivery of product related news could form an episode when the

information is delivered in two parts: a short introduction in an e-mail newsletter

containing a reference to a web site that contains full product specifications. A member

would need to perform the act of clicking on a link if he or she desired to complete this

episode (initiated by CN), thus the click-through would represent the required customer

interaction.

It is obvious that the degree to which a click-through is required for an e-mail message to

be considered successful can vary depending on the message content and desired

recipient activity. Viewing the message without clicking on a related link is by no means

38

worthless to the sender. One commonly used metaphor for demonstrating this is the

question “when was the last time you clicked on a newspaper ad?” Just because the

online media enables the immediate action of clicking for further information does not

mean it should be the only criteria used for measuring the effectiveness of marketing

communication. (Posman 2000)

According to Posman (2000), “the actual click-through means that the user is both

interested and prepared to take a minute at that given time to learn more about the offer.”

The information that a member has clicked on a link in an e-mail message should

therefore be considered as a confirmation of interest, but not a requirement. As the role of

e-mail messaging in CN is to help in building long-term dialogue, it would nevertheless

seem logical that if a member perceives CN’s regular e-mail communication relevant and

interesting, he or she would at some stage be willing to extend the episode by performing

the act of clicking on a link.

Member click-throughs can provide information on how the overall relationship is

developing. Applying the model for relationship development by Dwyer et al (1987, 16-

20) to the relationships between CN and its members, most of the new customers

registering into CN are likely to be in the exploration phase. Receiving benefits

throughout the membership and having satisfactory interactions during the expansion

phase can drive members closer to the point of achieving commitment. According to

Dwyer et al (1987, 19), commitment can be measured by inputs, durability and

consistency.

Click-throughs represent a form of communication resources that act as inputs into the

relationship, when the member invests his or her attention and time into CN’s e-mail

communication. Durability has to do with CN’s implicit promise of continuing to deliver

relevant content and not to exploit the member’s inputs by turning the dialogue into a

one-sided sales pitch. In return, the consistency of clicks from the member maintains the

relationship and gives CN the confirmation that member commitment does still exist.

39

3.3 Available Data

The second sub-problem of the study is “what information is available regarding the

communication process?” In this subchapter the possible current data sources and their

contents will be described.

3.3.1 Campaign Report Summaries

For each e-mail campaign sent by CN a campaign report is generated. These include both

a collection of summary values for selected campaign sending variables as well as

detailed click data on each click-through made by the recipients. The summary variables

available in these reports are:

• Number of recipients (= unique e-mail addresses)

• Number of e-mails successfully sent

• Number of HTML e-mails read

• Number of clickthroughs

• Number of hard bounces

• Number of soft bounces

• Number of unsubscribers

These variables constitute the key information that has been used for the regular

monitoring of e-mail campaign performance over a period of time. The problem with this

type of measures is that they view the communication purely from the perspective of

running an e-mail sending process, rather than maintaining a dialogue with individual

members. Message viewings and link click-throughs remain isolated events with no

relation to the past communication and interactions with members. This means the

information does not contribute to the development of a common knowledge platform

and added value cannot be created based on the reasoning process.

40

3.3.2 Campaign Click Data

The detailed response data found from the campaign reports include the following

variables:

• Message ID

• URL ID

• E-mail address

• Date and time of click-through

Due to limitations in the mailing platform used, the unique member identifier (CID) is

not included in the click data. This means the data has to be linked to member

information from CN database by using the e-mail address as the key field. The

shortcoming of this approach is that while the CID’s will always remain constant, the e-

mail addresses can vary over time if members update their contact information. Therefore

the accuracy of historical response data will steadily decrease if CID’s are not associated

to the response data. Another problem with using the e-mail address as key field is that

several CID’s may share the same address.

Since the click data is currently not imported into the CN database, there is no

categorization available for the URL’s. Depending on the contents of the e-mail

messages, the URL’s may lead to the CN web site, Nokia main web site, a dedicated

landing page (designed specifically for the promotion), a third party site or an

unsubscribe page. In order to enable the analysis of overall e-mail campaign

effectiveness, the URL ID’s should be encoded to include information regarding the

general purpose of the link.

The timestamp information recorded for each click-through makes it possible to monitor

the e-mail sending process by, for example, analyzing what is the distribution of clicks

over time compared to the time of message sending, which could give an indication of

the optimal timing for e-mail campaigns. Consecutive clicks on the same link by the

same recipient could be used for assessing members’ level of interest towards the subject

of the link. This could however lead to false conclusions in situations where problems in

41

web server performance increase the load times of the page and therefore make members

click on the links repeatedly.

3.3.3 CN Member Database

The CN Data Mart is a member database used for analytical purposes. It contains most of

the member and service related information available in CN production databases and its

data is updated on a daily basis. Following the categorization of customer profile data

into demographic and behavioral data (Novo 2000, 1), the contents of the Data Mart can

be described with the following summarization.

Demographic data:

• Gender and age

• Registration date and media

• Contact details

• Contact permissions

Behavioral data:

• Product registrations

• Digital content downloads

• Campaign contacts

• Inbound contacts

• Phone repairs and swaps

Although the Data Mart is used only for data analysis and campaign management

purposes, its design resembles a production database in the sense that the behavioral data

is mostly available in only transactional format, with only few fields summarized to

member level. While the transaction level data is useful in performing detailed analysis

of a particular behavioral item, the database design inhibits combining these items for

studying their interrelationships. The query structures become too complex for the end

user and query performance is reduced to unsatisfactory level. Since summary data fields

cannot currently be added into the Data Mart, certain types of data analysis require the

data to be exported into an external database.

42

3.4 Data Interpretation

The third sub-problem of the study is “how can the communication information be linked

to the customer relationship?” This chapter discusses first the different problems and

shortcomings related to associating the data to relationship concepts and then concludes

how the data items can be mapped to the theoretical framework.

3.4.1 Definition of a Member

The terminology used for labeling concepts of the CN information system can be

misleading if they are directly compared to their counterparts in the real (physical) world

without interpreting their meaning. One potential misinterpretation is related to the term

‘member’. (In the actual data model of the CN databases this concept is referred to as

‘customer’, but since the terms are in practice interchangeable, the database concept is

also referred to as ‘member’ in this document.)

CN operates in the area of business-to-consumer relationships; therefore the default

interpretation of a member is one physical person. Ideally the business information

systems should contain a corresponding model of reality, but in one cannot assume each

member record in the CN database to represent one physical person. A single person can

have multiple member records and member ID’s, or alternatively one member record

may be used by several physical persons. Exploration of customer data held in the CN

database has identified several occurrences of both cases and there is reason to believe

their magnitude to be of significant level.

The underlying logic behind CN’s registration process is that every unique person should

create one member account for him or herself and register all future product purchases on

this same account. However, in reality the person registering further Nokia products may

have either forgotten the information necessary for accessing his or her account

information, or alternatively forgotten about the prior interactions with CN completely,

thus not acknowledging the existing member account. This can easily lead to creation of

multiple membership accounts for one physical person. The reverse scenario, using one

member account for registering products from several physical persons, could occur if,

43

for example, people assume having more registered products on the same account will

entitle them to more benefits from CN.

3.4.2 Definition of Purchase

Unlike in many other lines of business where loyalty programs have traditionally been

used, there is no direct interaction between Nokia and the CN member when he or she

purchases a new product. Mobile phones are sold through retailers and operators, which

means CN is an entity independent from the retail channel. This creates an additional

challenge for collecting information on purchases made by CN members. Whereas a

grocery store chain needs to teach the members of its loyalty program to show a

membership card during the check out procedure, CN has to go one step further and get

the members to perform a separate registration procedure in addition to the purchase

transaction.

It is obvious that the barrier for members to identify themselves is higher when a

dedicated procedure is required for registering a purchase. This is likely to result in a

lower rate of data capture, thus decreasing the accuracy of member purchase history as a

measure for determining the loyalty program’s results.

3.4.3 Repeat Purchases

From the data point of view, it would be easy to assume that the number of registered

phones per each member would indicate the number of Nokia products he or she has

purchased. The shortcoming of this approach is that it ignores any previous customer

relationship the person may have with Nokia prior to signing up as a CN member. Also,

if an existing member accidentally registers his or her latest purchase to a new member

account, all historical information is lost. This could even lead to a situation where, in

terms of maintaining the common knowledge platform, it would have been better if the

member would have ignored new product registration altogether.

It is impossible for CN to identify when a member decides to purchase a mobile phone

from a competing brand, as this activity does not result in any natural interaction with

CN. It is equally impossible to determine whether a member with several registered

44

products has remained loyal to Nokia between the registrations or if he or she has bought

products from other brands during that time. Even though the majority of people are

likely to have only one mobile phone in use at any given time, the CN membership

cannot be used as proof of monogamous loyalty. A person who registers new Nokia

products just as often as the average CN member might in reality be highly interested in

mobile phones and switch between other brands in between the Nokia product

registrations. Still, by focusing only on the product registration data this person would

not be identified as a particularly potential buyer of the latest mobile technology and CN

might not provide him or her with the kind of treatment needed for increasing the brand

preference towards Nokia.

3.4.4 Dissolution

Although CN members have the opportunity to resign from the program, this is

performed only if the members explicitly request their information to be removed from

the records. This means there is no formal definition for relationship dissolution in a CN

membership. In reality, the relationship between CN and its member can be considered to

have come to an end when the two no longer have interactions, as suggested by

Holmlund’s (1997) model. The member may have either switched to using a competing

mobile phone brand or continued as a Nokia user, but due to the lack of contact there is

no longer any exchange of information.

Since there are no concurring financial implications (such as an annual fee) resulting

from CN membership, the easiest way for a member to discontinue the relationship is to

simply ignore it altogether. Member accounts in the CN database gradually become

inactive as no new data is recorded for them and contact details become out of date. As

time goes by, an increasing share of member accounts will fall into this category. This

should be taken into consideration when analyzing customer data, in order to avoid the

inactive members’ data from distorting or hiding the trends in active member behavior.

45

3.4.5 Framework Summary for Club Nokia

In figure 3, the available data items identified in chapter 3.3 are mapped to the

framework that was first introduced in chapter 2.7.

Figure 3: Mapping of available data items to the conceptual framework

The basis of knowledge on the member demographic profile (demographics, contact

details, contact permission, membership length, registration media, latest phone) is

created during the initial registration process, which does not necessarily yet mean the

existence of active dialogue. Through the communication process started based on this

knowledge and the interactions with members the demographic information may be

updated or supplemented.

Of the data available in e-mail campaign reports, bounces and HTML opens are more

related to the e-mail messaging process rather than the actual relationship between Nokia

and the CN members. Bounces need to be interpreted into member contactability

information in order for them to become a part of the common knowledge platform.

HTML opens present partial and (currently) non-identifiable data of e-mail message

reception that cannot as such be transformed into relationship knowledge. This also

applies to other summary variables such as CTR.

Communication

Interactions

Exchange of value

DIALOGUE

E-mail marketing

Direct

marketing

Interactive

marketing

MEMBER

RELATIONSHIP

Bounces

HTML opens

Campaign contacts

Click-throughs

Inbound contacts

Demographics

Contact details

Contact permission

Membership length

Product registrations

Downloads

CLUB NOKIA

NOKIA MOBILE PHONES

46

Behavioral knowledge on members is generated both through interactions in the dialogue

process and transactions in the value exchange process. Campaign contacts made using

e-mail can initiate an active dialogue between CN and its members. This can result in

identifiable interactions such as click-throughs or custom messaging visible as inbound

contacts. Positive experiences from the dialogue can increase member commitment

towards the loyalty program and result in new product registrations and usage of digital

content downloads (to some extent also phone repairs and swaps).

With this mapping of available variables in place, we can proceed to the empirical

research in which the campaign response data is analyzed using the conceptual

framework. The research will aim to derive new knowledge from the e-mail direct

marketing process of CN by linking member click-through interactions to other identified

variables and analyzing their dependencies.

47

4 DESCRIPTION OF THE RESEARCH METHODOLOGY

This chapter discusses the methods and data used in the empirical research and the

validity of results acquired from it.

4.1 Approach And Methodology

The empirical study was conducted using a deductive approach and quantitative research

methods. The material used in the research consisted of secondary data that was available

from two sources: the Club Nokia Data Mart (database) and e-mail campaign reports (flat

files). The content of these sources was described earlier in chapter 3.2.

One of the research objectives was to find a way to use existing CN member and

campaign data for analysis purposes, both in ad-hoc research and also on continuous

basis. Therefore the choice of data sources was naturally limited to internal resources.

Although the current format and location of the data meant there was a need for manual

processing before the analysis could be conducted, most of the variables included had

been generated through automated processes. The sample data was imported into a

personal database that could in theory also be implemented in the live environment, thus

making it possible to perform similar analysis on all CN members.

4.2 Available Data

The data available for analysis in CN was described in chapter 3.3. Since the resources

available for the study were in general identical to this description, only the limitations

specific to the study are reviewed here.

While the customer data and campaign aggregate data were complete, e-mail campaign

response data was only available from a limited time period. The reports covered a time

period starting from April 2002 and were available for a total of 49 campaigns in 23

countries. As one of the key phenomena to be studied was the effect of consecutive

campaign contacts, it was important that the sample contained CN members who had

received several mailings between April 2002 and September 2002 (the deadline set by

the data collection schedule). Therefore it was not practical to include members from

48

countries with only one campaign report available into the sample. It is also important to

remember that not all members have received all the mailings. This can be due to

campaign target group criteria, changes in member contactability or new members

having registered during the time period.

4.3 Population

The target population of the research was defined as “all Club Nokia (Europe, Africa &

Gulf region) members having been sent campaign e-mails between April 2002 and

September 2002”. The sampled population was limited to four countries (Austria,

Ireland, Spain, Switzerland) due to their best availability of campaign response data

(minimum five distinct campaigns). The member base of these countries covered around

12% of the e-mail contactable CN members at the time of the research.

It was clear that the 23 countries in the population would differ considerably in terms of

both their campaigns and member base profile. Since the available data as well as

processing resources were limited, it was not considered feasible to attempt to cover all

countries in the sample data. Focusing on four selected countries made it possible to

analyse variations in results between them, instead of relying only on average data from

the total population. As the purpose of the research was not so much in determining the

behavior of an average CN member rather than in exploring the possible applications of

individual level data, the selection of a limited sampled population was considered to be

the optimal choice.

4.4 Sample

The sample members were selected from the CN Data Mart by using systematic

sampling based on their unique member identifier (CID), which is a ten-digit number.

The last few digits of the CID are generated randomly for each member during their

registration to CN and can therefore be used for sampling purposes. The possibility to

use stratified sampling was inhibited by the structure of the source database that did not

allow using a selection criteria consisting of both customer and campaign variables if

reasonable query performance was to be preserved.

49

20% of the members in the sampled population were included in the sample, which

meant the inclusion of 137,946 members. This data volume provided satisfactory

accuracy and adequate processing performance. Behavioral data selected from the CN

Data Mart transaction tables was summarized to member level for analysis purposes.

Campaign contact data was selected separately based on the CID’s of the members

included in the sample, resulting in 557,872 rows of contact transactions.

Campaign response data was collected manually from campaign reports. Due to the lack

of a CID field in the reports, click data had to be joined to member data by using e-mail

address as the key field. During the data collection process, any duplicate clicks per

single link were removed. Campaign and link hierarchy was created specifically for the

sample data and identifiers were added into the click transactions. The click data was

included in its entireness and later linked to the sample members, resulting in a total

volume of 109,663 clicks, out of which the sample members performed 21,019 clicks.

The data was imported into an MS Access 2000 personal database operated on a desktop

PC with a PII/400 CPU and 320MB of memory. The size of the database file at the end

of the analysis phase was 115MB. A description of the database structure used can be

found in appendix B. Query performance with this setup was satisfactory throughout the

analysis phase.

4.5 Data Analysis

The data was analyzed by building crosstab queries and exporting the query results to

MS Excel for further manipulation. The click data was summarized to member,

campaign and link level to simplify query structure. Temporary calculated fields were

created by using subqueries when necessary, but the existing data categorization proved

to be adequate in most cases.

Since the CN Data Mart contained complete data sets for many of the variables used, on

some occasions this live data was queried for information that was not available in the

research database. This was done in order to acquire useful background information that

was excluded from the research database due to the limitation of including only contacted

members.

50

4.5.1 Calculation of Averages

Chart 1 shows the distribution of sample data between the four selected countries. As can

be seen from the table, the size differences between countries are quite significant. The

largest country, Spain, has almost ten times the amount of contacted members compared

to Ireland.

Chart 1: Sample data distribution

As discussed in the sampled population definition, it was not the main purpose of the

study to calculate average values for variables from the total member population of CN.

One of the intentions was to provide understanding on how significant the variations

between countries are likely be, thus estimating how well the results can be generalized

to members in different markets.

To ensure that campaigns and members in all selected countries were given equal

importance in the data analysis, the total average values in this document are not

calculated directly from the entire sample volume but from country based averages.

This method gives each country a 25% weight in the total averages. Many of the

Contacted Members Country

Distribution In Sample

AUSTRIA

16%

IRELAND

6%

SPAIN

57%

SWITZERL

AND

21%

51

variables in the report are also presented on country level to give the reader an

understanding of the significant the variations were.

4.5.2 Counting Clicks

As already mentioned in the description of the sampling process, multiple clicks on one

link by a single member were discarded from the data. This approach was adopted since

it was obvious by looking at the click timestamp data that the occurrence of multiple

clicks was often the result of server problems inhibiting the member from reaching the

destination page.

One type of link included in most of the e-mail messages was an unsubscribe link, which

can be used by the member for canceling the permission to send direct marketing e-mails.

While the purpose of these unsubscribe links is from the member point of view quite the

opposite to the other links provided (termination of e-mail dialogue as opposed to

acquisition of further information), they are nevertheless included in the general click

data used in calculating member click activity. The main reasons for adopting this policy

in the research was that the inclusion of unsubscribe link clicks in the sample data was

necessary if one wished to analyze them.

4.6 Research Reliability

Reliability is the extent to which an experiment, test, or any measuring procedure gives

the same results on repeated trials conducted by either the same person or anyone else

(McNeill 1990, 14-15). The use of secondary data from a computer environment means

the data collection process was unlikely to suffer from random errors. The data was

derived directly from information systems used in managing member data and sending e-

mail messages, therefore the results of a research repeated at a later time would likely be

identical to the current findings.

There is no evident indicator suggesting the data integrity would have been compromised

during the mechanical processing involved in its original collection nor the manual

processing conducted during the research. Although Club Nokia also sends e-mail

campaigns through various local e-mail vendors, all the campaigns included in the

52

sample had been conducted using the same vendor, which increases the reliability of the

campaign data.

4.7 Research Validity

Validity can be divided into internal and external validity. Internal validity refers to the

question of whether the results obtained within the study are true, whereas external

validity is concerned with whether the findings can be generalized to other populations,

settings or time periods. (Ghauri et al 1995, 33)

4.7.1 Internal validity

Compared to sampling methods commonly used in similar research projects, the volume

of sample data included was substantially higher. The availability of secondary data

resources that in many cases covered the whole population meant the research validity

was not in practice threatened by sample size. It is however important to remember that

the process through which the secondary data had originally been collected is not without

its flaws, which is why considerations of internal validity should not be ignored.

Customer entered data, such as gender and age, are subject to both intentional and

unintentional errors. Unlikely age values (0 years, 150 years etc.) are to some extent

excluded in the secondary data, but demographic data is also missing from a certain part

of the population. Contact information (in this case e-mail address) validity is defined

through automatic checking procedure as well as bounce reports on mailings sent, which

limits the existence of (relevant) invalid addresses in the member database to a short time

period between contacts. Credit purchase data cannot make a distinction between credits

bought and credits given for free; therefore the events could not be considered monetary

transactions and were therefore excluded from the analysis. Digital content downloads do

not include downloads paid through operator billing, thus capturing download activity

only partially.

The biggest threats to validity come from the conceptual mapping of customer data to

real world phenomena, as discussed in chapter 3.4. When compared to the average

repurchase cycle of mobile phones and Nokia brand loyalty measured in surveys, it

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becomes clear that only a part of the members update their product information when

acquiring a new Nokia phone. Another problem related to product updates is that some

members may create a new account when switching to a new Nokia model because they

do not remember their member ID or password needed to access their account

information. Reliable identification of defectors who have switched to another brand of

mobile phone is impossible. Even repeat registrations are not 100% sure information,

since some people may be using their membership account to register the phones of their

family or employees. All these factors have to be kept in mind when analysing customer

relationships through phone registration data.

Processing campaign response data required generating a key field to each of the data

entries. The primary key used in Club Nokia Data Mart, the CID, does not exist in the

click data; which is why the member e-mail address had to be used for linking the two

data sets. This causes problems in the aforementioned case where a customer has

registered two or more membership accounts that share the same e-mail address. These

duplicates had to be removed during the process of linking customer data to click data.

The logical method of performing the deduplication was to delete the account with an

older registration date, as it was more likely to be the inactive one. This process can

nevertheless cause marginal mismapping of the campaign response data.

4.7.2 External Validity

Limiting the sample population to a relatively short time period and to only some of the

Club Nokia countries due to practical reasons is bound to decrease the applicability of the

findings to the whole target population. The country selection was not performed with

the intention of covering the variation in markets within the population, as there was no

prior research on which the selection could have been based. In addition, the differences

between the contents of all sent e-mails could not have been analysed within the scope of

this study, which is why the attempts to increase internal validity through different

selection criteria of the sampled population would have been unlikely to produce

significant results.

The practices of using e-mail direct marketing in facilitating customer dialogue within

CN are constantly evolving. Therefore the findings of the study cannot be reliably

54

generalized to prior time periods. The findings should be considered as a snapshot from a

given time period that can be used in the immediate planning of dialogue related

activities and as a reference point for future research on the subject.

55

5 EMPIRICAL RESEARCH RESULTS

This chapter discusses the findings from the quantitative research performed on member

and response data from CN. The chapter starts with an overview of the campaign

response data characteristics. The response data is then studied alongside the variables

available from the Data Mart, categorized into demographic, behavioral, membership

length and dissolution data.

The term ‘click rate’ is used throughout the chapter to refer to the percentage of members

who have performed a click within a given time period. Unless specified otherwise, the

time period in question is the complete sample data period of 5.5 months, between April

and September 2002. The sample size is 137,946 CN members, except in selected cases

where the complete population for the selected countries was used by accessing CN Data

Mart directly in order to include data also on non-contacted members.

NOTE: CHAPTER 5 REMOVED FOR THE ONLINE VERSION OF THIS DOCUMENT

Originally this chapter contained a detailed analysis of Club Nokia customer data with

various tables and graphs. Even though the three year confidentiality agreement signed

for the publication of this thesis has expired on January 2006, I consider the statistics

contained in this chapter to represent the kind of information that is not intended for

wide scale distribution through the various Internet search engines. Therefore, out of

respect for the employees of Club Nokia who made it possible for me to conduct this

study and allowed me to use their live customer data in the analysis, the numerical data

used in the quantitative research part has been removed from this online document.

Chapter 6 still contains the conclusions drawn from the data analysis, thus serving the

possible academic applications that this thesis study may have.

In case you are genuinely interested in studying the detailed statistics upon which the

study was based on, the full version of this thesis paper is available in the library of

Haaga-Helia University of Applied Sciences, Helsinki, Finland.

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6 CONCLUSIONS

In this final chapter the findings from the empirical research are summarized,

recommendations for measurement system development are presented and areas

requiring further research are identified.

6.1 Summary of the Empirical Research Findings

In order to summarize the findings from studying the effect of available member

variables on the likelihood of clicking on e-mail links, examples of these effects are

presented in table 14. The table shows how selected variable values either increase or

decrease the average member click rate.

Table 14: Summary of studied variables’ effect on click rate

The top three factors increasing the average click rate are all member interactions. This

result is well in line with the assumption made in chapter 2.7, which stated that customer

behavior is a stronger predictor of future customer relationship than demographic

variables. However, the variable having the most negative effect on click rate is a

demographic variable, since members between 16 to 20 years of age were found to have

a considerably lower click rate than older members. This was one of the more unexpected

research findings.

The most notable identifiable event in the member history data is the registration of a

second Nokia phone, which increases the average click rate by 54%. Whether repeat

registrations are the cause or effect of e-mail communications is debatable, but it is safe

Variable type Variable value Effect on click rate

Phone registrations Has 2 or more registered phones + 54%Inbound contacts Has inbound contacts + 39%Digital content downloads Has digital content downloads + 30%Phone category Latest phone Classic or Mediaphone + 20%Phone category Latest phone Fashion or Premium + 16%Gender Is female - 3.5%Membership length Registered before 2001 - 13%Phone category Latest phone Expression or Basic - 14%Registration media Not Internet - 22%Age group Is 16-20 - 34%

57

to assume that these members perceive the CN membership to provide value to them and

are therefore willing to participate in an ongoing dialogue by updating their phone

information. In order to deepen the relationship with these customers, their past

interactions should be utilized in delivering them more customized content that would

keep up their level of interest and ensure they will also register their next Nokia

purchases to CN. Furthermore, special attention should be paid to guiding the existing

members to register new products to their existing member accounts, since previous

experience has shown this to be a problem area. The inability to capture consistent

member lifecycle data can have serious implications to developing a meaningful dialogue

with members.

Members who have made inbound contacts to CN careline are much more likely to

respond to e-mail contacts than the average member. There can be various different

reasons why members make inbound contacts, but with no categorization available it is

not possible to examine the data further. E-mail clicks may be the first step in the search

for information that leads a member to contact CN directly, or alternatively the interest

towards CN’s e-mail communications could be the result of a satisfactory response to

prior inbound contact. Either way, inbound contacts represent the purest form of dialogue

and can therefore be considered to enforce the relationship between CN and its members,

which also reflects into the e-mail communication. Due to the unstructured nature of

inbound contact data, using it for guiding the planned communication process can

however be difficult.

Digital content downloads are a form of interaction that the members can perform at any

given time. There is no need for milestone events, such as purchase of a new Nokia

product or problems related to the existing one. Downloads represent the more everyday

activities related to CN membership, even if regular (credit based) service usage is rare

among members. Although members are charged for the downloads, delivering

information on new digital content through e-mail can create added value for members

interested in them and make the communication more relevant, which may be one reason

behind the positive correlation between digital content downloading and clicking on e-

mail links.

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Out of the two available demographic variables, the most notable effect on click rate

comes from age, specifically from the youngest age group of 16-20. The younger end of

the member age spectrum is clearly less responsive towards e-mail messages. Due to the

group’s high share of contacted members and the whole member base, the effect on

overall click rate is considerable. Even though the younger audience may be more

challenging to form dialogue with, this issue should be addressed in order to make e-mail

communication targeted at this age group more relevant to the recipients. As it stands, the

youngest Nokia customers seem to be highly interested in joining CN but less so in

responding to its e-mail messaging, which could be a sign of the gap between perceived

and offered benefits from CN.

Differences in click rate by the product category of a member’s latest phone can be

mainly identified in the low responsiveness of Expression and Basic owners. The high

share of young members in these categories is clearly one of the main reasons for low

click rates, but there might also be a generally lower interest towards the mobile phone

and service related content of the CN e-mails among these members. At the opposite end

of Nokia’s product selection, members with Imaging phone (7650) were found to be a

highly responsive group and should not be ignored just because of their low volume or

recent product updates.

Member registration media, i.e. the starting point of the dialogue, is an important factor

in predicting the likelihood of a member to respond to e-mail messages. If a member

registers through mail or other media, he or she is 22% less likely to click on e-mail links

than a member who registers through the web site. However, the more important

difference is the 70% lower contactability by e-mail for non-web registrants. Therefore it

is in practice only possible to use e-mail for dialogue building with members who join

CN through the web site.

As could be expected, member click activity tends to decrease over time. The reduction

in click rate per membership year is not in itself very dramatic, but the more important

phenomenon is the loss of contactable members. While only 3% of contacted members

unsubscribe per year, on average one fourth of contacted members become uncontactable

each year due to invalid e-mail address. This high annual loss rate undermines the

potential for building long-term dialogue, since by the time the average person is at the

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stage of purchasing a new mobile phone, the e-mail address validity will have reduced to

half of its initial level at the time of registration. Members performing clicks have

considerably higher address validity rates, suggesting they are more interested in

maintaining the dialogue with CN.

Looking at members who have remained contactable over time, their activity in clicking

remains relatively high, but this does not automatically translate into a large number of

regular readers. The frequency of clicks was quite low for both new and old members in

the 5.5 month time period. 82.5% of clicking members have clicked on only one single

message and the share of members with three or more clicked messages is only 2.5%.

This results in a somewhat contradicting equation: members who remain contactable

through e-mail appear to maintain their interest towards the CN e-mails, but they click on

the links only occasionally. It would therefore seem that the current contents of the e-

mail messages may not be something that the members would actively look forward to

reading through or at least it may not contain a clear incentive to click. The data suggests

the e-mails manage to deliver independent pieces of information that interest the

members, but consistent response patterns are not achieved.

6.2 Recommendations

The final sub-question of the research was “how could the information gained from the

research be monitored on a long-term basis?” The recommendations for applying the

research findings into regular measurement of the e-mail direct marketing activities will

now be discussed in detail.

If we compare the previously available information on CN’s e-mail driven customer

dialogue, based on campaign level summary figures, to the new information provided by

the research, we can see they provide very different views on the same phenomenon.

Campaign reports show how CN is talking to its members, whereas member level data

gives an idea of how the message is received and, to some extent, how members are

talking back at CN. Even though the data only covers one very specific part of the

communication process, the click on a hyperlink, it clearly increases the amount of

knowledge held on registered CN members and their behavior towards e-mail direct

marketing activities.

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Some might question the relative importance of click-throughs compared to other

trackable interactions, such as product registrations or digital content downloads. The

findings in chapter 5.3.5 clearly show that click-through data is the richest source of

member interaction information currently available to CN, due to the high frequency of

clicks. Including this data into the analytical database would therefore considerably

increase the overall coverage of member information available for dialogue planning and

analysis of relationship development.

The extent to which ad-hoc research similar to this study could be performed on different

sample populations is in theory only limited by the availability of e-mail campaign

reports. Comparing results from different time periods could enable measuring short-term

effects of changes applied to CN’s e-mail communications. Repeating the analysis on the

same set of members included in this study would also be possible, if one wished to

examine the effects of e-mail direct marketing on relationship development over time.

Long-term monitoring of e-mail direct marketing effectiveness should not rely on ad-hoc

research, but rather the information systems should enable the automation of repeated

measuring activities. The following topics identify areas in which it would be beneficial

to modify or develop the contents of data stored in CN’s member database to better

support the analytical needs.

6.2.1 Time-series Data

The data included in the research sample consisted of a 5.5 month time period with

several campaigns targeted at the same audience, but it should nevertheless be considered

as a snapshot. It could be observed from the data that the average frequency of member

response to e-mails is too low to be accurately measured within this time frame, which

prevented any categorization based on response activity levels. The division was

therefore forced into an on/off approach of ‘has clicked’/’has not clicked’ analysis that

left much of the potential of customer level activity tracking unused.

Capturing historical response data from a longer time period is crucial for taking the

analysis further and looking into member behavior instead of just member profile. Until

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there is sufficient data to identify a member’s activity over different membership

lifecycle stages, one has to project the behavior history by comparing members with

different membership length, which leaves some room for speculation on data accuracy.

Capturing complete e-mail response data into the member database would make it

possible to customize the dialogue with a member based on is or her prior interactions

with CN. Customization does not have to mean constructing a unique message for each

member using an advanced content management application. For example, the

information that a member has clicked on an e-mail newsletter link during last three

months could be easily used in selecting the most responsive target groups to send

additional information when new mobile applications are launched. Response data on

individual campaigns could be a powerful tool to use with recurring events like yearly

snowboarding competitions.

As observed from the research data, 18.4% of members with clicks had responded to

more than one e-mail of the 5 to 6 messages sent to them. Even this repeat click rate from

a time period of only 5.5 months is considerably higher than the total average of 12.9%.

This supports the conclusion upon which also the RFM model is founded on: members

who have clicked are more likely to click again than members who have not. It can

therefore be assumed that an effective way to grow the size of the audience viewing the

information found behind the hyperlinks is to focus on the most responsive members:

those who have clicked in the past. Some members will always become uncontactable

regardless of the communication efforts, but CN should utilize the campaign response

data in making sure the active part of the member base remains willing to participate in

the dialogue.

6.2.2 Member Activity Level

The research found positive correlation between member click activity and other types of

identifiable interaction, such as product registrations, inbound contacts or digital content

downloads. Although the focus of this particular research was on the e-mail response

data, in the context of analyzing member dissolution it should not be treated separately

but rather be included in the set of other member activity indicators. The fact that

members with clicks are also more likely to have other interactions with CN supports the

62

assumption that these actions are interrelated and they form the natural path of an active

relationship. They are one type of possible acts in a CN membership and should be

considered as building blocks that grow into episodes, sequences and finally a

relationship, as suggested by Holmlund’s (1997) model.

The repurchase cycle of mobile phone products is quite long, which reduces the accuracy

of member value analysis conducted based on static, demographic variables. This type of

data mainly reflects the starting point of the dialogue, not its results or the development

of a relationship over time. As pointed out by Reinartz et al (2002, 9), just because a

group of customers was profitable in the past, does not mean it will continue to be so in

the future. Many nonloyal customers can be very profitable initially, but if a company

continues to invest in marketing to them after they have ceased their buying activity,

these customers can become unprofitable in the long run.

In order to improve the accuracy of member value measurement, the recency of last

interaction should be included into the analysis and used in conjunction with product

purchase information to determine the current state of the customer relationship.

Although the sample data suggests the frequency of interactions by a typical member is

not high enough to justify weekly or monthly follow-up, knowing whether the last time a

member was in contact with CN was two weeks or two years ago would be a strong

indicator of response likelihood. The information could be acquired by adding a field into

the member database which would calculate the latest date of interaction based on all the

transactional tables (product registrations, digital content downloads, inbound contacts,

repairs, swaps, and click-throughs when available).

6.2.3 Member Profile Changes

The current member data does not include information on when the variables stored in

non-transactional format have been updated. This issue concerns mainly the

contactability related data, such as contact permissions, contact details and their validity.

There is no historical information available regarding the changes in contactability data,

which makes it impossible to study how the potential of having a dialogue with members

develops over time and in relation to member life cycle. Adding information on the date

of last update of contactability related fields into the database would enable analysis of

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the reasons behind loss of direct contact to members, making it possible to take measures

aimed at preserving dialogue.

6.2.4 Response Categorization

If detailed click-through data becomes available in the member database, information on

the content of each e-mail message should also be recorded. Knowing only that a

member clicked through a link provided in a particular message is not enough to interpret

his or her behavior if the communication consists mainly of regular e-mail newsletters

containing information on various different topics. Unless the destination page behind the

link is described, it will be impossible to differentiate between links to product

information pages, CN front page, external campaign sites and the likes. It is not feasible

to expect the hyperlink structure of individual e-mail messages to be studied afterwards,

but rather the process of sending out campaign messages should include a generic

categorization of possible responses from recipients according to link purpose.

6.3 Further Research

The practical applications of using data from various electronic customer touchpoints that

a modern loyalty program can contain have not yet been widely studied. There are

numerous potential topics for further research that could expand our understanding of the

key phenomena related to building a customer dialogue through electronic media. The

following are some of these issues that were identified during the process of this study.

In order to attract campaign responses from customers you have to first be able to contact

them. As the CN case demonstrated, the validity of e-mail contact information collected

for a loyalty program can deteriorate very rapidly if customer dialogue does not remain

active. By the time an outbound e-mail message sent to the customer results in a hard

bounce, the actual contactability has most likely already expired a long time ago and the

bounce is just the final confirmation of this. It would be very useful for the e-mail direct

marketer to be able to identify potential loss of contactability well beforehand, not only

to reduce the amount of messages sent to obsolete mailboxes but to make it possible to

act on the situation and preserve the customer contacts. Methods for analyzing the

development of contact data validity in different environments should therefore be

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studied, as the validity information is likely to be available in most planned e-mail

communication programs.

When the collection of customer information is first initiated, all accounts in the

customer database are often considered active and valid. As time passes by, an increasing

share of the accounts will become inactive in the real world as a result of relationship

dissolution. Transforming this phenomenon into a database process that identifies the

inactive accounts is in many cases not performed until the volume of these accounts has

started to distort data analysis and weaken communication accuracy. Since the

phenomenon itself is of universal nature, the possible ways of taking it into consideration

when designing information systems and methods of filtering existing databases should

be studied. Although the exact definition of inactivity needs to be decided case by case,

based on the average fluctuations in customer activity, using common principles could

improve the reusability of models developed for tracking dialogue effectiveness.

The time period examined in this study was not long enough to allow the inclusion of the

time dimension into the data analysis. Continuous monitoring of relationship

development is however a time dependent process where the focus is on customer

migration between defined activity categories. The variables analyzed in this study

provide a snapshot from a given moment, but the data stored in transactional format

would also allow analyzing sequential events and constructing models to display the

interrelationships of different activities in a more illustrative manner. By gradually

increasing the level of detail involved, it could be possible to map the interactions

specific to the relationship in question onto a hierarchy similar to the model presented by

Holmlund (1997, 96). Understanding how this relationship structure can be made visible

from the available data would enhance both planning and measuring of the planned

communication process.

65

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APPENDIX A: EXAMPLE OF CLUB NOKIA E-MAIL NEWSLETTER

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APPENDIX B: DESCRIPTION OF RESEARCH DATABASE STRUCTURE