St. Petersburg University
Graduate School of Management
Master in International Business Program
Customer segmentation for B2B markets
Master’s Thesis by the 2nd year student
Concentration — International business
Olga Petukhova
Research advisor:
Assistant Professor
Froesen Johanna Pia Maria
St. Petersburg
2016
1
ABSTRACT
Master Student's Name
Master Thesis Title
Faculty
Main field of study
Year
Academic Advisor's Name
Description of the goal, tasks and main results
Petukhova Olga
Customer segmentation for B2B markets
Graduate School of Management
International Business
2016
Froesen Johanna Pia Maria
The master thesis touches upon the issue of
segmentation in B2B markets. The aim of the
research is to contribute to the development of
modern approach to market segmentation based
on the analysis of the efficient customer
portfolio, which allows the company operating in
business market to evaluate most profitable
clients, assess the risks and make a conclusion
concerning the allocating of the company’s
resources in the most efficient way. The task of
creating efficient customer portfolio is solved
with application of financial portfolio theory,
which demonstrates how investors can create an
optimal portfolio that will maximize returns
depending on the given risk level. In the research
the efficient portfolio frontier is constructed in
order to compare possible efficient portfolio
combinations with the current customer
portfolio. The analysis of the efficient customer
portfolio reveals which groups of customers
should be targeted and whose requirements and
expectations should be met by the company, and
thus contributes to the efficient market
segmentation.
Keywords
segmentation, B2B markets, customer portfolio
analysis, financial portfolio theory, Markowitz’s
model
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АННОТАЦИЯ
Автор
Название магистерской диссертации
Факультет
Направление подготовки
Год
Научный руководитель
Описание цели, задач и основных
результатов
Ключевые слова
Петухова Ольга Дмитриевна
Сегментация B2B рынков
Высшая Школа Менеджмента
Международный бизнес
2016
Йоханна Пиа Мария Фрёзен
Магистерская
диссертация
затрагивает
проблему сегментации B2B рынков. Целью
исследования является внести вклад в
развитие
современного
подхода
к
сегментации рынка, основываясь на анализе
эффективного портфеля потребителей, что
позволяет компании, функционирующей на
бизнес рынке, идентифицировать наиболее
выгодных клиентов, оценить риски и прийти
к заключению о наиболее эффективном
способе распределения своих финансовых
ресурсов. Задача создания эффективного
портфеля
потребителей
решается
посредством
применения
финансовой
портфельной теории, которая показывает, как
инвесторы могут создать оптимальный
портфель с максимальным уровнем дохода
при заданном уровне риска. В рамках
исследования
был
построен
график
оптимального
портфеля
(“граница
эффективности”) с целью сопоставления
возможных
вариантов
эффективного
портфеля с существующим портфелем
потребителей
компании.
Анализ
эффективного
портфеля
потребителей
позволяет выявить группы клиентов,
сотрудничество с которыми является
наиболее выгодным, и чьи интересы
представляют первостепенный интерес для
компании.
Таким
образом,
анализ
эффективного портфеля вносит вклад в
усовершенствование подхода к сегментации
рынка.
сегментация, B2B рынки, анализ портфеля
потребителей, финансовая портфельная
теория, модель Марковица
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Content
List of figures and tables ...............................................................................................................7
1. Introduction ...............................................................................................................................8
1.1 Research gap ......................................................................................................................10
1.2 Research question and research objectives .....................................................................11
1.3 The main definitions ..........................................................................................................12
2. Literature review .....................................................................................................................14
2.1 Core features of business markets ...................................................................................14
2.2 B2B customers ...................................................................................................................19
2.3 Segmentation and customer portfolio theory ..................................................................21
2.3.1 The main steps of the market segmentation ..............................................................22
2.3.1.1 Defining of the relevant market to be addressed .............................................22
2.3.1.2 Segmentation bases .............................................................................................23
2.3.1.3 Identifying and selecting the segments .............................................................27
2.3.2 Customer Relationship Management ........................................................................27
2.3.3 Customer Relationship Management (CRM) and customer portfolio theory .......30
2.3.4 Relationship between Financial portfolio theory and customer portfolio theory .31
Conclusions ..................................................................................................................................34
3. Methodology .............................................................................................................................36
3.1 The company description ..................................................................................................36
3.2 Analysis of the existing customer database .....................................................................37
3.3 Method of financial portfolio theory application to customer portfolio analysis ........41
3.4 The process of the customer portfolio optimization .......................................................46
3.5 Comparison of the current customer portfolio and the efficient customer portfolio
combinations ................................................................................................................................48
4. Findings ....................................................................................................................................50
4.1 Industries sales variability assessment ............................................................................50
4.2 Industries risk – reward dynamics assessment ...............................................................52
4.3 Efficient customer portfolio options ................................................................................54
4.4 Current customer portfolio analysis ................................................................................59
5. Discussion .................................................................................................................................61
5.1 Answer to research question.............................................................................................61
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5.2 Theoretical contributions ..................................................................................................62
5.3 Managerial implications ...................................................................................................63
5.4 Limitations and future research .......................................................................................65
References.....................................................................................................................................67
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List of figures and tables
Figure 1 Nested approach to segmentation....................................................................................25
Figure 2 CRM areas of influence ..................................................................................................29
Figure 3 Customers' distribution be location area .........................................................................38
Figure 4 Customers' distribution by industry type ........................................................................38
Figure 5 Range in the number of workers in the client - companies .............................................39
Figure 6 Distribution of client groups among location areas ........................................................40
Figure 7 Annual invoices dynamics 2013-2015 ............................................................................51
Figure 8 Annual costs dynamics 2013-2015 .................................................................................52
Figure 9 Return dynamics by industries ........................................................................................53
Figure 10 The efficient frontier portfolios .....................................................................................58
Figure 11 The efficient frontier portfolios and current customer risk and return..........................60
Table 1 Company "X"profile .........................................................................................................36
Table 2 Invoice from one unit / Costs per one unit .......................................................................43
Table 3 Monthly invoice/costs one client ......................................................................................44
Table 4 Monthly invoice from all clients ......................................................................................44
Table 5 Monthly costs-to-serve of all clients ................................................................................45
Table 6 Monthly return from all clients.........................................................................................45
Table 7 Industry Beta (calculation) ...............................................................................................46
Table 8 Risk of the entire portfolio (calculation) ..........................................................................49
Table 9 Reward ratio (calculation) ................................................................................................49
Table 10 Industry beta ...................................................................................................................53
Table 11 Industry average return ...................................................................................................54
Table 12 Efficient portfolio- option 1(return equal to 8%) ...........................................................55
Table 13 Efficient portfolio - option 2 (return equal to 7,5%) ......................................................56
Table 14 Efficient portfolio - option 3 (return equal to 7%) .........................................................56
Table 15 Efficient portfolio - option 4 (return equal to 6,5%) ......................................................57
Table 16 Efficient portfolio - option 5 (return equal to 6%) .........................................................57
Table 17 Risk -Reward ratio..........................................................................................................58
Table 18 Current customer portfolio (return equal to 7,1%) .........................................................59
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1. Introduction
The relations among businesses have rich historical background and systematic scientific
investigations in this field have been carried out. The key theoretical problems concerning marketing
on the whole and business-to-business management in particular are highlighted in the monographs
written by Kotler and Armstrong (2006, 2013, 2014), Bains et al. (2008), Wedel and Kamakura
(2000), West et al. (2006) and others. These outstanding economists laid the foundation of the
scientific research in this field, having investigated such burning issues related to B2B activity as
characteristic features of business organizations, strategic planning, evaluating market opportunities,
market segmentation etc.
Business-to-business market includes all organizations that buy goods and services for the
production of other goods and services or for the purpose of reselling or renting them to others at a
profit (Kotler and Armstrong 2014). One of the key factors of efficient marketing in B2B sphere is
segmentation. Segmentation is considered as a business process, which main goals are to divide the
market into homogeneous segments according to customers' needs and coordinate the company's
activity in accordance with the value of each segment (Smith 1956, Dickson and Ginter 1987, Douglas
1972, Wedel and Kamakura 2000, Brijs 2002, Block and Block 2005 etc). Efficient segmentation is
significant for any company because it results in identifying groups of customers with specific needs,
which allows to promote the right offer on the market and gain competitive advantage.
Although a lot of different approaches to segmentation have been worked out, thoroughly
analyzed and applied in various spheres of business activity, there are still some important issues that
are widely discussed by economists nowadays. Profound researches related to segmentation touch
upon such theories as customer relationship management (CRM) (Buttle 2001, Stone and Woodcock
2001, Parvatiyar and Jagdish 2001, Florez-Lopez and Ramon-Jeronimo 2008), Greenberg 2010,
Tohidi and Jabbari 2012 etc), value-based customer segmentation (Hogan et al., 2003, Raaij et al.,
2003, Chan Ch. 2008, Cheng and Chen 2009, Hiziroglu and Sengul 2012, Aeron et al., 2012,
Noorizadeh et al., 2013 etc), customer portfolio management (Rajagopal and Sanchez 2005, Terho
and Halinen 2007, Sackmann et al., 2008, 2010, Tarasi et al., 2011, Thakur and Workman 2016 etc).
Market segmentation implies choosing most profitable groups of customers to target (Wedel
and Kamakura 2000). It is essential for the company to allocate resources into those customers that
bring most profits and concentrate more on the requirements and expectations of the most profitable
customers (Noorizadeh et. al., 2013). Thus, deep insight into customers will contribute to achieving
sustainable competitive advantage in the market (Thakur and Workman 2016).
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The problem of retaining existing customers, acquiring new customers and maintenance longterm relationships with strategically significant ones in order to create superior value for both clients
and companies can be decided by means of implementation of the strategy of Customer Relationship
management (CRM) (Parvitiyar and Sheth 2001, Buttle 2001). This strategy implies establishment of
close relationship with customers so that the company could satisfy their needs and get necessary
information to innovate, study their preferences and reach higher efficiency in the company’s supply
(Heredero and Gomez 2014).
From a CRM perspective, market segmentation heavily depends on the data collected in the
customer database. The data included into customer database can be either generated internally by
collecting finance and sales records of the clients or sourced externally by conducting interviews,
marketing research campaigns and surveys (Buttle 2004). Customer database has two important
functions. Firstly, the base provides continuous description of companies’ clients. Secondly, by using
the information about existing customers the company can divide them into homogeneous groups and
identify potential target market (Gide and Shams 2011).
One of primary conditions for CRM is to estimate customer value with the aim to compare
investment alternatives (Sackmann et al., 2010). After identification of the most profitable customers,
companies can focus on building long-term partnerships with them and, as a result, enhance both
current and future performance of the company and its financial results.
In order to sustain continuous growth and achieve greater profitability customer management
tools should be applied at the customer portfolio level, which means that the company has to focus
not on few closest relationships but on wider managerial perspective, that takes into consideration the
company’s entire customer portfolio (Terho and Halinen 2007). Having examined customers in the
portfolio, the company can better understand the relative significance of each group of customers in
relation to sales and profits. Such knowledge will help the company to work out market strategies
aimed at retaining valuable customers, developing efforts in creating additional value with these
customers (Thakur and Workman 2016). Moreover, after conduction profound analysis of the existing
customer portfolio the company can not only reformulate its strategy and put efforts on the satisfaction
of the requirements of most profitable customers, but also can attract new customers with similar
characteristics. Thus, the analysis of customer portfolio represented in this work is supposed to be an
effective tool of efficient segmentation in B2B market.
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1.1 Research gap
Business–to–business market deals with marketing of products and services among
organizations and differs from business-to-customer market in a number of ways (Baines et. al 2008).
These differences, such as, for example, fewer number of customers, the necessity to build longlasting relations, the peculiarity of the decision-making unit and segmentation variables, predetermine
the direction of the economic and managerial investigations in this field (Blythe and Zimmermann
2005, Block and Block 2005, Baines et al. 2008). Being considered as an important factor of market
success, segmentation constantly draws the attention of scientists (Palmer and Millier 2004, Aeron et
al., 2012, Hiziroglu and Sengul 2012, Florez-Lopez and Ramon-Jeronimo 2008 etc).
In spite of the fact, that the role of efficient segmentation based on the strategy of customer
relationship management is not underestimated, analysis of customer portfolio models, which
represent significant tools proposed for relationship management is relatively rare in business (Terho,
Halinen 2007). Meanwhile, empirical tests which take into account costs to serve the customers and
allow to set most valuable customers, so that the efficient customer portfolio could be formed, are
considered as rather promising (Thakur and Workman 2016).
Another significant tool of achieving efficient segmentation is the application of financial
portfolio theory to customer portfolio, as it allows to define the variability in a customer portfolio, to
assess the similarity of market segments and to investigate their weights in the portfolio (Tarasi et al.
2011). The main idea of this approach is to show that market segments could be characterized in terms
of their risk and return. Although this approach is considered well suited for business-to-business
companies that serve market segments from different sectors of the economy, it is still not widely used
in the market.
Applying financial portfolio theory to the customer portfolio theory in this research resulted
in the construction of the efficient customer portfolio based on the industry type. The proposed model
allows to determine the optimal weights of the resources (costs) distribution among industries in the
portfolio, to evaluate them and finally decide which industries presented in the market are more
preferable to serve provided that company is aimed to achieve the predetermined level of return.
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1.2 Research question and research objectives
The aim of the research is to contribute to the development of modern approach to market
segmentation based on the analysis of the efficient customer portfolio, which allows the company
operating in business market to evaluate most profitable clients, assess the risks and make a conclusion
concerning the allocating of the company’s resources in the most efficient way. The issues touched
upon in the research gap section being taken into account, the research question of the thesis can be
formulated in the following way:
Research question
How can the approach to segmentation in b2b markets be supplemented by applying customer
portfolio analysis?
In order to answer the research question the following objectives were established. First of all,
to analyze the theoretical background of the issue with the aim to identify core features of B2B
markets, bases of segmentation and modern approaches to market segmentation enhancing. Secondly,
to analyze the existing customer database of the B2B Service Company “X” and identify the bases for
market segmentation. Thirdly, to apply financial portfolio theory to the analysis of the customer
portfolio. Finally, to analyze customer portfolio options and their applicability to market
segmentation.
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1.3 The main definitions
As this thesis concentrates on the segmentation in B2B markets the definitions of the notions
of B2B market and segmentation should be provided.
The business market definition has already been considered by a lot of marketers. B2B market
is concerned with various profit making organizations that buy products or components to use, to
resell or to produce other products (Baines et al., 2008). Besides buying and selling materials for
production goods, B2B markets deal with buying and selling equipment and maintenance services
(Kotler et al., 2013). Some specialists think that the definition of B2B market should be expanded and
complemented with the information concerning wider range of customers and include such institutions
as charities and hospitals and various levels of governments (Blythe and Zimmerman 2005).
As it was mentioned, B2B businesses include not only transactions of products and
components, but also services. Services that are considered in B2B markets can be divided into two
groups: operations-type services that are necessary continuously for operating of the business, such
as insurance, for example, and one-time services, which can include consulting projects, auditing
services and others (Block and Block 2005).
Thus, although many definitions of Business markets have already been proposed, the final
version can be modified to some extent and will comprise all types of customers involved and all
activities and functions it implies. Kotler and Armstrong (2014) state in their definition of business
market that it includes all organizations that buy goods and services for the production of other goods
and services or for the purpose of reselling or renting them to others at a profit. This definition is
accepted in this work.
Market segmentation concept has been developed since the middle of the XX-th century. At
this time it became obvious in industrial world that manufactures are supposed to focus not only on
decreasing of production costs but also on satisfying customers’ needs (Wedel and Kamakura 2000).
To identify groups of customers with specific needs meant for companies to promote the right offer
on the market and gain competitive advantage.
Smith introduced the first definition of segmentation in 1956. “Market segmentation is based
upon developments on the demand side of the market and represents a rational and more precise
adjustment of product and marketing effort to consumer or user requirements” (Smith 1956, p.3-8).
According to W.Smith, segmentation is an important tool that helps organizations to meet customer
needs. The idea put forward by W.Smith was developed to the concept that implied the identification
of groups of customers, which respond to the marketing mix in a different way (Dickson and Ginter
12
1987). Having divided the market into segments on the basis of their response to marketing strategies,
the company can adjust its marketing policies to the needs of every specific segment, and, as a result,
get greater profit (Douglas 1972). Market segmentation implies choosing most profitable groups of
customers to target (Wedel and Kamakura 2000). It is necessary for the company to allocate resources
to those customers that bring most profits and concentrate more on the requirements and expectations
of the most profitable customers (Noorizadeh et. al., 2013).
Since the time when Smith’s definition was introduced economists have proposed a lot of
different approaches to define the segmentation. Some scientists accentuate the idea that segmentation
as a global strategy which aim is selection of customers, while others consider segmentation as a
methodology, which relates to methods, used for customers’ clustering (Dibb and Stern 1995). T. Brijs
(2002) states that segmentation is the dividing of the market into homogeneous sub-markets
depending on customer demand, which results in the identification of groups of customers that respond
differently to the marketing mix. (Brijs 2002, p. 94).
Summarizing the key points of the main concepts we will consider the segmentation as a
business process which main goals are to divide the market into homogeneous segments according to
customers' needs and coordinate the company's activity in accordance with the value of each segment.
13
2. Literature review
Research question formulated above predetermines the theoretical issues that should be
outlined and thoroughly investigated. As the thesis deals with segmentation in B2B market it is
essential to consider the core features of B2B markets so that clear understanding of their peculiarities
could be achieved. The information related to the features that differentiate B2B markets from B2C
ones will allow to realize the main directions of economic investigations concerning the process of
segmentation, to understand the role of segmentation in efficient operation of the firm in the market
and outline the ways of its enhancing. The core features of B2B markets being considered, the
theoretical issues related to the process of segmentation should be covered. For this purpose, the main
steps of market segmentation and different approaches to segmentation will be studied. As the problem
of efficient segmentation implies the allocating resources into the customers that are most profitable
and requires concentrating on the needs of these customers, it seems essential to investigate the
strategy of Customer Relationship management that presupposes establishing necessary relations with
both existing and potential clients. Due to the fact that for efficient segmentation the customer
management strategy should be applied at the customer portfolio level (Terho and Halinen 2007), the
main theoretical points of customer portfolio theory will be considered. Besides, as according to the
latest investigations the problem of structuring of a customer portfolio could be successfully solved
with application of the financial portfolio theory (Tarasi et al., 2011), the main points of the latter and
its relation to customer portfolio should be touched upon.
2.1 Core features of business markets
Business markets are far larger than consumer markets (Blythe and Zimmerman 2005).
Among major features of business market its focus on the organizations’ purposes instead of the end
customer needs can be mentioned. In B2B environment the interest related to profit, the benefits of
people involved in the negotiations, and the interest of competing organizations are the main points
that should be taken into account (Block and Block 2005). In order to have clearer understanding of
specific features of B2B market we will take a closer look at such aspects as: complexity of demand
forecasting, identification of number of customers, complexity of products and services, buyer-seller
relationships and brand importance.
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Demand forecasting
Although business market is really large, the number of customers is significantly lower than
in consumer markets (Blythe and Zimmerman 2005, Kotler et al., 2013). However, the volume of
purchases is much higher and involves higher investments. B2B market faces some challenges, one
of which unique characteristics is derived demand (Block and Block 2005). The notion ‘derived
demand’ means that ultimate demand for company’s goods or services is related to the demand by
customer’s customers. With the changes of consumer preferences business markets should always
monitor the demand and be ready to adopt their offers according to these changes. Besides, business
market demand is “more volatile than consumer demand” (Kotler and Keller 2006). This can be
mainly explained by the fact that any changes in a finished product can somehow affect other
companies that are related to the buying organization or participate in a production process. Even
relatively small fluctuations in consumer demand may lead to significant increase in business demand
(Kotler and Armstrong 2014). On the other hand, business market demand can be considered as
inelastic, which implies that in the short run total demand for many business products does not change
greatly with price changes (Kotler et al., 2013).
Such peculiarities of the demand in B2B market require continuous monitoring of the market
so that predominating trends could be identified in due time, which would allow the company, offering
goods or services, be more flexible and get competitive advantage especially in the situation
aggravated by economic crisis. Thus, analyzing current customer database and revealing the
fluctuations in risk and return indicators at the customer portfolio level, which is accentuated in this
thesis, is believed to be important.
Complexity of products and services
Products and serviced provided in B2B market have their specific characteristics that should
be taken into consideration in the process of defining of clients’ needs, which is indispensable for
efficient segmentation.
Goods and services in B2B markets are different. The range of the products offered by business
markets can vary depending on their further applying (Baines et al., 2008). B2B products can be
materials that are used as parts of other products. These products are a part of finished products. An
example can be raw materials; components used to create the final product or finished goods. Another
example of B2B products is capital goods and services. These are products that are involved in the
production process and help in the creation of final product, such as buildings, manufacturing
15
equipment or computer systems. A number of B2B products and services are not involved in the
production process, but help organizations to achieve their goals. A good example here is services that
companies usually outsource in order to focus on their core activities, such as: delivery of goods to
the customer, hiring of consultants to create marketing campaign, maintenance of equipment etc
(Vitale and Giglierano 2002). A growing number of industrial companies try to switch from pure
product offerings to providing bundles of products and services or even integral systems (Ulaga and
Reinartz 2011).
As this thesis is based on analyzing of the data provided by a service company it is necessary
to touch upon some features of B2B activity involving service. A service in the market is supposed to
have some element of intangibility (in fact, there is a continuum of tangibility, which ranges from
highly intangible to highly tangible). Besides, any service implies interaction with customers, does
not lead to transfer of ownership, presupposes the change in condition and may not be closely
associated with a physical product (Payne 1993). The range of services offered in the market is wide
and each service has some specific features and strategical objectives. The main mission of the
company, that is considered in this thesis is defined by its management and includes strengthening the
corporate image and improving the everyday operations of customers through the company’s services.
Buyer-seller relationships
The process of segmentation implies concentrating on requirements of the most profitable
customers and building strong and trustworthy relations with them. As relationship with customers is
considered to be extremely significant in B2B activity on the whole and in B2B service in particular,
it is essential to touch upon such aspect as buyer-seller relationships in detail.
According to Grönroos (2011), buyer – seller relationship in a business market is a serious and
multi-step process, as suppliers create value not only by providing products and services to clients,
but also by supporting clients in their own business operations by providing resources that perform
specific functions for the client. Some researchers noticed that customers consider these offerings as
relational processes that can improve the productivity of their operations (Tuli et al., 2007). The key
conditions for the successful development of customer relationships are a deep understanding of the
client's business effective plan for the development of relations and the ability to link how service that
is offered can lead to customer's success (Baines et al., 2008).
Many researchers support this idea and highlight that understanding of the customer processes
is one of the main components in B2B processes (Blythe and Zimmerman 2005).
16
To develop long-term relationships with their customers the company needs open communications so
that it could become aware of customers’ wants and needs (West et al., 2006). Competently carried
out negotiations can be the driving factor for B2B companies’ success (Block and Block 2005).
Salespersons are key figures for B2B companies, because they have unique opportunities to translate
customers’ desired value back into their companies, understand how that value can be appropriated in
the form of revenue and other strategic benefits (Blocker et al., 2012, p.15).
As in B2B markets relationships between buyer and seller are close, future sales opportunities
depend to a large extent on mutual trust and satisfaction (Crosby et. al., 1990, Doney and Cannon
1997). The challenge of any salesperson is to create open relationships with customers, as it will help
to find out their needs and satisfy them. Long-term relationship in this case is a foundation of a
customer’s permanent repeat purchases; desire to increase the amount of purchases or to extend the
validity of contract. J. Blythe and A. Zimmerman having considered Newton’ typology of salespeople,
according to which four types of salespeople were identified: trade, missionary, technical and newbusiness, state that there is another kind of salesperson whose role is significant – a key-account
salesperson (Blythe and Zimmerman 2005).
which take place
This person is responsible for lengthy negotiations,
in conformity with a key-account scenario. The function of a key-account
salesperson is to deal with decision makers of different types and which is more challenging - with
decision-makers from different countries that means with people with different cultural backgrounds.
Another important factor concerning successful buyer-seller relationship is the professional
managerial and technical qualities of a seller. Quite often big deals are carried out by means of tenders.
That is why people who are involved into sales process should be high skilled professionals with a
solid technical product insight and be aware of all products or services characteristics, production
process and competitors offerings (Blythe and Zimmerman 2005).
Taking into account the fact that building solid and trustworthy relations is so significant in
B2B sector it is essential to consider the main aspects of customer relationship management as a
strategy that is aimed at retaining existing customers, requiring new customers and maintaining stable
relations with them. This strategy will allow the company to enhance both current and future
performance and its financial success. Customer relationship management will be touched upon in
detail further in this work.
17
Brand importance
Consideration of characteristic features of B2B market should undoubtedly include such
aspects as analyzing types of organizations involved, which influences the negotiation process, and
brand name that contributes greatly to the image of the company. As segmentation implies indicating
the needs and expectations of the customers and constructing long-term relations with them, such an
important tool of influencing the customer’s attitude and decision-making process as brand name
cannot be underestimated.
In B2B sector there is a link between branding strategy and companies financial performance.
Brand strategy includes brand positioning, brand name selection, brand sponsorship and brand
development (Kotler et al., 2013). Brand positioning implies delivery a specific set of features,
services and benefits to customers. Good name also contributes to company’s success provided that it
is easily remembered, pronounced and recognized. In order to develop its brand the company can
introduce line extensions, brand extensions, multibrands or new brands (Kotler et al., 2013). Branding
helps companies to provide customers with relevant information about products or services and as a
result increase the number of repetitive purchases among firms (West et al., 2006).
According to Aaker (1991), many industrial purchase alternatives tend to be toss-ups. The
decisive factor then can become what a brand means to a buyer. This proves the fact of band
importance to B2B organizations. When customers purchase a product or a service from top
companies with a famous brand name they can become more assured that high quality components
will increase their own product class. “Buying a familiar brand may involve additional comfort and a
“feel good” factor” (Mudambi 2003, p.527).
According to Keller (2009) brand name plays great role in the decision-making process,
because it sets expectations and guarantees reducing risks. As decisions in business are often complex
and characterized by uncertainty, the name of the brand can be a rather powerful tool in providing
valuable reassurance to business customers. The given ideas are relevant for service companies. Under
unfavorable economic circumstances, many customer organizations search for a service partner with
well-known brand name, which has proved its reliability and high level of the service quality. It allows
customers to concentrate on their everyday activities, while some aspects of their business that require
particular service is ensured by the company with a famous brand name.
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2.2 B2B customers
Another important characteristic of B2B market is types of customers, which also influences
decision-making process and predetermines the specific activities of the company aimed at long-term
relations construction. Nowadays companies are changing their approach from product centric to
customer centric (Wedel and Kamakura 2000). Customer centric approach unlike product centric
approach concentrates on customer’s wants and needs. In a customer centric approach companies
consider customers as assets and focus primarily on the possible ways of acquiring and retaining more
customers (Johnson and Selnes 2004). This can be explained by the fact that while competitors can
easily replicate products, customer base can be a source of competitive advantage (Harsha et al.,
2011).
Customers in a business-to-business context vary significantly from the customers in businessto-consumer context (Baines et al., 2008). Organizations do not consume the products themselves.
Unlike consumer market, business market consists of organizations of many types and sizes. Business
customers take into account their economic benefits when they make decisions, especially in tough
economic situation (Kotler et al., 2013). Meanwhile human and social factors are also important.
Business customers are involved into the process of collaborating reasonably and emotionally.There
are a lot of types of customers that participate in B2B activity. Among them can be named various
commercial organizations, governmental organizations and institutional organizations.
Commercial organizations
Commercial organizations can be categorized, as there are many types of business
organizations. Commercial organizations purchase goods with the aim, for example, to produce other
goods, to resell them to other organizations or final consumers (Blythe and Zimmerman 2005).
Baines et al., (2008) provide a classification of commercial organizations and divide them into
four groups: distributors, users, retailers and original equipment manufactures. These groups are
formed in accordance with the needs of customers. Distributors (or intermediaries) are responsible
for transferring goods from manufactures to final end user customers. Distributors usually provide
cash support to suppliers, storage, services or technical support to customers. Users purchase products
that are used up within the organization as components in their own equipment or to make the
equipment perform properly. Thus, products that are used by users participate in production process,
but are not the part of a finished product. Retailers purchase products and resell them to consumers.
Original equipment manufactures purchase all types of products from the capital facilities to finished
19
goods or raw materials. The key factor for them is quality of services and products. OEMs are
companies that buy products, include them within different other products and after that sell it under
auspices of its own brand name.
Governmental organizations
Government organizations constitute a large group of buyers of business purchases. The
volume of business purchases of this category is relatively high. Governmental projects involve large
number of stakeholders, can exceed the budget and past the planned date of completion. These factors
influence buying behavior (Baines et al., 2008). There are a lot of domains that need public
investments, such as: military, educational establishment, medical institutions etc. Blythe and
Zimmerman (2005) touch upon the main peculiarities of operating of government organizations. They
usually function under rigorous rules. Often deals are usually carried out by means of tenders.
However, as the winner of the tender is an organization that offers the lowest bidder, cooperation with
governmental organizations is not always profitable. Another important point concerning
governmental organizations purchases is the specifics of the products they buy. Not all goods (for
example, military hardware) are available to the general public and allowed to be sold by regular
businesses.
Institutional organizations
Institutional organizations include various non-profit organizations such as: charities,
churches, hospitals, universities etc. These organizations purchase goods and services in order to
satisfy their needs. Quite often their budgets are rather small and these organizations are in need of
financial support in a cash or product equivalent. Purchasing in institutional markets can be influenced
by political factors, some pressure from authorities. Meanwhile these organizations purchase a wide
range of products, materials, and services so that they could operate and achieve their goals (Baines
et al., 2008).
All types of customers (commercial, governmental and institutional organizations) can be
considered as potential clients of a service company operating in B2B market. Meanwhile, it should
be noticed, that the decision-making process could vary greatly depending on the type of the customer.
As segmentation implies identifying groups of customers with specific needs, which allows to
promote the right offer in the market, the type of the organizations involved becomes a significant
factor that cannot be underestimated.
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2.3 Segmentation and customer portfolio theory
The core features of business markets being considered, it is essential for this thesis to touch
upon the theoretical basis of the segmentation process itself. The main goal of market segmentation
is to leverage scarce resources to work out marketing strategies that will promote the adjustment of
the components of marketing mix, price, distribution, products and promotion, to particular needs of
different groups of customers (Baines et.al., 2008). According to Baines et al., (2008) there two main
approaches to segmenting markets: breakdown method and build-up method. The former admits that
all customers on the market are the same and the task is to find groups, which have particular
differences. The latter method admits that all customer are different and the task is to identify
customers, which are similar.
The problem of market segmentation implies first of all the decision of two questions: to select
variables, which will be used in defining segments and to put these variables together in a proper way
(Block and Block 2005). B2C market segmentation variables are geographic, demographic,
psychographic and behavioral (Kotler et al., 2013). Although B2B companies use some of these
variables, they also have other options for segmenting, such as: customer operating characteristics,
purchasing features, situational factors and personal characteristics (Kotler et al., 2013).
As Willan (2015) points out, due to the fact that usually B2B markets target audience is usually
smaller in comparison to B2C markets, the optimal number of segments also varies. Thus, while for
B2C markets the number of segments for more than 10 is a common practice, B2B markets are
generally divided into 3-5 segments. To some extent, this can be also explained by the fact that B2B
customers’ needs tend to be more rational and vary less than B2C consumers’ ones.
Efficient segmentation of customers can provide great advantages to the companies
performance. Identification of the major segments of customers assists B2B companies to improve
offerings, to understand the needs of the customers, to establish price and to organize effective
promotion (Florez-Lopez and Ramon-Jeronimo 2008). The understanding of these points will lead to
the retention of existing customers and to the attraction of new ones. Moreover, after revealing the
most profitable segments B2B companies will be able to direct all their efforts and budgets to serve
the most profitable and responsive markets segments (Blythe and Zimmerman 2005).
Thus, to have deeper understanding of the segmentation process it is necessary to consider in
detail its main steps, types of segmentation bases, on which different approaches are founded, define
the role of customer relationship management in segmentation. As this thesis is based on the data,
provided by a service company which already operates in the market, there is an opportunity to
21
evaluate the existing customers, apply customer portfolio theory so that to study the ways to enhance
both current and future performance of a service company in B2B market.
2.3.1 The main steps of the market segmentation
The process of market segmentation implies the division of heterogeneous market into specific
homogeneous segments (Payne 1993). It allows to reveal characteristic features of all segments,
identify their needs and analyze which of them are most beneficial for the firm. As B2B market is
considered rather stable and aimed at long-tern collaboration, the investments into the research of
proper segmentation are quite promising in a modern competitive world, because thorough
investigation related to the segmentation of the market will contribute to a large extent to the efficiency
of the company’s activity. Only stable segments can become a significant basis for the working out of
successful marketing strategy of the company (Wedel and Kamakura 2000).
In order to identify homogeneous segments in the market the following major steps should be
taken (Payne 1993):
-
To define the relevant market;
-
To identify the alternative bases for segmentation;
-
To study the identified bases so as to choose the best base or bases for segmentation;
-
To identify individual market segments, to assess their potential benefit for the firm;
-
To choose specific target segments;
-
To develop a positioning for the target segments selected.
Further, we will touch upon these steps of segmentation in detail.
2.3.1.1 Defining of the relevant market to be addressed
The first step of the market segmentation includes the determination of the group of customers,
which the firm deals with. The important issue here is to create a reliable database that provides upto date information about the company. The database should contain such important information as
addresses, telephone numbers, contact names of the people who are responsible for making decisions,
the longevity of companies in the market, the history of the business activity of the companies,
financial position in the market, participation in arbitration etc (Stevens 2016). Such data is considered
to be an essential tool to build efficient relationships with customers, both existing and potential ones,
it is useful for working out tactical activities such as addressable direct marketing (Baines et al 2008).
22
Alongside with creating the database, the company is supposed to tackle the following issue:
to identify its strategic goals thorough investigation of the mission of the company aimed at the future
with possible emphasis on five-year strategic marketing plan. The decision concerning the relevant
market to be addressed is taken according to the range of products or services that the company is
going to offer in B2B market, the types of the customers available and their geographical location
(Payne 1993).
Some companies are known to compile a questionnaire for customer companies’
representatives who participate in decision-making process. Decision-making unit of any company
can have particular requirements, which influence the purchase strategies (Baines et al., 2008). The
main goal of the questionnaire is to find out the customer’s attitude to the offered product or service,
to identify their requirements concerning the price, flexibility of payments, the frequency of delivery,
optimal size of orders, the most convenient time of delivery, preferences of design, potential benefits
for the customer etc. Such investigation can contribute to building most efficient relations with
customers based on satisfying mutual interests.
2.3.1.2 Segmentation bases
Identification of segmentation base is considered to be an important point of segmentation
process (Blythe and Zimmerman 2005). There are several approaches to the process of selecting the
right niche (Day 1990).
According to Willan (2015) one of the basic approaches to segmentation that is more
widespread and used by approximately 80 % of B2B marketers is feature based approach. The target
market is divided according to the factors that are called ‘firmographics’. Among them there are:
-
company activity;
-
company location;
-
company turnover;
-
number of employees.
In spite of the fact that this approach is widely applied, it does not consider some important needs of
the customers which can differ significantly. However, there are some evident positive moments in
this approach. First of all, it is rather easy to implement. The analysis of ‘firmographics’ factors is not
complex, so to identify segments and decide the question of belonging a customer to a particular
segment is rather obvious. To implement feature-based approach the firm can use the database of
customers to identify the main groups of customers. On this stage it is useful to study the competitors
23
experience, because there can exist groups of customers which are not yet discovered but can present
interest for the company. The next important step is to place the identified segments in order of
priority, taking into consideration the volume of sales and profit that is received from current
customers (Tarasi et al., 2011). The value-based approach that allows to reveal most profitable
customers will be considered in detail further.
The second basic approach to segmentation is needs based approach (Willan 2015). It implies
deeper level of realizing customers’ needs, benefits and attitudes, which allows to work out the best
offer in the market and prove the point that the company is interested not only in getting the highest
profit but also in constructing long-term relationships with its partners. However, this approach
requires more effort and seldom can be implemented without extensive research (Block and Block
2005: 73). It should be mentioned that this approach relies more on human factor and demands that
employees should elaboration their communicative skills. It may present some difficulty because
physiological factor should be taken into account: not all employees, even the most hardworking, can
perform brilliantly at personal communication. Setting up some special training for employees who
are involved into the process of communication with customers is essential (Blythe and Zimmerman
2005).
The needs based approach implies carrying out the in-depth interviews, which are focused on
identification of needs, behavior and attitudes of the customers in detail (Willan 2015). According to
Willan (2015) common bases of segmentation are supposed to be the following:
1) Behavior:
-
sort of service or product bought or rented;
-
peculiarity of decision making process;
-
risk awareness.
2) Needs:
-
specific functions of the product or service;
-
requirement to price, ways of payment, discounts;
-
requirement to the terms of delivery.
3) Attitudes:
-
critical notices concerning product or service;
-
view on perfect relations in B2B activity;
-
devotion to the sphere of business operating;
-
concerns about the image (in case of service industry);
24
-
level of knowledge of available products and services in question in the market.
According to Blythe and Zimmerman (2005) segmentation variables, which are dealt with in
feature based approach, can be referred to as identifiers, and those which can be considered in needs
based approach - as response profile.
The most reasonable and widely used approach to segmentation is considered to be the integral
approach, which was offered, by Bonoma and Shapiro – the nested approach (Bonona and Shapiro
1983) (figure1). Nested approach is more comprehensive because it is based on multi-step
segmentation (Plank 1985). In spite of the fact that nested approach was developed in the mid-1980s,
it is still significant for the segmentation of modern markets (Weinstein 2011).
Figure 1 Nested approach to segmentation
Source: Bonoma and Shapiro (1983)
This approach can be considered by economists as a “practical and comprehensive means for
segmenting business markets” (Weinstein 2011). The variables that are taken into consideration in
nested approach can be sufficient source of data necessary for effective segmentation (Palmer and
Millier 2004). Nested approach includes several steps (Plank 1985, Blythe and Zimmerman 2005).
25
The first one is demographics and it involves the analysis of the industry, of company size and
potential customers’ location. The second step is operations. On this stage it is important to reveal
customers’ needs and determine technologies that are used by them. Moreover, it is important to
identify the status of the user. Users can be either light or heavy: heavy user purchases in larger volume
than does a light user (Twedt 1964). This step is a foundation for customer segmenting. Moreover,
company whose aim is to provide service on B2B market for heavy users is supposed to take into
account the fact that if their size is not enough they will be not able to serve organizations of a larger
size, because their production capacity is not sufficient for such a client. As a result, they will not be
able to satisfy their customer’s needs completely.
The third step is purchasing approach. At this point B2B company should analyze purchasing
structure, customers’ attitude towards the firm and buyer-seller relationships. An important point here
is to analyze buying situations. In general, there are three types of buying situations: new buy, straight
rebuy and modified rebuy (Blythe and Zimmerman 2005). New buy situation arises when a buyer
purchases a product or service for the first time. In this case, the aim of the organization is to attract a
new customer and find the way to keep him. Straight rebuy occurs when the buyer purchases goods
from the company constantly and in equal volumes. In this case, if the customer is satisfied with the
price, quality, delivery time and there are no any unexpected changes, he will continue to purchase.
However, organizations are supposed to negotiate with the customer in order to ensure that the
customer is satisfied. Modified rebuy is a buying situation when a customer makes purchases
repeatedly, but wants to make some changes in the purchasing process contract. Changes can be
related to the size of purchases, form of delivery, redesign of the product packaging an etc. (Blois
1970).
The forth step is situational factors. Situational factors include urgency of order fulfillment,
size of order and product application (Bonoma and Shapiro 1983). In some emergency cases, urgency
of order fulfillment is extremely important. Some B2B companies create separate units to work
quickly with the emergency orders of their customers. The size of an order is also an important point,
as for some companies it is more profitable to work with clients who purchase in large volumes,
provided that their equipment is capable to produce necessary quantities. On the other hand, for small
companies sometimes it is easier to produce individual orders in small quantities. The last step is
personal characteristics of the buyer. Purchasing decisions are made by people and they can be
influenced by personal attitude, work experience, motivation and attitude to risk. There are clients
who are risk averse and try to avoid transactions that may lead to losses; and there are customers who
26
can be tolerant to risk. Risk tolerant customers are more willing to test new products and services,
while risk averse clients will prefer to use proven commodities (Shapiro and Bonoma 1984, Weinstein
2011). Nests and variables used in the nested approach are the bases that identify all necessary data
for effective B2B segmentation (Palmer and Miller 2004).
2.3.1.3 Identifying and selecting the segments
There are many factors that are considered to be significant in identifying and selecting
particular market segment. According to Payne (1993) among them there are: the size of the segment,
its special demands, the ability of the company to meet the requirements of the segment and others.
The process of selecting the best segments implies comparison different segments so as to define their
future attractiveness to the firm, resource demands and fit with firm strategy (Blythe and Zimmerman
2005). The assessment of the future attractiveness includes answering the following questions:
-
if the segment will show stable growing;
-
if it is large enough;
-
if it is profitable to serve;
-
if there any possible risks;
-
what the main demands of the customers are;
-
what sort of relations with the segment may be developed. (Blythe and Zimmerman 2005).
Payne (1993) noticed that another important factor, which is meaningful for identifying the
segment, is if it is responsive to marketing effort. If the response of the segment to changes in
marketing effort does not differ from the response of other segments, it is not necessary to identify it
as a separate segment. Besides, it is important to decide if the particular segment corresponds the
mission of the firm, if it will develop in accordance with the firm’s main strategy and organizational
requirements. J. Blythe and A. Zimmerman underline that it is essential to take into account the
demands on the company’s resources in relationships, technology, image, capital investment and
product / service development (Blythe and Zimmerman 2005).
2.3.2 Customer Relationship Management
It was mentioned above that market segmentation implies choosing the most profitable groups
of customers, indicating their requirements and expectations and allocating resources into these
groups. It means that efficient segmentation is influenced greatly by the process of constructing solid
27
and mutually profitable relations with strategically significant customers. That is way it is essential
for this thesis to consider in detail the strategy of customer relationship management, which is aimed
at retaining existing customers, requiring new customers and maintenance stable relations with them.
Since Customer Relationship Management (CRM) emerged in 1993 many researchers have
considered it as an important sphere of marketing research and practice (Terho and Halinen 2007;
Greenberg 2010). The term “customer relationship management” appeared in the information
technology vendor community and was related to IT-based customer solutions (Ryals and Payne
2001). According to Kim et al., (2001) the importance of CRM has increased significantly because of
continuous development of information technologies and highly competitive, dynamic business
environment, which arose on the markets. These developments lead to dramatic changes in client’s
behavior, which has become difficult to predict. The trend today shows that clients have turned into
the most valuable assets for the company (Heredero and Gomez 2014). To adapt to fast-moving
changes in social patterns companies started to implement dynamic CRM in order to split customers
into homogeneous groups by similar behavior and offer differential value (Kim et. al., 2001).
The way how organizations interpret CRM influences the ways of CRM implementation in
the companies. Some researches defined CRM only as e-commerce application (Khanna 2001). Others
viewed CRM as an integrated series of technologies and e-commerce capabilities aimed at managing
customer relationship (Stone and Woodcock 2001). However, both definitions do not take into
consideration the strategic aspect of approach to CRM that highlights the importance of CRM for the
process of retaining existing customers, acquiring new customers and maintenance long-term
relationships with strategically significant ones in order to create superior value for both clients and
companies (Parvitiyar and Sheth 2001 Buttle 2001). In our research we will assume that in B2B
companies CRM should be positioned in a strategic context, as all organizations are interested in
achieving success.
Some researches highlighted three main elements that should be included into CRM structure:
Clients - who are the main source of the company’s present financial efficiency and future
development.
Relationship maintaining – that means continuous interaction that takes into
consideration the interests of both sides (company – client). Effective management – as CRM
includes the improvement of the organization’s processes and changes in culture and employees
behavior at all levels of the organization (Tohidi and Jabbari 2012) and in all areas (Singh and
Agrawal 2003).
28
According to Florez-Lopez and Ramon-Jeronimo (2007) successful starting point for CRM is
to single out the pool of valuable customers and canalize all company energies on the needs
satisfaction of this customers. During the whole period of client lifecycle the CRM provides the data
necessary to identify current position of each client and deliver personalized actions (Figure 2)
(Heredero and Gomez 2014).
Figure 2 CRM areas of influence
Source: Heredero and Gomez (2014)
The acquisition phase is based on the overall business strategy of the company. Usually for
the companies reacquisition phase is more complicated as an acquisition one, because it is more
difficult to attract the customers again than to engage their attention for the first time. With the aim to
drive more personalized actions in order to retain existing customer base CRM puts a lot of efforts on
optimization, loyalty and cross-selling areas (Payne and Frow 2005).
To gain a sustainable competitive advantage and be able to assess current customer Pool
Company should develop, maintain and constantly update database with the insight about customers
(Florez - Lopez and Ramon - Jeronimo 2008, Thakur and Workman 2016). Customers’ database can
be defined as “a dynamic collection of facts about customers and prospects stored in computerized
form” (Block and Block 2005, p.64). Customer database has two important functions. Firstly, the base
provides continuous description of companies’ clients. Secondly, by using the information about
existing customers companies can divide them into homogeneous groups and identify potential target
market (Gide and Shams 2011). From a CRM perspective, market segmentation heavily depends on
the data collected in the customer database. The data included into customer database can be either
generated internally by collecting finance and sales records of the clients or sourced externally by
conducting interviews, marketing research campaigns and surveys (Buttle 2004). The database can
contain such information as (Block and Block 2005):
29
Nominal variables – those that do not have numerical value and any quantitative
relationship among them (for ex: clients’ company name, geographical location,
industry specification etc.)
Ordinal variables – those where values represent categories with an intrinsic ranking
(for ex. clients level of satisfaction, client status (heavy/ light use) etc.)
Ratio variables – interval variables that take into consideration that measurement
equal to zero implies that there is none of that variable (for ex: number of customers,
invoice from clients, amount of units sold, maintenance costs etc.)
The variables mentioned above help to differentiate the clients within the database from one
another and identify potential market segmentation variables. Within every customer database there
are some differences in the invoices that clients bring to the company and costs that the company
needs to bear.
2.3.3 Customer Relationship Management (CRM) and customer portfolio theory
One of primary conditions for CRM is to assess customer value and identify the most profitable
customers (Sackmann et al., 2010). In order to sustain continuous growth and achieve greater
profitability customer management tools should be applied at the customer portfolio level, which
means that the company has to focus not on few closest relationships but on wider managerial
perspective, that takes into consideration the company’s entire customer portfolio (Terho and Halinen
2007). Thus, it is essential to touch upon the relation between CRM and customer portfolio theory.
Having assessed the customer value with the aim to compare investment alternatives and
having identified the most profitable customers the company can focus on building long-term
partnerships with them and as a result enhance both current and future performance of the company
and its financial results. Customer’s value assessment is of a special importance for the B2B
companies that adhere to customer-centric approach and consider customers as their main assets
(Wiesel et al. 2008).
With the aim to predict long-term customer relationship companies can use a Customer
Lifetime value model that was introduced in 1930s but is still used extensively (Cermak 2015).
Customer Lifetime Value indicator can be defined as quantitative measurement of the company’s net
cash flows generated by the clients over the whole period of their relationship with the firm
(Hizirouglu and Sengul 2012). CLV is focused on the ways of maximizing profit and is based on the
30
analysis of clients’ behavior with the aim to identify customers with the greatest potential. Taking into
account the information which of the customers presents the highest value company can calculate
what amount of money could be spent on acquiring new customers.
From the perspective of B2B companies this indicator can help managers to differentiate
customers and offer more appropriate services for each of them. High quality services and
personalized offers provide an opportunity to increase loyalty and save long-term relations. Moreover,
it is important to notice that the value of the loyal customers is ever –increasing. However, more
detailed analysis requires incorporation of the risks associated with client relationship into the
evaluation. This can be done by applying the deviation from expected cash flows into calculations
(Sackmann and Kundisch 2010).
The basic customer evaluation approaches are focused on the assessment of each customer
individually (Hogan et al 2003). However, in order to come to a decision on retention or acquisition
strategy companies need to take into consideration not only the risk within a single client relation, but
also risk endowment of each client to the entire customer portfolio (Kundisch et. al., 2007). Despite
the fact that B2B companies usually have fewer clients than B2C it is not appropriate to assess
customers in such a manner. Some researches pointed out that while assessing customers individually
many companies “tend to overestimate the value of the top-tier clients and underestimate that of
bottom-tier customers” (Tarasi et al., 2011, p.1). Therefore, customer portfolio analysis was designed
in order to optimize earnings record for the “entire customer base by offering differentiated value
propositions” (Buttle 2004, p.100) to various customer segments. Customer portfolio evaluation takes
into consideration not only the risks related to each single customer, but also the contribution of the
risks associated with the customer to the entire customer portfolio (Ryals et al., 2007). Customer
groups constitute the market segments that represent the current customer database and implement the
output of the relationships between clients and company. Due to the fact that customers are company
assets, overall expenditures related to the clients should be considered as investments. As such
indicators as risks and returns are important in the process of assessment of customers value scientists
offer to use for the analysis of customer portfolio the concepts of financial portfolio theory (Tarasi et
al., 2011).
2.3.4 Relationship between Financial portfolio theory and customer portfolio theory
The application of financial portfolio theory to customer portfolio allows to evaluate the
similar and complementary features of market segments, and to investigate market segments weighs
31
in an efficient customer portfolio (Tarasi et al, 2011). Customers are assets that continuously generate
risky cash flows. However, while some clients can provide bigger but unsteady cash flows others can
generate more stable but lower returns. According to Tarasi (et al., 2011) in order to create optimal
customer portfolio customers need to be divided into separate groups according to different levels of
returns, vulnerability and variability. Customer groups constitute the market segments that represent
the current customer database and implement the output of the relationships between clients and
company. Due to the fact that customers are company assets, overall expenditures related to the clients
should be considered as investments.
The idea of portfolio theory lies in the financial investments. Financial portfolio theory
presents how investors can construct an optimal portfolio that will maximize returns depending on
given level of risk and consider that risk is an integral part of superior returns (Markowitz 1952).
Starting point in the Markowitz theory was the idea that rational investor will choose an investment
portfolio that maximizes utility by increasing expected portfolio’s yield for a given risk level or by
risk minimization for a predetermined level of expected portfolio’s yield. The optimal portfolio
consolidates the opportunities with positive cash flows and excludes prospective negative cash flows
(Markowitz 1987). Some researchers criticize the financial portfolio theory, as it is based on the
assumption that asset returns are normally distributed and that market changes cannot be predicted
(Kaplan 2009). Others also highlight that the model supposes that correlations between assets are
stable, however during significant market changes the correlation between the assets can arise even if
they were uncorrelated before (Hubbard 2009).
The main differences between financial portfolio and customer portfolio lie in the core of
assets, returns and uncertainty (Tarasi et al., 2011). Financial assets can be determined and straight
purchased. In the context of customers in order to enhance the value of the existing customers
company should develop CRM in order to be able to maintain mutually beneficial relations with them
and improve customer loyalty. Potential customers as assets are much more complicated as even if
they are already identified by the company and in the long view can be targeted there is no certainty
that the company will be able to close the deal with them (Kumar 2008). As can be seen from the
above the final price of a customer as an asset will include either cost of existing customer retention
or costs of potential customer acquisition. Another distinction feature of the customer portfolio in the
nature of assets is the complexity of the alterations introduction into the weight proportion of each
group of customers within the company portfolio. In order to make any changes, companies need to
32
reconsider their current strategy priorities and put a lot of effort into the satisfaction of the
requirements of most profitable clients and attraction of the new ones with similar characteristics.
Focusing on the differences that lie in the core of returns of both portfolios several points
should be highlighted. In financial portfolio returns can be calculated as changes in the value of the
investment that embody appreciation or depreciation and running yield (Markovitz 1987). For the
customer portfolio returns can be calculated as invoice minus cost to serve. Variable costs, such as
marketing investments, investment aimed at increasing customer loyalty are not always included into
serving costs as they go beyond and are not directly incorporated into costs (Raaij and Triest 2003).
Another differential characteristic of customer portfolio is the rate of return on investment. While in
financial portfolio this indicator does not depend on the size of the investment, in customer portfolio
small size of investments in relationships with existing or potential customers is likely to be
inadequate. Some researchers pointed out that performance of each group of customers within
customer portfolio can be affected by the weight of each group, due to returns to scale that can either
decreasing or increasing. Thus, while managing customer portfolio managers are able to control risks
and returns features of the portfolio. In the context of financial investments, determination of the
weight for a group of assets by investor, conversely do not influence risks and returns of the group
(Bowman and Narayandas 2004).
The last differences in financial and customer portfolio that should be examined lie in the
nature of uncertainty. In the customer portfolio the degree of risks is provided by the deviation of
clients invoice from the expected values. In financial portfolio context uncertainty is formed by the
deviation of the realized and expected returns. One more origin of uncertainty comes from the stability
of asset usage. While investor can affect the retention period of the financial assets, it is more
complicated process to affect customers’ decision to choose competitor (Kundisch et. al., 2008). In
spite of these strictures and differences financial portfolio theory is widely used in financial practices
and can be adopted in the other areas, such as strategic management, marketing and etc. (Zolkiewski
and Turnbull 2002, Cardozo 1983).
The usage of customer portfolio theory in CRM is a useful tool for weighted distribution of
scanty resources of the company between the groups of clients with the aim to maximize long-term
profitability at a given level of risk (Turnbull 1990). This approach is an alternative to traditional
approaches to relationship management in which the main purpose of managing relationships was
permanent development and transition to increasingly high level of interaction between partners
(Christopher et.al., 1991, Ford et.al., 1998). However, while traditional approaches assume that all
33
relationships are equally important for the company and should be developed, portfolio theory
approach highlights that companies should be able to abandon the relationships that have no value for
the company. Thus, the key advantage of portfolio theory application is an increase in the return on
invested in the development of relationships with partners’ resources.
However, if the company decides to use financial portfolio theory in a non-financial context
some limitations and modifications should be borne in mind (Gupta and Zeithaml 2006). First, the
process of portfolio optimization is based on the assessment of the existing customer base that is why
if the analysis identifies that serious adjustment to the current portfolio should be applied the process
of changes implementation can become rather expensive. Thus, firms should consider optimized
portfolio as an ideal one and try to implement changes systematically and constantly assess how the
changes in the customer base correspond to organizations performance (Kundisch et. al., 2008).
Secondly, companies should monitor the changes in the market conditions on a permanent basis,
because segments that were differentiated by the company and considered as uncorrelated can start
moving in the same direction (rise o fall) in the recession period (Markovitz 1959). Finally, the
portfolio optimization model does not take into consideration such factors, as customer loyalty, level
of satisfaction and the capability to continue partnership.
Conclusions
The analysis of the theoretical background related to segmentation in B2B markets allows to come
to the following conclusions:
1. B2B markets have characteristic features that differentiate them from B2C markets, for
example, fewer numbers of customers, the necessity to build long-lasting relations, the peculiarity of
the decision-making unit and segmentation variables. These characteristics predetermine economic
and managerial investigation related to segmentation.
2. Market segmentation implies choosing most profitable groups of customers and allocating
resources into these target groups. To get competitive advantage in the market it is essential for the
company to study needs and requirements of these customers and to concentrate on building longterm relations with them. According to modern approaches to segmentation in business markets, deep
insight into the needs of the customers is a significant factor that influences the process of efficient
segmentation.
3. The problem of constructing solid long-term relations with customers can be solved by means
34
of implementation of the strategy of Customer Relationship management (CRM), which implies
establishment of close relationship with customers so that the company could study their preferences,
satisfy their needs and reach higher efficiency of the company’s performance in the market.
4. In order to sustain continuous growth and achieve greater profitability customer management
tool should be applied at the customer portfolio level. It allows to realize the relative significance of
each group of customers in relation to sales and profits. The thorough analysis of the existing
customer portfolio contributes not only to the satisfaction of the requirements of most profitable
customers, but also to attracting new customers with similar characteristics.
5. The issue of constructing of the efficient customer portfolio can be tackled by application
of financial portfolio theory, which allows to evaluate the similar and complementary features of
market segments, and to investigate market segments weights. The usage of customer portfolio theory
is an important tool for distribution of scarce resources of the company among the groups of clients
with the aim to maximize long-term profitability at a given level of risk.
35
3. Methodology
3.1 The company description
All the data used in the research were collected from one international B2B service Company
“X”. Company “X” has operated in the Russian market for more than 20 years and acquired the
leading position on the market (Table 1).
Table 1 Company "X"profile
Founded
Business model
Geographical coverage
Target markets
Terms of contract
Company «X» profile
At the international level the company operates for almost 100 years
In Russian market - since 1993
B2B service company, that offers tangible goods with accompanying
services
More than 20 countries
Automotive, Food and Beverage production, Manufacturing, Real
Estate, Retail Trade, Service industries
The minimum term of contract - one year
Source: Created by author
In Russia Company “X” offers several types of services, but one of them is under detailed
consideration in this work. Company “X” is a service company and its offerings can be considered as
a “tangible good with accompanying services” (Kotler 1991). This indicates that after the contract
between company and client was signed and the commodity delivered, the Company “X” provides
clients with the service that implies collaboration on the constant bases. Meanwhile, the frequency of
the service depends on the needs of the customers. Usually, for the majority of clients the service is
provided every week. For managers of the Company “X” the testing of clients’ solvency and reliability
is quite important but not always possible. To be sure of client ability to pay Company “X” established
the minimum life of the agreement that accounts for twelve months. This requirement protects the
Company “X” interests and guarantees recoupment of costs related to commodity production. Due to
the fact that for the clients such terms of agreement could seem too severe and pressing it is of a
primary importance for the Company “X” to protect brand image to provide valuable reassurance of
commodity and service quality to business customers and prove its reliability. Company “X” is highly
interested in the client’s opinion regarding the quality of provided services in order to be able to
maintain long-term relationships with them. Every year the Company “X” applies to different research
36
agencies to conduct independent study focused on identification of the satisfaction level among
customers and possible ways of service quality improvement.
Due to the fact that not all the clients are equally important, Company “X” has started to
implement the strategy of CRM aimed at identification of the most valuable clients. Under current
unfavorable economic circumstances Company “X” searches for new measures that can lead to
expenses reduction, resources reallocation and efficiency improvement. That is why the research,
which includes the estimation of the customers’ value and construction of the efficient customer
portfolio, can be considered as a possible measure for identification the most valuable groups of
customers, concentrating on these segments and improvement of Company “X” performance.
3.2 Analysis of the existing customer database
The gathered data represents the part of the Company “X” client database and consists of the
information related to 680 customers for the year 2015. The clients included into the sample were
selected by the representative of the Company “X. Company “X” started the process of the CRM
implementation relatively recently and now the base includes only general facts about already served
organizations. The information included into the sample is: client identification number, geographical
location of the client (cities, regions), invoicing from each client for 2015 in euro, client activity and
the number range of employees in each client company for whom the company “X” provides service
(1-19; 20 – 99; 100 – 249; 250 – 999; >1000). In order to identify the possible market segmentation
variables, the current database of clients was analyzed with the help of SPSS program that provide
necessary tools to depict the information. The existing customers were divided into homogeneous
groups by different variables provided by the company, such as location area, sphere of activity and
the number of workers who used the service in a client - company.
According to the information about customers’ addresses 7 geographical location areas
(Moscow; Moscow Region, Saint Petersburg, Saint Petersburg region, Nizhny Novgorod, Volga and
South of Russia) that can be used as a possible market segmentation variable was identified. The
figure below represents the entire client database divided by location areas (Figure 3).
37
Figure 3 Customers' distribution be location area
Source: Created by author
The majority of the existing clients are presented in Saint Petersburg, Moscow region and
Moscow. However, due to the fact that the company has already started its operations in all the abovementioned location areas this variable can be considered as one of the possible market segmentation
criteria. The second client characteristic included into database was the client-company activity. The
description of the customers’ activity corresponds to the possibility to consolidate them into groups
by the industry. The industries that were outlined are the following: Automotive, Food and Beverage
production, Manufacturing, Real estate, Retail trade and Service (Figure 4).
Figure 4 Customers' distribution by industry type
Source: Created by author
38
The pie chart highlights that the majority of the client operates in Automotive and Service
industries and accounts for approximately 56% (206 clients) of the customer base. Due to the fact
that existing clients of the Company “X” can be divided into 6 Industries this variable can be used as
a possible criteria for the overall market segmentation.
The database of the Company “X” also contains the information regarding the range of the
number of workers in a client - companies that purchase commodities and use the service provided by
Company “X”. We used this information in order to divide the existing customer base into groups
according to the size of a served client-company. The groups are client - companies with the range in
the number of workers equal to: 1-19; 20-99; 100-249; 250-999 and > 1000 (Figure 5).
Figure 5 Range in the number of workers in the client - companies
Source: Created by the author
According to the information presented in the data sample, 6 industries were highlighted: 1 Automotive; 2 - Food and Beverage production; 3 - Manufacturing; 4 - Real estate; 5 - Retail trade; 6
- Service companies In order to evaluate the distribution of the client groups ( presented by each
industry type) among location areas in which Company “X” operates the graph was constructed
(Figure 6).
39
Distribution of client groups among location areas
200
Quantity (customers)
180
160
Service companies
140
Retail trade
120
Real estate activities
100
Manufacturing
80
Food and baverage production
60
Automative
40
20
0
Location
Figure 6 Distribution of client groups among location areas
Source: Created by the author
The above presented graph shows that in Moscow and Moscow region an overwhelming
majority of clients are companies that operate in an Automotive and Service industries. While in
Moscow the Retail industry also represents significant share in Moscow region it does not prevail and
yield to Food and Beverage and Manufacturing. In Saint Petersburg and Saint Petersburg region, the
distribution of clients vary significantly from that in Moscow and Moscow region. Despite the fact
that Automotive industry is prevalent in both cases, in Saint Petersburg there is also a great number
of clients, which operate in Manufacturing. Other industries are represented in equal volumes. In
Saint Petersburg region Manufacturing, Food and Beverage and Retail trade account for
approximately the same numbers. Nizhny Novgorod was indicated as a separate location area, as there
is significant number of clients, which are served there. In Nizhny Novgorod, while the vast majority
of clients operate in Service and Retail trade industries, Manufacturing and Automotive industries are
presented by lower numbers of customers. In the South of Russia, the leading served industries are
Automotive and Food and Beverage production. Companies that operate in Manufacturing, Retail
Trade and Service industries are presented in equal volumes. There are no clients it the South of Russia
that operate in the Real Estate industry.The last examined location area was the Volga region.
40
Automotive and Manufacturing industries account for equal number of clients. Similar to the South
of Russia location area in the Volga region Real Estate industry is not presented.
The representation of the industries by location area being analyzed, it should be mentioned,
that the trends in different regions vary greatly and each region requires thorough investigation and
application of customer portfolio theory, which will be considered in this thesis in detail on the base
of Saint Petersburg data.
3.3 Method of financial portfolio theory application to customer portfolio analysis
To test the applicability of the financial portfolio theory to the customer portfolio assessment
the data related to the customers that operate in Saint Petersburg were used. The sample of 680
customers provided by the Company “X” included 178 clients that operate in Saint Petersburg.
However, due to the fact that one of the primary conditions for successful usage of the modified
financial model in the analysis assumes that the clients work with the company of at least three years,
the sample was reduced to 132 customer’s observations between 2013 - 2015. It includes the
information about the customers that operate in Saint Petersburg region for at least three years and
constantly use the service provided by the company. The sample will be used in order to analyze the
current customer portfolio and to develop the efficient customer portfolio and extrapolate the result
of the analysis to the market segmentation. The company “X” provided information related to:
Geographic location (Saint Petersburg)
Description of client – company activity
Number of units in circulation for every month in 2013-2015
Annual invoice for 2013-2015
Annual costs-to-serve
Company “X” divided its costs – to serve each client into 4 parts: Material, Washing, Delivery
and Overhead. Currently the Company “X” does not take into account the costs to serve and focuses
only on the amount of invoice that each client brings to the company. However, as the costs vary
significantly from one client to another during the process of the second sample analysis costs to serve
were incorporated in order to find out if the industries with higher returns are the most profitable. In
this research aggregated costs that include all the above-mentioned parts will be used to calculate an
efficient customer portfolio.
41
The current research implies the usage of the Markowitz’s portfolio selection model. The
model represents a theory that estimates investment alternatives, which consolidate risks and profits
among all investment opportunities (Lee et al., 2011). The application of the Markowitz model implies
the usage of risk and return attributed to the clients’ invoices that can be calculated using purchasing
historical data (Markowitz 1987). Company “X” operates with all the clients presented in the sample
for more than three years. It allows to assume that relationships with clients are sufficiently stable and
past variability can be extrapolated for future variability (Balagopal and Gilliland 2005).
The core idea of the model is to minimize the total risk of the entire customer portfolio. The
minimization of the total risk leads to decrease in the risk of individual assets. According to Kim
(2006), the risk is determined by the size of covariance among the assets. The Markowitz model
establishes the minimization of the variance (V):
𝑁
𝑉=∑
𝑁
∑
𝑖=1
𝑗=1
𝜎𝑖𝑗𝜔𝑖 𝜔𝑗
The variance indicates the degree of risk associated with the level of the return of the customer
portfolio. The model implies several limitations. Firstly, the minimum level of return expected by the
investor (manager) should be reached.
𝑁
∑
𝜇𝑗𝜇𝑖 ≥ 𝐾
𝑗=1
Secondly, the overall amount that is available for investments should be applied and invested. Thirdly,
the sum of the weights of all the client groups included into portfolio should be equal to 1.
𝑁
∑ 𝜔𝑗 = 1
𝐽=1
Finally, due to the fact that existing customer portfolio already includes several groups of clients, the
manager could either impose restrictions on the minimum weight of each client group in the portfolio
or be prepared that some of the client groups will be excluded from the efficient portfolio as they are
not able to assist in the achievement of the desired level of returns.
𝑤𝑗 ≥ 0 𝑓𝑜𝑟 𝑗 = 1,2, … 𝑁
Where, V – variance, associated with the profit rate of the entire portfolio; K – minimum expected
(desired) profit rate; N – the amount of client group types to invest included in the portfolio; 𝜇𝑗 –
average profit rate of the client group j of the portfolio; 𝜔𝑗 – ratio invested in client group j in the
customer portfolio; j- client groups (1, 2,…,N);
42
The current research implies the usage of the modified Markowitz’s portfolio selection model
in order to find the optimal weights of each client group included in the portfolio (𝜔1 ; 𝜔2 ; 𝜔3 𝑎𝑛𝑑 𝜔4 )
that will minimize portfolio risk (V) and bring desired level of return for the Company “X” provided
that all the above mentioned conditions are met. The portfolio combinations offered by the model
comprise efficient portfolio options (from the risk - reward perspective) that Company “X” can use
in order to achieve higher returns or reduce the risks associated with the serving processes.
In order to apply the modified financial portfolio model to the assessment of the Company
“X” customer portfolio several data conversion procedures were made. By analyzing the description
of client – company activity, all clients were consolidated into groups by the industry. Four industries
were highlighted and encoded by figures from 1 - 4:
Automotive (1 group)
Real Estate (2 group)
Food and Beverage production (3 group)
Manufacturing (4 group)
Company “X” provided the information about monthly changes in the number of units that are
used by the client, annual costs and annual invoice for 2013-2015. In order to evaluate how the
monthly returns from each group of clients changed in the course of three years the following steps
were completed:
1) Due to the fact that company database contains information only about annual
invoices and costs related to one client, Invoice and Costs per 1 unit were calculated (table 2).
Table 2 Invoice from one unit / Costs per one unit
Variable
Invoice from one unit (for
each client)
Costs per one unit (for
each client)
Calculation
Explanation
Invoice (2013/2014/2015) /
Invoice from one unit was calculated in
Number of units served order to count up the monthly invoice
in one year (2013/2014/ 2015) from each client with whom company
operates
Costs per unit was calculated in order to
Costs (2013/2014/2015) /
Number of units served count up the company monthly costs to
in one year (2013/2014/ 2015) serve each client with whom company
operates
Source: Created by the author
43
2) After calculating invoice and costs for 1 unit for each client, received value was multiplied by
the monthly number of units of each client for 3 years (table 3).
Table 3 Monthly invoice/costs one client
Variable
Monthly invoice from 1
client (for each client, 36
months)
Monthly costs to serve 1
client (for each client, 36
months)
Calculation
Number of clothes units
× Invoice from 1 unit
Number of clothes units
× Costs to serve 1 unit
Explanation
Monthly invoice and costs to serve (for
each client) were calculated in order to
use it later for calculating the changes
in return from each group of clients in
the course of three years
Source: Created by the author
3) Existing customers were consolidated into 4 groups by industry in which they operate. By
using the information regarding monthly invoice from each client the sum of monthly invoices for
each group (Automotive, Real Estate, Food and Beverage production and Manufacturing) was
calculated (table 4).
Table 4 Monthly invoice from all clients
Variable
Monthly invoice from all
clients presented by
Automotive industry
(calculated for 36 months)
Calculation
Aggregated monthly
invoice from all clients
presented by Automotive
industry
Monthly invoice from all
clients presented by Real
Estate industry (calculated
for 36 months)
Aggregated monthly
invoice from all clients
presented by Real Estate
industry
Monthly invoice from all
clients presented by Real
Food and Beverage
production industry
(calculated for 36 months)
Monthly invoice from all
clients presented by Real
Manufacturing industry
(calculated for 36 months)
Aggregated monthly
invoice from all clients
presented by Food and
Beverage industry
Explanation
Aggregated monthly invoice was
calculated in order to evaluate later
the changes in the return for each
group of clients presented by 4
industries within 36 month period
Aggregated monthly
invoice from all clients
presented by
Manufacturing industry
Source: Created by the author
44
4) By applying the information regarding monthly costs to serve for each client the sum of
monthly costs to serve for each group (Automotive, Real Estate, Food and Beverage production and
Manufacturing) were calculated (table 5).
Table 5 Monthly costs-to-serve of all clients
Variable
Monthly costs to serve of
all clients presented by
Automotive industry
(calculated for 36 months)
Calculation
Aggregated monthly
costs to serve of all
clients presented by
Automotive industry
Monthly costs to serve of
all clients presented by
Real Estate industry
(calculated for 36 months)
Aggregated monthly
costs to serve of all
clients presented by
Real Estate industry
Monthly costs to serve of
all presented by Real
Food and Beverage
production industry
(calculated for 36 months)
Aggregated monthly
costs to serve of all
clients presented by
Food and Beverage
industry
Monthly costs to serve of
all clients presented by
Real Manufacturing
industry (calculated for 36
months)
Aggregated monthly
costs to serve of all
clients presented by
Manufacturing industry
Explanation
Aggregated monthly costs to serve were
calculated in order to evaluate later the
changes in the return for each group of
clients presented by 4 industries within
36 month period
Source: Created by the author
5) Finally, monthly level of return within 36 month period for each group of clients presented
by 4 industries was calculated (table 6).
Table 6 Monthly return from all clients
Variable
Monthly return from all
clients presented by
Automotive industry
(calculated for 36 months)
Monthly return from all
clients presented by Real
Estate industry
(calculated for 36 months)
Monthly return from all
clients presented by Real
Food and Beverage
Calculation
(Monthly invoice –
Monthly costs) /
Monthly costs
Explanation
Monthly level of return was calculated in
order to compare the returns dynamics
for each group of clients presented by 4
industries within 36 month period
45
production industry
(calculated for 36 months)
Monthly return from all
clients presented by Real
Manufacturing industry
(calculated for 36 months)
Source: Created by the author
3.4 The process of the customer portfolio optimization
Process of the current customer portfolio optimization starts with dividing existing customers
into groups by similar characteristics. Firstly, the customers should be grouped by one or several
variables. The current research implies the customers division according to the industry, as this
variable not only indicates the type of client activity, but also presupposes the common needs of the
clients such as the requirements to the commodity and service, the frequency of order and to some
extent the size of the order. Secondly, the process comprises the assessment of the highlighted groups
(marked by industry) sales variability. This includes also the analysis of the annual invoices and coststo-serve dynamics between the years 2013-2015. Thirdly, due to the fact that costs-to-serve are the
managers’ investments into clients and can vary from one group to another it is important to take them
into consideration while evaluating the portfolio performance. The clients that bring the highest
returns can at the same time present the highest risks for the organization. Thus, the next step of the
process includes risk-reward dynamics assessments of each of the highlighted groups (marked by
industry). Fourthly, after the assessment of the highlighted groups was made the most desirable
industries to serve can be highlighted.
Due to the fact that companies can segment the market by applying the results of customer
portfolio analysis the identification of the most desirebale industry can be potentially useful. In order
to highlight the most desirable industries to serve Industry beta can be used (table 7).
Table 7 Industry Beta (calculation)
Variable
Industry beta (𝜷)
Calculation
Ib =
𝐶𝑜𝑣(𝑋𝑖,𝑋𝑝)
𝑉𝑝
Explanation
Industry beta indicates the degree to
which each industry contributes to the
risk of the entire customer portfolio
Cov (xi, xp) – covariance between
industry return and the return of the
overall
Vp - variance of the return for the entire
customer portfolio
Source: Created by the author
46
The coefficient industry beta can be considered as a reliable measure of the stability of the
returns for each industry served by the company compared with the reference portfolio (Tarasi et al.,
2011). In financial theory beta coefficient is calculated in relation to the market portfolio, where the
market portfolio consists of the assets that comprise weights in proportion to the summarized market
value. However, due to the fact that the establishment of a comparable portfolio that comprises all
customer assets presented by the industries across all companies operated in the market is a
challenging task, some researchers offer to define existing customer groups as a market portfolio (Buhl
and Heinrich 2008; Ryals 2002).
Finally, in order to assess the current customer portfolio the efficient customer portfolio should
be constructed. The process starts with the covariance matrix construction, where all risk-reward
combination (between years 2013 -2015) related to the each of the highlighted groups (marked by
industry) are incorporated. First of all, to construct a covariance matrix the Excel add-ins tool –
Analysis ToolPak was used. By using the function covariance in analysis, ToolPack the returns
covariance matrix was designed. The weights (W1 - Automotive; W2 - Real Estate; W3 – Food and
Beverage production; W4 - Manufacturing) in the matrix represent the costs distribution of the
Company “X” among four highlighted industries. Diagonal elements of the covariance matrix
represent the variance of returns. The variance indicates the risks associated with the process of each
client group serving.
W1
W2
W1
W2
9,11004E-05
3,42497E-05
1,56348E-05
W3
W4
6,94979E-05
5,46937E-05
3,14734E-05
2,18718E-05
W3
7,34665E-05
4,50883E-05
W4
3,51724E-05
Secondly, due to the fact that covariance matrix should be symmetric, empty graphs were filled up.
W1
W2
W3
W4
W1
W2
9,11004E-05
3,42497E-05
3,42497E-05
1,56348E-05
6,94979E-05
3,14734E-05
5,46937E-05
2,18718E-05
W3
6,94979E-05
5,46937E-05
3,14734E-05
2,18718E-05
7,34665E-05
4,50883E-05
4,50883E-05
3,51724E-05
W4
47
After completing the covariance matrix construction another Excel add-ins tool - Solver was
used. The function Solver allows to set up some limits and conditions before developing an efficient
customer portfolio. Some of the restrictions are obligatory, for example:
The sum of shares (weights) of all client groups included into portfolio should be equal to 1
Shares (weights) of each client group should be greater than or equal to zero
Other limits, like the minimum weighting values of each group of existing customers in an efficient
portfolio can be set up at the discretion of the company manager. To complete the portfolio optimiation
the desired level of return should be settled. The customer portfolio is considered as an efficient if it
has the lowest risk for a desired and specified level of return or if it has the highest possible level of
return for a some level of risk (Tarasi et al., 2011).
The efficient portfolios developed bordered assortment of the possible portfolios. An efficient
portfolio frontier represents all efficient portfolio combinations that will provide to the company the
highest return at each risk level or the lowest risk for each level of return (Sackmann et al., 2010).
The efficient portfolio frontier can be created by using several desired returns and corresponding
deviation indicators. The risk and return of the efficient portfolios should be then depicted on the
scatter graph.
3.5 Comparison of the current customer portfolio and the efficient customer portfolio
combinations
There are several ways to compare current customer portfolio and an efficient one. The first
way is to compare current portfolio return and risk with the efficient frontier portfolios by putting the
point of the current portfolio on the efficient frontier graph. If management is primarily interested in
the risk evaluation the overall customer portfolio risk of the all portfolio combinations can be
calculated by using the formula that computing the variance of the entire portfolio (table 8).
48
Table 8 Risk of the entire portfolio (calculation)
Variable
Vp –Variance of portfolio
Calculation
2
Vp=
∑𝑁
𝑗=1(𝑥𝑗−𝑥𝑝)
Explanation
The formula allows to compare the
performance
of
the
portfolio
combinations.
𝑁−1
Source: Created by the author
Finally, to compare efficient customer portfolio and current customer portfolio managers can
calculate the risk - free proxy for the all portfolio combinations. According to Sharpe (1994) the
process of measuring the rate of return on risk of a portfolio forms the basis for the reward ratio (RR)
(table 9).
Table 9 Reward ratio (calculation)
Variable
Reward Ratio (RR)
Calculation
Explanation
Reward is measured as the return above
the risk free rate
𝑅𝑖
RR = 𝜎𝑖
RR – customer reward ratio
Ri – return for the portfolio
𝝈 - standard deviation of the return
Source: Created by the author
Reward ratio calculated for each portfolio presented in efficient portfolio frontier provides the
information about what portfolio is the most sustainable and profitably attractive.
49
4. Findings
To start the process of the current customer portfolio optimization the sample which included
132 customers with whom Company “X” operates in Saint Petersburg was used. All the clients
assessed worked with a Company “X” for at least three years (2013-2015). To test the applicability of
the financial portfolio theory to a customer portfolio purchases historical data that include annual
invoices from each of 132 clients and costs-to-serve were used.
Customer portfolio optimization implies the assessment of the resources allocation among the
existing customer groups with the aim to identify possible investment alternatives. First of all, the
client database was divided into groups distinguishing customers who operate in Automotive, Real
Estate, Food, Beverage production and Manufacturing industries. The attributing to highlighted
groups (marked by the industry) not only represent the information regarding client-company activity,
but also reflect their needs. Due to the fact that Company “X” has operated in the Russian market for
more than twenty years, the managers recognize what type of commodity or service, in what quantity
and with what frequency clients that operate in different industries need.
The construction of the efficient customer portfolio based on customer groups makes the
process of future potential customers’ identification more transparent, especially for the B2B
companies which already have a customer database and want to find out what customers are more
profitable for the company.
4.1 Industries sales variability assessment
The next step in the process of the analysis includes the assessment of the differences in sales
variability that exist among the customer segments presented by four industries (Automotive, Real
Estate, Food and Beverage and Manufacturing). The information regarding three years of purchase
historical data was used in order to evaluate these differences. The graph below reflects the annual
invoices from customers groups presented by four industries between 2013 - 2015 (figure 7).
50
Annual invoices dynamics
40000
Automotive
35000
Real Estate
Invoice
30000
25000
20000
Food and Beverage
production
15000
Manufacturing
10000
5000
0
2013
2014
2015
Year
Figure 7 Annual invoices dynamics 2013-2015
Source: Created by the author
Manufacturing industry exhibits stable growth pattern over three years. Real Estate and Food
and beverage production industries register modest growth. Automotive industry exhibits a slight
decline after 2014. The analysis indicates that there are significant differences in sales trends for
customers that operate in the above-mentioned industries. The company “X” takes into consideration
the information about the client-company activity and keeps it in its database. The construction of the
efficient portfolio based on the industry type can be used in order to determine the optimal weights of
the resources (costs) distribution among industries in the portfolio, to evaluate them and finally decide
which industries presented in the market are more preferable to serve provided that the company is
aimed to achieve the predetermined level of return.
Alongside with the invoices all customers cause costs (figure 8). The graph depicts the annual
costs to serve of customers groups presented by four industries between 2013 - 2015.
51
Annual costs dynamics
35000
Automotive
30000
Real Estate
Costs
25000
20000
Food and Beverage
production
15000
Manufacturing
10000
5000
0
2013
2014
Year
2015
Figure 8 Annual costs dynamics 2013-2015
Source: Created by the author
Along with an increase in invoices, Manufacturing industry exhibits stable growth pattern in
costs over three years. Automotive and Real Estate industries register modest growth. Only food and
Beverage production industry exhibits a slight decline in 2015. This decline can be resulted either
from reduction in the consumption of the service by the client or from the reduction of maintenance
costs to serve by Company “X”.
4.2 Industries risk – reward dynamics assessment
In order to assess the possible investment alternatives from the perspective of risk and return
both invoices and costs-to-serve were incorporated into analysis. As it was already mentioned before,
costs-to-serve by the company “X” consist of four parts that are material, washing, delivery and
overhead. The graph that represents annual costs dynamics represents that costs-to-serve vary
significantly from one industry to another (figure 12). Due to the fact that the industry that brings
higher returns also generates the largest costs for the Company “X” both of these indicators should be
incorporated into the customer groups’ evaluation. These data must be taken into consideration in the
process of current customer portfolio evaluation and efficient portfolio construction. In the course of
analysis monthly aggregated returns of four industries (1 - Automotive; 2 - Real Estate; 3 - Food and
Beverage production and 4 - Manufacturing) for 2013-2015 (36 months period) were calculated and
the dynamics of the returns is depicted on the graph below (figure 13).
52
Return dynamics by industries
0,12
return dynamics by industries
0,1
0,08
1
2
0,06
3
4
0,04
0,02
0
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435
Figure 9 Return dynamics by industries
Source: Created by the author
The graph represents that all industries contribute to the performance of the Company “X” and
have a positive level of return during the overall examined period. To evaluate the industries and to
identify which of them can be considered as the most desirable to serve, Industry beta coefficient was
calculated (Table 10).
Table 10 Industry beta
B1 (Automotive)
B2 (Real Estate)
B3 (Food and Beverage )
B4 (Manufacturing)
1,30512707
0,53020369
1,116560389
0,824064139
Source: Created by the author
Industry beta assesses the industries from the perspective risks and returns associated with
each of them. The results highlights that Automotive and Food and Beverage production industries
are the most valuable instruments in achieving greater profit, as they generate the highest returns for
the Company “X”. Besides, the variability of the returns and, as a result, the risks incorporated by
each industry is also different among the customers’ groups (marked by industry). Thus, Automotive
53
and Food and Beverage industries are at the same time the most risky industries to serve. In order to
determine the optimal weights (from the perspective of the company manager) of the costs distribution
among industries in an efficient customer portfolio management the right balance between expected
risk - reward combinations should be found. While using the financial portfolio theory Company “X”
manager should constitute himself as an investor who needs to find the best balance between the level
of desired returns and the risks inherent in achieving the returns.
Before constructing several efficient portfolios, the individual average return of each
emphasized industry was calculated (Table11).
Table 11 Industry average return
Average r
Automotive
8%
Real Estate
Food and Beverage
5,8%
7,7%
Source: Created by the author
Manufacturing
6,7%
For three years period (2013-2015) the average return generated by clients operated in the
Automotive industry was equal to 8%, in the Real Estate industry – 5,8%, Food and Beverage
production – 7,7% and Manufacturing – 6,7%. These figures indicate the maximum possible return
that the Company could achieve by serving each industry. All of the groups (marked by industries)
generate high level of return during the analyzed period. This can be explained either by the fact that
clients included into the sample provided by Company “X” were selected from top-tier clients or that
Company “X” has quite high markups on commodities or service.
4.3 Efficient customer portfolio options
Due to the fact that at the current moment Company “X” groups of clients that are analyzed in
the research generate high returns in the range from 5,8% - 8% the efficient portfolios will be
constructed with the aim to achieve the return equal to 6%; 6,5%; 7%; 7,5% and 8% by serving all
client groups together. In order to show how company “X” can achieve an increase in its monthly
returns, several efficient portfolios with different risk levels were constructed:
54
1. The first constructed portfolio offers maximum return of approximately 8% for a Company
“X”, but also has a maximum risk level (Table 12).
Table 12 Efficient portfolio- option 1(return equal to 8%)
w1
w1
w2
w3
w4
0,568514
0
0,424298
0,007188
w2
0,568513799
9,11004E-05
3,42497E-05
6,94979E-05
5,46937E-05
0
3,42497E-05
1,56348E-05
3,14734E-05
2,18718E-05
w3
0,424297871
6,94979E-05
3,14734E-05
7,34665E-05
4,50883E-05
w4
0,00718833
5,46937E-05
2,18718E-05
4,50883E-05
3,51724E-05
Source: Created by the author
The model with the maximum level of return (equal to 8% monthly) that can be achieved by
the company indicates, that in order to achieve such performance company needs to put its efforts on
the clients, which operate in Automotive, Food and Beverage and Manufacturing industries. The
weights of the costs distribution among these industries should account for 57%, 42% and 1%
respectively. This indicates that according to the Markowitz’s portfolio selection model, Company
“X” can achieve the desired level of return (equal to 8%) by serving mainly two groups of clients
presented by Automotive and Food and Beverage industries. Due to the fact that Automotive and Food
and Beverage production industries have the highest average return among the four highlighted
industries, that is why they were included by the model as the most valuable industries in achieving
the highest profitability. The diagonal values of the model represent the variance (risk) associated with
serving of each of the industry. Automotive and Food and Beverage production industries have the
highest variance, meanwhile, the serving of these two industries is quite risky. Besides, Real Estate
industry generates the lowest average return and is not able to contribute to Company’s “X” maximum
profit at the given level of returns (equal to 8%). However, it doesn’t mean that clients who operate
in Real Estate industry don’t bring returns to Company “X”. The model excludes them because they
can’t contribute to the achievement of the 8% level of return. Thus, for the Company “X” the
achievement of the level of return equal to 8% is possible, but it assumes that the company will put
all its efforts on serving two industries and take a greater risk. Moreover, as a Company “X” has
already a customer base that consists of all the industries analyzed in the current research the desire
to achieve such level of return will imply the termination of the partnership with the Real Estate
Industry.
55
2. The second solution was constructed under the condition that the expected return should be
equal to 7,5% (Table 13).
Table 13 Efficient portfolio - option 2 (return equal to 7,5%)
w1
w1
w2
w3
w4
0,297451
0
0,313644
0,388905
w2
0,29745096
9,11004E-05
3,42497E-05
6,94979E-05
5,46937E-05
0
3,42497E-05
1,56348E-05
3,14734E-05
2,18718E-05
w3
0,313644382
6,94979E-05
3,14734E-05
7,34665E-05
4,50883E-05
w4
0,388904658
5,46937E-05
2,18718E-05
4,50883E-05
3,51724E-05
Source: Created by the author
The decrease of the desired return by only 0,5% caused the situation when the weights of the
costs distribution among the highlighted clients groups changed significantly. The level of the desired
return is still high, so the model again excludes the client group presented by Real Estate industry.
However, other groups are presented in the portfolio with relatively equal weights of the costs
distribution and account for 30% - Automotive, 31% - Food and Baverage production and 39%
Manufacturing.
3. The third constructed portfolio offers return of approximately 7% (Table14). The table below
illustrates
that Manufacturing industry dominates in the portfolio and weight of the costs distribution accounts
for 61%. The costs to serve Food and beverage industry has a weight of 20% and two other industries
have relatively equal representation of approximately 9% each.
Table 14 Efficient portfolio - option 3 (return equal to 7%)
w1
w1
w2
w3
w4
0,088311
0,096477
0,200511
0,614701
w2
0,088310872
9,11004E-05
3,42497E-05
6,94979E-05
5,46937E-05
0,096477392
3,42497E-05
1,56348E-05
3,14734E-05
2,18718E-05
w3
0,200511104
6,94979E-05
3,14734E-05
7,34665E-05
4,50883E-05
w4
0,614700632
5,46937E-05
2,18718E-05
4,50883E-05
3,51724E-05
Source: Created by the author
Company “X” should pay more attention on the above-described portfolio as it includes all
client groups that are served by the company. In case if the managers of the Company “X” are looking
56
for achievement level of returns equal to 7%, the costs distribution among the analyzed industries
presented by the model can be considered as optimal from the perspective of Markowitz portfolio
selection model that implies both risk minimization and desired return (in this case equal to 7%)
achievement. As the company “X” management may be interested in less risky solutions several
portfolios that offer lower returns were constructed.
4. Thus, the fourth designed portfolio generates 6,5% return (Table 15). Due to the fact that the
Automotive industry can be considered as the most risky, when the desired level of return was reduced
the model excluded this industry and distributed the weights of the costs distribution among other
industries which are less risky and are able to bring the desired return of 6,5%. In this case, the weights
of the costs distribution of the Manufacturing, Real Estate and Food and Beverage production
industries account for approximately 54% , 38% and 8% respectively.
Table 15 Efficient portfolio - option 4 (return equal to 6,5%)
w1
w1
w2
w3
w4
0
0,376196
0,077777
0,546027
w2
0
9,11004E-05
3,42497E-05
6,94979E-05
5,46937E-05
0,376195845
3,42497E-05
1,56348E-05
3,14734E-05
2,18718E-05
w3
0,077777231
6,94979E-05
3,14734E-05
7,34665E-05
4,50883E-05
w4
0,546026923
5,46937E-05
2,18718E-05
4,50883E-05
3,51724E-05
Source: Created by the author
For the Company “X” the termination of the partnership with the Automotive Industry is not
appropriate, as the analysis of the existing customer database showed that customers who operate in
Automotive industry constitute a significant part of the entire customer database.
5. The last analyzed portfolio represents the solution that guarantee the return equal to 6%
(Table16).
Table 16 Efficient portfolio - option 5 (return equal to 6%)
w1
w2
w3
w4
0
0,844571
0
0,155429
w1
0
9,11004E-05
3,42497E-05
6,94979E-05
5,46937E-05
w2
0,844570728
3,42497E-05
1,56348E-05
3,14734E-05
2,18718E-05
w3
0
6,94979E-05
3,14734E-05
7,34665E-05
4,50883E-05
w4
0,155429272
5,46937E-05
2,18718E-05
4,50883E-05
3,51724E-05
Source: Created by the author
57
This portfolio is composed predominantly of the customers that operate in Real Estate industry
and the representation of the weight of costs is equal to 84%. Due to the fact that Real Estate segment
acted on its own is not able to provide Company “X” with necessary profit increase, model also
transfers the rest of the costs (equal 15%) to the Manufacturing industry customers, that can be
considered as more risky assets, but cause higher return.
The efficient portfolios discussed above bordered the set of the efficient portfolios (Markowitz
1959). The efficient frontier was constructed in order to compare the possible efficient portfolios
(figure 10).
The efficient Frontier Portfolios
8,50%
return %
8,00%
7,50%
7,00%
efficient frontier
portfolios
6,50%
6,00%
5,50%
0
2
4
Variance
6
8
10
(*10-5)
Figure 10 The efficient frontier portfolios
Source: Created by the author
The vertical axis of the graph represents the level of the desired returns that could be achieved
and the horizontal axis indicates the level of risk. The points on curve present the risk-reward value
of each efficient portfolio. In order to compare the portfolios risk – reward ratio proposed by Sharpe
(1994) was calculated (Table 17).
Table 17 Risk -Reward ratio
EP 1
EP 2
EP 3
EP 4
EP 5
Desired return
6,00%
6,50%
7,00%
7,50%
8,00%
Risk
1,774426206
2,780055379
4,100930357
5,722805392
7,692281378
Risk-Reward ratio
14,24368232
12,32782989
10,9309232
9,914179428
9,121419002
Source: Created by the author
58
Calculated risk-reward ratio highlights that the first efficient portfolio with the risk-reward
ratio equal to 14, 2 can be considered as the most preferable from the Shape’s perspective. However,
this coefficient is mainly focused on the risk – minimization and does not take into consideration the
fact that for a company with an existing customer database any significant changes in the decision
about investment alternatives imply the termination of the partnership with the clients. The choice of
the efficient portfolio for the Company “X” should depend also on the worthiness and significance of
any changes. Due to the fact that all client - groups in an analyzed sample bring sufficiently high level
of returns Company “X” may consider options that allow to reach higher level of return. In order to
take the final decision regarding which of the portfolio combinations present more attractive option
of the resources allocation for the Company “X” the current portfolio should be assessed.
4.4 Current customer portfolio analysis
In order to calculate current customer portfolio of the Company “X” the costs to serve were
considered as an investments and stated as weights in the model. In the current portfolio, the weights
of the costs distribution among four industries account for 23, 5%, 11%, 17, 5% and 48% respectively
(Table 18).
Table 18 Current customer portfolio (return equal to 7,1%)
w1
w2
w3
w4
0,234633605
0,106848417
0,174679206
0,483838772
w1
w2
0,234633605
0,106848417
9,11004E-05
3,42497E-05
3,42497E-05
1,56348E-05
6,94979E-05
3,14734E-05
5,46937E-05
2,18718E-05
Source: Created by the author
w3
0,174679206
6,94979E-05
3,14734E-05
7,34665E-05
4,50883E-05
w4
0,483838772
5,46937E-05
2,18718E-05
4,50883E-05
3,51724E-05
The calculated level of return of the current customer portfolio is equal to 7,1%. In order to
evaluate the performance of the current portfolio in comparison to that of the efficient portfolios the
results were depicted in the graph (Figure 11).
59
The efficient Frontier Portfolios and Current Portfolio risk and return
8,50%
return %
8,00%
7,50%
efficient frontier portfolios
7,00%
6,50%
current portfolio risk and
return
6,00%
5,50%
0
2
4
Variance
6
8
10
(*10-5)
Figure 11 The efficient frontier portfolios and current customer risk and return
Source: Created by the author
The graph highlights that the Company’s “X” current portfolio of customers can be considered
as efficient. However if the company wants to increase returns or to decrease the risks it can take into
consideration other risk – reward combinations presented on the graph and change the weights of each
industry in the portfolio. If the Company “X” wants to achieve higher returns (equal to 7,5%) it can
change the weights in the costs distribution to the values proposed in the second portfolio option
(Table 13). However, this will imply the termination of the partnership with client group presented by
Real Estate industry and focus primarily on the three other segments. Meanwhile, by decreasing the
level of the expected return to 7% Company “X” will be able to reduce risks by insignificant changes
in the weights in the costs distribution (Table 14). As a result, the Company “X” will be able to keep
high level of return without termination of any partnership.
To conclude, it should be mentioned that the analysis of the efficient portfolio options and
assessment of the existing customer portfolio provides managers with the information about potential
resources allocation among the customer groups and allows to identify the most valuable market
segments on which the company management efforts should be focused.
60
5. Discussion
For the purpose of the research, at first, the literature review for B2B markets, segmentation,
customer relationship management and customer portfolio management was done. The review of the
marketing and management literature showed that B2B sphere has specific characteristics and
deserves thorough investigations. The majority of the academic articles related to B2B market
highlight that it varies from B2C markets in a number of ways and that it is essential to investigate
such burning issues as characteristic features of B2B organizations, market segmentation variables,
customers value assessment, customer relationship management, customer portfolio theory etc. The
empirical study carried out in the current research allows to formulate the answer to the research
question that was stated in the introductory part of the master thesis.
5.1 Answer to research question
How can the approach to segmentation in b2b markets be supplemented by applying
customer portfolio analysis?
The answer to the research question required the investigation of the theoretical background
related to the problem of efficient segmentation. According to theoretical sources, the key success
factor for the efficient market segmentation is to define the most profitable groups of customers to
target and study their needs in detail so that the mutual collaboration could bring satisfaction and profit
both to the company and its customers in B2B market. To achieve greater profitability companies
need to focus their efforts not on the few closest relationships but on the entire customer portfolio.
The analysis of the existing customer database of the B2B Service Company “X” revealed
such indicators as: customers location, distribution of the client groups among location areas, the range
in the number of workers in the client-companies and customers distribution by industry type. In the
conducted research the customers were divided into groups according to the industry, as this variable
not only indicates the type of client activity, but also presupposes the common needs of the clients
such as the requirements to the commodity and service, the frequency of order and the size of the
order. The history purchases data, such as: annual invoices, annual costs-to-serve and number of units
in circulation, provided by the Company “X”, were applied to construct efficient customer portfolio
options. This task was solved with application of financial portfolio theory.
Financial portfolio theory shows how investors can create an optimal portfolio that will
maximize returns depending on the given risk level. In this case, risk can be considered as an integral
part of the superior reward achievement. Like financial investments, customers represent assets that
61
continuously generate risky cash flows. While some clients can provide great, but unsteady cash flows,
others can generate lower but more stable cash flows. The conducted research empirically proved
that customer portfolio performance could be analyzed and assessed in terms of financial portfolio
theory, which implies using Markovitz portfolio model as a basis for adaptation to customer portfolio
analysis. Besides, the following financial indicators were modified and applied to the analysis of the
industries presented in customer portfolio and to the comparison of the alternative portfolios: industry
beta, risk-reward ratio, portfolio variance.
Customer portfolio analysis incorporates both risks and returns associated with the highlighted
groups of customers that are served by the company. Efficient portfolio frontier was constructed in
order to compare possible efficient portfolio combinations with the current customer portfolio. Due
to the fact that Company “X” investments into clients are the costs that the company bares, current
customer portfolio represents the current costs distribution among the groups of customers and the
efficient portfolio - the optimal distribution of resources among the highlighted industries from risk –
reward perspective. The management of the company can use the information gathered from the
analysis in order to decide what risks they are ready to take in order to achieve desired return. Analysis
of the current customer portfolio showed that the portfolio risk - reward combination lies on the
efficient frontier. This indicates that portfolio can be considered as efficient. However, the analysis
revealed that the management of the Company “X” has two options. By changing the weights in the
costs distribution they can either take greater risk and achieve higher returns or decrease the desired
level of return, ensuring less risky operation.
Thus, the analysis of the customer portfolio can be considered as a way of supplementing to
the approach to segmentation in B2B markets and reveal which groups of customers should be targeted
and whose requirements and expectations should be met by the company. The usage of customer
portfolio theory can be considered as a useful tool for distribution of scanty resources of the company
among the client groups with the aim to maximize long – term profitability at a given level of risk.
5.2 Theoretical contributions
In a highly - competitive economies companies are always under the pressure and constantly
need to ensure that the clients they serve are cost efficient (Lumby and Jones 2001). The thesis
highlights and develops theoretical issues related to segmentation in B2B market, concentrating in
particular on B2B service activity. Such an activity is often based on long-term collaboration that in
turn implies dealing with relationship - oriented customers. From this perspective customer
62
relationship management touched upon in this research is considered as a strategy that is essential for
B2B service companies. Current research contributes to marketing theory for several reasons.
Firstly, according to Grinblatt and Titman (2002) financial portfolio theory is a useful tool that
can be applied not only in financial practices, but also can be modified and used during crucial
managerial decision making processes. Current research shows how theoretical approaches applied in
financial portfolio theory can be implemented in customer portfolio theory. By using the modified
financial portfolio model in the analysis of B2B Service Company activity the applicability of
financial theory to creating efficient customer portfolio was justified empirically. Secondly, according
to Terho and Halinen (2007) customer portfolio evaluation represents a significant tool for
improvement of the customer relationship management and can assist in competitive advantage
achievement. The research highlights how B2B organizations can gain a better understanding of the
importance of each client group contribution to the overall performance and work out the market
strategy for retaining the most valuable of the existing customers. The gathered knowledge can also
be extrapolated on the potential market and result in acquiring those customers who a supposed to
become more promising and profitable and with which long-lasting relations should be constructed.
Thirdly, modern researches highlights that the inclusion of costs to serve in calculations while
evaluating customers and identifying those of them who can increase shareholder value to a greater
extent is rather promising (Thakur and Workman 2016; Tarasi et al., 2011). The conducted research
presents an empirical example that prove that the incorporation of costs to serve in customers’ groups
is an inevitable part of customers assessment that allows to estimate risks attributed to the process of
each client group serving. Fourthly, the contribution to the customer portfolio management was made
by modifying the beta coefficient applied in financial theory to industry beta that describes the degree
to which each customer group presented by industry contributes to the entire customer portfolio.
Finally, for business – to – business companies that serve clients operating in various industries it is
crucial to find an optimal risk – reward balance. The model tested empirically in the current thesis
represents how the company can change the weights of the costs distribution within each industry in
order to increase returns or to decrease the risks associated with each customer group.
5.3 Managerial implications
The conducted research provides several implications for marketing managers, who always
are under pressure of the competition from other companies and need to demonstrate the ways to
63
increase shareholder value by means of competent resources allocation. Companies operating in the
market tend to overestimate the value of the bigger customers and underestimate that of the smaller
ones. Instead of analyzing the entire customer portfolio companies quite often look at their clients
individually and do not incorporate costs-to-serve related to the customers serving process (Homburg
et al., 2009).
Efficient market segmentation, which allows the company to promote the right offer on the
market and gain competitive advantage, implies allocating resources into those customers that bring
most profits and concentrate more on the requirements and expectations of the most profitable
customers. By applying financial portfolio concepts to the customer portfolio analysis the research
contributes to the marketing practices aimed at developing appropriate strategy for existing customers’
retention and potential customers’ attraction in a number of ways.
Firstly, the process of grouping existing customers of the B2B Service Company “X” into
groups by industry showed that for the analyzed company industry variable can be considered as a
good base for customers grouping, as it indicates not only the sphere of the client company activity,
but also the needs of the customers. While the cash flows and costs – to – serve within each client
group characterized by industry are relatively equal, the variability of the returns and as a result, the
risks incorporated by each of four highlighted industries in the thesis is different. Thus, such
unification allows managers to assess how each client group contributes to the profit achievement and
what risks are incorporated in the process of serving these customer groups. However, the analysis
proposed in the research allows managers to group the customers by other variables, such as size of
the client - company business, location area etc. if the company’s database contains this information.
Secondly, the conducted research draws the attention of the B2B companies to the entire
customer portfolio assessment. The costs-to-serve associated with each client groups are the
investments of the analyzed B2B Company “X”. Thus, the modified model suggests using costs
associated with each customer group within the portfolio as bases for resources distribution among
the highlighted industries. By using the information regarding an efficient resources distribution
among each client group managers can improve the performance efficiency by developing more
focused marketing strategy aimed at retaining existing customers.
Thirdly, by comparing the performance of the current customer portfolio with that of the
efficient portfolios managers could identify the possible ways of business processes improvement.
The construction of the efficient customer portfolio combinations allows managers to change the
weights of the costs distribution among the highlighted industries in order to achieve better risk64
reward balance. A manager is always to some extent an investor that should take the final decision
concerning the fact which customers are more preferable to serve. While some managers are adherents
of stability and predictability of portfolio performance, others are ready to take the risks in order to
achieve greater profits. The conducted research presents efficient compositions of customer portfolio
that can either outperform the current customer portfolio in terms of reward or minimize the risks
associated with the serving processes.
Fourthly, by applying customer portfolio theory to the client database and identifying the most
profitable customers, managers can enhance both current and future performance of the organization
and its financial results, by focusing on long-term partnerships with highlighted customers. Finally,
after assessing customers in portfolio and identifying the most valuable groups, managers could work
out strategies to attract new customers with similar characteristics and needs. Thus, the approach
proposed in this thesis becomes promising not only for the evaluation of existing customers, but also
for identifying potential customers in the market.
5.4 Limitations and future research
The conducted analysis allows to determine the risk – reward combinations of the customer
portfolio and to use these data for the effective segmentation in B2B market with the aim to reveal,
which groups of customers should be targeted and whose requirements and expectations should be
met by the company. However, there are some limitations that could be deeply analyzed in the future
research.
Firstly, the analysis were carried out on the base of the example of only one B2B Service
Company “X” that collaborates with clients for several years. Despite the fact that the financial
portfolio model was modified in a way to suit for the analysis of all B2B service companies, further
research should be conducted in order to test and confirm the applicability of the approach to other
B2B service companies. Secondly, the sample given by the B2B Company “X” constitutes only a part
of the all clients served in Saint Petersburg. Thus, the gathered results show the applicability of the
financial portfolio theory to the customer portfolio analysis, but does not present the real situation of
the overall company performance.
Thirdly, the applicability of the model depend on the company type. There is an important
condition that in order to apply financial portfolio theory to the customer portfolio analysis, companies
should have continuous monthly cash flows from each of the assessed clients for several years.
65
Moreover, companies should maintain the information in the client database regarding client company
activity, the volume of consumption of the clients and the costs to serve associated with each client.
However, usually the mentioned data are available to the majority of companies, as some of them are
prescribed in the contract and some can be calculated by using annual cash flows, costs and monthly
volume of transactions.
Fourthly, due to the fact that the Company “X” started to implement the Customer
Relationship Management approach relatively recently, the information gathered in the database does
not covered all potential variables and entire customers’ needs. Therefore, the analysis incorporated
the evaluation of the groups of clients divided by the industry. However, the methods used in this
research can be applied even if the customers will be divided into groups by other descriptive features
or even by groups of features.
Fifthly, the current research is based on the analysis of the existing customer portfolio of the
B2B Company “X” and does not take into consideration the possibility to expend the company
operations on the other potentially attractive market segments. However, this limitation presents the
possibility to apply the heuristic approach proposed by Buhl and Heinrich (2008) in the future
research. This approach allows to find an optimal problem solution of customer portfolio optimization
by incorporating fixed costs associated with client relationships to a manageable number of client
segments.
Sixthly, current research proposed methods to assess customers’ attractiveness by applying
past variability in clients’ purchases. The gathered results could be extrapolated to the future;
however, unstable market conditions can significantly affect clients’ needs and desirability. Thus, in
order to estimate the future customer value the future cash flow volatility associated with clients
should be added to the analysis in the future research. All of the above-mentioned limitations provide
a promising and forward – looking beginning of the future research.
66
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