PRODUCT DIFFERENTIATION AND PRICING STRATEGY IN THE
MARKET FOR ANTIBODIES
_______________
A Thesis
Presented to the
Faculty of
San Diego State University
_______________
In Partial Fulfillment
of the Requirements for the Degree
Master
of
Business Administration
_______________
by
Christina Gramatikova
Spring 2013
iii
Copyright © 2013
by
Christina Gramatikova
All Rights Reserved
iv
ABSTRACT OF THE THESIS
Product Differentiation and Pricing Strategy in the Market for
Antibodies
by
Christina Gramatikova
Master of Business Administration
San Diego State University, 2013
The market for research antibodies is extremely competitive and fragmented, thus
cost-leadership, differentiation and niche (focus) strategies are very difficult to examine and
formulate in Michael Porter's terms. Porter's generic strategies: cost-leadership,
differentiation and niche have become a dominant paradigm in competitive business
environments. Porter's model has been shown to be outdated; i.e. he states that the
combination of low-cost and differentiation strategies is unlikely to produce a sustainable
competitive advantage. In today’s market, however, to establish a sustainable competitive
advantage, many businesses especially those distributing research antibodies, pursue a
combination of generic strategies simultaneously. This thesis aims to refuel the debate about
what specific circumstances constitute an appropriate differentiation and pricing strategy. By
popularizing the idea that differentiation and low-cost are incompatible, Porter's legacy may
have served to misdirect corporate strategists. This thesis develops a framework that aims to
identify the contingencies under which the above propositions hold for the research antibody
industry.
This thesis examines key questions raised in the analysis of the research antibody
industry: (1) Will the strategy found among antibody suppliers resemble Porter's generic
strategies? (2) Are there performance differences among antibody suppliers pursuing
different types of strategies? (3) Are there differences in the product differentiation and
pricing strategies among antibody suppliers?
Multivariable cluster analyses were used to define strategic groups along current
antibody sellers using normalized data from external sources (market research reports).
Conjoint analysis survey design in combination with multivariable cluster analyses were used
to define strategic groups along the dimensions of cost, antibody applications (e.g. Western
Blot, IF, FC etc.) and product citations (as factors of differentiation in a combinatorial matrix
of nine buyer choices). A survey using nine consideration sets was executed, and data of the
responders analyzed using a combination of hierarchical clustering and non-negative matrix
factorization (NMF). The respondent profiles were inferred and characterized by their
dominant behavior. A course division was made between 'smart' and 'thrifty' buyers in favor
of the first. The behavior of the 'smart' buyers is driven predominantly by product citations
and less by application provided by the seller.
Findings suggest that antibody suppliers use integrated strategies combining elements
of cost-leadership and differentiation, an outcome which departs from classic Porter models.
The researcher’s survey results propose large differences in performance among current
players in the market for research antibodies. From the survey, the researcher infers that a
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successful strategy in the antibody market should be a blend of cost-leadership (47-42%)
quality differentiation (53-58%).
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TABLE OF CONTENTS
PAGE
ABSTRACT ............................................................................................................................. iv
LIST OF TABLES ................................................................................................................. viii
LIST OF FIGURES ................................................................................................................. ix
CHAPTER
1
INTRODUCTION .........................................................................................................1 Statement of the Problem .........................................................................................1 Background ..............................................................................................................3 Threat of New Entrants ............................................................................................5 Power of Suppliers .............................................................................................5 Power of Buyers .................................................................................................5 Availability of Substitutes..................................................................................6 Competitive Rivalry ...........................................................................................6 Purpose of the Study ................................................................................................6 Essential Questions on Validation of Porter’s Generic Strategy .............................7 Significance to the Field ..........................................................................................8 Limitations ...............................................................................................................9 Ethical Considerations .............................................................................................9 Outline of the Study ...............................................................................................10 2
REVIEW OF LITERATURE ......................................................................................11 Specialization by Product Type: Differentiation by Leading Applications ...........11 Setting/Sampling: Bioinformatics..........................................................................12 Biocompare ............................................................................................................12 Piper Jaffray ...........................................................................................................14 Most Recent Market Research Results ..................................................................14 Supplier Differentiators in the Information Search Stage......................................14 Supplier Differentiators in the Purchase Stage ......................................................19 Conclusions/Implications.......................................................................................22 Limitations/Weaknesses ........................................................................................23 vii
3
METHODS ..................................................................................................................24 Hierarchical Cluster (HC) Analysis .......................................................................25 Nonnegative Matrix Factorization (NMF) .............................................................30 Data Analysis .........................................................................................................32 Results ....................................................................................................................34 Using the Strategic Group Map as an Analytical Tool ..........................................34 4
RESULTS ....................................................................................................................36 Components of the Conjoint Study ........................................................................36 Attributes and Corresponding Levels: Attribute Definition ..................................37 Application .............................................................................................................37 Citations in Research Publications.........................................................................38 Price .......................................................................................................................38 Preference Model ...................................................................................................38 Data Collection and Stimulus Construction ...........................................................39 Stimulus Presentation.............................................................................................40 Measurement Scale ................................................................................................41 Survey Implementation ..........................................................................................41 Data Input...............................................................................................................42 Interpretation of Results with Multidimensional Scaling (MDS) ..........................42 Cluster Analysis .....................................................................................................43 Descriptive Statistics..............................................................................................43 Cluster 1: “Smart Shoppers” ..................................................................................44 Cluster 2: “Thrifty” ................................................................................................44 Customer Segmentation by Value Perception .......................................................46 5
DISCUSSION ..............................................................................................................48 Limitations of the Study.........................................................................................48 Conclusions ............................................................................................................49 REFERENCES ........................................................................................................................53 A CONSENT AGREEMENT INTRODUCTION ..........................................................56 B ALTERNATIVE ANALYSES USING LINEAR REGRESSION .............................58 C SURVEY QUESTIONS & RAW DATA ....................................................................69 viii
LIST OF TABLES
PAGE
Table 1. Technical Descriptions of Leading Applications.......................................................13 Table 2. Top Suppliers for Leading Antibody Applications....................................................15 Table 3. Supplier Differentiation Contributing to Decision Drivers in the Information
Search Stage. ................................................................................................................16 Table 4. Supplier Differentiation Contributing to Decision Drivers in the Purchasing
Stage. ............................................................................................................................20 Table 5. Supplier Differentiation Contributing to Decision Drivers in the PostPurchase Stage. ............................................................................................................21 ix
LIST OF FIGURES
PAGE
Figure 1. 2008 Antibody market estimated at $40 billion. ........................................................4 Figure 2. Buyer behavior - the decision-making process. .........................................................8 Figure 3. “Information stage” cluster analysis customer association data...............................26 Figure 4. “Information stage” cluster analysis company association. .....................................26 Figure 5. Information search stage non-negative matrix factorization cluster. .......................27 Figure 6. “Purchase stage” cluster analysis for customer association data..............................27 Figure 7. “Purchase stage” cluster analysis for supplier association data. ..............................28 Figure 8. Purchase stage non-negative matrix factorization cluster. ......................................28 Figure 9. “Post-purchase stage” cluster analysis customer association data. ..........................29 Figure 10. “Post- purchase stage” cluster analysis company association. ...............................29 Figure 11. “Post- purchase stage” stage non-negative matrix factorization cluster. ...............32 Figure 12. 9x9 Matrix for conjoint analysis.............................................................................40 Figure 13. Respondents divided between quality/price: science-oriented “smart
shoppers” (top) and “thrifty shoppers” (bottom). ........................................................45 Figure 14. Respondent segmentation by citations, price and applications. .............................47 1
CHAPTER 1
INTRODUCTION
This chapter will start out by providing the reader with a background to the elements
of the competitive environment in the market for antibodies1 for research use. It will then
move on to discuss the theoretical frameworks that have tried to explain competitive
advantage as it relates to strategy formulation and product differentiation from a broad
perspective. Of these frameworks Michael Porter’s five forces model is highlighted and
discussed further. The emphasis that is put on how strategists cope with competition leads the
reader into the problem discussion for the identified market. This section of the chapter
focuses on underlying drivers of profitability in the current market for antibodies in the
research sector. At the end of the chapter the reader will be provided with the purpose and
research questions of this thesis.
STATEMENT OF THE PROBLEM
According to Porter (1998) “competitive advantage is at the heart of a firm’s
performance in competitive markets.” As will be discussed in the next section, antibody
suppliers for the research and discovery market sector utilize multiple approaches to create a
competitive advantage through leveraging primary or supporting value chain activities.
Furthermore, the configuration of the five forces characterizes the industry structure and can
be extended to understand the challenges of smaller scale suppliers.
Bioinformatics, LLC2 (2012) market research report lists the most frequently used
brands: Abcam, Santa Cruz Biotechnology, Sigma-Aldrich, Life Technologies (Invitrogen),
1
Antibody is defined as a protein with the ability to recognize and bind to a substance, usually another
protein, with great specificity. Antibodies help the body’s immune system recognize and trigger an attack on
invading agents (Weaver, 2005).
2
Bioinformatics LLC provides market intelligence to major suppliers serving the life science, medical
device and pharmaceutical industries. Bioinformatics as a definition, is a field that involves the building and
manipulation of biological databases.
2
Cell Signaling Technology, BD Pharmingen, R&D Systems, Molecular Probes (Life
Technologies), Jackson ImmunoResearch Laboratories and Dako (Agilent).
Abcam, capturing 12.7% of the market, uses the Internet as a distinctive positioning
strategy. By essentially becoming an “Amazon for antibodies” with a large product portfolio
of 52,000+ products, and a broad customer base of over 250,000 individuals, Abcam seizes
market share through the breadth of its product portfolio and geographic reach (Piper Jaffray
Ltd., 2009). Another prominent antibody supplier, Cell Signaling Technology, has grown by
focusing on providing quality antibodies for a particular area of research, and by developing
technologies to address that same field (Bioinformatics, LLC, 2012). Thus, two strategies for
success currently exist in the industry and are exemplified by Abcam and Cell Signaling
Technology’s business models. These two models yield different benefits, Abcam seizing
market share with its large product portfolio, and Cell Signaling Technology, capturing
customer based brand loyalty. Neither supplier achieves superior strategic position through
leveraging lowest costs, only differentiating its products and services, or dominating a niche.
With hundreds of other suppliers to choose from, if customers’ purchasing behavior is
led by antibody quality and availability, how can new entrants and small scale suppliers be
equipped to leverage opportunities for growth? Technologically, it is possible for a new
entrant operating in this space to compete with large companies through the use of ecommerce, if by competition it is understood presenting offerings for sale to a global
marketplace just as a larger business would. Indeed, a global Internet presence with ecommerce capability is not difficult and, not that expensive to establish for any size of
business.
However it is not likely that competition in this context means that small and
relatively weak competitors will become a threat to an established business. Even though the
Internet does help to level the playing field, and offers opportunities to new entrants in ways
never before possible, new entrants typically will not have the marketing budget required to
capture the attention of a global audience. Therefore "having a presence" and "capturing the
attention" are different because of the proliferation of Internet approaches creating
substitution threats (Porter, 2001).
Considering the above statement by Porter this thesis intends to investigate suppliers’
and product differentiation strategies which capture the attention of customers through
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competitive advantages and positioning defining “what should our business be?”, “what is
our business?”, and “what will it be?” (Drucker, 1974).
In the market for antibodies for research use, supplier risk (limited brand loyalty),
limited intellectual property, and competition threaten customer acquisition and loyalty. And
customers’ purchasing patterns are related to price, availability of product, customer
satisfaction with antibody product features, and other differentiating factors created through
marketing. Therefore this study will investigate the primary factors which dictate choice of
supplier, and how these suppliers capture the attention of buyers.
The assessment of literature indicates that existing research on the market for
antibodies with an emphasis in competitive strategy can provide only limited insights to
practitioners. There is, for instance, still a shortage of research aimed at developing models
of brand choice as related to product differentiation that would make it easier for product and
marketing managers to understand and create necessary business strategies, and existing
theories can only provide fragmented answers. Similarly, Bioinformatics, LLC (2012) calls
for further research that focuses on the identification of buyer and buying behavior models,
and on the analysis of the success or failure of applications. Porter (2001) also stresses the
importance of understanding exactly how to capture economic benefits the Internet creates,
especially considering the rapid changes that are occurring within the area of e-commerce.
Clearly, there is a need for further study of antibody product differentiation with
respect to different competitive contexts, and the identification of factors crucial to the
success of various business models. Consequently, the problem area of the thesis can be
defined as: Assessment of Product Differentiation and Pricing Strategy in the Market for
Antibodies.
BACKGROUND
The total antibody market, consisting of therapeutic, diagnostic and research sectors,
is represented in Figure 1. This market was estimated at approximately $40 Billion in 2008.
The area of focus for this study is the research segment (antibodies for R&D), which is
competitive and fragmented with over 600 companies inhabiting the space, and 300+ of these
with an internet presence (Piper Jaffray Ltd., 2009).
4
Figure 1. 2008 Antibody market estimated at $40
billion.
Companies inhabiting the research sector range from Sigma-Aldrich, one of the
world’s largest suppliers of laboratory reagents, to individual research labs directly selling
antibodies they produce. Industry reports estimate that the total market is $2 billion
worldwide growing at 10-12% per annum according to Frost and Sullivan (Piper Jaffray Ltd.,
2009).The area in which this study is focused - sale of single antibodies as opposed to kits accounts for around 50% of this market. The antibody brands with the highest market share
are Abcam, Santa Cruz Biotechnology and Sigma-Aldrich. Piper Jaffray Ltd. (2009)
estimates that there are three companies with around 10% market share: Sigma-Aldrich,
Millipore and Life Technologies (Invitrogen).
Though there is wide variation in antibody quality between companies, these market
leaders attain competitive advantage by focus, cost leadership or differentiation strategy. Yet
competition goes beyond established industry rivals to include five forces which define an
industry’s structure and shape the nature of competitive interaction. In “The Five Forces That
Shape Strategy”, Porter (2008) defined these forces as: competitive rivalry, bargaining power
of buyers (customers), bargaining power of suppliers, threat of new entrants and substitute
products. Therefore to understand an industry competition and profitability one must analyze
the industry’s underlying structure in terms of the five forces.
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THREAT OF NEW ENTRANTS
The market for antibodies is fragmented, subsequently competition and rivalry among
antibody suppliers is high. Government policies also make entry easier indirectly by funding
research through grants and therefore increasing demand for antibodies for research use.
Overall low entry barriers mean that current market leaders must invest aggressively in
product development, marketing and advertising to differentiate their product, and to develop
cost or quality advantages to create customer loyalty. In this industry, some factors that limit
the threat of new entrants are:

Existing loyalty to major brands

Incentives for using a particular brand

High fixed costs

Demand-side benefits of scale (network effects)

Vertical integration
Power of Suppliers
Suppliers of antibodies for research use do not own the IP around their products and
most of their supply contracts are non-exclusive. There is little IP which can be generated
around the products, therefore supplier power in this industry is relatively weak. Antibody
suppliers have limited power since:

There are many suppliers (different sources) of similar products which may be
used for the same application from

There are many substitutes

Switching to another (competitive) product is not very costly
Power of Buyers
In the market for antibodies for research use, neither commercial nor non-commercial
customers have a large enough impact to affect a company’s margins and volumes. Buyers’
tastes are fragmented, with different buyers each desiring special features of an antibody and
are willing (and able) to pay a premium for it, since there is no standardized version. Thus
the demand for any particular product variety is small. Therefore buyers do not hold
substantial power since:
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
There are a large number of buyers

Customers purchase in small volumes

Switching to another (competitive) product is simple

Customers are price sensitive (elastic demand)
Availability of Substitutes
Bioinformatics, LLC (2012) states that the average antibody user switches between
seven different suppliers of antibodies annually. In 2012, on average, academic and industrial
antibody users indicated using 14 different primary antibodies in their research, and four
different secondary antibodies. These antibodies are also available to customers in a variety
of sizes to support their research studies. Therefore it is very likely that a buyer of reagents
would switch to a competitive product or service, since there are so many unique varieties
and brands. Furthermore if the cost of switching is low, then this poses a serious threat to
other suppliers. Other factors that can affect the threat of substitutes are the similarity of
substitutes i.e. different suppliers offering antibodies against the same target.
Competitive Rivalry
There are numerous equally balanced competitors in the antibodies for research
market sector. The major players mentioned in the previous section are similar in terms of
size and perceived resources, though there are many smaller sized labs supplying custommade antibodies. This vastly competitive market might also result from:

Many players of about the same size; the largest brand (Abcam) capturing only
12.7% of the market

Little differentiation between competitors’ products and services
PURPOSE OF THE STUDY
Related to industry fragmentation resulting from underlying economics that cannot be
overcome, Porter (1980) suggests strategic positioning of particularly crucial significance. He
suggests a number of possible strategic options for coping with a fragmented structure. This
thesis aims to explore these approaches by focusing on increased value added by suppliers
through specialization by product type or product segment, specialization by customer type,
and specialization by usage (research application) in the market for antibodies. By
uncovering which factors are highly correlated with repeat purchases of antibodies from a
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particular supplier, this study will update antibody suppliers and manufacturers seeking to
grow market share.
A highly fragmented market requires a customer-centered marketing approach that
goes beyond creating demand for a specific antibody, antibody family or custom service
(Bioinformatics, LLC, 2006). Suppliers hoping to differentiate their products must interact
with researchers, determine what products they need, and then provide enough information
about those products so that the researchers can identify the antibodies they want and need.
The drive to satisfy customers in narrowly defined segments in the industry has led
suppliers to offer wider arrays of antibodies. Delivering these unique antibodies with the
appropriate mix of features for these highly fragmented market segments requires estimates
of the value that customers place on each feature. Using a conjoint and multidimensional
scale-based approach, this study uncovers information about consumers’ responses to
different aspects of antibody attributes and their relationship to price.
ESSENTIAL QUESTIONS ON VALIDATION OF PORTER’S
GENERIC STRATEGY
Porter (2008) states that the five forces provide a starting point for sizing up a
company’s strengths and weaknesses. Most importantly, an understanding of industry
structure guides managers toward possibilities for strategic action. This may include
positioning the company to better cope with the current competitive forces or anticipate and
exploit shifts in the forces, while shaping the balance of forces to create a new structure more
favorable to the company. Specific to this industry, important relationships to analyze are
cost leadership, differentiation and focus strategies.
However because current management theories have evolved in the context of brickand-mortar vs. e-commerce firms (Kim, Nam & Stimpert, 2004), this thesis examines key
questions raised by the advent of e-business: Will the strategy types found among antibody ebusiness suppliers resemble Porter’s (1980) generic strategies? And can Porter’s generic
strategies or other strategy typologies be applied to e-business models, for example Abcam?
Similar to Kim et al. (2004), the researcher concludes that in this market, integrated strategies
that combine elements of cost leadership and differentiation will outperform cost leadership
or differentiation strategies.
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Considering these statements by Porter, and subsequent studies calling the exclusivity
of his generic strategies into question, we arrive at the purpose of this thesis: to explore
buyers’ purchase decision criteria when ordering antibodies, including purchasing drivers
during the information search stage, the purchasing stage, and post purchase decision stages
of the buying process outlined in Figure 2. The major objectives and research approach of
this report are as follows:
Figure 2. Buyer behavior - the decision-making process.

Assess decision drivers that are most important during the antibody purchase
decision stages, and evaluate differentiating factors that influence the decision to
purchase antibodies from particular suppliers
o Research application, price, and citations in research journals
SIGNIFICANCE TO THE FIELD
From a consumer standpoint antibodies are a vital part of biomedical research. As
such it can be difficult for life scientists to find the right antibody that suits their interests.
Many factors such as specificity, reproducibility, price, etc. have to be taken into account
when choosing an antibody for an experiment. This study will facilitate the understanding,
expansion of the field, and relevant product offerings from suppliers since more competition
can mean lower prices and antibodies directed against more and more targets. The growth of
9
the industry also facilitates research in wide-ranging fields of study, and the survey
conducted as part of this research will uncover important information about antibody users’
attitudes, perceptions and behaviors and what they value so that companies can use that
knowledge as a part of their strategizing and competitive advantage when it comes to
penetration of different customer groups.
Different companies often focus on different groups of proteins3 for antibody tags.
Taking into account the number of antibody possibilities and the work involved in producing
them, there are a number of different companies attacking different areas and approaches.
This study will also benefit those companies, since a good part of the industry is selling
primarily to repeat buyers, and there are differences when selling to repeat buyers and firsttime buyers that have important consequences for industry structure (Porter, 1980).
The findings from this study will help antibody suppliers understand what types of
attributes their customers value most. This information will help suppliers better understand
their strengths and weaknesses vis-à-vis their competitors. Using this study as a guide,
suppliers will be able determine which factors or channels to concentrate on when trying to
promote their antibodies to both current and new customers.
LIMITATIONS
Although research has reached its aims, there were some unavoidable limitations.
First, because of the time limit, this research was conducted on a small size sample who were
authors of research publications. Therefore to generalize the results for larger groups, the
study could have involved more participants of different levels. Second, the population of the
experimental group is small; most survey participants are located in the United States,
Europe and Asia although participation was open to researchers worldwide.
ETHICAL CONSIDERATIONS
The following guidelines were put into place for the research period: the dignity and
wellbeing of respondents was protected at all times; and the research data remained
confidential; collective findings are anonymous and not linked to individuals.
3
Proteins are polypeptides with unique amino acid sequence. Sometimes the term protein denotes a
functional collection of more than one polypeptide (Weaver, 2005).
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OUTLINE OF THE STUDY
The remaining parts of this thesis build on this introductory chapter. Chapter 2, the
literature review, provides an overview of previous literature relevant to the problem area of
this study. Chapter 3 contains a problem discussion based on the previous literature review
and the results from the antibody questionnaire with focus on conjoint methods. This
discussion leads to the development of a research problem, the formulations of research
questions, and ends up with a conceptual framework. Chapter 4 outlines the chosen research
methodology for this study; Chapter 5 presents the empirical data and related analysis as well
as the results of the study and theoretical and managerial contributions. Finally, the thesis
ends with suggestions for further research.
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CHAPTER 2
REVIEW OF LITERATURE
The market for antibodies within the research sector was discussed in the previous
chapter. It also was pointed out that the approximated $2 billion research market is growing
10-12% per annum (Biocompare, 2009), and that the adoption of product differentiation, cost
leadership, or focus strategy has implications for different market actors (i.e., buyers,
suppliers/manufacturers). As indicated in the previous section, the industry is competitive
and fragmented, which leads to an increased need for the development of competitive
advantages based on formulated competencies. The lack of research within the field is the
motivation for this study. Based on this notion the problem area for this thesis is: “product
differentiation and pricing strategy in the market for antibodies.”
A review of the existing literature on the market for antibodies reveals that related
critical issues have not been researched extensively. With this in mind, this study has had to
rely on available technical reports, especially those concerning both commercial and noncommercial uses of antibodies.4
This chapter presents and evaluates an overview of previous research related to the
problem area. Based on this overview the researcher formulates the research problem and
relevant research questions for this study in Chapter 3. The literature overview will also be
used for the development of the frame of reference.
SPECIALIZATION BY PRODUCT TYPE: DIFFERENTIATION
BY LEADING APPLICATIONS
Product differentiation is described by Porter (1976) as resulting from a combination
of physical characteristics of the product and selling efforts by the manufacturer. Therefore
differentiation is a result of consumer choice, and choice depends on the attributes of
products and the investment consumers make in obtaining information about them.
4
Commercial antibody users are those from the pharmaceutical and biotechnology industries, while noncommercial users are those consumers in governmental and academic institutions (Biocompare, 2009).
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Antibodies have multiple attributes that define value to the user: antibody form, antibody
optimization for stated applications, available validation data from supplier, conjugation
options, lot-to-lot consistency, multi-functionality, pricing, sizing options, species crossreactivity, and specificity and sensitivity. These are attributes on which the user seeks
information from various sources and subsequently bases his purchase decisions.
One type of exploration in The 2012 Market for Antibodies technical report was an
investigation the effects different selection criteria have on users choosing antibodies for
leading applications i.e. market insights into common antibody applications (Bioinformatics,
LLC, 2012). The purpose of the investigation was to isolate the important criteria antibody
users consider when selecting antibodies for any leading application, and then assess which
related antibody features offer a strategic advantage or potential vulnerability from a supplier
standpoint. Table 1 presents selected definitions of leading antibody applications that will be
mentioned in the following section(s). Tortora, Funke, & Case (2004), Lipman, Jackson,
Trudel, & Weis-Garcia, (2005), Cooper, Robert, & Hausman (2007) and Chemicon
International. Inc., (1998) define and explain technical definitions that will be further used
throughout this study.
SETTING/SAMPLING: BIOINFORMATICS
The 2012 Market for Antibodies, report was based on responses to a 35 question
online survey conducted by Bioinformatics, LLC based in Arlington, Virginia
(Bioinformatics, LLC 2012). The questionnaire consisted of 23 closed or partially closeended questions and 12 open-ended questions designed to encourage participation and to
meet the objectives of the study. The questionnaire was distributed to registered members of
the Science Advisory Board and additional qualified life scientists provided by
Bioinformatics, LLC. A total of 1,096 scientists from around the world participated in the
survey between February 22nd through March 1st, 2012.
BIOCOMPARE
The Antibody Report Market Overview and Industry Survey – Premium consisted of
39 antibody-related questions and five demographic questions (Biocompare, 2009). The
survey was administered online between February 3rd and February 27th, 2009, and the data is
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Table 1. Technical Descriptions of Leading Applications
Author
Description of Application
Tortora, Funke, & Case, (2004)
Western blotting, serological* test, is used to identify bacterial antigens in a patient’s
serum. Proteins (including bacterial proteins) in the patient’s serum are separated by a
process called electrophoresis, and then are transferred to a filter by blotting.
Lipman, Jackson, Trudel, & Weis-
The spatial expression of an antigen relative to an individual cell, or in the context of
Garcia, (2005)
whole tissue, can be analyzed with antibodies using immunofluorescence and
immunohistochemistry. Both applications involve preparing samples (cells or tissue
sections) in a manner that retains their three dimensional structure, immobilizing them
on glass slides, probing them with antibodies and visualizing the antigen antibody
microscopically.
Cooper, Robert, & Hausman,
Immunofluorescence: antibodies can be used to visualize proteins in intact cells. For
(2007)
example cells can be stained with antibodies labeled with fluorescent dyes, and the
subcellular localization of the antigenic proteins visualized by fluorescence
microscopy.
Chemicon International Inc.,
Detection of antigens in tissues is known as immunohistochemistry, while detection
(1998)
in cultured cells is generally termed immunocytochemistry. For both, there is a wide
range of specimen source, antigen availability, antigen-antibody affinity, antibody
type, and detection enhancement methods.
Tortora, Funke, & Case, (2004)
The term flow cytometry can be used to identify bacteria in a sample without
culturing the bacteria. This method detects the presence of bacteria by detecting the
difference in electrical conductivity between cells and the surrounding medium.
Tortora, Funke, & Case, (2004)
An analytic biochemistry test/research application) called enzyme-linked
immunosorbent assay (ELISA) is widely used because it is fast and can be read by a
computer scanner. In a direct ELISA, known antibodies are placed in the wells of a
microplate, and an unknown bacterium is added to each well (Tortora, Funke, & Case,
2004). A reaction between the known antibodies and the bacteria provides
identification of the bacteria.
Note.*The science dealing with the immunological properties and actions of serum.
14
tabulated and presented in the report. The total sample was 787 comprising of respondents
working in academic and governmental institutions as well as biotechnology and
pharmaceutical companies. Seventy-six percent of respondents were from the U.S., 13
percent from Europe, and six percent from Asia.
PIPER JAFFRAY
Piper Jaffray’s proprietary survey conducted on behalf of Abcam, consisted of 25
scientists from university, government, company and research institute laboratories. Thirtysix percent of respondents were from the U.S., 28 percent from the UK, and 36 from the rest
of the world. Surveys were sent to scientists who had recently published papers using
antibodies from antibody suppliers.
MOST RECENT MARKET RESEARCH RESULTS
The issues that Bioinformatics explored were the prevalence of common antibody
applications, “best in class” antibody suppliers by application, selection criteria for
antibodies by application, and problems with antibodies by application. These criteria were
defined by Bioinformatics LLC, and applied to the respondents of their questionnaire.
In Table 2, a list of best-in-class suppliers by antibody application are presented. Cell
Signaling Technology is the top supplier for two of the leading applications, as are Life
Technologies (Invitrogen) and Abcam. Cell Signaling Technology is considered best-in-class
for their Western blotting antibodies, the most popular application. While Santa Cruz
Biotechnology - second in terms of overall market share - (Bioinformatics, LLC, 2012), is
not ranked as the top supplier in any of the leading antibody applications. In this section of
aggregate, the majority of the survey respondents in the report choose antibody suppliers
based upon reputation and extensiveness of product offerings (Bioinformatics, LLC, 2006).
However, the actual selection process on a supplier-specific basis is much more nuanced.
SUPPLIER DIFFERENTIATORS IN THE INFORMATION
SEARCH STAGE
The process of gathering information about antibodies is complicated, and the
suppliers’ contribution to product differentiation equals the influence they exert on the
Molecular Probes
Jackson Immuno
Research
Laboratories
Cell Signaling
Technology
Abcam
Santa Cruz
Biotechnology
Jackson Immuno
Research
Laboratories
Pierce
3rd
4th
5th
6th
9th
8th
7th
Abcam
Abcam
Sigma-Aldrich
R&D Systems
BD Pharmingen
Molecular Probes
Invitrogen
Santa Cruz
Biotechnology
R&D Systems
Cell Signaling
Technology
Sigma-Aldrich
Dako
BD Pharmingen
Santa Cruz
Biotechnology
Sigma-Aldrich
Invitrogen
Abcam
Cell Signaling
Technology
Invitrogen
Cell Signaling
Technology
Immunocytochemistry
Abcam
Immunohistochemistry
Beckman Coulter
BD Horizon
eBioscience
BD Pharmingen
2nd
Invitrogen
Cell Signaling
Technology
Flow Cytometry
1st
Immunofluorescence
Western Blots
Rank
Table 2. Top Suppliers for Leading Antibody Applications
Upstate
Other
BD Pharmingen
Jackson Immuno
Research
Laboratories
Invitrogen
Abcam
R&D Systems
ELISA, Sandwich
format
Chemicon
Sigma-Aldrich
Santa Cruz
Biotechnology
Sigma-Aldrich
Jackson Immuno
Research
Laboratories
BD Pharmingen
BD Pharmingen
Invitrogen
R&D Systems
Cell Signaling
Technology
Abcam
Other techniques
Santa Cruz
Biotechnology
Cell Signaling
Technology
Abcam
R&D Systems
Invitrogen
ELISA, peptide
15
16
purchase decision of the antibody buyer. Table 3 presents how supplier influencers are
applied in various interrelating ways.
The supplier controls and embodies some of the attributes the antibody user may
desire. One example of this is the supplier’s reputation and image reflecting on the quality
and the image of the product (Porter, 1976). Another example is the way in which the
supplier can influence the sale of antibodies is through the provision of information. The
technical datasheet displayed on the supplier website can have a major influence on the
purchase of the antibody. Therefore information on a supplier’s website is an important
decision driver in the information search stage prior to purchase.
Other elements in the information search stage of the buyer process can be seen in
Table 2. These are set of considerations on which the buyer places a utility value, and on
which the buyer would base fully informed choice during the information search stage. These
considerations vary among suppliers and Porter (1976) explains that the buyer will not
consider all product attributes to be equally important, rather they will contribute differently
to his utility. Each buyer can be viewed as having a priority ranking of informational
elements based on their contribution to his utility. And since the elements the buyer values
vary across suppliers, the value of the individual information sources will vary. The results
represented in Table 3 are the percent of respondents who ranked the displayed criteria as
“very high” on an 11-point scale.
Table 2 indicates that previous experience with a brand is a major driver in the
information search stage of the purchase decision process of antibodies for the suppliers:
Molecular Probes (Life Technologies), Jackson ImmunoResearch Laboratories, Dako
(Agilent), eBioscience and Chemicon (Millipore). Personal recommendations from
colleagues are another major consideration factor for customers when they are deciding from
which supplier to purchase. Personal recommendations are more important than previous
experience with a brand, especially for Abcam, Santa Cruz Biotechnology, Sigma-Aldrich,
Life Technologies (Invitrogen), and Cell Signaling Technology. The third column indicates
that citations in journals or academic publications, along with colleagues’ recommendations
are the most important consideration criteria for R&D Systems. For this specific supplier,
these criteria outweigh the importance customers place on previous experience with a brand.
77%
68%
84%
72%
67%
71%
60%
72%
77%
81%
72%
74%
86%
89%
77%
88%
Abcam
Santa Cruz
Biotechnology
Sigma
Aldrich
Invitrogen
Cell Signaling
Technology
BD
Pharmingen
R&D Systems
Molecular
Probes
Jackson
Immuno
Research
Laboratories
Dako
eBioscience
52%
70%
66%
73%
75%
71%
65%
68%
78%
42%
58%
63%
73%
Citations/references
in journal articles
and other
publications
64%
80%
85%
71%
Colleague/Coworker's
recommendations
Previous
experience
with the brand
64%
43%
61%
52%
53%
52%
59%
46%
40%
40%
56%
Information
on a supplier's
website
46%
34%
34%
36%
37%
37%
52%
27%
25%
17%
31%
Product
Promotions
29%
27%
36%
30%
32%
22%
25%
35%
28%
17%
23%
Information
within supplier's
print catalog
Table 3. Supplier Differentiation Contributing to Decision Drivers in the Information Search Stage
17%
16%
11%
25%
20%
24%
23%
25%
15%
8%
20%
Discussions
with sales
reps
15%
11%
13%
16%
16%
28%
17%
22%
15%
8%
21%
15%
5%
13%
14%
16%
13%
13%
13%
13%
6%
18%
Email(s) from
suppliers
(table continues)
Presentations
by Supplier
reps at trade
show/conference
17
58%
80%
69%
60%
75%
66%
62%
80%
Roche
Applied
Science
Zymed
BioSource
Other
Suppliers
48%
57%
62%
53%
54%
49%
50%
46%
54%
50%
45%
49%
36%
29%
45%
37%
29%
30%
44%
35%
35%
26%
36%
32%
27%
25%
14%
20%
46%
33%
16%
23%
Source: Bioinformatics, LLC. (2012). The 2012 market for antibodies (Report No. 12-001). Arlington, VA: Bioinformatics LLC.
72%
60%
73%
63%
AbD Serotec
65%
52%
Pierce
62%
61%
GE
Healthcare
67%
70%
76%
62%
Calbiochem
61%
64%
76%
71%
Upstate
56%
76%
74%
81%
Chemicon
Table 3. (continued)
20%
22%
19%
18%
10%
18%
22%
23%
14%
11%
24%
20%
8%
25%
12%
16%
26%
19%
8%
14%
12%
20%
15%
10%
4%
10%
16%
21%
2%
7%
18
19
In Chapter 3, frequency data of the percentages in Table 2 will be analyzed further to
draw general observations of the large proportion of these suppliers whose customer
preferences can be roughly divided into strategic clusters based on their marketing strategies.
The suppliers have customers who prefer certain differentiators (differentiated marketing
strategy), which is a direct reflection of the suppliers’ strategic group. In spite of that fact, the
strategic groups identified are not equivalent to market segments or segmentation strategies
but are defined on the basis of a broader conception of strategic posture (Porter, 1980).
The strategic groups in the antibody market may be present for a wide variety of
reasons, such as companies’ differing initial strengths and weaknesses, or differing times of
entry into the business. However, once groups have been formed, the companies in the same
strategic group generally resemble one another closely in many ways besides their broad
strategies (Porter, 1980). They tend to have similar market shares and also to be affected by
and respond similarly to external events or competitive moves in the industry because of their
similar strategies (Porter, 1980). This latter characteristic is important in using a strategic
group map as an analytical tool. Such a map of strategic groups will be further discussed in
Chapter 3.
Noticeably, a single degree of product differentiation is not appropriate for
characterizing antibody suppliers, therefore hierarchical clustering will be performed to
identify structural characteristics ascribed to what customers prefer from certain groups in the
information search stage of the purchase decision process.
SUPPLIER DIFFERENTIATORS IN THE PURCHASE STAGE
During the purchase stage of the decision making process, antibody users exemplify
different behaviors due to the different levels of data they have gathered during the
information search stage and necessity of the antibody to their workflow.
At this stage, the importance of a supplier’s selling efforts and his control of product
attributes depends on the customer’s process of choice (Porter, 1976). The customer is
willing to expend varying amounts of (costly) effort in buying different antibodies and
considers varying sets of attributes during the purchase decision stage. The supplier will be
more or less influential in the purchase decision, depending on the importance of product
attributes controlled by the supplier, the perceived benefit of the range of antibody
20
information disseminated by the supplier relative to the availability and cost of other sources
of information. The following table based on respondent data gathered by Bioinformatics,
LLC (2012) shows that during the purchasing stage of the decision process, in-stock
availability and delivery time are the most important drivers for top industry performers
when deciding who to purchase from. Also, maintaining sufficient inventory of frequently
ordered antibodies may be the easiest way to prevent customer switching. Table 4 shows the
importance of decision drivers in the purchasing stage.
Table 4. Supplier Differentiation Contributing to Decision Drivers in the Purchasing Stage
In-Stock
Availability
Delivery Time
Ease of
Ordering
Shipping
Charges
Product
Promotions
56%
Range of
Products
Offered
54%
Abcam
61%
43%
44%
33%
Santa Cruz
Biotechnology
65%
55%
45%
45%
56%
35%
Sigma Aldrich
59%
54%
39%
49%
42%
29%
Invitrogen
70%
63%
54%
52%
38%
38%
Cell Signaling
Technology
74%
64%
68%
57%
51%
51%
BD Pharmingen
69%
67%
74%
57%
42%
48%
R&D Systems
78%
76%
59%
61%
61%
51%
Molecular Probes
82%
79%
61%
59%
50%
45%
Jackson
ImmunoResearch
Laboratories
75%
61%
70%
48%
41%
39%
Dako
63%
64%
51%
53%
47%
35%
eBioscience
70%
69%
71%
63%
56%
42%
Chemicon
65%
58%
47%
51%
42%
35%
Upstate
64%
59%
39%
43%
53%
39%
Calbiochem
63%
62%
44%
46%
44%
31%
GE Healthcare
65%
59%
47%
61%
63%
50%
Pierce
69%
65%
54%
57%
47%
37%
AbD Serotec
63%
48%
44%
44%
38%
28%
Roche Applied
Science
73%
65%
53%
59%
59%
47%
Zymed
66%
57%
56%
44%
51%
30%
BioSource
73%
70%
46%
56%
42%
32%
Other Suppliers
69%
73%
48%
69%
44%
44%
Source: Bioinformatics, LLC. (2012). The 2012 market for antibodies (Report No. 12-001). Arlington, VA:
Bioinformatics LLC.
21
This market report suggests availability from a supplier and brand reputation are the
top factors that influence respondents’ choice of individual suppliers (Bioinformatics LLC,
2012). In Chapter 3, a hierarchical cluster analysis along with a representative non-negative
factorization matrix will be analyzed to characterize an alternative approach for exploring
supplier similarities. This method will capture overall consumer consideration behaviors that
cluster suppliers based on global similarities in their expression data. Table 5 displays the
most important consideration factors during the post purchase evaluation of the consumer
decision making process.
Table 5. Supplier Differentiation Contributing to Decision Drivers in the Post-Purchase
Stage
Consistent
Product
Performance
92%
Supporting
Documents
Technical
Support
Guarantee/Wa
rranty
Customer
Service
53%
59%
47%
47%
Santa Cruz
Biotechnology
87%
57%
57%
59%
39%
Sigma Aldrich
85%
60%
59%
53%
48%
Invitrogen
93%
65%
67%
55%
42%
Cell Signaling
Technology
98%
74%
75%
74%
64%
BD Pharmingen
85%
57%
56%
54%
67%
R&D Systems
90%
75%
76%
69%
63%
Molecular Probes
100%
68%
75%
66%
68%
Jackson
ImmunoResearch
Laboratories
93%
68%
58%
52%
45%
Dako
91%
65%
65%
56%
44%
eBioscience
97%
68%
69%
53%
62%
Chemicon
95%
63%
56%
58%
46%
Upstate
94%
67%
65%
69%
45%
Calbiochem
83%
61%
62%
50%
48%
GE Healthcare
88%
70%
68%
60%
48%
Pierce
96%
51%
67%
57%
55%
AbD Serotec
90%
47%
49%
41%
37%
Roche Applied Science
88%
63%
55%
55%
54%
Zymed
92%
69%
72%
60%
57%
BioSource
92%
59%
62%
56%
52%
88%
50%
62%
54%
54%
Abcam
Other Suppliers
Source: Bioinformatics, LLC. (2012). The 2012 market for antibodies (Report No. 12-001). Arlington, VA:
Bioinformatics LLC.
22
The percentages of the post-purchasing stage respondent ratings reveal that customers
unanimously rate consistent product performance above all other criteria for any given
supplier in their overall evaluation of the brand.
CONCLUSIONS/IMPLICATIONS
Several conclusions can be made about antibody users’ responses to the information
search, purchase and post-purchase stage Bioinformatics survey questions. First through
repeat purchasing, the respondents of the Bioinformatics 2012 survey have accumulated
knowledge about antibodies purchased from certain suppliers and competing brands. Among
all consideration sets for the three stages, the effect of pricing is noticeably missing. The
results from Bioinformatics, LLC (2012) report indicated that previous experience with the
brand and colleague/coworker’s recommendations are the top two informational criteria upon
which customers consider a purchase. Then customers consider in-stock availability, delivery
time and range of products.
However, a linkage Piper Jaffray Ltd., (2009) speculates about between citations and
price is not explored in the Bioinformatics investigation. Piper Jaffray’s 2009 survey
revealed that academic publications were cited as the primary method of identifying new
antibody suppliers, and that Abcam’s product portfolio growth (6.5% per annum/21%
CAGR, 2002-08, PJ est.) is driven by both rising prices and increases in use of antibodies as
product data builds and the number of academic publications increases.
The Abcam report deduces growth in research antibody sales is driven by an
increasing number of applications for antibodies in research, in particular in the cell biology
and cancer fields. Similarly, Bioinformatics, LLC (2012) demonstrates specifically that
leading antibody application are important purchase decision making criteria on many levels
such as antibody sensitivity and specificity, antibody optimization and lot-to-lot consistency.
Bioinformatics’ conclusions are consistent with the most important features which contribute
to respondents’ overall satisfaction. However, Bioinformatics identified pricing as a potential
antibody feature vulnerability, but the reason why is not explored by either Piper Jaffray or
Bioinformatics.
While one report discusses price as an element of consideration under leading
application classification, and the other makes an overall supposition that price is ranked as
23
the fourth consideration of surveyed customers, behind antibody quality and availability
(Piper Jaffray Ltd., 2009), neither analyze the effect price has on product differentiation.
There are a number of disputes as to which user criteria for decision making rank
highest in the customer decision making process across all stages - applications, citations in
research journals etc. - none discuss attribute tradeoffs in consumer preferences.
LIMITATIONS/WEAKNESSES
There are several limitations in these technical reports. Among the limitations was the
lack of assessment customers’ valuation of different product attributes in relation to other
attributes. Secondly, the reports do not present uniform data that is normalized, meaning
values measured on different scales are not aligned on a common scale. All matrices
presented earlier provide different parameters, and different scales cause difficulty in analysis
of data sets. Therefore there is a need to collect the data and then normalize it, since it is very
difficult to compile and analyze data not based on a common scale. Consequently, research
cannot be unbiased without normalizing compiled data. This study cannot reach conclusions
using fragmented data.
These weaknesses point for the need to normalize presented product attribute
matrices through proper unbiased statistics. Through comparison of methods like hierarchical
clustering and the non-negative matrix factorization (NMF) robust algorithm, patterns of
interest in product differentiation become more apparent. Ultimately there is the need for the
application of the conjoint analysis framework which identifies utilities (part-worth) values
used by respondents making trade-offs and choosing among objects having many attributes
and/or characteristics (Smith & Albaum, 2005).
24
CHAPTER 3
METHODS
The literature review has attempted to identify, present, and summarize recent
existing research on the topic in order to clarify what is unknown about differentiation
strategies in this industry. Yet a fundamental element to the discussion is the role a supplier’s
pricing strategy plays in the customer decision making process.
In formulating dynamic competitive strategy it is presumed that firms respond to
competitors' moves by using a combination of marketing actions, one of which is price
adjustment. In the market for research antibodies, it does not appear price is the optimal
marketing instrument with which to react to a competitor's action. Instead, the tables
presented in the previous chapter show that a combined strategy consisting of the
introduction of new quality, validated products with citations in research journals, and
leading research applications, is generally associated with differentiation strategy. Still the
role of pricing in customer trade-offs for these attributes remains vague and permits a range
of interpretations.
How do various suppliers respond to competitive action in terms of awareness
advertising, image advertising, distribution, salesforce, promotions, repositioning of existing
products and introduction of new products? This chapter explains the identification of
strategic competitive clusters, with regard to consumer taste preferences, contributing to
industry structure. This identification is an important step toward understanding product
differentiation, and can facilitate the development of industry mapping. This part of the study
presents a broad industry analysis that illustrates strategic dimensions, except for antibody
product differentiation which will be discussed in the following chapter.
A strategic group is a group of firms in an industry following the same or a similar
strategy along strategic dimensions (Porter, 1980). In Tables 3, 4, and 5 , complex strategic
groups are identified by multiple customer preferences and considerations which determine
behavioral purchase factors. Since antibody suppliers have heterogeneous differentiation,
then the detection of product differentiation is difficult as one set of product features and
25
attributes can be important for one supplier, but not another. Therefore, the classification of
the strategic groups of complex features customers consider in the purchase decision process
is difficult.
The nonnegative matrix factorization (NMF) approach assumes that complex
differentiators can be described by sets of simple and measurable characteristics, with each
group representing sets of supplier differentiators that behave in a strongly correlated
fashion. NMF has been used in this context to find relevant common sub-sets of supplier
differentiators that correspond to latent concepts in the reviewed literature. Due to the nondeterministic nature of NMF, results might differ from one run to the other (Pascual-Montano
et al., 2006). To minimize this effect and in order to select the best factorization results, it is
crucial to repeat the process using different random initialization for matrices W and H. The
non-negative matrix decomposition can be described as: V ≈ WH where V ∈ Rm×n is a
positive data matrix with m variables and n objects, W ∈ Rm×k are the reduced k basis vectors
or factors, and H ∈ Rk×n contains the coefficients of the linear combinations of the basis
vectors needed to reconstruct the original data (Pascual-Montano et al., 2006).
HIERARCHICAL CLUSTER (HC) ANALYSIS
To investigate the degree of dissimilarity (relative distance) in consideration factors
in the purchase decision process for antibodies, the percentage frequency counts in Figures 3,
4, 5, 6, 7, 8, 9 and 10 were converted to normalized vectors/matrices with respect to the total
count for all responses, and hierarchical clustering (HC) was used to explore and visualize
the relationships among the vectors. The researcher performed HC using the Statistica data
mining and predictive analytics software, which assigns statistical significances to clusters.
Statistica 7.0 provides various options for distance metrics between vectors (StatSoft Inc.,
2004). In this experiment the researcher explored the two most-used options: Euclidean or
Chebychev distance.
For identification of similarities among companies (suppliers) and their profiles as
defined by customer preferences (vectors), i.e. eight parametric stimuli for the information
search stage: previous experience with the brand, colleague/coworker's recommendations,
citations/references in journal articles and other publications, information on a supplier's
26
Figure 3. “Information stage” cluster analysis customer association data.
Figure 4. “Information stage” cluster analysis company association.
27
Figure 5. Information search stage non-negative matrix factorization cluster.
Figure 6. “Purchase stage” cluster analysis for customer association
data.
28
Figure 7. “Purchase stage” cluster analysis for supplier association
data.
Purchase stage analysis of matrix H x W: 6 var (H) x 21 suppliers, or COMs (W)
Result: 3 major groups (BD/CST - SCB/R&D – Sigma:Invitrogen)
BD
BD
JIL
JIL
Abcam
Abcam
CST
CST
eBio
eBio
Zymed
Zymed
SCB
SCB
R&D
R&D
GE
GE
Roche
Roche
Upstate
Upstate
Sigma
Sigma
Calbio
Calbio
BioSource
BioSource
Other
Other
MolProb
MolProb
Chemicon
Chemicon
Pierce
Pierce
Dako
Dako
AbD_Sero
AbD_Sero
Invitrogen
Invitrogen
Cluster#3 shift up
NMF param: f3, rho/max=0.9394/0.9259, 100/200 random runs
Figure 8. Purchase stage non-negative matrix factorization cluster.
29
Figure 9. “Post-purchase stage” cluster analysis customer association
data.
Figure 10. “Post- purchase stage” cluster analysis company
association.
30
website, product promotions, information within supplier's print catalog, discussions with
sales reps, presentations by supplier reps at trade shows/conferences and email(s) from
suppliers, were used in a multivariable exploratory technique provided by Statistica 7.0,
specifically employing complete linkage and Chebyshev distance metric (Cantrell, 2008).
Chebyshev distance has been chosen due to its capability to penalize vector values with large
distances among only a few of them (even between just two), a characteristic the researcher
wanted to exploit in these experiments (LaTorre, Peña, Gonzalez, Cubo, & Famili, 2007).
Secondly, Chebyshev distance is a suitable metric for these types of distributions - those
whose average magnitude is independent of the dimensionality of the matrix (Chariatis,
2007).
HC was implemented through updating a stored matrix of distances between clusters
as each pair of clusters is merged. The distance between clusters can be assessed in various
ways, and Statistica includes several options. The two options investigated in this study were
complete linkage, which defines the distance between clusters to be the maximum distance
between component vectors, and average linkage, which defines the distance between
clusters to be the distance between their centroids (Anderberg, 1973). The steps in an HC
solution that show the clusters being combined and the values of the distance coefficients at
each step are shown by dendrograms. The lengths of the branches in the dendrograms
represent distances, and the significance of each cluster is shown on the branches. The
package also provides a facility to draw a rectangle around clusters significant at a given
threshold, usually 0.95.
NONNEGATIVE MATRIX FACTORIZATION (NMF)
Lee and Seung (1999) proposed non-negative matrix factorization NMF, a matrix
factorization method, A ≈ W x H, where the elements of A, W, and H are all non-negative.
NMF imposes non-negative constraints to detect variable behaviors, in contrast with the
approaches used by other linear representation clustering methods.
NMF is an algorithm based on decomposition by parts that can reduce the dimension
of expression data from thousands of consumer considerations to a handful of consideration
sets (Wang, Hsiao, Hsieh, & Lin, 2012). When applying NMF to a matrix A, the matrix A
31
can be factored into two matrices W and H with Am x n ≈ Wm x k x Hk x n, where the columns k
of matrix W are called companies.
After HC, the researcher chose to use NMF to analyze and visualize the relationships
among the vectors. Beginning with the collection of n w-dimensional frequency-vectors
representing the n ontology members in the input list query. The w dimensions represent the
w selected n-purchase decision considerations. These vectors are arranged as columns of a
matrix M of dimensions w x n. The researcher used NMF to factor the M matrix into two
non-negative lower rank (f) matrices:
M = WH
where f is the number of factors or ontological features, W is a w × f projection matrix and H
is the coefficient matrix of dimension f × n. The column vectors of the W projection matrix
are called ontological features, due to the fact that they are collections of related n-purchase
decision considerations. The columns of H project the original n-purchase decision
considerations vectors in this new low rank space spanned by the W matrix. To perform the
NMF calculations the researcher used the bioNMF5 web-server application developed by
Pascual-Montano et al. (2006). The researcher used the cophenetic correlation coefficient (ρ,
Rho) as well as a clustering heat-map to assess the stability of the factorizations at different
ranks (f) (Devarajan, 2008).
The cophenetic correlation coefficient is used as a measure of the robustness of the
method in producing stable clusters for a given number of factors (f). Usually the value of f
is selected at the point where the magnitude of the cophenetic correlation coefficient shows a
significant peak (ρmax). In general, higher values of f will reveal more localized and specific
semantic features in the domain. In this case the researcher looked for the value f at which
the cophenetic correlation coefficient is closest to one (ρmax) because it represents the
degree of fit of classification to the set of data.
The researcher used a modified version of bioNMF to cluster MMPs based on RP
patterns. The resulting matrices were exported from bioNMF and visualized using Windows
XP and assembled in MS PowerPoint/Word and Adobe Illustrator CS2. Figures 5, 8, and 11
5
bioNMF: a web-based tool for nonnegative matrix factorization in biology
32
Figure 11. “Post- purchase stage” stage non-negative matrix factorization cluster.
are visual representations of the bioNMF output, representing the information stage purchase
factors/frequencies in Table 3 in Chapter 2.
DATA ANALYSIS
For purposes of determining how supplier differentiators are positioned in terms of
competitive offerings and consumer views about the grouping of suppliers, preclassificatory
clustering procedures were utilized. The object was to formulate rather than test
categorizations of data (Green & Tull, 1978). Because secondary data was used, it was
assumed that the data are partially heterogeneous, that is, that clusters already exist. In
Statistica 7.0 the complete linkage (or maximum method, furthest neighbor) option was used
to cluster the two closets points between members of a cluster, then the criterion for joining
points to clusters, or clusters to clusters involved maximum distance (Green & Tull, 1978).
In the information search stage, previous experience with the brand,
colleague/coworker's recommendations, citations/references in journal articles, other
33
publications and information on a supplier's website consisted of the first cluster
(informational marketing activities). Then, product promotions, information within suppliers’
print catalogs, discussions with sales reps, presentations by supplier reps at trade
show/conferences, and emails from suppliers consisted of a second cluster (sales-related
promotional activities). And ultimately the two clusters were joined to create the leading
cluster fusing marketing and sales activities.
For the purchase stage, in-stock availability, delivery time, range of products offered
and ease of ordering were initially clustered. This was followed by the larger cluster
engulfing all these considerations along with shipping charges and product promotions. In
the post-purchase stage, consistent product performance swamped all other considerations
creating one large cluster. These initial consideration clusters led to the transposition of the
matrices for analysis of how companies are strategically grouped together. However because
of the formidable problems associated with statistical inference in cluster analysis, NMF was
used as an ad hoc procedure to provide a rough check on the clustering results.
Visual similarities between the HC and NMF company analyses led to identification
of strategic dimensions on which the suppliers for antibodies defer:

Specialization: The degree to which a supplier focuses its efforts in terms of the
width of its line, the target customer segments, and the geographic market served.

Brand identification: The degree to which a supplier seeks brand identification
rather than competition based mainly on price or other variables. Brand
identification can be achieved via advertising, sales force, or a variety of other
means.

Technological leadership: The degree to which the supplier seeks technological
leadership versus following imitation. It is important to note that the supplier
could be a technological leader but deliberately not produce the highest quality
product in the market; quality and technological leadership do not necessarily go
together (Porter, 1980).
The strategic groups in the industry displayed in the NMF matrices are designed to be
a frame a reference between looking at the industry as a whole and considering each
company separately. Ultimately every company is unique, and thus classifying the suppliers
into strategic groups rises questions of judgment about what degree of strategic difference is
important.
34
RESULTS
Since a number of results have been reported from the HC analysis, it seems useful to
recapitulate the findings according to managerial questions that preceded the development of
this study.
USING THE STRATEGIC GROUP MAP AS AN ANALYTICAL
TOOL
Charting directions of strategic movement is an important use of the strategic group
map. The NMF approach allows for an identification of directions in which companies are
moving and shifting from an industry-wide point of view and helps answer “what should our
business be?”, “what is our business?”, and “what will it be?” thus aiding management to
anticipate the future, and relates the companies to their environment.
The strategic group map also helps to identify mobility barriers defined by Porter
(1980) as barriers to shifting strategic position from one strategic group to another i.e. for the
large aggregators protecting the technology, brand image and established network of
servicing, and for the market nichers, the experience, economies of scale, and relationships
with customers. Therefore a close analysis of this map can be very illuminating in predicting
threats to the various groups and probable shifts in position among companies (Porter, 1980).
The findings of the exploratory indicated that during the information search stage of
the purchase decision process for antibodies customers included in their ideal set of
marketing benefits (supplier differentiators) which are grouped into three major clusters
based on their relationship to suppliers. The first cluster consists of informational marketing
activities, and the second cluster of sales and promotional activities, which finally converge
into one cluster encompassing sales and marketing. The NMF clustering of companies yields
three groups: a specialized and value driven sub-cluster consisting of Santa Cruz
Biotechnology, Dako (Agilent), Chemicon (Millipore), Upstate and AbD Serotec. Marginal
groups consist of eBioscience and Pierce, and the unique e-commerce differentiator, Abcam.
The narrow/middle sub-cluster consists of Sigma-Adrich, Roche, JacksonImmuno Research,
Zymed (Life Technologies) and Molecular Probes (Life Technologies). And finally, in the
last sub-cluster are broadly-differentiated suppliers such as BD, Life Technologies
(Invitrogen), R&D Systems and Calbiochem.
35
In terms of marketing activities directed toward antibody users, these three major
strategic groups are carved out by the NMF method. Using this dimension, customers clearly
define certain clusters’ marketing (brand) activities as having a pronounced effect on their
ultimate behavior toward purchase, and these purchase behaviors subsequently reinforce the
very identity of differentiation strategy among suppliers.
36
CHAPTER 4
RESULTS
This thesis set out to investigate the current product differentiation and pricing
strategies in the market for antibodies with application of conjoint analysis. Considering that
no similar study has been conducted before (to the author’s knowledge), the contribution is
believed of value to the strategy field. In order to fulfill the purpose, a thorough attempt has
been made to identify, gather and analyze the existing relevant literature both economic
theory and industry market research reports. As a complement to the literature review, the
non-negative matrix factorization technique (NMF) was used to provide new insights and
relevant information about the complex hidden relationships in the experimental data sets
provided by Bioinformatics, LLC (2012).
In consumer choice modeling phase of this study, antibody users were surveyed in
order to obtain their input on the topic and to gain some insight into the relationship between
multiple antibody attributes defined by recent literature as having a significant effect on
customer’s psychological judgments in regard to purchasing antibodies. Comparing
respondent’s answers with findings from the literature indicates that literature (industry
reports), and methods applied for the analysis of multidimensional datasets are seemingly
consistent.
COMPONENTS OF THE CONJOINT STUDY
In terms of enhancing benefits and cutting costs, customers wish all their needs would
be satisfied at once, so it is the supplier’s objective to understand which needs (product
attributes) are most important for the customer. This understanding enables a supplier to use
its scarce resources in an optimal way, thus creating the most value for the customer.
Therefore the first step in conjoint analysis is to define important attributes that will be used
in the study. According to Green and Srinivasan (1978), steps following attribute
identification should include: model selection, data collection, construction of an
37
experimental design, stimulus presentation, assignment of a measurement scale,
administration of survey and evaluation of the survey results.
ATTRIBUTES AND CORRESPONDING LEVELS: ATTRIBUTE
DEFINITION
To define the particular attributes and corresponding levels needed to accurately
perform this study, past research in the market for antibodies, and more specifically past
research in the research segment of the industry, was considered. In the 2009 Antibody
Report, Biocompare performed a survey to determine important attributes, specifically:
antibody usage, antibody pricing, and purchase behavior or users of research antibodies. The
section assessing the antibody-based applications chosen in this study is almost parallel to the
applications included in Biocompare’s surveys.
In this study, a limited number of characteristics are used based on the product
features identified by literature as most impact on antibody users’ satisfaction. As mentioned
previously Bioinformatics, LLC (2012) identified antibodies optimized for applications as
one of these characteristics. And Piper Jaffray Ltd. (2009) identified citations in research
publications as the most important factor in choosing an antibody supplier. Price was the
third consideration used in this model since it had not been previously explored in relation to
applications and citations.
APPLICATION
Applications are simple qualitative and/or quantitative analyses to ascertain whether
an epitope6 is present within a solution, cell, tissue, or organism, and if so, where (Lipman et
al., 2005). A variety of research antibody applications have proven to be important
considerations in previous antibody supplier analyses. Bioinformatics, LLC (2012) identified
western blotting as the most common application with 83% or respondents indicating they
employ this technique in their research. immunofluorescence (69%), flow cytometry (55%),
immunohistochemistry (54%) and immunocytochemistry (50%) are other top applications.
Bioinformatics states that the use of flow cytometry has increased over the past three years
with more than half of respondents indicating that they now use this application compared to
6
A specific region on the surface of an antigen against which antibodies are formed; also called antigenic
determinant (Tortora et al., 2003)
38
44% in 2009. The three most commonly used applications were included in this study:
western blot, immunofluorescence and flow cytometry.
CITATIONS IN RESEARCH PUBLICATIONS
According to Piper Jaffray Ltd. (2009), journal references are a major contributor for
identifying new antibody suppliers, with 80% of respondents indicating that they used this
frequently during the information search and purchase decision process. However,
Bioinformatics, LLC (2012) was able to identify the weight of importance of citations in
publications relative to suppliers, stating that journal references are more influential for Dako
(Agilent) customers than they are for any other supplier. At the same time, favorable pricing
is a more important factor for those ordering from Abcam, Santa Cruz Biotechnology, and
eBioscience.
PRICE
Price is technically not a product attribute, but it is explored here since in this market
it is not a major contributing factor in product selection as it is in other industries. As
mentioned previously in market research analyses, price is generally in the top four purchase
considerations of customer surveyed (Piper Jaffray Ltd., 2009), but never the primary
decision factor. Bioinformatics’ study reports price as one of the top three factors influencing
the decision to purchase antibodies, but cites importance is relative to suppliers. For instance,
favorable pricing is a top consideration when ordering from Abcam and Santa Cruz
Biotechnology whereas availability from suppliers and brand reputation are top influencers
across the line for nearly all other suppliers. Therefore price is consistently seen as a
relatively important product attribute, but the extent to which antibody users trade off price
versus other factors is unknown. Superior supplier profitability is achievable by finding and
exploring synergies between customer needs and selling capabilities (Nagle & Holden,
2002), and price plays an integrative role in strategy, therefore its influence should be fully
understood.
PREFERENCE MODEL
The basic conjoint analysis model may be represented by the following formula
(Malhotra, 2010):
39
m
U (X )  
i 1
ki
 x
j 1
ij
ij
where
U(X)
= overall utility of an alternative
αij
= the part-worth contribution or utility associated with the j th level (j, j = 1, 2, . . . ki)
of the i th attribute (i, i = 1, 2, . . . m)
xjj
= 1 if the j th level of the i th attribute is present = 0 otherwise
ki
= number of levels of attribute i
m
= number of attributes
Once the attributes and corresponding levels were identified, a preference model was
outlined. Equation one specifies the preference model.
Ri= B1 + B2(P1) + B3(P2) + B4(P3) + B5(P4) + B6(P5) + B7(A1) + B8(A2) + B9(A3) (1)
+ B10(C1) + B11(C2) + B12(C3) + Ei
Where:
Ri = Rating given by survey respondent on a scale of 0-10;
P1-P5 = variables for price levels: $98, $120, $199, $250, $300;
A1-A3 = variables for research applications;
C1-C3 = variables for citation: no citations, one citation, more than two citations;
The customer’s ranking of his or her preference for each product is the amount of utility, that
consumer gets from consuming that specific product combination (Evans, 2008).
DATA COLLECTION AND STIMULUS CONSTRUCTION
Using the full-profile method would result in respondents evaluating all 45
(5x3x3=45) hypothetical products. Therefore, a fractional factorial design, which allows
respondents to only analyze a portion of the hypothetical products, was used to reduce the
number of products to be evaluated. There were a few considerations for choosing the
fractional designs vs. the complete set full-profile. First, from a user perspective, it is optimal
to use a minimal amount of combinations (product profile cards) so as to not overload the
respondent. Secondly, the product profile card number should be a number with a square root
so as to have an analysis matrix of at least two sets of parameters. A 3x3 matrix was chosen,
since more than 9 profile cards is too many, especially when considering some respondents
40
use mobile device browsers/applications, and more options might be difficult to view
depending on the screen size. There is also a probability that more options might overwhelm
the respondent’s memory. Therefore the choice of using a 3x3 combination is optimal for
short-term memory. The partial ranking is essential in practical conjoint analysis to collect
data efficiently to relieve respondents' task burden (Lam, Koning, & Franses, 2010).
In order to reduce the number of products to be evaluated, a 3x3 matrix was created
by matching two gradients of product values along the diagonal of the matrix, i.e. the three
antibody applications (WB, IF, FC) in increasing complexity/value/investment, and three
levels of citation (0, 1, >2). The 3x3 applications with citations were aligned with increasing
pricing for the 9 cards as displayed below in Figure 12.
Western Blot No Citations One Citation Two or More Citations Western Blot, IF Western Blot, IF, FC $98 $120 $199 Western Blot Western Blot, IF Western Blot, IF, FC No Citations No Citations No Citations $120 $199 Western Blot One Citation Western Blot, IF One Citation $199 $250 $300 Western Blot Western Blot, IF Western Blot, IF, FC Two or More Citations Two or More Citations Two or More Citations $250 Western Blot, IF, FC One Citation Figure 12. 9x9 Matrix for conjoint
analysis.
This matrix designs presents mutually independent (orthogonal) hypothetical products
to avoid any redundancy in the data and allow the representation of each of the attributes and
their respective levels in an unbiased manner.
STIMULUS PRESENTATION
Verbal stimulus presentation was adapted in the Qualtrics survey development page
in the form of short fragment sentences describing the product. Pictorial representations were
not used since the combinations of attributes were not necessarily in the form of an object.
41
The verbal descriptions presenting the information were displayed in sequential order starting
in the first column moving to the third column (top to bottom) of Figure 12 for respondents
to view and rank. Participants were asked to order the product descriptions from least to most
appealing (1=most appealing, 10=least appealing). This forced ranking exercise indirectly
revealed the participants' priorities and preferences.
MEASUREMENT SCALE
The attributes selected for ranking were those in which differences do exist among
suppliers or through which differentiation can be achieved. In this nonmetric version of
conjoint analysis, the dependent (criterion) variable represents a ranking of the alternative
product profiles and is ordinal-scaled. Ranking procedures require the respondent to order
stimuli with respect to the designated properties of interest (Smith & Albaum, 2005). Rankorder questions increase the power of the measurement scale by including the characteristic
of order to the data.
SURVEY IMPLEMENTATION
A short questionnaire design was used to assess the consumer tradeoffs between
attributes, since antibodies are a complex product. The problems associated with long
questionnaires call for experimental designs and estimation methods that recover the
heterogeneity in the part-worths with shorter questionnaires (Lenk, DeSarbo, Green, &
Young, 1996).
The surveys were administered online through Qualtrics survey software from
October 1, 2012, to October 31, 2012. Staff scientists, postdoctoral fellows, graduate
students, principal investigators, research associates, lab manager/supervisors,
technician/research assistants from the North America, Europe and Asia were asked to
participate in this survey. The participants’ emails were collected using the following
Boolean tag: "antibody [TI] 2012/07:2012/09[DP]" submitted to PubMed
[ncbi.nlm.nih.gov/pubmed] which revealed a total of 896 publications. These 896
publications were downloaded to a dedicated MS-SQL server and all e-mails submitted by
the authors were extracted using a server driven application written in C# (C Sharp). To
identify users of specific research applications, the same procedure was used: Entrez web
42
search of MEDLINE that is part of NCBI (Gibney & Baxevanis, 2011). The search engine of
Entrez is controlled by specific tags in Boolean syntax Tags[TI=title, TIAB=title and
abstract] "x" =phrase, query result of counts =X of PMIDs:

“western blot” [TIAB] 2012/09:2012/10[DP] = 897 publications

“flow cytometry” [TIAB] 2012/09:2012/10[DP] = 852 publications

“immunofluorescence” [TIAB] 2012/09:2012/10[DP] = 505 publications
Each of these PMIDs were extracted from Entrez using the "Send To" export control,
selecting (1) "File" and (2) Format = "PMID List." The lists were then used to extract top
ranking words in the pool and control the recall of e-mail extraction. These PMIDs were also
submitted in proprietary MS-SQL DB, which is hosted on a MS server. Once e-mails were
extracted, they were downloaded as text files and cleaned in Microsoft Excel.
DATA INPUT
After administering all of the surveys, the data was initially extracted into MS Excel.
Those respondents who failed to complete all three questions of the survey were eliminated
from the dataset. Next the data was factored into a matrix for import into Statistica and NMF
and saved as a text file. This was the same input technique as was previously used in Chapter
3 for visual strategic clustering of suppliers in the industry.
INTERPRETATION OF RESULTS WITH
MULTIDIMENSIONAL SCALING (MDS)
Traditional conjoint analysis uses dummy variable regression for estimation of partworth utility values; where if an attribute has ki levels, it is coded in terms of ki - 1 dummy
variables (Malhotra, 2010). However since regression analysis is widely used for prediction
and forecasting, and for investigation of independent and dependent variable relationships, it
was deemed out of scope for this study. Instead, the goal of this study is aggregate-level
analysis through combination of direct evaluations with those derived from the evaluations of
the conjoint stimuli. Through multidimensional scaling (MDS), it is possible to estimate a
model at the aggregate level and still retain some individual differences (Malhotra, 2010).
MDS portrays psychological relations among stimuli – either empirically-obtained
similarities, preferences, or other types of orderings – as geometric relationships among
points in a multidimensional space (Smith & Albaum, 2005). For this study a geometric
43
distance model was adopted using Euclidian distance measure. As in Chapter 3, single
linkage cluster analysis was performed where the shortest distance is a method of calculating
distances between clusters in hierarchical clustering.
CLUSTER ANALYSIS
Conjoint analysis segments respondents according to their sensitivities to different
product attributes. Therefore there is the need for cluster analysis which helps to identify user
segments which value certain antibody attributes more than others. With this method, the
object is to separate people into groups such that similarity of objects is maximized within
each group (Smith & Albaum, 2005). Statistica analytics software was used to divide
respondents with similar coefficients into homogeneous groups/clusters. This helped to
investigate useful conceptual classifications derived from grouping respondents, generate
conclusions through data exploration and attempt to determine if types defined through NMF
are present in the data set.
The application of this clustering technique is useful for differentiation and focus
strategy through market segmentation. With clustering it is possible to identify the customers
segments which would be attracted to an offered product positioning (quality vs. price).
DESCRIPTIVE STATISTICS
A total of 138 surveys were collected; however, only 100 were usable – resulting in a
usable response rate of approximately 75% percent. Out of the entire sample, 71 percent (the
majority) of the respondents reported that they work at an academic institution, eight percent
at a clinical/hospital institution, seven percent were from pharmaceutical institution, and six
percent were employed at biotechnology companies. The remaining results are displayed in
Appendix A.
From the second question of the survey, 23 percent of the respondents of the total
sample reported that they are either a principal investigator or staff scientist, 17 percent are a
graduate student, and 16 percent reported that they are a professor or instructor. In addition,
12 percent reported their position as postdoctoral fellow, and 11 percent as research assistant
and technician.
44
The sample was international, with 69 percent of respondents from North America
including U.S., Canada and Mexico. Ten percent of respondents were from China or Japan,
and 19 percent were from Europe, including both EU and non-EU countries.
CLUSTER 1: “SMART SHOPPERS”
There were 53 respondents assigned to the first cluster, comprising 53 percent
(rho=0.9986) of the total usable sample. These respondents were split, placing very high
importance on either antibody applications and citations or price. Figure 13 shows the two
clusters with their evaluation of attributes presented quite prominently. Part of this consumer
segment most likely values antibody quality in the form of product performance, images and
laboratory techniques displayed on the website and in-depth product information sheets.
These respondents are also highly interested in antibody performance which is equivalent to
quality. The other part of this segment would likely be interested in a lower price point, but
also the full range of applications for antibodies. Therefore this 53 percent cluster was termed
smart shoppers since they seek price savings but are not willing to compromise product
quality and performance. These knowledgeable consumers effectively use their marketplace
skills and resources to read and acquire information, while taking advantage of price savings,
leading to enhanced utility and greater overall value.
CLUSTER 2: “THRIFTY”
The second cluster is the smaller of the two. It is comprised of 47 respondents,
making up 47 percent (rho=0.9986) of the total usable sample. Because this cluster only
valued low price, sacrificing the range of applications and reputation from citations in
research journals, they were termed “thrifty.” This segment is atypical in that it challenges
the predominant assumption that, in this industry, price in inconsequential. This segment
consists of customers who do not make feature-benefit trade-offs and cannot be convinced to
pay more for a product with a unique value added or one from a supplier with a sterling
reputation. They believe from prior experience that they know what they need and what the
product is worth paying for. They might not be swayed by sales efforts or promotional
information since they have concluded in advance that evaluating the differentiation of
higher-priced suppliers is a waste of time.
45
Figure 13. Respondents divided between quality/price: science-oriented “smart shoppers”
(top) and “thrifty shoppers” (bottom).
46
CUSTOMER SEGMENTATION BY VALUE PERCEPTION
Figure 14 delves even further into the 53 vs. 47 percent split, identifying exactly how
many respondents placed their highest importance on each attribute. An additional three
clusters are derived, creating a 37, 42, 21 separation (rho=0.9831). These smaller clusters
show the linkage between customer needs, displaying behavior that includes the relationship
of needs to desired attributes or wants, to consideration sets, and consideration sets to
preference orderings and choice. Forty-two percent of respondents valued a lower price point
($98-199) if the product would allow them to perform at least western blot and
immunofluorescence techniques. Thirty-seven percent were interested in a full range of
applications, at least one citation in a publication, and a moderate price point ($120-250).
Twenty-one percent of respondents were driven by the number of applications available with
no specific preference for number of citations in publications, and full range of pricing.
This cluster granularity is consistent with segments mentioned in recent market
research reports, but it also brings to light another behavioral pattern on the price-quality
continuum, which is that antibody users are attracted to low-mid pricing as long as the value
added (by application) is worth the cost. The hierarchical clustering in Figure 14 reveals two
other segments that do not fit with the linear view of markets described both in theory and
practice. These segments consist of value buyers, represented by degree of differentiation
positioned against alternatives, and price buyers, represented by cost-leadership positioning.
These are viable consumer segments that value a combination of supplier differentiation and
cost-leadership. Therefore this empirical investigation builds on Porter’s generic strategy
concepts by showing that cost advantages and differentiation are not absolute and clear-cut.
His generic strategy theory is not only dependent on the industry, but also on the dynamic ecommerce marketplace forces. This study dictates an optimal pricing strategy is not to be the
lowest, but rather synchronized with applications and citations in a 42:37:21 blend. The
optimal strategy is the one in which these trade-off values are applied to a value-based
marketing strategy for maximum customer acquisition.
47
Figure 14. Respondent segmentation by citations, price and applications.
48
CHAPTER 5
DISCUSSION
Various technical reports have sought to identify the sources of product
differentiation and the effects of price in the research antibody segment of the industry.
Despite the lack of concurring literature, Bioinformatics, LLC (2012) comprehensively
discusses how antibody users are affected by specific product differentiators and supplier
induced differentiation at every level in the customer purchase decision making process.
The purpose of this quantitative study was to extend the research on suppliers’
contribution to customer satisfaction and repurchasing through product differentiation which
leads to growing market share. This investigation also sought to determine which aspects of
antibodies are most important to end users and how suppliers should focus on in order to
create satisfied customers. Previous research using conjoint analysis at the consumer level
and NMF analysis at the industry level is inexistent; therefore the intent of this study was to
fill a gap and offer further research in the area.
Preferences were obtained from this tradeoff study in which respondents were asked
to choose between nine products with three attributes and up to three levels each. Each
respondent provided responses to between nine comparisons involving a fraction of the
attributes. The result was a definition of market segments with preferences for an optimal
combination of features, and identification of marketing opportunities for future
combinations perhaps not currently available.
LIMITATIONS OF THE STUDY
This study has coupled the ideas of cluster analysis and MDS into a hybrid model of
categorical-dimensional structure. However, no fully defensible procedures are currently
available to construct a test of the statistical reliability of clusters. The lack of appropriate
tests stems from the difficulty of specifying realistic null hypotheses (Green & Tull, 1978).
This is because cluster techniques are viewed as preclassification, where the object is to
formulate rather than test categorizations of data. Despite the formidable problems associated
with statistical inference in cluster analysis, the researcher tried a few ad hoc procedures to
49
provide rough checks on the clustering results. In Appendix B data diagnostics of
respondent rankings are presented along with pairwise sampling of the “53 percent” segment
to confirm the goodness of fit of a normal model to the data. To test for normality, segment
histograms of the sample data were compared to a normal probability curve (Appendix B).
Since the sample size was small, the data were regressed against quantiles of a normal
distribution with the same mean and variance as the sample. Lack of fit to the regression line
suggests a departure from normality. By just looking at the simple linear regression, it is
clear a linear function is inadequate in describing the relationship between the rankings of the
products (R2=..38). Therefore the model cannot be used to predict future outcomes; the low
coefficient of determination value is explained by the small sample size.
A respondent trading-off research applications and citations, for example, did not
choose between applications or citations, or between prices. As a result it was not possible to
obtain fixed-effect estimates of the entire vector of part-worths for any specific respondent.
Moreover, even if all levels were included for each respondent, constraints on the length of
the questionnaire preclude collecting a sufficient number of trade-offs for accurate estimation
of individual respondent part-worths. In all, a total of 81 paired-comparisons were available
for analysis, but linear regression was only performed on the major cluster segments
(Appendix B).
CONCLUSIONS
The results and analysis from this survey are important to the prosperity of the
research antibody industry because of the emergence of multiple successful supplier
approaches to capturing market share. The different approaches for gaining market share and
maintaining loyal customers has been discussed extensively in several market research
reports. However not one of these reports go in depth to explore consumer psychological
preferences. The purpose of this study was to uncover just that – the consumer preferences
for different antibody attributes and various pricing options.
From a theoretical point of view Porter’s generic strategy is a very appealing
performance paradigm, which on the surface applies to the market for research antibodies. It
seems to offer a solid framework for building and sustaining a competitive advantage.
However in his discussion of fragmented industries Porter (1980) hinted that the efficacy of
50
generic strategies might be contingent on industry structure. The findings of this study
indicate exactly that, it is simply not accurate to say that either differentiation or cost
leadership or focus, are equally viable. Instead, success in the market for research antibodies
is really a composite of numerous combinations of these generic strategies. Given that both
cost leadership and product differentiation have broad and focused variants, the question
becomes, which variant should a supplier choose? The conjoint analysis framework with
MDS techniques used in this pilot study help to answer that question.
Looking at all 100 usable surveys collected (Appendix C), results for the total sample
met the a priori expectations. The findings implied that the top attribute that influenced
customers’ decision to purchase an antibody for research use was quality, measured by
validated data and information through citations in research publications. And as expected,
more than half of the respondents chose leading application as an important criterion in the
decision making process. What was unique to this segment was that price was perceived as
an equally important attribute. Competitive pricing was seen as an equally important
characteristic as was the offer of prevalent applications along with quality information.
Grouping the total sample in clusters relayed that different respondents placed
different levels of importance on certain attributes. This study first identified two major
segments, and then an additional three categories of consumers.
The citation/quality group displayed in Figure 14 was 37 percent (rho=0.9831) of the
sample and placed greatest relative importance on antibodies with at least one citation in a
research journal. Also in Figure 14 displays the largest sub-category consisting of 42 percent
(rho=0.9831), was the price driven/application insensitive group who placed almost greatest
relative importance on lower price and antibodies with at least western blotting application.
This group did consider more than one research application a salient attribute. And finally the
third group consisting of 21 percent (rho=0.9831), of the sample was focused on the variety
of applications, quite often not placing importance on the number of citations.
Although these three clusters in Figure 14 were initially part of 53/47 (rho=.9986)
split segmentation, they present several distinguishing characteristics that could prove to be
viable marketing information. For example, not all clusters preferred quality category as the
total sample did. In fact, the 42 percent cluster actually preferred the $120 price (mean =
2.142857, SD = 0.7830967), which was the second to lowest price category listed. In
51
addition, the 21 percent cluster differed from the total 53/47 split because they were the only
group show partial indifference toward citations in research journals but heavier importance
on applications. Another example is the 37 percent who do not mind a higher price point as
long as it is accompanied by a good range of applications and at least one citation.
These examples help to justify the purpose of clustering the consumers into different
segments for marketing purposes. Including the occupational demographic variables into
each cluster can communicate even more valuable information about consumer preferences
for research antibodies to marketers. For example, those consumers in the “42 percent”
cluster who prefer lower pricing were mostly principal investigators and staff scientist in
academia. As such, these lead scientists take direct responsibility for completion of a funded
project, directing the research and reporting directly to the funding agency. Therefore it is
likely that they are price sensitive. And those “21 percent” cluster who preferred a wider
array of whole, applications are more likely to be graduate student in academia or a
clinical/hospital environment, as well as professors and research assistants who likely
appreciate favorable pricing but are more concerned with the product performance.
This study considered a relatively small number of attributes. Since some of the
attributes that were not included in this study could possibly have some importance to
consumers and therefore influence their buying intention, other research could be done in the
future to further examine the consumer preferences for the full extent of applications, as well
as a larger variety of pricing and sizing options. Future research could focus on those
antibody users that typically do not end up purchasing the product, and the reasons they do
not purchase it. Even items that were included in the occupational demographics and rating
section of this survey could be used in a future conjoint study to determine the influence of
such things as the brand/supplier relationship to attributes and the derivation of an individual
customer’s “ideal point.”
The ideal points considered here were the price and trade-offs which the customer
prefers over all other points in the attribute space. And since the ideal points ultimately
formed more than two clusters, suppliers can offer more than one product configuration
successfully. Since customers did not indicate a preference for a similar product offering, a
focus strategy is viable in this industry. However, the sustainability of the focus strategy is
tied to external factors such as the diverse and shifting nature of customer preferences.
52
Similarly, the viability of a cost leadership strategy is dependent on customer price sensitivity
which is also indicated in this study, but sensitivity is not the only condition for successful
cost leadership. Instead a successful cost leadership strategy should stem from economies of
scale, learning effects or preferential access to inputs or distribution channels. And finally,
product differentiation is clearly identified in the conjoint framework used in this study. The
weights that customers place on price and other attributes are inversely related; therefore a
product differentiation strategy is viable if customers give weight to product attributes other
than price. But customers’ attachment to product attributes other than price is not the only
condition upon which to base a successful differentiation strategy. Instead, a firm must be
able to build and sustain noticeable differences in its product offerings through brand image,
and pre and post sales service. Because of the variety of exogenous factors and the nature of
the fragmented industry, suppliers can and should pursue more than one generic strategy
simultaneously. This strategy can be modeled after the 42:37:21 split of consumer
preferences in this study. This reasoning contradicts Porter, who would say this is a recipe for
“below average performance,” and would cause a company to get “stuck in the middle.” This
study indicates the view is dated, though not irrelevant. Managers in the industry must not
only be able understand the strategic implications of exogenous variables and consumer
behavior, but actively create practical models for linking external factors with appropriate
internal responses stemming from customer preferences.
53
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56
APPENDIX A
CONSENT AGREEMENT INTRODUCTION
57
Study Title: Product Differentiation and Pricing Strategy in the Market for Antibodies
Dear Researcher,
I am a graduate student in the Business Administration Department at San Diego State
University and am studying differentiation strategy in the market for antibodies. You've been
selected this study because of your area of research and expertise. Your participation in this
survey will help make improvements to pricing structure, product offering, marketing
communications and promotions of antibodies offered by life science-oriented companies.
Your privacy is important to us; your answers will be combined with others, and will never
be linked with you personally.
Participation is confidential. Study information will be kept in a secure location at San Diego
State University. The results of the study may be published or presented at professional
meetings, but your identity will not be revealed. Participation is also anonymous, which
means
that no one will know what your answers are.
I would be happy to answer any questions you have about the study. You may contact me at
[email protected] if you have study related questions or problems. If you have any
questions
about your rights as a research participant, you may contact the Institutional Review Board at
San Diego State University at 619-594-6622.
The survey would only require approximately 5 minutes of your time. If you would like to
participate, continue to the link presented below:
https://atrial.qualtrics.com/SE/?SID=SV_07ZusgOYTvMiia1
With kind regards,
Christina Gramatikova
858-531-2704
58
APPENDIX B
ALTERNATIVE ANALYSES USING LINEAR
REGRESSION
59
Test Regression - Linear
WB_IF_FC_$199_0 v WB_IF_$120_0
n
53
R2
0.38
Adjusted R2
SE
0.37
1.5
Term Coefficient
Intercept
3.957
Slope
0.4994
95% CI
Sum
squares
of variation
Model
Residual
Total
SE
2.797 to 5.116
0.3219 to 0.6768
Mean
square
DF
67.3
107.6
175.0
1
51
52
67.3
2.1
t statistic
0.5776
0.08840
F statistic
31.91
DF
6.85
5.65
p
51
51
<0.0001
<0.0001
p
<0.0001
Scatter Plot with Fit
12
Linear fit (3.957
+0.4994x)
95% CI
10
95% Prediction interval
WB_IF_FC_$199_0
8
6
4
2
0
0
2
4
6
WB_IF_$120_0
8
10
60
Histogram
35
Normal Fit
(Mean=8.1, SD=1.1)
30
Frequency
25
20
15
10
5
0
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
WB _$98_0
95% CI Notched Outlier Boxplot
Median (9.0)
95% CI Mean Diamond
Mean (8.1)
5
n
5.5
6
6.5
7
7.5
WB _$98_0
8
8.5
9
53
Mean
95% CI
SE
8.1
7.8 to 8.4
0.15
Variance
SD
95% CI
1.2
1.1
0.9 to 1.4
CV
13.8%
Skewness
Kurtosis
-0.80
-0.51
-Smirnov D
p
0.34
<0.0001
Median
97.3% CI
9.0
7.0 to 9.0
Range
IQR
4
2.0
Percentile
0th
25th
50th
75th
100th
5.0
7.0
9.0
9.0
9.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
1
Normal Fit
(Skewness=-0.80, Kurtosis=0.51)…
0
Normal Quantile (Z)
9.5
-1
-2
-3
5
5.5
6
6.5
7
7.5
WB _$98_0
8
8.5
9
9.5
61
Histogram
20
Normal Fit
(Mean=6.1, SD=2.3)
18
16
14
Frequency
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10
WB_IF_$120_0
95% CI Notc hed Outlier Boxplot
Median (7.0)
95% CI Mean Diamond
Mean (6.1)
1
n
2
3
4
5
6
7
WB_IF_$120_0
8
9
10
53
Mean
95% CI
SE
6.1
5.5 to 6.8
0.31
Variance
SD
95% CI
5.2
2.3
1.9 to 2.8
CV
37.2%
Skewness
Kurtosis
-1.05
0.05
-Smirnov D
p
0.23
<0.0001
Median
97.3% CI
7.0
6.0 to 8.0
Range
IQR
8
3.0
Percentile
0th
25th
50th
75th
100th
1.0
5.0
7.0
8.0
9.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
3
Normal Fit
(Skewness=-1.05, Kurtosis=0.05)
(D = 0.23, p = 0.0000)
2
Normal Quantile (Z)
1
0
-1
-2
-3
1
2
3
4
5
6
WB_IF_$120_0
7
8
9
10
62
Histogram
16
Normal Fit
(Mean=7.0, SD=1.8)
14
12
Frequency
10
8
6
4
2
0
2
3
4
5
6
7
8
9
10
WB_IF_FC_$199_0
95% CI Notched Outlier Boxplot
Median (8.0)
95% CI Mean Diamond
Mean (7.0)
Outliers > 1.5 and < 3 IQR
2
n
3
4
5
6
7
WB_IF_FC_$199_0
8
9
10
53
Mean
95% CI
SE
7.0
6.5 to 7.5
0.25
Variance
SD
3.4
1.8
1.5 to 2.3
95% CI
CV
26.1%
Skewness
Kurtosis
-0.83
-0.14
-Smirnov D
p
0.21
<0.0001
Median
97.3% CI
8.0
7.0 to 8.0
Range
IQR
7
2.3
Percentile
0th
25th
50th
75th
100th
2.0
6.0
8.0
8.3
9.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
2
Normal Fit
(Skewness=-0.83, Kurtosis=-0.14)
(D = 0.21, p = 0.0000)
Normal Quantile (Z)
1
0
-1
-2
-3
2
3
4
5
6
7
WB_IF_FC_$199_0
8
9
10
63
Histogram
16
Normal Fit
(Mean=6.0, SD=1.6)
14
12
Frequency
10
8
6
4
2
0
3
4
5
6
7
8
9
10
WB_$120_1
95% CI Notched Outlier Boxplot
Median (6.0)
95% CI Mean Diamond
Mean (6.0)
3
n
4
5
6
7
WB_$120_1
8
9
10
53
Mean
95% CI
SE
6.0
5.6 to 6.4
0.22
Variance
SD
2.5
1.6
1.3 to 2.0
95% CI
CV
26.4%
Skewness
Kurtosis
0.03
-1.20
-Smirnov D
p
0.16
0.001
Median
97.3% CI
6.0
5.0 to 7.0
Range
IQR
6
3.3
Percentile
0th
25th
50th
75th
100th
3.0
4.7
6.0
8.0
9.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
3
Normal Fit
(Skewness=0.03, Kurtosis=-1.20)
(D = 0.16, p = 0.0015)
2
Normal Quantile (Z)
1
0
-1
-2
-3
3
4
5
6
7
WB_$120_1
8
9
10
64
Histogram
12
Normal Fit
(Mean=4.7, SD=2.6)
10
Frequency
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10
WB_$199_2
95% CI Notched Outlier Boxplot
Median (5.0)
95% CI Mean Diamond
Mean (4.7)
1
n
2
3
4
5
6
WB_$199_2
7
8
9
10
53
Mean
95% CI
SE
4.7
4.0 to 5.4
0.36
Variance
SD
6.7
2.6
2.2 to 3.2
95% CI
CV
55.1%
Skewness
Kurtosis
0.07
-1.10
-Smirnov D
p
0.13
0.024
Median
97.3% CI
5.0
4.0 to 6.0
Range
IQR
8
4.3
Percentile
0th
25th
50th
75th
100th
1.0
2.7
5.0
7.0
9.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
2
Normal Fit
(Skewness=0.07, Kurtosis=-1.10)
(D = 0.13, p = 0.0237)
Normal Quantile (Z)
1
0
-1
-2
1
2
3
4
5
6
WB_$199_2
7
8
9
10
65
Histogram
16
Normal Fit
(Mean=4.0, SD=1.7)
14
12
Frequency
10
8
6
4
2
0
1
2
3
4
5
6
7
8
9
WB_IF_$199_1
95% CI Notc hed Outlier Boxplot
Median (4.0)
95% CI Mean Diamond
Mean (4.0)
1
n
2
3
4
5
6
WB_IF_$199_1
7
8
9
53
Mean
95% CI
SE
4.0
3.5 to 4.4
0.23
Variance
SD
95% CI
2.8
1.7
1.4 to 2.1
CV
42.3%
Skewness
Kurtosis
0.01
-0.48
-Smirnov D
p
0.15
0.004
Median
97.3% CI
4.0
3.0 to 5.0
Range
IQR
7
2.3
Percentile
0th
25th
50th
75th
100th
1.0
2.7
4.0
5.0
8.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
3
Normal Fit
(Skewness=0.01, Kurtosis=-0.48)
(D = 0.15, p = 0.0043)
Normal Quantile (Z)
2
1
0
-1
-2
1
2
3
4
5
6
WB_IF_$199_1
7
8
9
66
Histogram
18
Normal Fit
(Mean=2.5, SD=1.4)
16
14
Frequency
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
WB_IF_$250_2
95% CI Notc hed Outlier Boxplot
Median (2.0)
95% CI Mean Diamond
Mean (2.5)
Outliers > 1.5 and < 3 IQR
1
n
2
3
4
5
WB_IF_$250_2
6
7
8
53
Mean
95% CI
SE
2.5
2.1 to 2.9
0.19
Variance
SD
1.9
1.4
1.2 to 1.7
95% CI
CV
56.4%
Skewness
Kurtosis
1.03
0.91
-Smirnov D
p
0.22
<0.0001
Median
97.3% CI
2.0
2.0 to 3.0
Range
IQR
6
2.0
Percentile
0th
25th
50th
75th
100th
1.0
1.0
2.0
3.0
7.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
4
Normal Fit
(Skewness=1.03, Kurtosis=0.91)
(D = 0.22, p = 0.0000)
3
Normal Quantile (Z)
2
1
0
-1
-2
1
2
3
4
5
WB_IF_$250_2
6
7
8
67
Histogram
16
Normal Fit
(Mean=3.9, SD=1.9)
14
12
Frequency
10
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10
WB_IF_FC_$250_1
95% CI Notched Outlier Boxplot
Median (4.0)
95% CI Mean Diamond
Mean (3.9)
1
n
2
3
4
5
6
7
WB_IF_FC_$250_1
8
9
10
53
Mean
95% CI
SE
3.9
3.4 to 4.4
0.26
Variance
SD
95% CI
3.7
1.9
1.6 to 2.4
CV
49.4%
Skewness
Kurtosis
0.80
0.27
-Smirnov D
p
0.17
0.001
Median
97.3% CI
4.0
3.0 to 4.0
Range
IQR
8
3.0
Percentile
0th
25th
50th
75th
100th
1.0
2.0
4.0
5.0
9.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
3
Normal Fit
(Skewness=0.80, Kurtosis=0.27)
(D = 0.17, p = 0.0007)
2
Normal Quantile (Z)
1
0
-1
-2
-3
1
2
3
4
5
6
7
WB_IF_FC_$250_1
8
9
10
68
Histogram
20
Normal Fit
(Mean=2.7, SD=1.8)
18
16
14
Frequency
12
10
8
6
4
2
0
1
2
3
4
5
6
7
8
WB_IF_FC_$300_2
95% CI Notched Outlier Boxplot
Median (3.0)
95% CI Mean Diamond
Mean (2.7)
Outliers > 1.5 and < 3 IQR
1
n
2
3
4
5
WB_IF_FC_$300_2
6
7
8
53
Mean
95% CI
SE
2.7
2.2 to 3.2
0.24
Variance
SD
3.2
1.8
1.5 to 2.2
95% CI
CV
65.5%
Skewness
Kurtosis
0.87
-0.27
-Smirnov D
p
0.21
<0.0001
Median
97.3% CI
3.0
1.0 to 3.0
Range
IQR
6
2.0
Percentile
0th
25th
50th
75th
100th
1.0
1.0
3.0
3.0
7.0
(minimum)
(1st quartile)
(median)
(3rd quartile)
(maximum)
Normality Plot (Q-Q)
3
Normal Fit
(Skewness=0.87, Kurtosis=-0.27)
(D = 0.21, p = 0.0000)
Normal Quantile (Z)
2
1
0
-1
-2
1
2
3
4
5
WB_IF_FC_$300_2
6
7
8
69
APPENDIX C
SURVEY QUESTIONS & RAW DATA
70
Survey Questions & Results 10/31/2012 1. In which type of institution do you work? # Answer 1 Academic 3 2 4 6 5 7 Pharmaceutical Biotechnology Government Total 102 74% 8 6% 7 5% 6 4% 11 8% 3 2% 1 1% 138 100% Private Research % Clinical/Hospital Other Response 71
2. Which position best applies? # Answer 3 Research Director 6 Response % 10 7% 10 7% 31 23% 14 10% 0 0% 0 0% 1 1% 1 1% 3 2% 19 14% 17 13% 25 19% 4 3% 135 100% Lab Manager/Supervisor Principal 11 Investigator/Staff Scientist 5 2 15 7 16 4 1 14 9 18 Technician/Research Assistant Business Development Director Procurement/Purchasing Agent Account Manager/Sales Consultant Department Head Professor/Instructor Postdoctoral Fellow Graduate Student Other Total 72
3. Please rank antibody attributes by rearranging according to your preference of application(s), price, and importance of citation(s). (drag and drop)WB =Western Blot, IF =Immunofluorescence, FC=Flow cytometry * Peer reviewed citations in MEDLINE/PubMed # Answer 1 2 3 4 5 6 7 8 9 Responses 1 WB / $98/ No citations 25 6 4 2 6 4 19 6 31 103 9 25 11 15 7 15 7 13 1 103 16 8 25 14 15 4 13 2 6 103 6 9 10 21 9 11 11 24 2 103 7 12 11 21 38 11 2 1 0 103 18 18 13 7 11 21 7 7 1 103 0 2 1 8 8 13 34 16 21 103 2 14 13 10 8 12 8 31 5 103 20 9 15 5 1 12 2 3 36 103 2 3 4 5 6 7 8 9 WB / $120/ One citation in MEDLINE WB / $199/ Two or more citations in MEDLINE WB, IF, FC / $120/ No citations WB, IF/ $199/ One citation in MEDLINE WB, IF/ $250/ Two or more citations in MEDLINE WB, IF, FC/ $199/ No citations WB, IF, FC/ $250/ One citation in MEDLINE WB, IF, FC/ $300/ Two or more citations in MEDLINE Total 103 103 103 103 103 103 103 103 103 ‐