Enabling disruptive innovations through the use of … Gurtner...instance, Sony’s ﬁrst portable transistor radio and the ﬁrst personal computer were new-market innovations because

ORI GIN AL PA PER

Enabling disruptive innovations through the useof customer analysis methods

Ronny Reinhardt • Sebastian Gurtner

Received: 29 October 2010 / Accepted: 10 June 2011

� Springer-Verlag 2011

Abstract The theory of disruptive innovation has had a profound effect on aca-

demic literature and management mindsets. Nevertheless, the processes that are

required to develop disruptive innovations are not yet well understood. An essential

part of creating disruptive innovations is gathering the right information on potential

and current customers. The research questions that are addressed in this paper deal

with the suitability of customer analysis methods for providing this information. The

customer analysis model that is formulated in this paper summarizes the results of a

literature review regarding the requirements of customer analysis for the success of

disruptive innovations. With insights on context, customers, constraints and effects,

the model reveals what information is needed to successfully shape the disruptive

innovation process. Following the literature on disruptive and radical innovation, a

group of eight customer analysis methods is selected and assessed. The analysis

reveals that none of the existing methods can generate all of the required infor-

mation. By combining and modifying the associated methods, the requirements of

the proposed model and, by extension, the market can be met. Managers who follow

the suggestions of this paper will develop a better understanding of current and

potential customers and, therefore, unveil the potential of disruptive innovations.

Keywords Disruptive innovations � Customer analysis � Market research �Strategic management � Innovation management

JEL classification M10 � M13 � M31

R. Reinhardt � S. Gurtner (&)

Technische Universitat Dresden, Helmholtzstrasse 10, 01062 Dresden, Germany

e-mail: [email protected]

URL: www.gruenderlehrstuhl.de

R. Reinhardt

e-mail: [email protected]

URL: www.gruenderlehrstuhl.de

123

Rev Manag Sci

DOI 10.1007/s11846-011-0069-2

1 Introduction

The latent assumption that innovations have to improve a product based on the

opinions of what mainstream customers value most was eliminated by Christensen’s

theory of disruptive innovation (Christensen and Bower 1996; Christensen 1997;

Christensen and Raynor 2003). Christensen’s theory reveals that entrants are able to

disrupt industries by developing products that initially are not attractive to a large

part of the market. Disruptive innovations are associated with high uncertainty,

emerging markets and an unknown customer base. This uncertainty leads

incumbents to pull resources away from disruptive innovation projects and, instead,

move them toward sustaining innovation projects. Scholars describe this as the

‘‘Innovators dilemma’’ (Christensen and Bower 1996). However, over time, a

disruptive innovation can improve in terms of what customers value in the

incumbent’s market. When customer needs are sufficiently met, the disruptive

innovation begins to replace the established technology, and, simultaneously, the

entrant replaces the incumbent as the new market leader.

A narrow definition of customer orientation that focuses on current customers

with current needs provokes the idea of ‘‘Innovator’s Dilemma’’ however, a broader

definition of customer orientation that includes potential customers and latent needs

is a starting point for solving the ‘‘Innovator’s Dilemma’’ (Danneels 2004;

Christensen 2006; Slater and Mohr 2006). Furthermore, innovators have all

identified the assessments of customers’ needs and the determination of potential

customers and potential new products or services as key problems of the innovation

process (Meiren 2005). In a longitudinal, multiple-case study, McDermott and

O’Conner (2002) observed that too much effort was put into the technical

development of new products rather than the analysis of potential markets and the

refinement of business plans. For these reasons, customer analysis methods are

necessary and, if conducted appropriately, can assist in successful management of

the disruptive innovation process.

Although there are various innovative methods that can foster the development of

disruptive innovations (Slater and Mohr 2006), no attempt has been made to assess

the methods that have been established for classical innovation projects for the

purpose of developing disruptive innovations. Additionally, no practical tool has

been specifically developed for fostering disruptive innovations (Hauser et al.

2006). Researchers have frequently explored how customer research and customer

orientation should be conducted to successfully detect and develop disruptive

innovations (Danneels 2004; Yu and Hang 2009).

This paper aims to close this gap by developing a model for assessing customer

analysis and market research methods. The paper is organized as follows. In chapter

2 we start by discussing the literature on disruptive innovation. We develop a model

containing requirements concerning customer analysis methods for disruptive

innovations in chapter 3. In chapter 4 we describe the method and the sample used

to assess customer analysis methods. Then, using the model from chapter 3 and the

method illustrated in chapter 4, we assess a variety of innovative customer analysis

methods with regard to their appropriateness for detecting and developing disruptive

innovations. Bringing together the results of this analysis, chapter 6 discusses a

R. Reinhardt, S. Gurtner

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combination of partially modified methods with the potential to successfully shape

the disruptive innovation process. The last chapter constitutes limitations and future

research on the topic.

2 Disruptive innovation theory

The theory of disruptive innovation was first developed by Clayton M. Christensen

in the late 1990s; however, there are several different definitions and interpretations

of this term (Markides 2006). This section clarifies the term ‘‘disruptive innovation’’

and emphasizes the need for a consistent definition.

Disruptive innovation is a term that attempts to define a specific innovation type

from a market point of view rather than a technology perspective (Govindarajan and

Kopalle 2006a). Disruptive innovations offer a novel set of product attributes in

comparison to the established technology. Due to the innovation being less valued

by mainstream customers in comparison to other product categories, the innovation

is often neglected by incumbent firms, although the capability to innovate is usually

less important for incumbents (Christensen and Bower 1996). Traditional market

research methods are applied in the context of disruptive innovations, and, because

mainstream customers do not want disruptive innovations, incumbent firms often

dismiss such projects outright (Christensen and Bower 1996). Over time, a

disruptive innovation steadily improves its performance in ways that are valued by

the established market. Mainstream customers switch to the disruptive innovation

when requirements concerning their focal attributes are met and the new technology

offers other advantages (Christensen and Bower 1996). They may also switch if the

overall unit price of the innovation is lower than that of the existing technology

(Adner 2002). At that moment, the disruptive innovation replaces the established

technology in the mainstream market, and the entrant replaces the incumbent (Tellis

2006).

In developing the theory of disruptive innovation, Christensen and Raynor (2003)

have defined ‘‘disruptive innovation’’ by introducing the terms ‘‘new-market

disruptions’’ and ‘‘low-end disruptions.’’ New-market disruptions are innovations

that create an entire new market. Due to various barriers that are presented later in

this research, some people are not able to consume a certain product despite having

an unfulfilled need. Simultaneously, a new-market disruption offers a novel set of

product performances that are not initially valued by current customers. For

instance, Sony’s first portable transistor radio and the first personal computer were

new-market innovations because their customers were people who did not own the

antecedents of radios or computers (Christensen and Raynor 2003). Users of tube

radios did not value the portability of Sony’s radio and were dissatisfied with the

sound of the new device. In contrast, teenagers and young adults were amazed by

the new technology because they could listen to music away from home. Thus, new-

market innovations compete against non-consumption when they first appear and

not incumbent products (Christensen and Raynor 2003).

Low-end disruptions, however, do not create new markets. These innovations are

typically business model innovations that serve the least demanding customer in a

Enabling disruptive innovations

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given market segment (Zollenkop 2006). Low-end disruptive innovations are less

expensive and are usually of lower quality. Christensen and Raynor (2003) name

several examples of low-end disruptions, such as steel mini-mills, discount retailing

and Korean car manufacturers in North America.

Although low-end disruptions and new-market disruptions are separate pro-

cesses, they often overlap (Christensen and Raynor 2003). For instance, netbooks

created a new market for people who lacked the money to buy a regular laptop or

needed a much smaller device. At the same time, however, netbooks attracted the

least demanding customers in the laptop market.

In contrast to disruptive innovations, Christensen defines sustaining innovations

as innovations that ‘‘improve the performance of established products, along the

dimensions of performance that mainstream customers have always valued’’

(Christensen 1997, p. XV). These innovations can be radical or incremental in

character (Govindarajan and Kopalle 2006a). Researchers commonly equate

sustaining innovation with incremental innovation and disruptive innovation with

radical innovation (Sandberg 2002, Raulerson et al. 2009); however, these

classifications represent distinct phenomena (Govindarajan and Kopalle 2006a).

In addition, there has not been a differentiation between competence-enhancing and

competence-destroying innovations (Christensen 2006), as the distinction between

these types of innovation is exclusively based on the customers’ conceptions of the

products’ attributes in conjunction with the characteristics of the markets.

3 Theoretical framework

If inadequate methods of customer analysis and customer orientation are applied,

the ‘‘Innovator’s Dilemma’’ will take its course. The conducting of simple surveys,

interviewing of current customers and neglecting of emerging markets can lead to a

termination of innovation projects (Lynn et al. 1996; Denning 2005); however,

customer analysis is a necessary and promising aspect of successful innovation

development, even if firms face a high level of uncertainty (Steinhoff 2006;

Govindarajan and Kopalle 2006b). The disruptive innovation theory generates

several requirements for customer analysis.

An important finding of Christensen’s research is that current customers can

obstruct the development of disruptive innovations. Moreover, they can set the

incumbent on the wrong track (Christensen and Bower 1996). Henderson (2006)

argues that senior and middle managers face a cognitive barrier in that they do not

see disruptive opportunities when listening to current customers. Many scholars are

coming to the conclusion that businesses must stop listening to current customers

and start searching for potential new customers.

However, ignoring current customers can only be an effective strategy in the

disruptive product development process. Recognizing disruptive opportunities

requires analyzing the saturation levels of the current customers’ performance

dimensions (Paap and Katz 2004). Hence, both current and potential customers

should be analyzed in order to obtain valuable information regarding the evolution

of needs and preferences, although current customers should not be directly


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interviewed or integrated into the disruptive innovation process. Targeting

customers who are outside of the established market can be necessary to create a

successful disruptive innovation (Gilbert 2003).

The requirements for the analysis of customers in general as well as current and

potential customers in particular are presented in the subsequent sections. Ten

research questions (RQ) are posed regarding customer analysis methods. In

addition, an integrative customer analysis model is developed.

3.1 The requirements for customer analysis methods concerning all customers

One of the major issues of customer orientation in new product development is the

exploration of the current, latent and future needs of customers (Ulwick 2002).

These needs determine the emergence of new markets and affect preferences and

saturation levels for both products and their attributes. Their investigation is,

therefore, a necessary requirement for successful customer orientation in the face of

disruptive innovations (Slater and Mohr 2006; Yu and Hang 2009). Empirical

research has confirmed this hypothesis (Slater and Narver 1998). Unfortunately,

directly asking customers does not necessarily reveal their needs, (Ulwick 2002;

Slater and Mohr 2006) and focusing on ultimate solutions is less valuable than

suggesting benefits and outcomes (Alam 2006). Researchers (Christensen 1997;

Paap and Katz 2004; Slater and Mohr 2006) emphasize the necessity of observing

customers who are using the product and utilizing other methods that focus on direct

needs. Consumers are not able to give the required information when they are

unaware of their needs due to the absence of a product that serves those needs in the

most basic manner. In this case, customers are satisfied and cannot imagine a better

solution (Slater and Mohr 2006). In sum, innovative research methods have to be

conducted in order to extract the current, latent and future needs of customers (Yu

and Hang 2009). Thus, the following question is formulated:

RQ 1 Which method is suitable for analyzing the (1a) current, (1b) latent and (1c)

future needs of customers?

In addition to determining needs, sensitivity analyses regarding the impact of the

environment on customers have to be implemented (Paap and Katz 2004). If

customers’ environment changes, customer analysis methods should be able to

predict what kinds of needs will emerge and which performance attributes become

more or less important.

RQ 2 Which method aids the assessment of the environment’s impact on

customers’ needs?

Disruptive innovations possess a new set of product attributes that underperform

in the mainstream market but generate value for customers who are outside of the

mainstream market. Customer analysis methods, therefore, need to identify the

preferences of not only current but also potential customers. The value of a

product’s attributes from customers’ perspectives is particularly worth investigating

(Schmidt 2004). In order to aid the understanding of the processes that underlie

customers’ changing preferences, Adner (2002) introduces the terms ‘‘preference


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overlap’’ and ‘‘preference symmetry.’’ Preference overlap describes ‘‘the extent to

which development activity that is valued in one segment is also valued in another

segment’’ (Adner 2002). For instance, customers in the notebook computer market

value innovations that improve hard disk drive capacity. This is also the most

critical attribute in the desktop computer market (Adner 2002). Preference

symmetry describes the degree of symmetry of the preference overlap. In the

previous example, the ratio of the number of notebook users who value

improvements in the desktop computer market to the number of desktop users

who value improvements in the notebook market constitutes the preference

symmetry of the notebook and desktop markets. Adner (2002) concludes that

disruption is more likely to occur if preferences are asymmetric; hence, one firm

will have greater incentives to invade the other market than vice versa. This model

emphasizes the necessity of determining the preferences of current and potential

customers, as they are indispensable input parameters.

RQ 3 Which method is suitable for analyzing preferences?

Henderson (2006) examines the relationship between conventional confectionery

and energy bars with regard to two performance dimensions: nutritional value and

taste. She concludes that, like the performance improvements of disruptive

innovations, preference shifts can also change the market. In this case, it is more

plausible to assume that customers shifted their preferences from high-taste, low-

nutrition products (conventional confectionary) to lower-taste, higher-nutrition

products (energy bars) than to assume that energy bars have improved in taste to

such an extent that they became disruptive to the conventional confectionary

market. Hence, identifying changes in customer preferences is a key competence for

recognizing disruptive opportunities and threats. Furthermore, it is important to

realize that disruptive innovation can follow more than one preference shift at the

same time. This phenomenon implies that incumbents could possibly leapfrog a

disruptive threat by creating an entirely new attribute instead of either emphasizing

the attribute that is valued by their current customers or the advantage of the

disruptive innovation (Charitou and Markides 2003).

RQ 4 Which method can assist in predicting preference shifts?

3.2 The requirements for customer analysis methods concerning current

customers

Unfortunately, a snapshot of current customers’ preferences for product attributes is

necessary but not sufficient for revealing disruptive opportunities. Information on

the saturation levels of current performance dimensions must also be obtained (Paap

and Katz 2004). Indications of saturation levels include the willingness to pay for

improvements in certain performance dimensions and alteration rates of preferences

meaning that a declining demand for major performance improvements can signal a

saturation level (Paap and Katz 2004; Christensen et al. 2004).

RQ 5 Which method is suitable for analyzing saturation levels?


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Detecting over-served or overshot customers is an objective of analyzing the

specific utilities and saturation levels (Schmidt 2004). The decreasing of partial

utilities in close proximity to saturation levels serves as a strong indicator of

opportunities for low-end disruptive innovations (Christensen et al. 2004). Anthony

et al. (2008) define an overshot customer as ‘‘a particular customer segment for

which existing products or services are more than good enough.’’ These customers

are forced to buy products that exceed their needs and are simultaneously compelled

to pay a price that exceeds their willingness to pay. Appropriate customer analysis

methods should be capable of spotting the point when ‘‘a technology becomes good

enough in a given market segment’’ (Christensen 2006).

RQ 6 Which method is suitable for detecting overshooting?

Furthermore, investigations should be directed at emerging drivers, while other

drivers reach their saturation level (Paap and Katz 2004). For instance, if the

performance attributes of computers, such as CPU speed, size of internal memory

and hard disk capacity are sufficient for people who want to read and write e-mails,

browse the internet and use a word processing program, then innovators must

discover what other attributes will matter to these customers. Prospective attributes

may include size, usability, price or some combination of these traits. When either

incumbents or entrants are able to reveal these emerging attributes a disruptive

innovation can be developed. Various researchers (Christensen and Bower 1996;

Paap and Katz 2004; Markides 2006) adhere to the theory that customers switch

technologies when their dominant need is sufficiently satisfied and other product

attributes of the new technology better satisfy less important needs. Adner (2002)

challenges the idea that desktop computer users, for instance, always had the latent

need for smaller, less energy-consuming hard disks. He argues that after customers’

needs in the focal product attribute (e.g. capacity) were satisfied, the overall unit

price became the relevant characteristic, which led to disruption. This example

implies that customer analysis has to focus on levels of saturation and

simultaneously observe the customers’ willingness to pay for a unit rather than

exclusively focusing on performance/price ratios. Because it is likely that both

effects play a decisive role in the purchasing decision, both the level of saturation

and the customers’ willingness to pay for a unit have to be investigated through

appropriate methods. Both effects can be subsumed under the requirement of

‘‘detecting emerging drivers.’’

RQ 7 Which method can assist in predicting emerging drivers?

3.3 The requirements for customer analysis methods concerning potential

customers

In addition to analyzing current customers, examining potential customers and

integrating them into the disruptive innovation development process is another

important layer of successful customer orientation (Enkel et al. 2005; Govindarajan

and Kopalle 2006b).


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First, the scope of the analysis has to be determined. Researchers point to

knowledge of multiple emerging markets as a factor in the success of disruptive

innovations (Gilbert 2003; Yu and Hang 2009). Tellis (2006) likewise argues that

future market orientation is a critical success factor for developing disruptive

innovations. Markides (2006) adds that a necessary precondition for a successful

innovation is the ability to enlarge the market either by attracting new customers or

by boosting the existing customers’ consumption. Christensen and Bower (1996)

point out that several prototypes have been developed by incumbents, but were

never introduced to the market. In particular, the case of the Kittyhawk hard disk

drive (Christensen 1997) demonstrates that a focus on a specific market can be

harmful to the development of the product because product features may be aligned

with a specific demand in that market segment. Moreover, the importance of

analyzing markets and not industries needs to be emphasized. If a disruptive

innovation is part of the industry but not part of the market, the innovation may be

ignored (Charitou and Markides 2003). Charitou and Markides (2003) state that

incumbents that are already way up-market (i.e. high price, high performance) do

not need to engage in low-end disruptive innovations. Independent of this

hypothesis, it can be assumed that an in-depth analysis of multiple adjacent

markets is necessary even if they belong to different industries. Hence, the capacity

to recognize different emerging markets during the product development process is

a key ability of customer analysis methods.

RQ 8 Which method is suitable for identifying emerging markets?

In addition to assessing multiple emerging markets, innovators should consider

non-consumption as an opportunity for a disruptive new product or service. Non-

consumers are people who lack skills, money or equipment, and, as a result, they do

not have the means to consume the existing products (Christensen and Raynor

2003). Instead, non-consumers have to hire someone who is more competent to

satisfy their needs. Finding customers who desire an innovation that satisfies a

previously unmet need could unveil new-market disruptive opportunities.

RQ 9 Which method is suitable for detecting non-consumption?

In order to reveal non-consumption, a more in-depth analysis is required.

Anthony et al. (2008) give hints on how to identify non-consumption. Four barriers

to consumption are mentioned: skill, wealth, access and time. Individuals who lack

the expertise to solve a problem or must often hire someone else to fix that problem

face skill-related constraints. An example of the opportunities that are present in

exploiting wealth-related constraints is low-cost airlines. Prior to their emergence,

only the wealthier portion of the population travelled by airplane. Access barriers

are usually related to concepts like inconvenience, a lack of mobility and overly

large products. The last constraint describes a situation where too much time is

needed for proper consumption. The Nintendo Wii, for instance, significantly

reduces the time that is required to start playing a video game. Assessing customers

that have stopped consuming and analyzing the time that is invested in using a

product are important objectives of customer analysis. Naturally, some of these


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barriers are interdependent and can simultaneously occur. Analyzing them correctly

and drawing the right conclusions requires methodical support.

RQ 10 Which method is suitable for revealing barriers to consumption?

3.4 A customer analysis model for disruptive innovations

An analysis of the literature has revealed three layers of requirements for customer

analysis methods that are targeted at disruptive innovations, which include the

current customers’ perspectives, the potential customers’ perspectives and their

combined perspectives (Fig. 1). The model implies that different measurements are

required depending on the type of customer. The layers can be divided into four

tiers. The first tier contains contextual factors that influence customers. In particular,

environmental occurrences can influence customers’ needs. In the same vein, the

appearance of multiple emerging markets can determine customers’ needs. The

second tier contains the perspective on customers’ needs and preferences and,

therefore, constitutes the basis of the analysis. The third tier focuses on distinct

constraints. Potential customers face barriers to consumption, whereas current

customers possess saturation levels. Consequently, the fourth tier, which focuses on

effects, reveals the results of these constraints (i.e., overshooting, emerging drivers,

preference shifts and non-consumption).

It is important to note that analyzing every tier and every layer is necessary to

obtain a comprehensive understanding of the associated customers and markets.

Attempting to ascertain environmental impacts without considering latent needs and

saturation levels will be just as unsuccessful as investigating preference shifts

without understanding the environmental impacts that might have caused these

shifts.

Context Customer Effects

CurrentCustomers

Current& PotentialCustomers Environmental

Impacts (RQ 2)Current, Latent,

Preferences(RQ 3)

Relation

Multiple Markets(RQ 8)

BarrierstoConsumption

(RQ 10)

Non-Consumption(RQ 9)

Constraints

Saturation Levels (RQ 5)

Emerging Drivers (RQ 7)

Overshooting(RQ 6)

Preference Shifts(RQ 4)

PotentialCustomers

Emerging

Needs (RQ1)Future

Fig. 1 Assessment model for customer analysis methods


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4 Methods

4.1 Sample

One of the key implications of Christensen’s theory is that traditional market

research methods fail in the face of disruptive innovations (Christensen 1997);

however, innovative methods have been developed to cope with high uncertainty

and customers’ inabilities to express their needs. Most of these methods have been

particularly developed to deal with radical innovations; however, radical innovation

and disruptive innovation share some but not all characteristics. For that reason, we

will assess the main method from each innovative customer analysis method

category, as presented by Steinhoff (2006). These categories are termed ‘‘explor-

ative and qualitative methods,’’ ‘‘future analyses,’’ ‘‘ethnographic methods’’,

‘‘simulation-based methods,’’ ‘‘intensive user collaboration’’ and ‘‘iterative exper-

imentation.’’ Alternatives to the main method of each category are not assessed

because they represent only slight modifications of the main method with

unchanged modes of action. Nevertheless, conjoint analysis and Christensen’s

job-based segmentation method are also evaluated; although neither fits into the

categories, they have both been proposed to be used for disruptive innovation

analysis (Christensen and Raynor 2003; Schmidt 2004).

4.2 Assessment

Because a knowledge-based assessment of the methods is required, an expert

evaluation that is similar to the Delphi method was conducted (Dalkey and Helmer

1963, Rowe and Wright 1999). An assessment of the customer analysis methods

was performed by relying on the model and three experts with comprehensive

knowledge of the methods. Initially, the experts independently assessed each

element of the model so as to avoid subjectivity. For each method and element of

the model, the three experts gave one of the following ratings: the method neither

addresses nor has the potential to address the attribute, the method weakly addresses

the attribute or has the potential to address the attribute, or the method explicitly

addresses the attribute or has an extraordinary potential to address the attribute

(scale according to Table 1). Subsequently, conflicting opinions on few assessment

elements were discussed based on the original papers until a consensus was reached.

5 Results

The results of the experts’ evaluations and discussions are given below. A brief

summary of each method and key statements concerning the assessment results are

presented. Table 1 summarizes the customer analysis methods, illustrates their

assessment regarding the elements of the model and answers research questions 1

through 10.

The umbrella methodology (Noori et al. 1999) is a future analysis. First, a

scenario analysis is conducted. Various scenarios with different environmental


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settings as well as different user needs and requirements are developed. Then, an

analysis is performed in order to determine how environmental parameters need to

change so that a specified scenario emerges. This process is called backcasting.

During the product development process, these environmental patterns are

monitored in order to determine which scenario will emerge and, therefore, which

product features will be most valued by customers. This methodology investigates

both emerging markets and environmental changes. Additionally, megatrends

regarding future needs and preference shifts could be recognized. Because

customers are not integrated directly, other requirements of the model, such as

revealing barriers to consumption and identifying needs and preferences of current

customers, are not satisfied.

Empathic Design (Leonard and Rayport 1997) is an ethnographic method. This

method focuses on observing customers, non-consumers or customers’ customers in

their natural environments. Usually, an interdisciplinary team conducts the

Table 1 Results of the assessment of the customer analysis methods


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observations and captures the data. Afterwards, the team members reflect upon their

observations and analyze the data. In the next step, they brainstorm for solutions and

develop a prototype. Because this methodology is designed to uncover latent needs,

it scores high on this requirement. In addition, barriers to consumption and

overshooting can be identified by this method; however, information regarding

emerging market parameters, environmental impacts and preference shifts cannot be

obtained.

The lead user method developed by von Hippel (1986) integrates innovative

users into the product development process. The theory behind this methodology is

that there are people who develop needs ahead of the mass market. Finding these

users can provide insights on future needs. Frequently, lead users develop a solution

to their specific problem but have no interest in commercially exploiting it. At this

point, a company needs to incorporate the problem and the solution of the lead user

and adjust it for the mass market. This method addresses the task of revealing future

needs and integrating customers into the early stages of the development process;

however, disruption is facilitated when incumbents assess the needs of their most

powerful customers (Christensen and Bower 1996). At first glance, this statement

seems to contradict the lead user theory, but, in general, this is not the case. The

success of managing disruptive innovations depends on the choice of the lead user.

Picking a suitable lead user who has characteristics that differ from those that are

required for the sustaining innovation process is a key capability in order to obtain

valuable information (Danneels 2004; Christensen 2006).

Information acceleration (Urban et al. 1997) is a simulation-based method that

tries to determine the market potential of innovations and customers’ reactions to

prototypes in a future environment. Initially, future scenarios and product

information are created and simulated on a computer. The test subjects then

interact with the simulation software and retrieve information on the future

environment, product attributes and competitive products. The perceptions,

preferences and buying decisions of the customers are obtained, and the market

potential of the product is subsequently calculated. This methodology requires the

creation of future scenarios and, hence, promotes the practice of assessing the

impact of environmental changes. In addition, an attempt is made to forecast market

potential and sales, which constitute some but not all of the emerging market

parameters. Urban et al. (1997) claim that this methodology can more precisely

determine customers’ needs than conjoint-analysis because customers obtain

information by actively searching for it.

The probe and learn process described by Lynn et al. (1996) is an iterative

process of probing and learning. Early on, prototypes of products are introduced to

customers. As a result, information regarding the market as well as customers’

needs, desires and suggestions for improvements can be obtained. Afterwards, an

improved version of the product is launched. The product may be introduced to a

different market segment, and information on customers, markets and the product

itself are once again collected. This iterative process is continued as long as it is

necessary, and it aims to reduce the uncertainties of emerging markets. Furthermore,

the probe and learn method attempts to determine the needs of current and potential

customers during the probing period. The requirements for evaluating


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environmental impacts and revealing preferences can potentially be fulfilled through

this process.

Customer idealized design (Ciccantelli and Magidson 1993) is an explorative

method and is similar to focus groups. Potential or current customers are asked to

develop their ideal product in the context of group work and not consider technical

constraints. This is a valid method for determining latent needs (Anthony et al.

2008). Moreover, future needs and barriers of consumption can be determined.

Conjoint-analysis (Green et al. 2001) is a technique that identifies consumers’

preferences and values with regard to product attributes. Customers usually have to

assess attributes based on pairwise comparisons. Afterwards, the relevance of each

attribute is calculated. In addition to calculating utilities, this method can detect

overshooting as well. As mentioned above, decreasing partial utilities and

decreasing marginal values can be predictors of overshooting. Making modifications

and applying this method multiple times could reveal preference shifts, saturation

levels and emerging drivers.

Christensen and Raynor (2003) propose a job-to-be-done approach. They argue

that activity-oriented market segmentation is a critical success factor for innovations.

They reject common methodologies in which segmentation is conducted through

demographics, psychographics, price point or product type, and, instead, they

propose a job-based segmentation method. Their model is based on the assumption

that customers ‘‘hire a product’’ for a certain job. For example, a fast food restaurant

that is interested in analyzing the milkshake market could create segments that are

categorized by psychographics and demographics, which would separate men from

women, older from younger customers and so forth. Alternatively, the firm could

analyze the ‘‘jobs’’ that the milkshakes get hired to perform. Christensen and Raynor

(2003) illustrate two jobs in this example. First, buyers in the morning hire a

milkshake to act as a distraction from the boring commute and as a snack between

breakfast and lunch. Secondly, parents hire a milkshake to gratify their kids. Both

jobs demand different products, and, if segmentation is accomplished by relying on

demographics, this important information on customers’ needs can be lost due to

arithmetic averaging within the segments. By using the job-to-be-done approach, the

requirements concerning the identification of needs, preferences and overshooting

are met. Other factors of the model, such as environmental impacts, saturation levels

or barriers to consumption, cannot be measured using this method.

6 Discussion and implication

The proposed customer analysis model summarizes the results of a literature review

on the requirements of customer analysis with regard to disruptive innovations. An

analysis of current and potential customers leads to a better understanding of market

environments, needs, preferences and barriers. The model also provides detailed

insights into the customer perspective of disruptive innovation theory. The model

can be used as a basis for the assessment of different kinds of customer analysis

methods, and it also helps to create new integrated customer analysis methods that

are able to generate all of the relevant information.


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Our assessment of current customer research methods has shown that there is no

integrated tool that meets all of the requirements of the model; however, a

combination of methods has the potential to successfully analyze both current and

potential customers. For instance, the umbrella methodology can assess the

environment’s impact on customers and can investigate multiple emerging markets.

Moreover, empathic design can determine both current and potential customers’

actual needs as well as reveal barriers to consumption. In addition, conjoint analysis

can be used to examine current customers’ preferences and saturation levels as well

as emerging drivers. Other combinations, such as the lead user method, empathic

design and the probe and learn method, are also possible candidates for meeting the

model’s requirements.

An idealized process of customer analysis is shown in Fig. 2. As a first step

relevant environmental influences, such as cultural and social trends, the legislative

framework or economic context must be identified. Furthermore, related and

emerging markets must also be determined.

Next, both current and potential customer needs must be assessed. Specially

trained personnel have to observe consumers’ and non-consumers’ behaviors in

their natural environments in order to detect their most important needs. They must

focus their observations on triggers of use, interaction with customers’ environ-

ments, user customizations and unarticulated needs (Leonard and Rayport 1997). As

an important precondition for all of these observations, the observers must be

familiar with the theory of disruptive innovation, and therefore have the capability

to unveil true needs and identify specific phenomena, such as barriers of

consumption and non-consumption. Furthermore, searching for people who have

developed simpler, easier-to-use solutions with less functionality could improve the

overall picture of both current and future needs.

Afterwards, the environmental impacts, needs and barriers that have been

identified need to be linked together. Similar to a scenario analysis or umbrella

methodology, a bundle of factors and the impact of each factor on customers’ needs

Fig. 2 Continuous process of customer analysis


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need to be modeled. Then, a scenario-based simulation could reveal under which

circumstances in the future needs change and how they can be served. A similar

reasoning is applicable to emerging markets. Because the observed needs are linked

to different markets, insight into the evolution of needs can be developed.

A conjoint analysis can then be used to measure the preferences of both current

and potential customers. This analysis has to be conducted multiple times in regular

intervals in order to recognize preference shifts and preference alteration rates. The

accumulated information on preferences must be linked to the needs and impact

factors that were defined earlier in order to successfully identify saturation levels,

overshooting and emerging drivers. The emerging drivers can then be integrated

into the next round of conjoint analysis.

This process is a starting point to detect disruptive opportunities and generate

appropriate innovations. As Henderson (2006) points out, companies must develop

an understanding of entirely different markets, each of which contains different

needs and different customers. In addition, there are barriers that obstruct the

cognition of disruptive concepts (Thomond et al. 2004). Understanding all of the

relevant factors and the different layers of the phenomenon through the use of

the customer analysis model and the process described by our study will enhance the

possibility of creating a disruptive innovation.

7 Limitations and further research

This research focuses on the role of customer analysis in the process of creating

disruptive innovations. In addition to investigating customers, further parameters

have to be considered in terms of ascertaining the disruptive potential of an

innovation. The determination of market parameters (Adner and Zemsky 2005), the

collection of information about new technologies (Paap and Katz 2004), the

assessment of distribution channels (Christensen and Raynor 2003) and a

competition analysis are other important steps that are required for the success of

disruptive innovations.

The proposed model is static, and no examinations were conducted in order to

assess the appropriateness of customer analysis methods in the early or late stages of

the innovation process. Hence, future research could identify appropriate customer

integration methods for each stage of the innovation process.

Moreover, the weight of the requirements that were proposed in the assessment

model could be investigated because it is very unlikely that all of these factors are

equally important. The developed model focuses on an idealized application of

customer analysis. Accordingly, research could be directed on barriers to the use of

customer analysis tools.

In addition, the model focuses on benefits. No attempt has been made to evaluate

the costs of each method. For instance, using information acceleration requires

developing future scenarios, implementing information into the simulation software

and finding customers to participate in the study. Analyzing costs and benefits could

generate valuable insights on which combination of methods should be used under

various budget constraints.


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Finally, this study is of a theoretical nature, and the model developed in this study

was conducted through a literature analysis. Thus, empirical validation is needed. In

a longitudinal study, researchers could use the customer analysis process developed

in this paper and data on disruptive new products to investigate the various factors

of the model and examine its predictive power. Comparisons to established analysis

methods and to the proposed combinations of methods would yield additional

insights for practitioners and researchers alike. In order to determine different levels

of disruption Sood and Tellis (2011) have introduced a precise definition that

eliminates some of the tautological issues when conducting empirical research on

disruptive innovation.

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