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Putting One-to-One Marketing to Work: Personalization, Customization and Choice



One-to-one marketing advocates tailoring of one or more aspects of the firm's marketing mix to the individual customer (Peppers and Rogers 1997; Peppers, Rogers and Dorf 1999; Shaffer and Zhang 2002). One-to-one marketing represents an extreme form of segmentation, with a target segment of size one. There are two forms of one-to-one marketing: personalization and customization. Personalization is when the firm decides, usually based on previously collected customer data, what marketing mix is suitable for the individual. A good example is's personalized book and music recommendations (Nunes and Kambil 2001). The e-commerce arena is replete with other instances of personalization. allows readers to get personalized news articles of interest, in Canada screens houses for buyers depending on their preferences for location, size and features. Customization is when the customer proactively specifies one or more elements of his or her marketing mix. Dell computer allows customers to customize the computer they order. The MyYahoo feature at allows users to specify elements of their home page such as the weather forecast, reports on their favorite stocks, or priorities given to local sports news. The purpose of this paper is to summarize key challenges and knowledge gaps in understanding the choices that both firms and customers make in a personalization/customization environment. We start with a summary of personalization and customization in practice, and then draw on research in economics, statistical, and consumer behavior to identify what we know and do not know. We conclude with a summary of key research opportunities.
Putting one-to-one marketing to work: Personalization,
customization, and choice
Neeraj Arora &Xavier Dreze &Anindya Ghose &
James D. Hess &Raghuram Iyengar &Bing Jing &
Yogesh Joshi &V. Kumar &Nicholas Lurie &
Scott Neslin &S. Sajeesh &Meng Su &Niladri Syam &
Jacquelyn Thomas &Z. John Zhang
Published online: 16 September 2008
#Springer Science + Business Media, LLC 2008
Abstract The tailoring of a firms marketing mix to the individual customer is the
essence of one-to-one marketing. In this paper, we distinguish between two forms of
one-to-one marketing: personalization and customization. Personalization occurs
when the firm decides what marketing mix is suitable for the individual. It is usually
based on previously collected customer data. Customization occurs when the
customer proactively specifies one or more elements of his or her marketing mix. We
summarize key challenges and knowledge gaps in understanding both firm and
customer choices in one-to-one markets. We conclude with a summary of research
Keywords One-to-one marketing .Customization .Personalization .Marketing mix .
Market Lett (2008) 19:305321
DOI 10.1007/s11002-008-9056-z
N. Arora
A.C. Nielsen Center for Marketing Research, Wisconsin School of Business,
University of Wisconsin-Madison, Madison, WI 53706, USA
X. Dreze :R. Iyengar :S. Sajeesh :Z. J. Zhang
The Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340, USA
A. Ghose :B. Jing
IOMS Department, Leonard N. Stern School of Business, New York University, 44 West 4th Street,
Suite 8-94, New York, NY 10012-1126, USA
J. D. Hess
Department of Marketing, University of Houston, Houston, TX 77204-6021, USA
Y. Joshi
Department of Marketing, Robert H. Smith School of Business, University of Maryland,
College Park, MD 20742, USA
V. Kumar
Georgia State University, 35 Broad Street, Atlanta, GA 30303, USA
1 Introduction
One-to-one marketing advocates tailoring of one or more aspects of the firms
marketing mix to the individual customer (Peppers and Rogers 1997; Peppers et al.
1999; Shaffer and Zhang 2002). One-to-one marketing represents an extreme form
of segmentation, with a target segment of size one. There are two forms of one-to-
one marketing: personalization and customization. Personalization is when the firm
decides, usually based on previously collected customer data, what marketing mix is
suitable for the individual. A good example is Amazon.coms personalized book and
music recommendations (Nunes and Kambil 2001). The e-commerce arena is replete
with other instances of personalization. allows readers to get
personalized news articles of interest; in Canada screens houses for buyers
depending on their preferences for location, size, and features. Customization is
when the customer proactively specifies one or more elements of his or her
marketing mix. Dell computer allows customers to customize the computer they
order. The MyYahoo feature at allows users to specify elements of their
home page such as the weather forecast, reports on their favorite stocks, or priorities
given to local sports news. Figure 1illustrates these definitions.
The purpose of this paper is to summarize key challenges and knowledge gaps in
understanding the choices that both firms and customers make in a personalization
customization environment. We start with a summary of personalization and
customization in practice and then draw on research in economics, statistical, and
consumer behavior to identify what we know and do not know. We conclude with a
summary of key research opportunities.
N. Lurie
College Management, Georgia Institute of Technology, 800 West Peachtree Street, NW, Atlanta, GA
30308-0520, USA
S. Neslin
Amos Tuck School of Business Administration, Dartmouth College, Hanover, NH 03755, USA
M. Su
Guanghua School of Management, Beijing 100871, China
N. Syam (*)
C.T. Bauer College of Business, University of Houston, Houston, TX 77204-6021, USA
J. Thomas
Integrated Marketing Communications (IMC), Northwestern University, 1845 Sheridan Road,
Evanston, IL 60208-2101, USA
While our terminology is consistent with that of others (e.g., Murthi and Sakar 2003; Syam et al. 2005),
these terms are often used interchangeably in the literature.
306 Market Lett (2008) 19:305321
2 Current practices
2.1 Personalization
Perhaps the most popular example of personalization is Amazon uses
collaborative filtering to determine what music or books to recommend to users
(Linden et al. 2003; Blattberg et al. 2008). Indeed, the Internet has provided many
opportunities for personalization. In, the website can recognize the user
and fishout the previous trends of the user and the searches that he or she has
made. The system then introduces appropriate related links on the website as the user
browses. Search engines like Google and AltaVista analyze the types of searches the
user undertakes over time. When the user searches for a similar topic on the search
engine, the engine can respond faster and more efficiently.
The services industry has made ample use of personalization. For example, Sprint
can analyze customer usage to determine the appropriate calling plan for the
During the summer, the Portola Plaza Hotel
in California relies mainly
on tourism. The hotels objectives are to increase revenue and reduce inventory.
Invitations are sent to previous customers directing them to a personalized URL that
gathers information about guest preferences. After detailed analysis, mails are sent to
these customers offering them discounts and other promotional offers to encourage them
to come to the hotel. Using this procedure, the hotel saw a significant increase in revenue
and was successful in retaining its customers. Personalization is practiced by many
insurance companies like ICICI-Lombard, which uses a customer survey to prepare
personalized insurance plans. Harrahs Entertainment personalizes many promotions
and incentives based on what it learns from customerdata (Watson and Volomino 2001).
The practical advantages of personalization lie in greater customer satisfaction
and higher profits. For example, Malthouse and Elsner (2006) show in a field test
Firm initiated Customer initiated
Degree of
Personalization Customization
Fig. 1 Understanding personal-
ization and customization
Sprint Nextel,, Retrieved on
Aug 10, 2007.
Personalized Marketing Software Overcomes Demand Generation Challenges in the Business-to-
Consumer Marketplace,, Retrieved on Aug 09, 2007.
Market Lett (2008) 19:305321 307
that personalizing the copy used in a book offer increases response rates
significantly. Notably, we are not aware of systematic study that shows personal-
ization yields higher customer satisfaction in the long run, although the Portola Plaza
Hotel example above certainly suggests so.
One concern is invasion of privacy. Personalization thrives on data, driving
companies to stretch the envelope on what data they collect.
The customer wonders,
How do they know I want that book?This may be why Amazon now explains its
recommendations. Second, personalization is expensive. It requires data and
expensive software for implementation. Whether personalization pays out ultimately
depends on the accuracy of the personalizationdid we recommend the right book
to the right person? This cannot be taken for granted.
2.2 Customization
Whereas Amazon serves as the prototypical example of personalization, Dell
Computer plays that role for customization in the computer industry. The customer
can order a computer from Dell according to his or her needs and likes. Thus, the
computer is custom-made for the user. The long-term payoff of this strategy is
difficult to determine and confounded with Dells reliance on the Internet channel
and its recent woes. However, it certainly appears that customization was part of the
value proposition that propelled Dell into being a major player.
Customization has been applied in a variety of other industries. Many restaurants
allow customers to suggest alterations to the stated menu. A recent study found that
81% of motorcyclists would like to have the motorcycle seats made-to-order.
Sporting goods giant Adidas-Salomon has utilized customization (Berger and Piller
2003). Faced with intense competition to launch the right new products, Adidas has
begun production of shoes which are codesignedby the customer. A survey by
Berger (Adidas) and Dr. Piller (TUM, Munich, Germany) has shown that customers
prefer these shoes to the standard ones. Companies like Spreadshirtand Levis
also customize apparel. In consumer durables, IKEAand Bemz Furniture
started a joint venture to provide custom-made furniture. The BMW-Miniis a very
common car in Germany and, now, it comes with a customized roof design too. One
can design the roof of the car online and then the car is custom-made. Marelli
Motors, responding to competition from new entrants, has decided to produce only
custom-made electric motors.
Other examples abound. Banks such as Garanti Ban, Turkey and Deutsche Bank
have begun to offer checkbooks and credit cards in any design that suites the
customer. In the pharmaceutical industry, VURUis a pill box used to carry
nutritional supplements in customized daily packs. The customer is given the
freedom to choose how he or she wants his box to look like and what it should
E-week Business magazineNovember 27, 2000, Retrieved on Aug 9, 2007.
Mass-customization in clothing,, retrieved on August 10,
Mass-customization and open innovation news, March 2006,
mass_customization_open_i/furniture_home/index.html, retrieved on August 09, 2007.
InfoworldDecember 13, 1999, Retrieved on Aug 9, 2007.
308 Market Lett (2008) 19:305321
contain. This product is already on the market and comes with all the details that are
considered pertinent for the medicines inside.
An obvious potential advantage of customization is greater customer satisfaction.
While the proliferation of examples cited above implicitly bears testimony to this,
the long-term impact and profitability of customization has not to our knowledge
been analyzed systematically. Another potential advantage suggested by the above
examples is strategicwe often see customization emerging in highly competitive
industriesrestaurants, banking, apparel, computerswhere product differentiation
is crucial but difficult to achieve. Customization itself is a point of differentiation
(Dell) and moreover ensures the product itself is unique.
A potential disadvantage of course is cost. For example, the customized Adidas
shoes are about 30% more costly than the standard line. Again, the question is
whether the incremental volume and strategic advantages overcome this cost.
Customization could also make the purchase decision difficult by making the choice
task very complex. Huffman and Kahn (1998) and Dellaert and Stremersch (2005)
demonstrate the psychological difficulty of trading off the higher utility derived from
customized products with the complexity of making the choice. Another potential
problem is the Pandoras box of raising customer expectations. For example, the
BMW mini customer may decide that he or she wants everything customizedfrom
the interior to the hub caps. How does the customer react when BMW says, Sorry,
we cant allow you to customize everything?
2.3 Forces shaping the evolution of personalization and customization
The challenges in implementing personalization and customization will have to be
met by future advances in three steps of delivering one-to-one marketing (Black and
Thomas 2004): (1) collecting the data, (2) transforming the data into insights, and (3)
operationalizing the results.
The key issue in collecting data is customer data integrationthe collection of
customer data at all touch points.There are two dimensions to this issue. First is
the extent of integration (360° view being the ideal). Second is for how many
customers the firm has the integrated data. Neslin et al. (2006) review this issue with
respect to integration across sales channels and question whether it is necessary to
have a 360° view for all the firms customers. A company may be able to obtain a
360° view easily for 40% of its customers because they are members of the
companys loyalty program. Obtaining such data for the next 60% may be
prohibitively expensive. Perhaps the company can leverage the insights from the
40% sample to the 60%, although they are obviously not randomly generated
This leads us to the second challengedeveloping insights. Companies that
perform the most sophisticated personalization and customization have strong
capabilities in statistical modeling. Some of the sophisticated techniques that are
used to analyze customer behavior are being standardized into computing packages
like SAS and BUGS, and this software will have to become more accessible in the
Service customizationFebruary 28, 2007,
open_i/service_customization/index.html, retrieved on August 9, 2007.
Market Lett (2008) 19:305321 309
future. In addition, companies will have to learn how to analyze text data
(Coussement and Van den Poel 2007) generated by customer emails, blogs, news
groups, chat forums, and virtual communities. Coca Cola, Harley Davidson, and
Disney have invested in learning from brand communities.
The final step, operationalization, requires close coordination among marketing,
information technology, and production. The challenges can exceed the organiza-
tions capabilities. This suggests more partneringto bring about personalization
and customization. One such example is Lands End,a catalog retailer. The data
for customizing jeans or shirts are collected at the Lands End website. But Lands
End relies on externally provided software to translate customer specifications for
jeans and shirts into final patterns that are then used to produce the clothing.
3 Empirical challenges in personalization
3.1 The issues
A distinguishing feature of personalization versus customization is its reliance on
statistical analysis of customer data to predict customer response to prices,
promotions, or communications. There are two key issues. First, how accurate are
these predictions? Personalization relies on assigning the right marketing effort to
the right customer. But, if the cost of misclassification is large, the firm may be
better off not personalizing. For example, distracting and annoying the customer by
recommending a series of books in which he or she has no interest may be worse
than making no recommendation at all. The second issue is how far the firm should
go toward the ultimate goal of one-to-one marketing? Figure 2depicts the choices
available to the firm. It may be that the loss in precisionin going one-to-nmay be
worth avoiding the errors of misclassification in going one-to-one.
3.2 What we know
The research on these issues can be classified into two categories: supportive and
doubtful. Among the supportive research, Rossi et al. (1996) first quantified the
benefits of adopting one-to-one pricing by utilizing household purchase history data
and empirically found that individual personalization improves 7.6% over mass
optimization. Later, Ansari and Mela (2003) found that the content-targeting
approach can potentially increase the expected number of click through by 62%.
Level of Personalization
(No Personalization)
(Extreme Personalization)
(Segment Personalization)
Fig. 2 Firm choice on level of personalization
310 Market Lett (2008) 19:305321
Furthermore, Arora and Henderson (2007) showed customization at individual level
can enhance the efficiency of embedded premium.
Among the doubtful stream of research, Zhang and Wedel (2007) investigate the
profit potential of various promotion programs customized at different levels in
online and offline stores. The three levels of customization are (1) mass market (one-
to-all)each customer receives the same coupon, (2) segment (one-to-n)each
member of the same customer segment receives the same coupon, and (3) individual
(one-to-one)each customer receives an individualized coupon. They found that the
incremental benefits of one-to-one promotions over segment- and market-level
customized promotions were small in general, especially in offline stores. Note that
it is possible that one-to-one promotions may significantly increase response rates,
perhaps due to steeper discounts, but that may not translate into significant profit
3.3 What we need to knowfuture research
It is not resolved whether traditional consumer choice models can be used
successfully to personalize the marketing mix. We need to know what methods
predict most accurately. There are two directions to go heremore complex or
simpler. On one hand, complex machine learning algorithms (e.g., see Blattberg et
al. 2008) might be required. Or, given the data limitations and specification issues
that can wreak havoc with complex models, simple models such as RFM may be the
best way to go. RFM stands for RecencyFrequencyMonetary Value and uses
information about a customers most recent purchase, her frequency of purchase, and
the dollar value of her past purchase to predict her likelihood of purchasing a product
in the future. This can be done on an individual customer basis and can therefore be
used to provide personalized recommendations of future product purchases.
Regarding level of personalization, Malthouse and Elsner (2006) provide encourag-
ing support for one-to-npersonalization (using relatively simple statistical analysis).
This support is in the form of a field test, which should be the litmus test for research
in this area.
More broadly speaking, we need decision support systems for weighing the costs of
incorrect personalization and helping managers decide when to personalize and to whom.
4 Economic models of firm choice related to personalized pricing
4.1 Key results in literature
From an economic standpoint, the promise of personalization is to enable firms to
estimate their customersvaluations and, hence, implement finer price discrimina-
tion. A number of theoretical papers (Shaffer and Zhang 1995; Bester and Petrakis
1996; Fudenberg and Tirole 2000) have shown that, among equally matched
(symmetric) firms, offering personalized pricing, while being optimal for each
practicing firm, makes all firms worse off. Choudhary et al. (2005) examine a
vertically differentiated duopoly and show that the higher-quality firm can be worse
off with personalized pricing.
Market Lett (2008) 19:305321 311
Of course, in reality, competing firms are rarely equally matched. For instance,
firms may differ in terms of how many loyal customers they each may have and
hence their targeting strategies may differ in terms of offering discounts to own vs.
the rivals customers. In that case, Shaffer and Zhang (2000) show that personalized
pricing can alleviate price competition and benefit firms (earn higher profits in
equilibrium). In a more general model, Shaffer and Zhang (2002) consider both
horizontal and vertical differentiation, with a positive cost of targeting customers.
They show that the firm with more loyal customers can earn higher profits in
equilibrium when both firms engage in one-to-one promotions. Ghose and Huang
(2006) allow symmetric firms to offer a continuum of qualities and show firms can
avoid a Prisoners dilemma and are better off when they engage in one-to-one
pricing. This happens because firms can provide higher qualities to each consumer
without the fear of intrafirm product cannibalization.
When we look at personalization in the presence of strategic consumers, the
results once again paint a complex picture. Villas-Boas (2006) shows that a
monopolist is worse off by offering one-to-one promotions because strategic
consumerscan sacrifice their purchase in the first period so that they are not
identified as existing customers in the next period. However, Chen and Zhang
(2007) show that targeted pricing is profitable, in the presence of strategic customers,
only in a competitive setting.
Allowing consumers to haggle is another mechanism by which firms can personalize
pricing. When costs of haggling are heterogeneous, Desai and Purohit (2002)showthat
firms may find it profitable to allow consumers to haggle than follow fixed-price policy.
The above papers assume perfect information that the firm has a perfect predictive
model (a debatable assumption as we discussed earlier). Chen et al. (2001)show,
perhaps paradoxically, that mistargeting softens price competition because firms fear
offering low prices to the wrong customers. Liu and Serfes (2004)alsoconsider
imperfect information in a spatial price discrimination model and find that firms
unilaterally commit not to price discriminate when the quality of information is low.
Another important issue associated with personalized pricing is whether firms
should engage in first-degree or second-degree price discrimination when custom-
izing their products. Ulph and Vulkan (2001) use the Hotelling framework to study
this relationship in a duopoly and show that a firm is always better off using first-
degree price discrimination if it also mass customizes and vice versa.
A critical issue is that the cost of personalization also plays an important role in
the decisions being made by firms. Dewan et al. (1999) show that as the cost of
personalization decreases, firms provide more and more personalized products
compared to standardized products when they can employ second-degree price
discrimination whereas Chen and Iyer (2002) show that firms will invest in
personalization if customers are sufficiently heterogeneous and the cost of
personalization is high which again requires understanding of context where one
prediction is more robust than the other one and vice versa.
4.2 Future research
While we have generated important theoretical insights regarding personalized
pricing, there still are a number of unanswered questions. How does the growth rate
312 Market Lett (2008) 19:305321
of any market impact firmspersonalization strategies? Are there synergies between
personalization and branding? Currently, firms have a lot of information about their
existing customers but not about competitors customers. There needs to be further
research on conditions under which sharing information with competitors can be
profitable (see Chen et al. 2001). Although a few empirical papers (e.g., Ansari and
Mela 2003) have examined the role of personalized communication in reducing
information overload and aiding customer decisions, this also remains a fruitful area
for future theoretical research. Further research is also needed to understand firm
strategies when firms have different cost functions arising due to operational
efficiencies enjoyed by a firm. Another area is the role of personalization when firms
adopt nonlinear pricing schedules.
5 Firm choice in product customization
5.1 Product proliferation vs. product customization
A familiar presumption in marketing is that a consumer realizes higher utility when
the product better matches her ideal preference. Therefore, when costs to achieve this
match are sufficiently low, firms with some monopolistic power may reap higher
profits by providing better-matching products. Product customization and product
proliferation are two popular strategies for improving the preferenceproduct match,
with some firms actively pursuing both. A firm pursuing product customization first
invites each individual consumer to reveal her preference and then produces and
delivers a product with the closest match possible. A firm pursuing product
proliferation does not hold such one-to-one dialogs with consumers. Instead, it offers
many variants and the customer chooses the most appealing product. Product
proliferation is observed in many markets such as breakfast cereal, yogurt, toys,
apparel, books, and consumer electronics.
5.2 Customization, competition, and firm profitability
Although customized products increase consumer utility, firms do not always gain
from adopting mass customization, frequently because customization reduces
product differentiation in a competitive context. In fact, if two or more firms offer
a consumer the product that perfectly addresses her taste, then Bertrand competition
will drive prices down to the second-highest marginal cost (Lederer and Hurter
1986; Thisse and Vives 1988). Nevertheless, if a firm does not pursue customization
but its competitors do, then the former would probably become worse off. In many
product categories today, the technological ingredients underlying product custom-
ization are relatively mature and readily accessible to all firms. This implies that
adopting customization cannot ensure competitive advantage and, indeed, studies
have shown that pursuing customization may lead to a prisoners dilemma (Dewan et
al. 2003; Thisse and Vives 1988).
So, will customizing firms ever be able to escape the curse of prisoners dilemma?
The answer is yes. As Dewan et al. (2003) further show, when firms differ in the
timing of adopting customization, the early adopter may achieve a first-mover
Market Lett (2008) 19:305321 313
advantage. Such first-mover advantage can be sustained if there are salient learning-
curve effects and/or scale economies. It is mainly for such reasons that Dell
Computers, the first to offer customized PCs on the Internet, maintains its industry
leadership today. invented the book recommendation system based on
collaborative filtering. Due to the inherent scale economies in collaborative filtering,
Amazon is widely believed to provide more relevant recommendations than its
If the product has multiple attributes of keen interest to consumers, then the firms
may relax price rivalry by judiciously choosing which attribute(s) to customize
(Syam et al. 2005). In a two-dimensional spatial model, a surprising insight of Syam
et al. (2005) is that the competing firms may choose to customize an identical
attribute but not both attributes, achieving matched partial customization.In a
model of both horizontal and vertical differentiation, Ghose and Huang (2006)
investigate a duopoly where one or both firms tailor both its prices and product
qualities based on consumerswillingness to pay. They also show that a prisoners
dilemma situation does not arise even when the firms are ex ante symmetric.
5.3 Constraints and challenges in mass customization
Generally, it is economically viable for firms to tailor those attributes most valued by
consumers and yet not too costly to customize. Currently, firms in many industries
(e.g., apparels, cars, and computers) customize only a fraction of the product
attributes and allow limited options for each of these attributes. However,
technological advances may lower these costs. We might then be left with the
prisoners dilemma of all firms customizing on allattributes. For instance, many
apparel makers are very happy to accommodate customersrequest regarding size,
color, or fabric but are reluctant to alter their basic styles due to concerns of
compromising their brands. Customization can even prove harmful for status goods
because an objective of such goods is to project an image of exclusivity (Amaldoss
and Jain 2005). Syam and Kumar (2006) show that, in competing exclusively with
customized products, firms may lose all differentiation advantages, and they may
therefore not want to eliminate their standard products.
Branding (Keller 2001) may become more important in a customization
environment. Companies may have to rely more on the rich associations and
experiences consumers have with brands rather than product attribute differences
per se.
6 Consumer perspectives
6.1 Preference formation
Substantial research shows that consumers often construct their preferences on-the-
flyas a function of task and contextual characteristicsincluding the ease with
Collaborative filtering uses transaction data of related previous purchases as input and, all else equal,
more transaction records render more relevant recommendations.
314 Market Lett (2008) 19:305321
which attributes can be evaluated (Hsee 1996), information format (e.g., Bettman et
al. 1998; Jarvenpaa 1989; Payne et al. 1992), response mode (e.g., Slovic 1995), the
particular attributes used to recommend products (Häubl and Murray 2003), and the
particular alternatives available for consideration at a given time (Bettman et al.
1998; Simonson and Tversky 1992). In other words, instead of being exogenous,
preferences are in fact endogenous to the particular task and information
environment facing the consumer.
These findings question two important assumptions of product personalization
and customization (Simonson 2005). The first assumption is that consumer
preferences are stable or evolve in a predictable fashion. Preference stability is
critical for personalization because previous choices are used to predict future
choices. Preferences are likely to be more stable when the information environment
itself does not change and when consumers have made repeated choices in a product
category (Hoeffler and Ariely 1999). To the extent that the Internet allows
consumers to learn from the product experiences and social information of other
similar consumers (Chevalier and Mayzlin 2006; Forman et al. 2006), this should
also enhance preference stability, allowing personalization.
The second assumption is that preferences revealed by consumer choices truly
maximize utility. Suppose a customer heavily weighs price in her product choices. The
typical inference is that the customer has high price sensitivity. Another possibility,
however, is that product information was presented in a way that made price
comparisons easy and this accentuated the importance of price (Lynch and Ariely
2000). Similar effects may occur from using Internet-based shop bots that facilitate
price comparisons (Iyer and Pazgal 2003). Thus, if prior choices are to be used to
personalize product offers, care must be taken in designing the offer to mirror the
environment under which the data driving the personalization were obtained.
The danger for product customization is that customers may realize after
designing their idealproduct that their actual preferences correspond more closely
to standardized products (Syam et al. 2007). Customer uncertainty about their
preferences is less likely to be an issue in business-to-business settings, where
buyers have greater experience and expertise (Alba and Hutchinson 1987; Huffman
and Kahn 1998). To the extent that a market is characterized by dramatic changes in
market offerings, however, even experienced buyers may not knowor may be
overconfident intheir preferences. One way to reduce preference uncertainty is to
provide buyers with interactive tools that allow them to visualize and experience
customized products prior to purchase (Lurie and Mason 2007) or to learn from the
experiences of others (Chevalier and Mayzlin 2006; Forman et al. 2006). Additional
research is needed to examine the extent to which such Internet-based tools reduce
6.2 Information search and processing
One of the greatest challenges to implementing customization is the extent to which
consumers are willing and able to process and act on all the attribute options
(Huffman and Kahn 1998). Information overload may lead to poor-quality choices or
a failure to purchase (Huffman and Kahn 1998; Iyengar and Lepper 2000; Lurie
2004). As the number of potential alternatives can be the factorial product of the
Market Lett (2008) 19:305321 315
number of attributes, having consumers choose among menus of attributes can
reduce perceived choice complexity and increase satisfaction with choice processes
and outcomes (Huffman and Kahn 1998). Other research suggests that menu-based
choice can allow firms to assess consumer preferences for a larger number of
potential products than traditional conjoint approaches (Liechty et al. 2001).
Moreover, menus of product attributes can be personalized (i.e., firms can offer
different menus of attribute levels to different customers). This suggests a promising
personalizationcustomization hybrid model.
Whether for economic, technological, or information overload reasons, product
customization will typically only be available for a subset of attributes. As a result,
consumers will be presented with a default product that can be modified. Research
suggests in such cases that consumers are likely to stick with the default (Johnson et
al. 2002; Samuelson and Richard 1988). If the default is chosen with the consumers
interest in mind, for example, automatically setting aside money for retirement
unless the consumer opts out, there can be significant welfare advantages (Johnson
and Goldstein 2003; Thaler and Benartzi 2004). Also, Hsee and Leclerc (1998)
suggest it may be more profitable to offer consumers a single product because
offering multiple options may lead to perceived losses in consumersminds.
Finally, research on cognitive lock in (Johnson et al. 2002; Zauberman 2003)
suggests that there may be advantages to engaging the consumer in customization.
Getting consumers to provide attribute importance weights which are used to create
customized recommendations can reduce consumer effort and therefore increase
loyalty (Häubl et al. 2004).
6.3 Consumer acceptance of product customization
Although one-to-one marketing should increase satisfaction, there are situations in
which consumers may prefer standardized products. Consumers with collectivist, as
opposed to individualist, orientation react more positively to products that meet
group, as opposed to individual, preferences (Kramer et al. 2007). Because
preferences are labile, customers may be as satisfied with products that seem
customized even if differences from standardized products are minimal (Simonson
2005). There is evidence that, although recommendations for superior options
increase choice satisfaction and confidence, recommendations that go against
consumersinitial preferences can have the reverse effect (Fitzsimons and Lehmann
2004). Research also suggests that consumer acceptance of personalized offers
depends on the ease with which consumers can see how recommendations were
developed (Kramer 2007).
Although personalized products may be appreciated by consumers, personalized
prices may not be, particularly if such prices are seen as unfair. Prices are most likely
to be viewed as unfair when consumers can see that they are paying a premium
relative to others for a similar product (Xia et al. 2004). For example, Anderson and
Simester (2007) find that charging more for large-sized clothing in a catalog leads to
significant declines in sales and profits. To the extent that personalized pricing is
unlikely to reveal price premiums, since each customer only sees one offer, such
approaches may be successful. However, there is risk here, as Amazon learned when
it suffered a severe backlash for charging different prices for the same DVD to
316 Market Lett (2008) 19:305321
different customers (Morneau 2000). If price offers are based on purchase history,
then other aspects of the offer should also be personalized to minimize comparisons
on the basis of price alone (Xia et al. 2004). Other research suggests that price
customization (e.g., through negotiation) is less likely to be seen as unfair since
consumers participate in price setting (Haws and Bearden 2006). On the other hand,
personalized pricing through the distribution of coupons or customized emails are
becoming increasingly common (Tezinde et al. 2002).
6.4 Translating attributes into benefits
The benefits provided by a collection of attributes are often in the interaction of
these attributes (Randall et al. 2007). This increases the information-processing
burden for customers. Novice consumers may be less able to customize their own
products (Sujan 1985). For such consumers, specifying the relative importance of
product benefits (such as performance and data storage in the case of a personal
computer), rather than selecting preferred product features (such as processor speed
and hard drive size), increases the perceived fit of the customized product offering
(Randall et al. 2007). At the same time, as expertise increases, greater benefits are
perceived from interfaces that allow the buyer to specify product features. Internet-
based opinion sites and review forums can go a long way in helping consumers
translate attributes into benefits.
6.5 Future research
The customer is central to one-on-one marketing much more than is the case in
traditional mass marketing. Therefore, it is important to understand the different aspects
of consumer behaviors as they pertain to customization. While some research has
addressed these questions, much more remains to be done. To what extent will the need
for uniqueness help the movement towards customized products? Will the consumers
desire to seek comfort in familiar products detract from the widespread adoption of
customized products? Clearly, this will depend on the product category and the way
consumers make decisions for different types of products. How do consumers deal with
the risk that manufacturers will not be able to precisely customize according to their
specifications? More research should address these and other questions.
7 Summary and conclusions
This paper has reviewed two major forms of one-to-one marketingpersonalization
and customizationand identified areas for future research. We started with an
overview of one-to-one marketing in practice and structured our discussion from the
perspectives of empirical work, economic analysis, and psychology. Each of these
perspectives yields its own wish listfor future research. We conclude by
synthesizing a few issues that draw on all three disciplines:
&When should the firm engage in one-to-one marketing? Traditional concepts in
economics, such as price discrimination, and in psychology, such as information
Market Lett (2008) 19:305321 317
processing, have long supported tailoring the marketing mix to each consumer.
But why is one-to-one such a recent phenomenon? It may be that advances in the
data analysis and technology opened the door. But undoubtedly there are other
conditions under which one-to-one marketing is advisable.
&When should the firm embrace personalization as opposed to customization?
Clearly, data play a key role, as personalization is only possible if reliable and
projectable customer data are available. However, economic and psychological
analyses that pit customization versus personalization are needed.
&Which elements of the marketing mix should be personalized or customized?It
may be that price should be personalized and product should be customized, but
what conditions favor one form over the other? Consumer responsechoice
plays a key role here.
&To what degree should the firm personalize? Should personalization be at the
individual level, the segment level, or somewhere in between? This surely is a
statistical issue in terms of how accurately we can predict consumer choice, but
undoubtedly there are economic and psychological factors as well.
&How far should the firm go in customization? Should the firm allow the customer
to customize 10%, 50%, 90%, or the product? This depends on competitive issues
but also on the customers ability to choose, i.e., fully design, his or her products.
&How can we combine personalization and customization?Onewaywe
mentioned was to personalize the product to some extent, e.g., suggest a certain
style of apparel, but then have the customer customize the specifics. Where
should we draw the line between personalization and customization?
&Can personalization or customization be a source of competitive advantage?It
would appear that the ability to predict customer choices accurately could be
sustainable and lead to a long-term advantage in personalization. We discussed
economic forces that could draw firms into a prisoners dilemma in custom-
ization if the critical features of a product become customizable cheaply and
customers are capable of defining their needs. Perhaps there is a way firms can
differentiate themselves in their expertise in various aspects of the customer
choice process, such as information processing or alternative evaluation, which
can lead to sustainable advantages and higher industry profits.
Clearly, personalization and customization present many challenging questions for
academics and ultimately practitioners. Our review suggests that some important advances
have been made, but clearly more contributions are needed from economics, psychology,
and statistical analysis. We hope this paper will help guide these future endeavors.
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... Regardless of whether in AI-based robots and items or the delivery then-shopping model, customization is accomplished through the cooperation between a relic (robot, item, programming) and a buyer. It is significant that the framework spearheaded by Stitch Fix lies halfway among "customization" and "personalization", as indicated by the qualification drawn [28]. The last is included when firms choose which items are appropriate for given individual buyers, putting together their choice with respect to already gathered client information; the previous when shoppers effectively indicate what item they need. ...
... The last is included when firms choose which items are appropriate for given individual buyers, putting together their choice with respect to already gathered client information; the previous when shoppers effectively indicate what item they need. Strikingly, [28] notice as a mainstream case of personalization the collective sifting utilized by Amazon to set up what music or books to prescribe to its clients. As it was seen, Stitch Fix returned to this and different encounters, building up a model that all the more intently takes after customization appropriate. ...
... This authentic irregularity opens up another space for reflection for researchers and promoting administrators the same. Taking this view, it would appear to be sensible to state, as certain writers have done as of now [42], that AI applications in the realm of promoting gracefully new lymph to the worth co-creation measures made famous by the worth co-creation hypothesis and the administration prevailing rationale of [28]. ...
Various recently-introduced applications of artificial intelligence (AI) operate at the interface between businesses and consumers. This paper looks at whether these innovations have relevant implications for marketing theory. The latest literature on the connection between AI and marketing has emphasized a great variety of AI applications that qualify this relationship. Based on these studies but focusing only on the applications with a direct impact on the relationship at the very heart of marketing, i.e., the one between firms and consumers, the paper analyzes three categories of AI applications: AI-based shipping-then-shopping, AI-based service robots, and AI-based smart products and domestic robots. The main result of this first analysis is that all three categories have to do, each in their own way, with mass customization. A discussion of this common trait leads us to recognize their ways to mass customization that – unlike the traditional approach developed thanks to flexible automation and product modularity technologies – place the customization process within a broader perspective of consumer needs management. This change in approach means that marketing should focus more on managing consumers’ needs than directly on the satisfaction of those needs. This finding marks a genuine discontinuity that opens up a new space for reflection for scholars and marketing managers alike. Retraction Notice: This paper has been retracted from the journal after receipt of written complains. This journal is determined to promote integrity in research publication. This retraction is in spirit of the same. After formal procedures editor(s) and publisher have retracted this paper on 10th November-2020. Related policy is available here:
... Dienstleistungsangebote, die im Sinne einer Personalisierung auf die individuellen Bedürfnisse eines Nutzers zugeschnitten sind, stellen einen wesentlichen Bestandteil einer erfolgsversprechenden Wertgenerierung dar (Müller et al. 2002). Damit die Personalisierung von Dienstleistungen möglichst komfortabel für den Kunden durchgeführt werden kann (Montgomery/Smith 2009), erfolgt die Identifizierung und Erfüllung der Nutzerbedürfnisse automatisiert und ohne proaktivem Zutun des Nutzers (Arora et al. 2008). So werden etwa personenbezogene Daten aus dem Kontext oder der Interaktion mit dem Kunden mittels Technologie identifiziert. ...
... Dieser zentrale Aspekt der Personalisierung bildet einen starken Kontrast zur Customization, bei der die Angabe von Nutzerpräferenzen proaktiv aus Kundensicht erforderlich ist (z. B. über Produktkonfiguratoren im Online-Shop) (Arora et al. 2008). So nimmt bei der Personalisierung auch die individuelle Wertschaffung für den Nutzer einen höheren Stellenwert ein, als bei der Customization (Arora et al. 2008). ...
... B. über Produktkonfiguratoren im Online-Shop) (Arora et al. 2008). So nimmt bei der Personalisierung auch die individuelle Wertschaffung für den Nutzer einen höheren Stellenwert ein, als bei der Customization (Arora et al. 2008). Nutzerbezogene Daten und die damit verbundene Personalisierung des Dienstleistungsangebots gelten daher als ausschlaggebend für die Stiftung eines Mehrwerts für den Nutzer. ...
Aktuelle technologische Entwicklungen ermöglichen zunehmend vernetzte, kontextsensitive und proaktive personalisierte Dienstleistungen. Mit den steigenden Erwartungen von Nutzern, wächst der Druck auf Anbieter mit den technologischen Möglichkeiten Schritt zu halten, um wettbewerbsfähig zu bleiben. In Anbetracht der Relevanz aktueller Entwicklungen werden in diesem Artikel Nutzenpotentiale der automatisierten Personalisierung durch eine strukturierte Literaturanalyse in den Anwendungsbereichen Smart Home, Smart Mobility, Health Care und E-Learning identifiziert und Implikationen einer wertgetriebenen Entwicklungsperspektive diskutiert.
... Toolkits should be interactive, allowing customers "to visualize and experience customized products prior to purchase or to learn from the experience of others" (Arora, et al., 2008). Therefore, they should be designed with features that enable the user to obtain feedback about the co-design process and positive reinforcement about her progress through her self-design experience. ...
... Instead of a mass marketing approach which could be less effective in the current digital economy, personalization could be a more practical and significant way of contributing both to customers' shopping experience (Alalwan, 2018). In other words, a high level of customers' expectations and needs matching could be attained by a high level of personalization on the targeted online platforms (Arora et al., 2008;Lal and Dwivedi, 2010;Shareef et al., 2017). For instance, Alalwan (2018) found that the level of customization existing in social media advertising predicts not only the customer's purchase intention but also the customer's perception that such ads are really useful as well as entertaining. ...
Purpose This study aims to examine the impact of mobile interactivity dimensions (active control, personalization, ubiquitous connectivity, connectedness, responsiveness and synchronicity) on customer engagement. Design/methodology/approach A quantitative field survey study was conducted to collect the required data from actual users of mobile shopping in three countries: Jordan, the United Kingdom (UK) and Saudi Arabia. Findings The results are based on structural equation modelling and support the impact of five dimensions of mobile interactivity: active control, personalization, ubiquitous connectivity, responsiveness and synchronicity. The impact of connectedness is not supported. The results also support the significant impact of customer engagement on customer loyalty. Research limitations/implications This study only considered the shopping activities conducted by mobile channels, while other channels (e.g., online channels, traditional channels and social media shopping channels) are not considered. Furthermore, the current model does not consider the impact of personal factors (e.g., technology readiness, self-efficacy and user experience). The results of the current study present a foundation that can guide marketers and practitioners in the area of mobile shopping. Originality/value This study enriches the current understanding of the impact of mobile interactivity on mobile shopping, as well as how mobile interactivity can enhance the level of customer engagement.
... Instead of a mass marketing approach which could be less effective in the current digital economy, personalization could be a more practical and significant way of contributing both to customers' shopping experience (Alalwan, 2018). In other words, a high level of customers' expectations and needs matching could be attained by a high level of personalization on the targeted online platforms (Arora et al., 2008;Lal and Dwivedi, 2010;Shareef et al., 2017). For instance, Alalwan (2018) found that the level of customization existing in social media advertising predicts not only the customer's purchase intention but also the customer's perception that such ads are really useful as well as entertaining. ...
One of the main aspects of the Web 2.0 revolution has been social commerce that has resulted in many people across the world increasingly engaging with commercial activities over social media platforms. However, the academic and research interest in social commerce is still low, and more studies are required to accelerate awareness of the most important issues relating to social commerce, in particular, social trust and value cocreation. Thus, the present study aims to propose a conceptual model that is intended to enable greater understanding of the causal interactions between social commerce constructs, social trust, and customer value cocreation. We collected data using a sample of 300 followers and fans of online Facebook communities, and we analysed them by using a structural equation model. The results show that social commerce constructs positively impact on social trust. Furthermore, we found that social trust positively impacts on the three dimensions of customer value cocreation. We found that social trust mediates the relationship between the social commerce and customer value cocreation dimensions. The paper presents a considerable theoretical contribution for being the first study that links social commerce constructs with social trust. The linkage between social commerce constructs, social trust, and customer value cocreation dimensions will also be beneficial for social media marketing strategists and managers.
... Instead of a mass marketing approach which could be less effective in the current digital economy, personalization could be a more practical and significant way of contributing both to customers' shopping experience (Alalwan, 2018). In other words, a high level of customers' expectations and needs matching could be attained by a high level of personalization on the targeted online platforms (Arora et al., 2008;Lal and Dwivedi, 2010;Shareef et al., 2017). For instance, Alalwan (2018) found that the level of customization existing in social media advertising predicts not only the customer's purchase intention but also the customer's perception that such ads are really useful as well as entertaining. ...
Social media commerce has been one of the fastest-growing areas over recent years. However, only a limited number of studies have addressed the related issues of social media commerce. It was also noticed that extant literature did not explore or link the impact of social commerce constructs on social trust and how this could impact the customer value co-creation. Hence, the current research aims to identify this gap and to propose a conceptual framework that highlights the linkage between social commerce constructs, social trust, and customer value co-creation. In line with this, a number of exploratory interviews were conducted to gain further understanding about how the customer’s perception of customer value co-creation and social trust could be affected by the role of social commerce. Accordingly, the current model proposes that social commerce constructs (second-order; ratings and reviews, recommendations and referrals, and forums and communities) impact social trust, which in turn affects customer value co-creation dimensions (functional value, hedonic value, and social value) in social network sites (SNSs). Theoretical and practical implications are provided.
This paper considers price competition in a market where two firms sell a homogenous service to a continuum of customers differing with respect to some exogenous characteristic. Our paper's novelty consists of explicitly acknowledging a distinctive property of many services in that firms incur customer‐specific service costs after the contract is signed. Hence, not only the customers' willingness‐to‐pay and as such demand but also the firms' supply are related to customer characteristics. In this paper, we shed light on the implications thereof for optimal pricing and market segmentation strategies in a monopoly as well as a duopoly market. Importantly, we stress the profitability of services by demonstrating that firms in highly competitive industries still earn positive expected profits in equilibrium. This article is protected by copyright. All rights reserved
Der Beitrag beschäftigt sich mit den Vorteilen („Licht“) und Nachteilen („Schatten“) der Automatisierung und Personalisierung von Dienstleistungen. Zwar werden beide Bereiche in der Wissenschaft und Praxis häufig gemeinsam behandelt, jedoch werden sie hier getrennt thematisiert, um die Bereiche differenzierter behandeln zu können. Für die Automatisierung und Personalisierung von Dienstleistungen werden jeweils die begrifflichen Grundlagen geschaffen, die Treiber für die zukünftige Entwicklung adressiert, die vielfältigen Einsatzfelder thematisiert, die Vor- und Nachteile explizit herausgearbeitet und schließlich verschiedene Forschungsfragen benannt. Der Beitrag schließt mit einer Zusammenfassung und einem Ausblick auf automatisierte Dienstleistungen als Zukunftsdisziplin des Dienstleistungsmanagements.
Mobile Apps etablieren sich zunehmend als wichtiges Instrument zur Vermarktung und Erstellung von Dienstleistungen. Aufgrund der individuellen Nutzung mobiler Endgeräte, auf denen die Apps gespeichert werden, eignen sie sich in besonderer Weise für eine personalisierte Ansprache von Interessenten und Kunden. Im vorliegenden Beitrag werden Grundlagen und Gestaltungsmöglichkeiten einer Personalisierung auf der Grundlage von Persönlichkeitsmerkmalen der App-Nutzer beschrieben. Ausgehend von den Ergebnissen einer empirischen Untersuchung bei 230 Probanden / -innen werden außerdem Hinweise für die praktische Umsetzung der persönlichkeitsbasierten Personalisierung gegeben.
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We evaluated 4 “experience industry” strategies for enhancing the quality of immediate experiences for 4-H youth: theming, adding multisensory experiences, personalizing interactions, and providing memorabilia. These strategies are commonly used by theme parks, restaurants, resorts, attractions, and other experience industry organizations, but their application to youth services is sporadic. 4-H youth (n = 30) participated in a series of 8 outdoor recreation activity sessions. Each activity session, 1 per week for 8 consecutive weeks, was structured using a unique combination of the 4 strategies. Participants completed questionnaires measuring 5 dimensions of experience quality after each activity session. Theme and personalization of experiences were found to significantly increase experience quality.
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A large number of visualization tools have been created to help decision makers understand increasingly rich databases of product, customer, sales force, and other types of marketing information. This article presents a framework for thinking about how visual representations are likely to affect the decision processes or tasks that marketing managers and consumers commonly face, particularly those that involve the analysis or synthesis of substantial amounts of data. From this framework, the authors derive a set of testable propositions that serve as an agenda for further research. Although visual representations are likely to improve marketing manager efficiency, offer new insights, and increase customer satisfaction and loyalty, they may also bias decisions by focusing attention on a limited set of alternatives, increasing the salience and evaluability of less diagnostic information, and encouraging inaccurate comparisons. Given this, marketing managers are advised to subject insights from visual representations to more formal analysis.
Customers are now active collaborators in creating value. Companies are increasingly engaging in mass customization and offering consumers a "choiceboard" (or a menu of choices) of various features and options for configuring their own products and services. The authors discuss the use of experimental choice menus for assessing customers' preferences and price sensitivities for the variety of features and options that might be offered by a firm in its choiceboard. The proposed approach directly analyzes customers' portfolio of choices from each of several experimental menus by estimating the utility for each menu item as a function of its characteristics, its price, and other specific attributes such as multifeature discounts. The authors accommodate customer heterogeneity in the utilities, allow for correlation of the utilities across items, and incorporate constraints in menu choices. Various technical issues and methodological contributions are discussed. The authors illustrate the approach in a commercial application of a customized Web-based information service, which is typical of offerings in the information economy. To assess predictive performance, the authors compare the proposed approach with alternative traditional approaches. The authors conclude with a discussion of the types of insights that can be obtained from this approach to menu choices and the managerial implications of these findings.