Creating Effective Online Customer Experiences

Abstract

Creating effective online customer experiences through well-designed product web pages is critical to success in online retailing. How such web pages should look specifically, however, remains unclear. Previous work has only addressed a few online design elements in isolation, without accounting for the potential need to adjust experiences to reflect the characteristics of the products or brands being sold. Across 16 experiments, this research investigates how 13 unique design elements shape four dimensions of the online customer experience (informativeness, entertainment, social presence, and sensory appeal) and thus influence pur- chase. Product (search vs. experience) and brand (trustworthiness) characteristics exacerbate or mitigate the uncertainty inherent in online shopping, such that they moderate the influence of each experience dimension on purchases. A field experiment that manipulates real product pages on Amazon.com affirms these findings. The results thus provide managers with clear strategic guidance on how to build effective web pages.

Full text

Article
Creating Effective Online
Customer Experiences
Alexander Bleier, Colleen M. Harmeling, and Robert W. Palmatier
Abstract
Creating effective online customer experiences through well-designed product web pages is critical to success in online retailing.
How such web pages should look specifically, however, remains unclear. Previous work has only addressed a few online design
elements in isolation, without accounting for the potential need to adjust experiences to reflect the characteristics of the products
or brands being sold. Across 16 experiments, this research investigates how 13 unique design elements shape four dimensions of
the online customer experience (informativeness, entertainment, social presence, and sensory appeal) and thus influence pur-
chase. Product (search vs. experience) and brand (trustworthiness) characteristics exacerbate or mitigate the uncertainty
inherent in online shopping, such that they moderate the influence of each experience dimension on purchases. A field experiment
that manipulates real product pages on Amazon.com affirms these findings. The results thus provide managers with clear strategic
guidance on how to build effective web pages.
Keywords
online customer experience, online design elements, online retailing, Taguchi design, web design
Online supplement: https://dx.doi.org/10.1177/0022242918809930
With more than 350 million products listed on Amazon.com
alone (360pi 2016), success in the increasingly competitive
online domain depends on sellers’ ability to orchestrate verbal
and visual stimuli (i.e., design elements) on product web pages
to effectively convert page visitors into buyers (Schlosser et al.
2006). Insights into which design elements make for effective
product web pages are however still largely based on manag-
ers’ intuitions or, at best, ad hoc A/B testing. Academic
research typically focuses on a single design element or just
a few across a limited number of products or brands. It also
often neglects the mechanisms through which design elements
affect purchase or employs theoretical perspectives (e.g., infor-
mation processing) that conceptually limit their effects a priori
to a single function (e.g., information transmission). Yet each
encounter with a product web page—the virtual space that
presents a product and illustrates its value to the customer—
evokes a multidimensional experience that goes beyond a pure
conveyance of factual information (Brakus, Schmitt, and Zar-
antonello 2009; Lemon and Verhoef 2016). The objective of
this research is therefore to understand how online design ele-
ments shape multidimensional customer experiences to influ-
ence purchase and how these experiences should be customized
depending on the products or brands sold.
The online customer experience at the heart of this research
comprises a customer’s subjective, multidimensional psycho-
logical response to a product’s presentation online. We argue
that this experience goes beyond cognitive (informativeness)
and affective (entertainment) dimensions typically conceptua-
lized in extant research (Novak, Hoffman, and Yung 2000) and
also includes social (social presence; Wang et al. 2007) and
sensory (sensory appeal; Jiang and Benbasat 2007a) dimen-
sions. Furthermore, we identify 13 web page design elements,
such as product descriptions, photos, and comparison matrices,
that each may help shape the online experience and are ubiqui-
tous in a wide range of industries and web page formats. This
multidimensional framework more closely resembles the con-
ceptualization of offline experiences (Brakus, Schmitt, and
Zarantonello 2009; Lemon and Verhoef 2016) and helps more
accurately capture the mechanisms by which design elements
affect product purchase.
How effectively each experience dimension elicits pur-
chases, however, may vary depending on characteristics of the
offered products and brands that exacerbate or alleviate the
uncertainty inherent in online shopping (Bart et al. 2005;
Schlosser et al. 2006). First, the degree to which consumers
Alexander Bleier is Assistant Professor of Marketing, Frankfurt School of
Finance & Management (e-mail: a.bleier@fs.de). Colleen M. Harmeling is
Assistant Professor of Marketing, Florida State University (e-mail:
charmeling@business.fsu.edu). Robert W. Palmatier is Professor of
Marketing, John C. Narver Chair of Business Administration, University of
Washington (e-mail: palmatrw@uw.edu).
Journal of Marketing
1-22
ªAmerican Marketing Association 2018
Article reuse guidelines:
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DOI: 10.1177/0022242918809930
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can evaluate a product solely on the basis of factual information
(search qualities) rather than needing direct physical experi-
ence (experience qualities) implies the level of uncertainty
associated with assessing that product online (Hong and Pavlou
2014). Second, customers may also be uncertain about the
accuracy and truthfulness of sellers’ product presentations, yet
a brand’s trustworthiness may alleviate this uncertainty
(Pavlou, Liang, and Xue 2007). We leverage our multidimen-
sional online framework of the online customer experience to
investigate how these two primary sources of uncertainty deter-
mine the effects of each experience dimension on purchase
(Dimoka, Hong, and Pavlou 2012).
To ensure the broad scope and generalizability of our
research, we collaborate with a specialized online content
agency and four Fortune 1000 firms, diverse in their industries,
brands, and products (i.e., consumer packaged goods, con-
sumer electronics, industrial electronics, and consumables).
In Study 1, we conduct large-scale online experiments that
involve 16 products from 11 brands, for which the online con-
tent agency created 256 unique “Amazon look-alike” product
web pages. On these pages, we manipulated 13 design elements
according to an orthogonal array design (Taguchi 1986) and
then tested the pages among 10,470 randomly assigned respon-
dents. With the resulting data, we estimate a joint model that
isolates the relative influences of each design element on each
dimension of the online customer experience, the relative
effects of each experience dimension on purchase, and the
moderating influences of product type and brand trustworthi-
ness on the effects of the dimensions on purchase. A field
experiment in Study 2 tests these effects with real Amazon
product pages, on which we used design elements to create
specific experiences to observe the effects on sales.
We offer three main contributions to the literature. First,
data from 16 experiments in Study 1 expand insights into
online customer experiences and identify four dimensions—
namely, informativeness, entertainment, social presence, and
sensory appeal—that act as the underlying mechanisms by
which design elements influence purchase (Novak, Hoffman,
and Yung 2000; Rose, Clark, and Hair 2012). Prior online
research has mainly focused on informativeness and entertain-
ment; however, we show that the effects of social presence are
just as strong as those of informativeness, and sensory appeal
offers additional insights. Second, we find that uncertainty
about the offered product and its seller’s brand influences the
effects of the customer experience dimensions on purchase.
Using actual product web pages on Amazon.com, a field
experiment in Study 2 validates the lab results to show that
search products benefit from a more informative experience
but experience products benefit from a more social experience.
Third, we establish an online customer experience “design
guide,” with actionable advice for marketers on how to strate-
gically orchestrate design elements to shape effective online
experiences in an era of increased web design importance
(Wolfinbarger and Gilly 2003). Specifically, we depict how
to evaluate the design elements that currently constitute their
digital inventory, which new elements to invest in and develop,
and how to negotiate and assess contracts for premium content
with online retailers.
Dimensions, Moderators, and Antecedents
of the Online Customer Experience
In contrast with brick-and-mortar retail, customers assess prod-
ucts online not through physical interaction but through verbal
and visual stimuli (design elements) deployed on product web
pages. A broad stream of research conceptualizes offline
experiences as consisting of multiple, separate, but related
dimensions (e.g., cognitive, affective, sensory, social, physical)
(Brakus, Schmitt, and Zarantonello 2009; Lemon and Verhoef
2016; Schmitt 1999; Verhoef et al. 2009). Yet research has
treated online experiences far more simplistically (Novak,
Hoffman, and Yung 2000; Steenkamp and Geyskens 2006),
often a priori limited to their informativeness (see Table 1).
In line with the four basic systems—cognition, affect, rela-
tionships, and sensations—commonly studied in psychology
and sociology (Anderson 1985; Pinker 1997), we conceptualize
the online customer experience as consisting of four dimen-
sions: informativeness (cognitive), entertainment (affective),
social presence (social), and sensory appeal (sensory). Consis-
tent with our multidimensional perspective, we do not expect a
one-to-one relationship between any specific design element
and an experience dimension (Brakus, Schmitt, and Zaranto-
nello 2009).
We next introduce and review each dimension of the online
customer experience. Then, we explain why the influence of
each dimension on consumers’ purchase decisions might
depend on the uncertainty associated with specific products
or brands. Finally, we present the design elements that manag-
ers can use to build product web pages to shape customer
experiences (see Figure 1).
Dimensions of the Online Customer Experience
Defined as “the extent to which a website provides consumers
with resourceful and helpful information” (Lim and Ting 2012,
p. 51), informativeness is the primary cognitive dimension of
the online customer experience. It captures a web page’s con-
tribution to helping the consumer make a pending purchase
decision, which involves thinking, conscious mental process-
ing, and, typically, problem solving (Gentile, Spiller, and Noci
2007). Informativeness captures the functional aspect and
value of the experience to the customer (Verhoef et al. 2009)
and is generally impersonal, outcome oriented, and objective
(Schlosser, White, and Lloyd 2006). This fact-based dimension
pertains to the information that remains after interacting with a
web page, which can improve attitudes toward a website
(Hausman and Siekpe 2009; Hsieh et al. 2014).
Customer interactions with products online can evoke affec-
tive responses and might be enjoyed for their own sake, without
regard for functional considerations. Entertainment,orthe
immediate pleasure the experience offers, regardless of its abil-
ity to facilitate a specific shopping task (Babin, Darden, and
2Journal of Marketing XX(X)

Table 1. Relevant Research on the Effectiveness of Design Elements on Product Web Page Performance.
Categorization of Design Elements Based on Form
Theoretical Perspective
Tested Underlying
Mechanisms
(Design Element
Function)
Product
Web Page
Performance Key Findings
Verbal Elements Visual Elements
Combined Verbal
and Visual Elements
Studies
Linguistic Style
Descriptive Details
Bulleted Features
Return Policy Information
Product Feature Crop
Lifestyle Picture
Picture Size
Product Video
Expert Endorsement
Comparison Matrix
Customer Star Ratings
Recommendation Agent
Content Filter
Cooke et al. 2002 PPInformation processing None Product evaluation When unfamiliar products are presented independently, additional descriptive
detail improves product evaluations. When presented alongside other
attractive products from a recommendation agent, descriptive detail worsens
product evaluations.
Ha
¨ubl and Trifts
2000
PPInformation processing None Purchase decision quality The use of recommendation agents and comparison matrices decreases the size
but increases the quality of customers’ consideration sets and also improves
purchase decision quality.
Hauser et al. 2009 PPPInformation processing None Purchase intentions Website content can be customized through the strategic selection of design
elements to maximize purchase intentions, based on customer information-
processing styles inferred from past browsing behaviors.
Huang, Lurie, and
Mitra 2009
PP P Information processing Search depth (time spent on
website)
Likelihood of product
purchase
Customer star ratings, expert endorsements, and multimedia presentations (e.g.,
product videos) are more effective for experience than search goods in driving
purchase.
Ludwig et al. 2013 PPInformation processing None Conversion rates Linguistic style can signal a customer’s similarity to other customers of a product,
which can influence purchase.
Roggeveen et al.
2015
PVividness Sensory appeal Product preference,
willingness to pay
Product videos increase a web page’s vividness and create experiences that mimic
real products, ultimately enhancing customers’ preferences and willingness to
pay.
Shi and Zhang
2014
PInformation processing None Consumer price and
promotion sensitivity
Recommendation agents vary in effectiveness, depending on the customer’s past
experience and decision processes.
Song and Zinkhan
2008
PPSocial presence
(interactivity)
Website communication,
controllability,
responsiveness
Website effectiveness
(satisfaction, loyalty,
attitude, quality)
Content filters that hinder access to information on a website can reduce
responsiveness of the site. A more conversational linguistic style increases
perceptions of the website as communicative, controllable, and responsive,
which enhance perceptions of website effectiveness.
Wang et al. 2007 PPSocial presence (social
response theory)
Website socialness, pleasure
and arousal, and flow
Website patronage
intentions
Social presence, website informativeness, and entertainment are key dimensions of
the online experience that interact to increase patronage intentions. More
conversational linguistic styles can increase perceived social presence and
encourage purchases.
Zhu and Zhang
2010
PUnspecified None Product demand The influence of customer star ratings on product demands is weaker for popular
products and for products designed for offline use.
This study PPPPPPPPPPPPPMultidimensional
customer experience
Informativeness,
entertainment, social
presence, sensory appeal
Product purchase Design elements can be used to create four distinct experience dimensions
(informativeness, entertainment, social presence, and sensory appeal) that vary
in the degree to which they influence purchase. based on a product’s search
versus experience qualities and the trustworthiness of the brand.
Notes: To derive a list of relevant research, we examined articles pertaining to online product marketing published in the last ten years in Journal of Marketing,Journal of Marketing Research,Marketing Science, and Journal of
Consumer Research. To be included, the research needed to be empirical in nature and focus on product web page design elements available to manufacturers that sell through a retailer website. We exclude studies of
retailer-controlled website design elements (e.g., navigation), email marketing, online advertising, word of mouth, or search.
3

Griffin 1994), is thus a key dimension of the online customer
experience. Entertainment reflects an appreciation for the
“spectacle” experienced on the web page, involves the fun and
play of online shopping, and accords more than just an
achievement-oriented purchase opportunity (Childers et al.
2001; Mathwick, Malhotra, and Rigdon 2001). As such, enter-
tainment can trigger arousal in web page visitors (Hsieh et al.
2014) and reduce cart abandonment in online stores (Kukar-
Kinney and Close 2010).
To match the benefits of offline experiences, online sellers
increasingly work to provide a sense of social presence on their
web pages (Wang et al. 2007). Social presence refers to the
warmth, sociability, and feeling of human contact that a web
page confers (Gefen and Straub 2003). Extant research shows
that the social presence of a website can increase perceived
tangibility and feelings of psychological closeness to a product
(Darke et al. 2016). It can also increase pleasure, arousal, and
flow during online shopping (Wang et al. 2007), as well as
purchase intentions (Hassanein and Head 2007) and loyalty
(Cyr et al. 2007).
Finally, the sensory component of the customer experience
includes aspects that stimulate sight, sound, smell, taste, or
touch (Gentile, Spiller, and Noci 2007). Zajonc (1980) suggests
that sensory-level processing and retrieval occurs automati-
cally and drives preferences. In an online environment, sensory
appeal refers to “the representational richness of a mediated
environment as defined by its formal features” (Steuer 1992,
p. 81) or the way a web page stimulates the senses. Perception
of beauty and aesthetically pleasing stimuli are part of sensory
appeal (Schmitt 1999). Although the online environment limits
the scope of sensory experiences, sensations can be evoked
through imagery (e.g., pictures, videos) (Elder et al. 2017).
Thus, sensory appeal can affect perceptions of product
performance (Weathers, Sharma, and Wood 2007) and purchase
intentions (Schlosser 2003).
Uncertainty and the Moderating Role of Product Type
and Brand Trustworthiness
Online shopping often comes with uncertainties that do not
arise offline and that might affect how certain experience
dimensions influence purchase (Dimoka, Hong, and Pavlou
2012; Pavlou, Liang, and Xue 2007). First, online, customers
cannot touch and feel the merchandise in which they are inter-
ested, which can create uncertainty in product assessment
before purchase (Kim and Krishnan 2015). This uncertainty
tends to be more severe for experience products, for which the
most relevant attributes are discoverable only through direct
physical contact, than for search products, whose most relevant
attributes are assessable from presented information without
physical interaction (Hong and Pavlou 2014; Weathers,
Sharma, and Wood 2007). How consumers attend to and inter-
pret product information differs between search and experience
products (Huang, Lurie, and Mitra 2009). Thus, the most effec-
tive type of experience for selling these two types of products
might also differ. For example, Weathers, Sharma, and Wood
(2007) show that web pages that appeal to the senses may be
more beneficial for experience products, whose evaluation
requires sensory information.
Second, the physical separation between customers and
products requires customers to have faith in the accuracy and
truthfulness of the product web page. Yet they may experience
uncertainty about online sellers’ ability and integrity to convey
product information, depending on the trustworthiness of the
seller brand (Pavlou, Liang, and Xue 2007). Trust reflects the
“willingness to rely on an exchange partner in whom one has
Verbal Elements
•Linguistic style
•Descriptive product detail
•Bulleted product features
•Return policy information
Webpage Design Elements
Verbal/Visual Elements
•Customer star ratings
•Expert endorsement
•Comparison matrix
•Recommendation agent
•Content ilter
Online Customer Experience
Informativeness
(cognitive)
Entertainment
(affective)
Visual Elements
•Product feature crop
•Lifestyle photo
•Photo size
•Product video Social Presence
(social)
Customer Purchase
Sensory Appeal
(sensory)
Factors Affecting Uncertainty
Product Type
(search/
experience)
Brand
Trustworthiness
Figure 1. Designing the online customer experience.
Notes: Constructs in italics were experimentally manipulated across 16 products and 11 brands. N ¼10,470.
4Journal of Marketing XX(X)

confidence” (Moorman, Zaltman, and Deshpande 1992,
p. 315). A significant stream of research shows the importance
of trust online (Urban, Sultan, and Qualls 2000), in which
sellers’ trustworthiness determines customers’ research and
purchase decisions (Gefen, Karahanna, and Straub 2003; Hoff-
man, Novak, and Peralta 1999). Trust online is also closely
connected with web design (Shankar, Urban, and Sultan
2002; Urban, Amyx, and Lorenzon 2009). Several studies sug-
gest that low trustworthiness can be overcome through purpo-
seful web page design (Schlosser, White, and Lloyd 2006;
Wang, Beatty, and Foxx 2004) or by customizing content to
customers’ preferences (Urban et al. 2009). Specific experience
dimensions might also be instrumental to alleviating low trust-
worthiness. Bart et al. (2005) show that entertaining online
experiences may compensate for an initial lack of trust in a
brand. Social presence may serve a similar purpose (Gefen and
Straub 2003). Extant work suggests that the product- and
brand-related uncertainty inherent in online shopping can influ-
ence the effects of experience dimensions on purchase. We thus
focus our moderation analysis on product type and brand trust-
worthiness as the respective primary determinants of these two
types of uncertainty (Hong and Pavlou 2014; Pavlou, Liang,
and Xue 2007), instead of other product, brand, or service
attributes.
Design Elements That Create the Online Customer
Experience
The product web page is at the heart of the online customer
experience. It consists of basic design elements, defined as
verbal and/or visual stimuli that provide the building materials
for any given page. To identify the most important elements,
we reviewed ten years of research on website design published
in Journal of Marketing,Journal of Marketing Research,Mar-
keting Science, and Journal of Consumer Research, as well as
various specialized journals. Our focus was on design elements
that relate directly to the product presentation and are typically
available to firms selling through retailers such as Amazon; we
excluded structural elements, such as navigation, menus, icons,
and overall organization, that operate at the website level and
are under the control of the host retailer. Although they operate
through many aliases, we identified 13 elements that we clas-
sify by their form (see Appendix A) into three groups: verbal
elements that use text and typographical features, visual
elements that use images and pictures, and combinations of
both. Table 1 summarizes research on each of these 13 design
elements.
Verbal elements. Verbal elements involve the written word. In
this category, we consider linguistic style, descriptive detail,
the number of bulleted features, and return policy information
statements. The most basic aspect of textual elements is the
way information is presented. The linguistic style in which
verbal content is conveyed or the characteristics of the text—
including word choice and use of questions, certain pronouns
(you, your), and adjectives—can affect product conversions
and consumer perceptions of website effectiveness (Ludwig
et al. 2013; Song and Zinkhan 2008). Song and Zinkhan
(2008) provide preliminary evidence that these effects occur
through the impact of linguistic style on social presence. To
capture the degree of elaboration of the product descriptions on
a web page, we examine descriptive detail. Providing more
attribute information generally increases product evaluations
and purchase likelihood (Cooke et al. 2002; Hauser et al.
2009). The number of bulleted features indicates how many
product features appear in an abbreviated list at the top of the
web page. Though prevalent on many product web pages, to
our knowledge, research has not empirically investigated its
effects on purchase. Return policy information refers to
whether the web page contains information about the terms
by which customers may return the product.
Visual elements. Visual elements subsume all content presented
in photographic or illustrated form and can convey symbolic
meaning and pictorial information (Scott 1994). We investigate
feature crops, lifestyle photos, photo size, and product videos.
Unlike pictures of the product as a whole, feature crops zoom
in on a key product feature that would otherwise not be visible.
Lifestyle photos connect the product with customers’ lives,
such as by depicting people using it or living with it in a regular
setting. They explicitly capture or imply human interaction
with the product (Babin and Burns 1997). We also investigate
photo size. Park, Lennon, and Stoel (2005) show that larger
product images can increase purchase intentions. Finally, a
product video can demonstrate the product and its key features.
Videos including human voices can serve as cues for human
characteristics and influence perceptions of social presence and
sensory appeal (Moon 2000; Roggeveen et al. 2015).
Combined verbal and visual elements. Customer star ratings,
expert endorsements, comparison matrices, recommendation
agents, and content filters all combine verbal and visual quali-
ties. Customer star ratings are aggregations of user-generated
product ratings, depicted visually with a series of stars and next
to the total number of reviews (Chevalier and Mayzlin 2006).
Expert endorsements are also product evaluations, but assem-
bled from distinguished experts in the category, such as product
testing firms, and generally include a graphic depiction, such as
a seal (Ansari, Essegaier, and Kohli 2000). Comparison
matrices are tables to compare the focal product with other
products from the same category on multiple characteristics.
Product information is typically presented as pictures of alter-
natives (columns) and text describing attributes (rows). Rec-
ommendation agents combine verbal and visual information to
generate a list of alternatives to the focal product (Lamberton
and Stephen 2016). Comparison matrices and recommendation
agents can improve purchase decision quality (Ha¨ubl and Trifts
2000; Knott, Hayes, and Neslin 2002). Content filters, such as
“show more” buttons, allow customers to dictate what, when,
and how much verbal and visual content appears on the web
page (Hauser et al. 2009; Mathwick and Rigdon 2004). Of the
combined elements, star ratings have received most empirical
Bleier et al. 5

attention, though studies typically test their effects directly on
purchase, without considering underlying mechanisms (Cheva-
lier and Mayzlin 2006; Hauser et al. 2009; Ludwig et al. 2013;
Zhu and Zhang 2010). Table 1 shows evidence for the effects of
design elements on purchase, while the underlying mechanisms
remain mostly unclear.
Testing Product Web Page Design, the
Online Customer Experience, and Purchase
We extend research on design elements and the online cus-
tomer experience with two studies. In Study 1, we aim to (1)
understand the relative importance of each of the four online
customer experience dimensions as key mediators in the rela-
tionship between web page design elements and customer pur-
chase, (2) determine which of the 13 design elements are most
useful in creating each experience dimension, and (3) assess
how product type and brand trustworthiness influence the
effects of the experience dimensions on purchase. In Study 2,
we manipulate real Amazon product pages from the insights
gleaned from Study 1 to assess the effects on actual sales.
Study 1: Design, Dimensions, and
Implications of Online Customer Experiences
We partnered with four Fortune 1000 firms in multiple indus-
tries (i.e., consumer packaged goods, consumer electronics,
industrial electronics, and consumables) (Appendix B) and
tested our conceptual model with 16 products (4 per firm),
representing 11 brands. Together with a specialized online
content agency, we designed and created mock Amazon prod-
uct web pages for each product that varied the 13 design ele-
ments on two levels each, according to an orthogonal array
design (Taguchi 1986). On Amazon.com, vendors can select
from a range of module templates and then manage the content
of each module within the retailer’s restrictions. Appendix C
shows an example web page.
1
Experimental Stimuli
Appendix A provides a summary of the two manipulated levels
for each of the 13 design elements. For verbal elements, we
manipulated linguistic style as either a journalistic tone (Level
1) or conversational tone (Level 2). For the journalistic tone,
the neutral product descriptions featured few or no adjectives,
no self-relevant words (e.g., “you,” “your”) (Carmody and
Lewis 2006; Song and Zinkhan 2008), no questions, and no
exclamation points. For the conversational tone, the descrip-
tions were more engaging and included adjectives, self-
relevant words, words that insinuate instantaneous gratification
(e.g., “fast,” “instant,” “quickly”), and self-reflective questions
(e.g., “Wouldn’t it be great to have high-speed Internet
everywhere?”) (Ahluwalia and Burnkrant 2004; Ludwig et al.
2013). Although linguistic style determines how product
descriptions convey information, it does not affect the actual
amount of information presented. To manipulate this facet, we
used the descriptive detail design element. At Level 1, product
descriptions contained approximately one-third the amount of
information (i.e., number of attributes discussed) that they con-
tained at Level 2. We manipulated bulleted features as either
three (Level 1) or five (Level 2) bullets on the web page;
previous research indicates that these numbers are relevant
(Shu and Carlson 2014). Return policy information was the
absence(Level1)orpresence(Level2)ofthestatement
“Return Policy: Items can be returned within 30 days of
receipt” on the page.
For visual elements, we manipulated the feature crop ele-
ment by either not replacing (Level 1) or replacing (Level 2)
one of the product hero shots with a close-up picture of a
specific feature of the product. A lifestyle picture, which con-
nects the product with the real world in an actual usage situa-
tion, was either not included (Level 1) or included (Level 2) to
replace one of the hero shots. At Level 2 of the picture size
design element, all pictures were 25%larger than at Level 1.
Product video indicated the absence (Level 1) or presence
(Level 2) of a video about the product.
For combined verbal and visual elements, we manipulated
customer star ratings, by either excluding (Level 1) or includ-
ing (Level 2) the average star rating for the product.
2
We
manipulated expert endorsement using a quality seal from a
fictitious third-party product rating agency, to avoid any poten-
tial effects of familiarity with existing agencies, that might
differ across respondents. At Level 1, there was no seal, while
at Level 2, this seal replaced one of the hero shots. We manipu-
lated the comparison matrix element as the absence (Level 1)
or presence (Level 2) of a table that compared the focal product
with similar products from the same firm and category on key
product characteristics. The recommendation agent featured
either the absence (Level 1) or the presence (Level 2) of a
section that displayed links to related products, again from the
same firm and category. For these two elements, we purposely
used products from the same manufacturer, to avoid any influ-
ences of additional brands for which consumers might hold
distinct views. The content filter element either did not permit
(Level 1) or permitted (Level 2) consumers to control the
amount of verbal and visual content shown on the page, using
“show more” buttons to reveal or hide parts of the modules.
Experimental Design
Testing the effects of such a large number of elements poses a
considerable empirical challenge. A full-factorial design would
have required building and analyzing 131,072 experimental
1
We designed this study to align with the context of Amazon.com, the largest
online retailer; most online retailers follow a similar approach. We disguised
the brand to protect the confidentiality of the participating firm.
2
To assess the unique effects of this element, we included no actual written
customer reviews on the page, used 4.5/5 stars for all manipulations, and held
the number of reviews constant across conditions.
6Journal of Marketing XX(X)

cells as web pages (2
13
combinations of design elements per
product 4 firms 4 products). With such an approach, we
could have investigated all potential interaction effects among
design elements, but it would have been infeasible to execute.
We therefore adopted a Taguchi (1986) orthogonal array
design, which reduced the required number of cells to 256
(16 combinations of design elements per product 4 products
4 firms). Thus, we can feasibly investigate the simultaneous,
causal direct effects of all 13 design elements.
Method
Sample and procedure. We recruited 10,470 workers via Ama-
zon Mechanical Turk for our 16 experiments (one per product).
Respondents, randomly assigned to one of the 16 experimental
cells within each experiment, were presented with the corre-
sponding web page and instructed to explore it for at least 45
seconds. Next, they completed a questionnaire with demo-
graphic questions, items for manipulation and realism checks,
and preexisting scales to measure purchase intentions and the
four experience dimensions (see Appendix D).
Measures. Appendix A contains the results of our manipulation
checks, which are all significant (p<.01), indicating success-
ful manipulation of the design elements. In addition, we used
two items to assess the realism of our web pages: “I could
imagine an actual web page to look like the one I just saw”
and “I believe that this web page could exist in reality” (a¼
.90) (Darley and Lim 1993). Respondents’ answers to these
items, on a seven-point scale (1 ¼“strongly disagree,” and
7¼“strongly agree”), indicated that our created web pages
established sufficient realism (M
composite score
¼5.41,
SD ¼1.29).
To assess the accuracy of our measures, we first conducted a
confirmatory factor analysis. The results indicate a good fit of
our measurement model to the data (w
2
(80) ¼2441.75, con-
firmatory fit index [CFI] ¼.98, Tucker–Lewis index [TLI] ¼
.98, root mean square error of approximation [RMSEA] ¼.05,
standardized root mean residual [SRMR] ¼.03). Moreover, in
support of convergent validity, all standardized factor loadings
are greater than .70 and significant at the 1%level. For each
construct, the average variance extracted (AVE) exceeds .50,
and the composite reliability is greater than .70. Cronbach’s
alpha values above .70 indicate internal consistency. In support
of discriminant validity, all AVEs are greater than the squared
correlations of the focal construct with any other construct (see
Table 2).
To evaluate multicollinearity among the experience dimen-
sions, we first calculated the variance inflation factors for each
construct. All values (informativeness ¼1.55, entertainment ¼
2.18, social presence ¼2.01, sensory appeal ¼2.58) fall below
the critical value of 5. Next, we examined the eigenvalues of
their correlation matrix. The condition number (k¼7.15) is
well below the critical threshold of 30. Altogether, these results
indicate that multicollinearity does not pose a concern. Last, we
conducted an exploratory factor analysis, which confirmed that
all items loaded onto their intended constructs (see Web
Appendix A). For the remaining analysis, we calculated com-
posite scores using the average of all scale items for each
construct.
To investigate the extent to which product type and brand
trustworthiness moderate the effects of the experience dimen-
sions on purchase, we collected additional data.
3
To capture a
product’s search versus experience focus (i.e., its type) unaf-
fected by the web pages on which it appeared in our experi-
ments, we first presented 452 respondents with randomly
selected hero shots of the 16 products and then asked them to
complete a questionnaire with corresponding search and expe-
rience quality measures (Weathers, Sharma, and Wood 2007).
Each respondent rated two products. We then computed the
average of the difference between the two items, which cap-
tured each product’s search and experience qualities over all
respondents. We similarly captured brand trustworthiness by
presenting 341 respondents with the logo of one of the 11
brands in our sample, along with a list of its associated product
categories. Each respondent rated a single brand on six trust-
worthiness items (Schlosser, White, and Lloyd 2006), which
we then averaged across respondents. Appendix D shows all
measurement items.
Results
To test our conceptual model, we combine the data from our 16
experiments (one for each product) and estimate a joint model
Table 2. Descriptive Statistics and Correlations
Variable M SD CR CA 12345
1. Informativeness 5.29 1.11 .90 .89 (.75)
2. Entertainment 4.16 1.49 .94 .93 .53 (.83)
3. Social presence 3.65 1.52 .95 .95 .39 .57 (.87)
4. Sensory appeal 3.97 1.34 .86 .85 .51 .61 .62 (.66)
5. Purchase intentions 3.91 1.77 .95 .95 .40 .55 .42 .43 (.88)
Notes: Means and standard deviations are based on composite scores; CA ¼Cronbach’s alpha; CR ¼composite reliability. AVE values are in parentheses.
3
Though not part of our conceptual framework, in an exploratory analysis we
also tested for the moderating effects of product type and brand trustworthiness
on the relationships between each design element and experience dimension.
Consistent with our conceptualization, only 11 of the 104 potential moderating
effects were significant, confirming the nomological validity of our model (see
Web Appendix B).
Bleier et al. 7

using covariance-based structural equation modeling with
maximum likelihood estimation. This approach allows us to
test the relative importance of each experience dimension as
a mediator of the link between design elements and purchase
intentions, while controlling for customer heterogeneity in
terms of age, gender, income, and education.
Mediation tests. To confirm the relevance of each experience
dimension as a mediator of the effects of design elements on
purchase, we ran a series of nested models and compared their
chi-square values with that of our proposed model (Table 3).
Model 1 is our proposed model with all four experience dimen-
sions as mediators. Models 2–5 test a set of three-dimension
models in which we removed the paths from each experience
dimension to purchase intentions, one by one. Models 6–15 test
all other possible combinations of experience dimensions.
Model 1 achieves good fit (w
2(16)
¼437.77, p<.01;
CFI ¼.980; TLI ¼.880; RMSEA ¼.050; SRMR ¼.009) and
performs significantly better than any alternative model; each
experience dimension partially mediates some design ele-
ments. We thus focus on the results of Model 1 with all four
experience dimensions in the remainder of our analyses.
4
Effects of experience dimensions on purchase. Columns 1–4 in
Panel A of Table 4 represent the effects of experience dimen-
sions on purchase intentions. In general, entertainment exhibits
the strongest effects (b¼.387, p<.01), followed by informa-
tiveness (b¼.118, p<.01), social presence (b¼.118,
p<.01), and sensory appeal (b¼.060, p<.01).
Effects of design elements on experience dimensions. Panel B of
Table 4 contains the effects of each design element on each
experience dimension, while accounting for the effects of all
other design elements. Customer star ratings emerge as a strong
driver of all experience dimensions (all bs.131, all ps<.01).
The same is true for picture size (bs.147, ps<.01). When
we control for the impact of all other elements, return policy
information and expert endorsement do not contribute signifi-
cantly to any experience dimension (ps>.05).
Column 5 of Table 4 further indicates that eight design
elements exert significant effects on the informativeness
dimension. The strongest effects stem from including customer
star ratings (b¼.211, p<.01), more bulleted features
(b¼.181, p<.01), a comparison matrix (b¼.168,
p<.01), more descriptive detail (b¼.153, p<.01), and larger
pictures (b¼.152, p<.01). Including a product video
(b¼.058, p<.01), a recommendation agent (b¼.049,
p<.05), and a lifestyle picture (b¼.047, p<.05) also drives
this dimension, though to a lesser extent.
Column6ofTable4showsthatninedesignelements
substantially influence entertainment. The most important are
picturesize(b¼.147, p<.01) and customer star ratings
(b¼.135, p<.01), which exert much stronger effects than a
comparison matrix (b¼.081, p<.01), more bulleted features
(b¼.077, p<.01), descriptive detail (b¼.064, p<.01), or
product video (b¼.056, p<.01). Using a conversational
linguistic style (b¼.052, p<.01) and including a product
feature crop (b¼.049, p<.05) also drive entertainment.
Column 7 of Table 4 shows that ten elements are relevant for
social presence. The most important are picture size (b¼.171,
p<.01), linguistic style (b¼.165, p<.01), customer star
ratings (b¼.162, p<.01), and lifestyle pictures (b¼.144,
p<.01). Comparably less important are bulleted features and
product feature crops (both b¼.042, p<.05). The effect
strengths of product videos (b¼.089, p<.01), descriptive
detail (b¼.088, p<.01), and a comparison matrix (b¼.064,
p<.01) lie somewhere in between. Including content filters
significantly decreases social presence (b¼–.087, p<.01).
Ten elements are also relevant for sensory appeal, as Col-
umn 8 of Table 4 shows. The most important are picture size
(b¼.190, p<.01) and product video (b¼.184, p<.01).
Linguistic style (b¼.069, p<.01), lifestyle pictures
(b¼.062, p<.01), product feature crops (b¼.055,
p<.01), and recommendation agents (b¼.048, p<.05) exert
positive but weaker effects. In between are the effects of cus-
tomer star ratings (b¼.131, p<.01), a comparison matrix
(b¼.104, p<.01), and more descriptive detail and bulleted
features (both b¼.099, p<.01).
5
Moderators of the relationship between experience dimensions and
purchase intentions. Panel C of Table 4 reports the moderation
results of our joint model. For search (experience) products, the
informativeness dimension of the experience becomes more
(less) important (b¼.019, p<.05), consistent with extant
research suggesting that consumers extract only minimal direct
information from advertisements for experience goods (Nelson
1974) and that information is more pertinent for search than
experience goods (Franke, Huhmann, and Mothersbaugh
2004). To assess experience goods, product attribute informa-
tion is less useful, perceived purchase risk is often high (Maity
and Dass 2014), and consumers turn to alternative signals on
the web page (Eroglu, Machleit, and Davis 2003). Accordingly,
we find that social presence (b¼–.023, p<.05) and sensory
appeal (b¼–.022, p<.05) are less (more) important for search
(experience) products. Heightened social presence and greater
sensory appeal can reduce perceived performance uncertainty
(Cyr et al. 2009; Weathers, Sharma, and Wood 2007), so they
are more important for purchase decisions involving experi-
ence products. For search products, consumers instead can
gather sufficient factual information from the web page, so
social presence and sensory appeal become less vital.
In addition, for more (less) trustworthy brands, informative-
ness is a more (less) important dimension of the online expe-
rience (b¼.022, p<.05), while entertainment becomes less
(more) important (b¼–.028, p<.01). This finding aligns well
4
Web Appendix C contains the results of the univariate effects for each of the
16 experiments.
5
Web Appendix D presents the indirect effects of design elements on purchase
intentions through each experience dimension.
8Journal of Marketing XX(X)

with previous research showing that information and argu-
ments provided by credible sources are more persuasive to
consumers (Petty, Cacioppo, and Heesacker 1981). Thus, the
more trustworthy a brand, the more consumers actually engage
with the information on its product web pages, and the more
they find this information relevant and helpful to their purchase
decisions. By contrast, entertainment is more important for
brands perceived as less trustworthy. When brand trustworthi-
ness is low and consumers experience more uncertainty
(Pavlou, Liang, and Xue 2007), entertainment has a greater
impact on purchase, a finding that aligns with previous research
(Bart et al. 2005).
Discussion: Creating Effective Customer Experiences
Finding that a product’s type and brand trustworthiness affect the
impact of each experience dimension on consumers’ purchase
decisionsimplies that marketers should use design elementsstra-
tegically to evoke specific types of experiences for different prod-
ucts and brands. To aid this effort, in Figure 2 we present a design
guide that illustrates and summarizes when to rely on which type
of experience and how to build it through design elements.
Although customer star ratings and picture size are relevant for
all experience types, we highlight specific design elements that
are particularly strong facilitators of distinct experience dimen-
sions. To this end, we provide percentage differences in the effect
sizes of each design element on each experience dimension, rela-
tive to its effects on all remaining dimensions.
Informative experiences are dominated by outcome-oriented
information and are most effective for search products and
brands that are generally well-trusted. Bulleted features exert
their strongest effects on this experience type (83%stronger
than their effects on any other experience dimension). A com-
parison matrix can also shape this dimension especially well
(62%more effective than for any other dimension), as can more
descriptive detail (54%more effective) and recommendation
agents (nearly equally effective at driving sensory appeal, but
150%more effective than driving any other dimension).
Entertaining experiences are pleasurable in their own right,
apart from any anticipated performance implications. We find
that these experiences are especially important for less trust-
worthy brands. Although most design elements exert some
effect on this dimension, no one design element appears
uniquely or more suited to shape it than any other dimension.
Social experiences convey a degree of human presence in
the encounter. These experiences are especially effective for
experience compared with search products. Linguistic style and
lifestyle pictures drive this dimension particularly well (respec-
tively, 139%and 134%more effective in shaping it than the
other dimensions).
Sensory experiences activate consumers’ senses and are
especially beneficial for experience products. Product videos
exert their strongest effects on this dimension (106%stronger
than on any other dimension). Product feature crop is another
important element to this dimension (29%stronger effects than
on the other dimensions).
Table 3. Study 1 Results: Model Comparison.
Model
Experience Dimensions Included as Mediators
Nw
2
d.f. CFI TLI RMSEA SRMR AIC DChi-Square DAICInformativeness Entertainment Social Presence Sensory Appeal
1PPP P10,470 437.770 16 .980 .880 .050 .009 513310.390 – –
2PP P10,470 574.109 17 .973 .850 .056 .011 513444.729 136.339 (1)** 134.34
3PPP10,470 1593.168 17 .924 .576 .094 .015 514463.788 1155.399 (1)** 1153.40
4PP P10,470 565.179 17 .974 .853 .055 .010 513435.799 127.41 (1)** 125.41
5PPP 10,470 469.428 17 .978 .878 .050 .010 513340.048 31.658 (1)** 29.66
6P10,470 2982.424 19 .857 .287 .122 .031 515849.044 2544.655 (3)** 2538.65
7P10,470 912.065 19 .957 .785 .067 .014 513778.685 474.295 (3)** 468.29
8P10,470 2686.159 19 .872 .358 .116 .027 515552.779 2248.39 (3)** 2242.39
9P10,470 2572.495 19 .877 .386 .113 .024 515439.115 2134.725 (3)** 2128.73
10 PP 10,470 683.774 18 .968 .831 .059 .012 513552.394 246.004 (2)** 242.00
11 PP10,470 1850.985 18 .912 .535 .099 .018 514719.605 1413.216 (2)** 1409.22
12 PP10,470 2031.622 18 .903 .489 .103 .019 514900.242 1593.852 (2)** 1589.85
13 PP 10,470 650.085 18 .970 .840 .058 .011 513518.705 212.316 (2)** 208.32
14 PP10,470 702.009 18 .967 .826 .060 .011 513570.630 264.24 (2)** 260.24
15 PP10,470 2057.166 18 .902 .482 .104 .020 514925.786 1619.397 (2)** 1615.40
*p<.05.
** p<.01.
Notes:Pindicates an existing path between the experience dimension and purchase intentions. AIC¼Akaike information criterion. The Dw
2
and DAIC refer to differences of a specific model relative to Model 1.
Results based on a model without moderating effects.
Bleier et al. 9

Table 4. Study 1 Results: Effects of Design Elements on Experience Dimensions and Purchase Intentions.
A: Effects of Experience Dimensions on Purchase Intentions
a
Experience Dimensions
(1) (2) (3) (4)
Structural Path Informativeness Entertainment Social Presence Sensory Appeal
Experience dimension !purchase intentions .118** (12.004) .387** (35.422) .118** (11.154) .060** (5.246)
B: Effects of Design Elements on Experience Dimensions Experience Dimensions
(5) (6) (7) (8)
Structural Path Informativeness Entertainment Social Presence Sensory Appeal
Verbal Elements
Linguistic style !experience dimension .035 (1.830) .052** (2.686) .165** (8.573) .069** (3.583)
Descriptive detail !experience dimension .153** (7.998) .064** (3.298) .088** (4.571) .099** (5.170)
Bulleted features !experience dimension .181** (9.443) .077** (3.986) .042* (2.206) .099** (5.131)
Return policy information !experience dimension .031 (1.627) –.005 (–.257) .006 (.336) .009 (.445)
Visual Elements
Product feature crop !experience dimension .007 (.371) .049* (2.529) .042* (2.205) .055** (2.844)
Lifestyle picture !experience dimension .047* (2.437) .037 (1.916) .144** (7.514) .062** (3.205)
Picture size !experience dimension .152** (7.946) .147** (7.591) .171** (8.916) .190** (9.906)
Product video !experience dimension .058** (3.016) .056** (2.882) .089** (4.633) .184** (9.550)
Combined Verbal and Visual Elements
Customer star ratings !experience dimension .211** (11.023) .135** (6.947) .162** (8.442) .131** (6.830)
Expert endorsement !experience dimension .023 (1.223) .016 (.823) .036 (1.896) .019 (.972)
Comparison matrix !experience dimension .168** (8.782) .081** (4.166) .064** (3.325) .104** (5.416)
Recommendation agent !experience dimension .049* (2.534) .019 (1.001) –.024 (1.249) .048* (2.475)
Content filter !experience dimension –.014 (–.751) –.011 (–.588) –.087** (4.532) –.023 (1.183)
C: Moderation of Effects of Experience Dimensions on Purchase Intentions
b
Experience Dimensions
(9) (10) (11) (12)
Structural Path Informativeness Entertainment Social Presence Sensory Appeal
Experience dimension product type (search/experience) !purchase intentions .019* (1.981) .002 (.183) –.023* (2.105) –.022* (1.960)
Experience dimension brand trustworthiness !purchase intentions .022* (2.211) –.028** (2.598) .000 (.042) .005 (.417)
*p<.05.
** p<.01.
a
Controlling for direct effects of design elements and consumer demographics.
b
Direct effect of product type (search/experience) on purchase intentions: b¼.152** (19.385); direct effect of brand trustworthiness on purchase intentions: b¼.044** (5.441).
Notes: Columns denote affected experience dimensions; brepresents the standardized coefficient; z-values are in parentheses. Model fit: w
2
(d.f.) ¼1475.63 (106), CFI ¼.94, RMSEA ¼.04, SRMR ¼.02.
10

Study 2: Field Experiment to Test the Effect
of Online Experience Designs on Sales
Study 1 provides a framework for designing online customer
experiences and customizing them to specific product or brand
factors. The lab experiments provide strong internal validity
across design elements, experience dimensions, and modera-
tors. In Study 2, we also aim to provide a compelling test of
external validity. We conduct a field experiment with real
products and sales on Amazon.com to test the finding from
Study 1 that, for products high in search qualities (search prod-
ucts), an informative experience can increase product sales
while a social experience may suppress them.
Experimental Design and Research Context
In this study, we collaborate with one of our partnering firms
and manipulate the content on two of its product pages on
Amazon.com. Using a difference-in-differences approach, we
observe the resulting changes in sales volume compared with a
control product page, over a period of two months. To inves-
tigate the extent to which search products benefit from a more
informative versus a more social experience, we first carefully
selected three search products (wireless Internet routers) with
similar characteristics and sales trends in the four weeks before
the launch of the experimental treatments (prelaunch) from our
partner firm’s inventory.
6
For the next four weeks (postlaunch),
we adapted the web pages of two products as either more
informative (Treatment 1) or more social (Treatment 2) and
left the third page unchanged (control condition). The
difference-in-differences analyses reveal the respective
changes in daily sales of the two adapted web pages, compared
with the unchanged control page. With this design, we can
disentangle the treatment effects of more informative or social
page designs from time trends and determine whether changes
in sales are attributable to the adjusted page designs or unob-
served shifts in consumer preferences.
We took several steps to reduce potential confounding
effects. First, to ensure homogeneous customer characteristics
across the two experimental periods, all product information on
the Amazon search results pages, from which consumers enter
the actual product web pages (e.g., product name, hero shot,
stockkeeping unit [SKU]), remained constant during the
experiment. Second, the price of all products remained con-
stant, and no promotion activity occurred during the experi-
ment. Third, because Amazon publishes seller-submitted
product content with varying time lags, we excluded the days
around the launch of the treatment content from our analyses
(Ma, Ailawadi, and Grewal 2013). Fourth, consumers do not
visit particular product web pages at random, so we account for
self-selection effects in the page views of the treatment pages
relative to the control page by supplementing our analyses with
controls for observable selection variables.
The experimental design thus employs two treatment con-
ditions and a control condition. Treatment 1 tests the effective-
ness of a more informative experience by increasing the
descriptive detail on the page, adding additional bulleted fea-
tures, and adding a comparison matrix. Treatment 2 tests a
more social experience, created through a conversational tone
and the addition of lifestyle photos, in line with Study 1. The
control product web page remained unchanged. To measure the
performance of each web page, our partner firm provided
access to Amazon Premium Analytics, from which we obtained
daily sales and customer star rating data one month before the
launch of the treatment pages (prelaunch) and one month after
(postlaunch).
Empirical Analysis
In our difference-in-differences approach, we compare the dif-
ference in daily product sales on each of the two treatment
pages between the pre- and postlaunch period with the corre-
sponding difference in sales for the unchanged control web
page:
Pjt ¼b0þb1Ijþb2Itþb3IjItþejt;ð1Þ
where P
jt
represents daily sales from web page j at time t and is
a random error term, clustered across the two periods. Our
design contains two treatment web pages (informative experi-
ence and social experience) and a control web page across the
two periods (pre- and postlaunch). As a conservative test, we
run two separate analyses that compare the informative and
social experience with the control condition. In both analyses,
I
j
is 1 for the treatment (informative or social, respectively) and
0 for the control condition, so that b
1
represents the mean
difference in sales between these two conditions. Furthermore,
I
t
is 1 for the postlaunch period and 0 for the prelaunch period,
so that b
2
reflects the mean difference in post- relative to pre-
launch sales. Finally, b
3
is the estimate of the respective treat-
ment effect, or the change in sales due to the informative or
social experimental treatment, after we control for systematic
differences across conditions and common time trends:
b3¼½EðPjtjj¼1;t¼1ÞEðPjt jj¼1;t¼0Þ
½EðPjtjj¼0;t¼1ÞEðPjt jj¼0;t¼0Þ:ð2Þ
In Equation 2, b
3
also represents the incremental economic
impact of customizing the web page design to create a partic-
ular online experience. A key assumption of the difference-
in-differences approach is that the time trends in sales are
identical in the treatment and control conditions, absent the
treatments themselves. If this assumption holds true, we can
interpret the deviation of the difference in sales between the
treatment and control conditions as causal treatment effects. To
verify this parallel trends assumption, we collected data at a
third period, two months before the launch of the treatments,
and ran a model similar to Equation 1, except that we compared
6
To select the most appropriate products for this test, we audited the firm’s
current product categories to identify those with at least three similar search
products with sufficient daily sales. From this set, we then selected three
wireless Internet routers as prototypical search products.
Bleier et al. 11

A: Informative Experiences
When to Focus on Informative Experiences
•Webpages for search products should emphasize informative
experiences.
•More trusted brands should pursue informative experiences.
B: Entertaining Experiences
When to Focus on Entertaining Experiences
•Less trusted brands should focus on entertaining experiences.
How to Build Informative Experiences
•Provide more descriptive detail by adding information about
additional product attribute.
•Use ive rather than three bulleted features that summarize key
product attributes.
•Provide a comparison matrix that compares the focal product to
other related products along key attributes.
•Employ a recommendation agent that suggests other related
products for purchase.
How to Build Entertaining Experiences
•No particular design element has its strongest effect on this
type of experience.
Linguistic style
Descriptive detail
Bulleted features
Product feature crop
Lifestyle picture
Picture size
Product video
Customer star ratings
Comparison matrix
Recommendation agent
Effect Size
.00
.02
.04
.06
.08
.10
.12
.14
.16
.18
.20
Content ilter
Linguistic style
Descriptive detail
Bulleted features
Product feature crop
Lifestyle picture
Picture size
Product video
Customer star ratings
Comparison matrix
Recommendation agent
Effect Size
.00
.02
.04
.06
.08
.10
.12
.14
.16
.18
.20
Content ilter
When to Focus on Social Experiences
•Webpages for experience products should focus on social
experiences.
When to Focus on Sensory Experiences
•Webpages for experience products should emphasize sensory
experiences.
Linguistic style
Descriptive detail
Bulleted features
Product feature crop
Lifestyle picture
Picture size
Product video
Customer star ratings
Comparison matrix
Recommendation agent
C: Social Experiences D: Sensory Experiences
Effect Size
How to Build Sensory Experiences
•Employ a product video that uses both audio and dynamic
visuals to present the product.
•Use a product feature crop that highlights a key characteristic of
the product by zooming in on this attribute.
How to Build Social Experiences
•Use a more conversational linguistic style by adding adjectives,
self-relective question, and pronouns (“you,” “your”).
•Include a lifestyle picture that features the product in use.
•Avoid content ilters, such as a “show more” button, that allow
customers to dictate what, when, and how much verbal and
visual content appears on the webpage.
-.06
-.04
-.02
-.08
.00
.02
.04
.06
.08
.10
.12
.14
.16
.18
.20
-.10
Content ilter
Linguistic style
Descriptive detail
Bulleted features
Product feature crop
Lifestyle picture
Picture size
Product video
Customer star ratings
Comparison matrix
Recommendation agent
Effect Size
.00
.02
.04
.06
.08
.10
.12
.14
.16
.18
.20
Content ilter
Figure 2. Design guide for creating effective online customer experience.
Notes: Only significant effects (p<.05) are shown; gray bars represent universally effective design elements across all experience dimensions,
black bars depict uniquely more effective elements for a specific dimension than for all other dimensions, and white bars indicate the remaining
elements.
12 Journal of Marketing XX(X)

this earlier period with the prelaunch period to determine the
trends across the three experimental groups, before the treat-
ments. The interaction between the period and experimental
group is nonsignificant (p>.10), confirming the parallel trends
and supporting the comparison of the treatment and control
conditions.
Because b
1
represents a product fixed effect, it eliminates
time-invariant, product-specific unobservable variables and
reduces the threat of bias (Gill, Sridhar, and Grewal 2017). In
addition, although each product may attract slightly different
customers, suggesting that a selection bias is possible, we hold
the firm-controllable page entry decision criteria (product
name, hero shot, SKU, and price) constant throughout the
experiment. Thus, customer characteristics across conditions
should be time invariant, and we can interpret b
1
as a customer
fixed effect that reduces this self-selection bias. However,
some page entry criteria, such as a product’s average star rating
or number of reviews (Mudambi and Schuff 2010), are outside
the firm’s control and time variant, so they could introduce
some customer differences across experimental conditions that
b
1
would not capture. To address this potential bias, we add a
vector of control variables X
jt
to Equation 1, which we use to
calculate the daily difference in average customer star rating
and number of reviews for each treatment page compared with
the control condition:
Pjt ¼b0þb1Ijþb2Itþb3IjItþdXjt þejt:ð3Þ
Results
Model-free evidence. Before the launch, sales did not differ
between the control condition and the informative product page
(Treatment 1), but the social product page (Treatment 2)
achieved higher sales (M
control
¼3, M
info
¼3, M
social
¼
734).
7
After the treatment launch, in support of our findings
in Study 1, sales increased for the informative page (M
info
¼
152), decreased for the social page (M
social
¼394), and
decreased slightly in the control condition (M
control
¼.1), rela-
tive to the counterfactual trend we calculated on the basis of the
time trend in the control condition and the sales levels of each
experimental condition before the experiment.
Difference-in-differences analysis. To test these effects more for-
mally, we run two separate models, one for Treatment 1 (infor-
mative) and one for Treatment 2 (social), in which we account
for possible time-variant changes among customers who visit
the product pages (Equation 3). In Model 1 (Table 5), the
treatment effect of the informative experience is positive and
significant (b
3
¼151.980, p<.01); increasing web page infor-
mativeness improves sales of search products. By contrast, in
Model 2, the treatment effect of the social experience is nega-
tive and significant (b
3
¼–337.180, p<.01), confirming the
detrimental effects of a social experience for search products.
8
Together, these field results corroborate our insights from
Study 1: Search products benefit from more informative
experiences, while more social experiences can have detrimen-
tal effects on sales of these products.
General Discussion
In an era in which web design is becoming increasingly impor-
tant (Wolfinbarger and Gilly 2003), sellers’ success depends on
their ability to employ design elements on product web pages to
evoke effective customer experiences that not only convey
information but also entertain, imply human interactions, and
mimic sensory experiences from the offline world. Through 16
large-scale experiments and a field study, we show how firms
can use online design elements to drive purchase behaviors by
customizing experiences according to the product or brand
being sold. Our findings offer important theoretical contribu-
tions to customer experience management (e.g., Grewal, Levy,
and Kumar 2009; Verhoef et al. 2009) and actionable manage-
rial implications.
Theoretical Contributions: Understanding the Online
Customer Experience
Our multidimensional conceptualization of the online customer
experience reveals why the effectiveness of any given design
element may vary with the offered product or brand. It adds to
extant research that examines the direct effect of design ele-
ments on purchase decisions without addressing their underly-
ing mechanisms (Cooke et al. 2002; Hauser et al. 2009). It also
moves beyond unidimensional, predominantly information-
processing perspectives (see Table 1). Although informativeness
is a key dimension by which design elements affect purchase
decisions, social presence is just as important, and entertainment
is even more so. Accounting for sensory appeal adds further
insights. We show that the function of design elements is not
limited to the cognitive information they convey, because they
also carry affective (entertainment), social (social presence), and
sensory (sensory appeal) value that influences purchases. We
also show that only a multidimensional perspective can help
determine the most effective use of design elements for a given
product or brand. Further research should thus account for and
test the multiple ways design elements drive purchase.
The multidimensionality of our research also led to the dis-
covery of unexpected relationships that may guide researchers
in the online domain toward identifying emerging, substantive
trends and relevant constructs. For example, the effects of
social presence on purchase are just as strong as those of infor-
mativeness, an insight that provides a foundation for examining
recent trends such as the inclusion of chat options on websites
7
We transformed all values by a constant, in accordance with our
nondisclosure agreement.
8
As a robustness check, we tested a single model in which we dummy-coded
each treatment condition versus the control condition. The substantive results
remained unchanged.
Bleier et al. 13

to enable visitors to interact directly with firms. Firms now use
chatbots, based on artificial intelligence, that can conduct con-
versations via voice or text. An information-processing view
might regard chatbots as merely providers of product or trans-
actional information, but our findings suggest that they can also
convey social presence. Further research might examine how
the linguistic style (a key driver of social presence) of a chatbot
should be calibrated to optimize the customer experience.
Moreover, our consolidation of design elements, addressing
the many labels used in extant work, and our test of their
relative effects reveal which elements have the greatest impact
on the customer experience and thus suggest priorities for
research. In allowing each design element to freely influence
each experience dimension, we were able to identify the core
function of each element (information, entertainment, social
presence, or sensory appeal). Lifestyle photos, for example, are
a key driver of social presence. In our study, they were pro-
duced by the seller. Yet companies such as Rent-the-Runway
encourage customers to post photos of themselves using the
firm’s products (clothing) directly on product web pages. Fur-
ther research could examine the implications of customer- ver-
sus firm-produced lifestyle photos. Our framework may also
guide research on emerging features that allow customers to try
products virtually using webcams (e.g., glasses at FramesDir
ect.com). These and other forms of in-page product trials war-
rant further investigation to determine their value for each
dimension of the online customer experience.
Our research also provides insights into the role of product
type and brand trustworthiness online, by showing how they
influence the relevance of each experience dimension for pur-
chase decisions. Search products benefit more from informa-
tive experiences but less from social experiences. Highly
trustworthy brands benefit from more informative experiences,
but less trustworthy brands gain from more entertaining experi-
ences. The finding that brand trustworthiness may increase
consumers’ willingness to process greater amounts of informa-
tion demands further examination, especially as research sug-
gests a decline in brand value when other sources of
information become more readily available to consumers
(Simonson and Rosen 2014).
Managerial Implications: Designing the Online
Customer Experience
The product web page is a key tool for managers, who can
strategically use design elements to create a customer experience
that turns web page visitors into buyers. Our findings apply to
both sellers showcasing their offerings through online retailers’
websites and the retailers themselves. The production, curation,
and publishing of high-quality photos, videos, and copywriting
are nontrivial tasks that require significant resources.
We offer a two-step design guide to show how sellers can
generate sales through effective online customer experiences.
First, sellers must determine the most beneficial experience,
based on the search versus experience focus of the product to
be sold and the trustworthiness of their brand. The measures we
employ can help firms gather this information from current and
potential customers. Second, firms should leverage this product
and brand knowledge and apply the design guide derived in
Study 1 (Figure 2) and validated in Study 2, to select relevant
design elements for their product web pages. For experience
products, social experiences should be built by employing a
conversational linguistic style and lifestyle photos. Sensory
experiences are also beneficial and can be built through product
videos and product feature crops.
Firms need to consider the customer experience in assessing
their existing digital assets. Managers often default to a logic
that suggests that if a design element exists in the firm’s digital
inventory, it should be used on the page (more-is-better
approach). Yet we show that certain design elements can
induce unfavorable customer experiences for specific products
or brands. An essential part of the process is thus to also deter-
mine which elements not to use. If the firm does not already
own certain design elements, our design guide suggests where
it should allocate its resource investments to produce valuable
new elements. For example, investing in high-quality imagery
can benefit any product or brand, but the most appropriate
amounts of textual detail and linguistic style depend on the
product type (search vs. experience focus).
Our design guide can also inform contract negotiations
between sellers and retailers. Many retailers offer premium
Table 5. Study 2 Results: Field Experiment Testing Customized Online Customer Experiences.
Model 1: Model 2:
Informative Experience Treatment Social Experience Treatment
Treatment effect 151.980** (34.604) –337.180** (73.800)
Time dummy –9.390 (25.815) 87.600 (92.633)
Treatment condition dummy –.367 (24.183) 730.730** (51.567)
Average customer star ratings –1576.786 (2303.567) –845.080 (3482.683)
Number of reviews –9.925 (18.049) –28.200 (22.334)
Observations 122 122
R
2
.29 .69
*p<.05.
**p<.01.
Notes: Standard errors are in parentheses.
14 Journal of Marketing XX(X)

content options that require additional financial investments
from sellers. Amazon, for example, offers multiple tiered cate-
gories (e.g., Basic AþContent, Premium AþContent) that
provide access to additional design elements or configurations.
For some products, these investments grant access to necessary
design elements; for other products, investing in premium con-
tent might not be necessary or could even be disadvantageous.
For example, premium content modules might support larger
pictures and more visually stimulating content (e.g., scrolling
pictures), but they also restrict the number of characters avail-
able to describe product features and benefits. Such designs can
induce social or sensory experiences, but they likely are less
effective at creating informative experiences. Thus, a lower-
cost alternative may be more attractive to a seller that wants to
provide mainly informative experiences.
Our design guide is also relevant for retailers. The more
conversions sellers generate on a retailer’s website, the greater
are its earnings. Yet retailers also must provide an infrastruc-
ture to support the digital content and guarantee adequate page
load and transaction speeds. Helping sellers build effective web
pages as efficiently as possible is in the retailer’s best interest.
With our design guide, retailers can develop tutorials to help
sellers improve the effectiveness of their product web pages, as
well as recommend available design elements to those sellers,
based on the products and brands they market. This approach
could improve conversions but also lessen storage demands, by
reducing ineffective content. With our design guide and a dedi-
cated customer experience mindset, sellers and retailers can
work together strategically to maximize the performance of
their product web pages.
Limitations and Future Research Directions
Although our research setting and design allowed us to deter-
mine the effects of various design elements on dimensions of
the online customer experience and purchases, this work is not
without limitations. Our results show no effects of return policy
information or expert endorsement on any experience dimen-
sion, after we account for the impact of the other elements.
Additional research might explore these elements further to
determine any circumstances in which they prove effective.
In addition, no design element exerts a particularly strong
effect on the entertainment dimension. Thus, research could
analyze other design elements that might prove especially
instrumental in shaping this dimension. Although purchase is
our final outcome of interest, an extended version of our frame-
work might address how product web page design elements
influence consumer decision-making quality, long-term satis-
faction, product returns, or social media behavior (Ha¨ ubl and
Trifts 2000; Simonson and Rosen 2014).
Researchers could also investigate how the effects we find
translate to mobile environments and whether the same design
elements induce similar or different experiences. We focus on
design elements most relevant to the product presentation, and
thus website elements such as navigation warrant further inves-
tigation. Research could also examine the design of landing,
overview, or checkout web pages, which we do not consider in
our study. Our experimental design is based on a Taguchi
(1986) orthogonal array design, which is rare in marketing
research. We recommend its application in similar, seemingly
intractable research settings to facilitate the simultaneous
manipulation of multiple experimental factors, as might be
required for advertising or product design studies. We focus
on product web pages, but a design perspective could also
improve understanding of other domains in which verbal and
visual stimuli build customer experiences, such as user manuals
or mobile apps. As online shopping environments continue to
approach the richness of the offline retail world, research
should further investigate the value of design for providing
unique experiences, customized to the specific characteristics
of the products and brands sold.
Bleier et al. 15

Appendix A. Manipulated Constructs, Definitions, Operationalizations, and Manipulation Checks.
Design Elements Aliases Definition
Operationalizations Means
Level 1 Level 2
Level
1
Level
2 t-Value p-Value
Verbal Elements
Linguistic style Socio-oriented, concept-oriented,
functional content, social content,
linguistic style, message
personalization
Characteristics of the text, including
word choice, elements such as
questions, certain pronouns (you,
your), and adjectives (Ludwig et al.
2013)
Product descriptions
have primarily an
unemotional tone.
Product
descriptions have
primarily an
emotional tone.
2.97 3.51 –16.03 .00
Descriptive detail Item-specific information The degree of elaboration of the
product descriptions on the web
page (Cooke et al. 2002)
Baseline number of
words of product
descriptions.
Number of words of
product description is
25% more than at
Level 1.
4.66 5.24 –20.01 .00
Bulleted features Product claims Product features that appear in
abbreviated list form on the web
page (Shu and Carlson 2014)
Web page contains a list
of three bulleted key
product features.
Web page contains a list
of five bulleted key
product features.
3.72 6.13 –5.96 .00
Return policy
information
Transaction facilitation information Visibility of product return
procedures and instructions
(Bower and Maxham 2012; Song
and Zinkhan 2008)
Web page shows no
product return policy
information.
Web page shows
product return policy
information.
3.13 5.27 –40.20 .00
Visual Elements
Product feature
crop
Cropped objects Compared with photos that show the
whole product, feature crops zoom
in on a certain aspect of the
product (Peracchio and Meyers-
Levy 1994).
No picture with only a
specific part of the
product.
At least one picture
shows only a specific
part of the product.
3.72 4.76 –18.02 .00
Lifestyle picture A photo of the product in use (Babin
and Burns 1997)
No picture shows the
product in use.
At least one picture
shows the product in
use.
2.34 3.22 –16.21 .00
Picture size Static picture The portion of the page with visual
elements (Jiang and Benbasat
2007a; Park, Lennon, and Stoel
2005)
Baseline picture size Pictures are 25% larger
than at Level 1.
4.03 4.74 –28.91 .00
Product video Multimedia presentations, dynamic
product presentation
Video of the product in use (Huang,
Lurie, and Mitra 2009; Roggeveen
et al. 2015; Weathers, Sharma, and
Wood 2007)
Web page contains no
product video.
Web page contains at
least one product
video.
1.83 5.06 –61.75 .00
Customer star
ratings
Online reviews, customer reviews Aggregated user-generated product
ratings posted on the product web
page in the form of stars (1 to 5)
and number of ratings (Chevalier
and Mayzlin 2006; Ludwig et al.
2013; Mudambi and Schuff 2010;
Web page contains no
consumer star rating.
Web page contains
consumer star rating.
2.34 5.51 –85.98 .00
(continued)
16

Appendix A. (continued)
Design Elements Aliases Definition
Operationalizations Means
Level 1 Level 2
Level
1
Level
2 t-Value p-Value
Weathers, Sharma, and Wood
2007)
Expert
endorsement
Third-party seals, expert evaluation,
authoritative third-party
recommendations, expert opinion
Product evaluations assembled by
distinguished experts in the
category (Ansari, Essegaier, and
Kohli 2000; Huang, Lurie, and Mitra
2009)
Web page does not
contain a seal of a
third-party expert
certifying the
product’s quality.
Web page contains a
seal of a third-party
expert certifying the
product’s quality.
3.12 4.54 –27.08 .00
Comparison
matrix
Decision aids, product comparisons,
shopping agent
Table organized as an alternatives 
attributes matrix that compares the
focal product with a small number
of alternative products along a set
of attributes (Ha
¨ubl and Trifts
2000)
Web page does not
contain a table that
allows for easy
product comparison.
Web page contains a
table that allows for
easy product
comparison.
2.64 5.46 –57.40 .00
Recommendation
agent
Next product to buy, cross-selling
hyperlinks, shopping agent,
electronic agents, recommendation
systems, infomediaries, referral
services
Tool that provides a screening
function by weeding through many
alternatives, based on similarities to
the focal product (Ansari,
Essegaier, and Kohli 2000; Cooke
et al. 2002; Ha
¨ubl and Trifts 2000;
Knott, Hayes, and Neslin 2002)
Web page does not
include links to
related products.
Web page includes links
to related products.
3.73 5.72 –38.44 .00
Content filter Collaborative filtering agents,
information control, decisional
control
Tool that allows the customer to
determine what, when, and how
much verbal and visual content is
presented (Wang et al. 2007;
Weathers, Sharma, and Wood
2007)
Consumers cannot
control the amount of
verbal or visual
content shown to
them at once.
Consumers can control
the amount of verbal
or visual content
shown to them at
once.
4.32 4.94 –13.38 .00
Notes: All means and t-values are calculated using 10,470 observations.
17

Appendix B. Description of Firms Participating in Study 1.
Firm
Annual
Sales ($B)
Number of
Employees
Number of
Products
Online Type of Products
Number of
Online
Channels
Firm
Age Headquarters
Private/
Public
A $1.6 1,725 2,000 SKUs Consumer electronics, home networking 16 32 U.S. Private
B $3.2 13,300 1,000 SKUs Supplements 5 44 U.S. Private
C $12.0 13,000 2,000 SKUs Consumer packaged goods, personal care, household 5 129 U.S. Private
D $33.1 185,965 1,000 SKUs Business electronics, consumer electronics 30 179 France Private
Notes: Data provided by Private Company Financial Intelligence (privco.com) and COMPUSTAT.
7. Product video
11. Expert
endorsement
3. Bulleted features
(3 bullets; 5 bullets)
9. Return policy information
8. Content
ilter
12. Comparison
matrix
13. Recommendation agent
1. Linguistic style
(journalistic; conversational)
2. Descriptive detail
(less; more)
4. Product feature
crop
5. Lifestyle picture
6. Picture size
(small; large)
10. Customer star
ratings
All design elements manipulated as absent (Level 1) vs. present (Level 2), except where noted.
Appendix C. Example product web page.
Appendix D. Constructs and Measures.
Constructs (Scale Sources)
Online Experience Dimensions
Informativeness (adapted from Luo 2002)
Information obtained from the product page is useful.
I learned a lot from using the product page.
I think the information obtained from the product page is helpful.
Entertainment (adapted from Hausman and Siekpe 2009)
Not fun/fun
Not enjoyable/enjoyable
Not at all entertaining/very entertaining
(continued)
18 Journal of Marketing XX(X)

Acknowledgments
The authors thank content26, the Marketing Science Institute, and the
Center for Sales and Marketing Strategy at the University of Washing-
ton for their help conducting this research.
Associate Editor
Venkatesh Shankar served as associate editor for this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) received an MSI Research Grant for this project.
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