Multi-Channel Price Differentiation:
An Empirical Investigation of Existence and Causes
1 Agnieszka Wolk, Ph.D. University of Frankfurt, School of Business and Economics,
Department of Marketing, Mertonstr. 17, 60054 Frankfurt am Main, Germany,
2 Christine Ebling, Ph.D. University of Technology Sydney, Sydney, Australia,
Multi-channel price differentiation:
an empirical investigation of existence and causes
Price differentiation has long been recognized as a strategy that companies can use to increase
profits when consumers’ tastes and valuations of a good differ. Operating multiple distribution
channels (e.g., offline and online stores) that have varying degrees of functionality and are
differently valued by consumers gives companies an opportunity to apply differential prices in
these different contexts. Nevertheless, existing empirical studies suggest that multi-channel
retailers charge uniform prices through their different distribution channels to preserve channel
consistency and avoid consumer irritation. In this paper, we study channel-based price
differentiation and empirically determine the extent of its occurrence among multi-channel
retailers. Additionally, we analyze factors that influence a company’s decision to engage in
channel-based price differentiation. The results show that multi-channel retailers recognize the
opportunity to increase their profits and increasingly engage in channel-based price
differentiation; this finding contradicts existing empirical studies on price dispersion. Consistent
with microeconomic theory, it seems that price differentiation mostly occurs among big
companies with market power that can separate markets.
Key words: price differentiation, multi-channel pricing
Research in marketing and economics has long acknowledged that price differentiation can be a
profitable pricing strategy (Phlips, 1989). In a market with heterogeneous tastes and different
product valuations, companies may increase their profits by segmenting consumers and charging
differential prices, which allows for the extraction of additional consumer surplus. Empirical
studies show that profit may increase by up to 34% when companies engage in price
differentiation over profits using a uniform pricing strategy (Khan & Jain, 2005). As a result,
researchers advocate using price differentiation whenever possible (Phlips, 1989). Among the
various forms of price differentiation that exist, self-selection price differentiation has received
special attention from researchers and practitioners due to its numerous advantages, which
include its low cost and the high ease of its application in addition to its high profitability (Khan
& Jain, 2005; Phlips, 1989). In the case of self-selection price differentiation, a company offers
multiple product versions at various prices and allows consumers to choose the one that best
suits their preferences (Mussa & Rosen, 1978).
Whereas various types of self-selection price differentiation have been widely used by
companies (e.g., versioning, coupons, different prices for damaged goods, intertemporal pricing),
technological developments constantly offer new applications that may help to increase profits.
The growing popularity of the Internet has led many conventional retailers to initiate online sales
and turn themselves into multi-channel retailers that offer their consumers the choice between
online and offline distribution channels (Zettelmeyer, 2000). As online and offline channels
differ in many respects, such as the level of convenience, risk or transparency (Chiang &
Dholakia, 2003), consumers develop heterogeneous channel preferences, leading to different
channel valuations and price sensitivities (Chu, Chintagunta, & Vilcassim, 2007; Degeratu,
Rangaswamy, & Wu, 2000; Kacen, Hess, & Chiang, 2003). As a result, operating multiple
distribution channels provides an opportunity to apply channel-based price differentiation, where
companies can charge different prices for the same product offered through an online and offline
channel and allow consumers to self-select their preferred channel-price combination.
Surprisingly, while theoretical work acknowledges the possibility of channel-based price
differentiation for multi-channel retailers (Dulleck & Kerschbamer, 2005; Dzienziol, Eberhardt,
Renz, & Schackmann, 2002; Zettelmeyer, 2000), recent empirical studies in the price dispersion
literature fail to find any evidence for its occurrence (e.g., Ancarani & Shankar, 2004; Pan,
Ratchford, & Shankar, 2002; Tang & Xing, 2001). Also, practitioners argue in favor of uniform
pricing in this context to prevent customer irritation (Asheraft, 2001). However, there is no
empirical study that explicitly analyzes the occurrence of channel-based price differentiation
among multi-channel retailers (Neslin et al., 2006). Therefore, in a first study, we analyze
whether multi-channel retailers charge different prices for the same product through online and
offline channels and ascertain the size of the price differences (i.e., price gaps).
The results of this first study clearly indicate that retailers engage in channel-based price
differentiation. However, this study also shows that not all multi-channel retailers equally engage
in channel-based price differentiation. Unfortunately, little research has focused on the question
of whether to price differentiate (Anderson & Dana, 2007). While this topic has received some
attention in the theoretical economic literature (e.g., Anderson & Dana, 2007), the empirical
research in this area is scarce, with only Iyer and Seetharaman (2001) having worked on the
question. As a result, we collected additional data and conducted a second study to analyze
factors that influence a company’s decision to engage in channel-based price differentiation,
specifically the influence of market-specific, retailer-specific, and product-specific factors on the
probability of observing channel-based price differentiation, its extent and direction.
The remainder of the paper is organized as follows. First, we review the existing literature on
price differentiation in distribution channels and multi-channel pricing. Next, we discuss the
conditions for successful channel-based price differentiation based on a microeconomic
rationale. In particular, we focus on the impact of market, product, and retailer characteristics on
the retailer’s decision whether to price discriminate. Thereafter, we describe the data as well as
its limitations with respect to the non-statistical sampling procedure and the factor
operationalization used. We present the results of the two empirical studies, discuss their
implications and conclude with directions for future research.
Various studies have considered price differentiation in the context of a distribution system.
Jeuland and Shugan (1983) propose the use of quantity discounts as a means to assure channel
cooperation in the context of a single manufacturer-retailer distribution channel, while McGuire
and Staelin (1983) propose two-part tariffs in these settings. Gerstner, Hees, and Holthausen
(1994) study price differentiation within a distribution channel when a manufacturer issues
coupons that can be used by the retailer as another means of channel coordination. The authors
show that such a pull strategy alleviates issues of double marginalization and channel
miscoordination. Besanko, Dubé, and Gupta (2003) analyze the manufacturer’s and retailer’s
ability to engage in price differentiation by issuing segment-specific coupons to consumers in the
context of a vertical channel.
While these studies focus on within-channel price differentiation strategies, Iyer (1998) develops
a theoretical model that analyzes a manufacturer selling through competing retailers using
contracts to induce price-service differentiation among retailers. Also, Cavero, Cebollada, and
Salas (1998) and Dulleck and Kerschbamer (2005) theoretically analyze the application of
different distribution channels to price differentiate along the quality of advice. Dzienziol et al.
(2002) go one step further and focus specifically on an online and offline distribution channel for
financial services; they recognize the opportunity for channel-based price differentiation in this
context. Similarly, Zettelmeyer (2000), who analyzes the pricing and communication strategies
of multi-channel retailers, accounts for the possibility of channel-based price differentiation in
his theoretical model.
Also, from a consumer perspective, channel-based price differentiation seems to be feasible
because price differences across online and offline channels could be easily justified by
differences in channel characteristics. Consumers derive different utility from various
distribution channels (Chu et al., 2007), which, in turn, leads to differences in channel valuations.
Kacen et al. (2003) show that the willingness to pay for a product purchased through an offline
channel can be 8% - 22% higher than the willingness to pay for a product purchased through an
online channel. Similarly, Jensen et al. (2003) find that consumers’ perceptions of prices differ
for online and offline channels.
Therefore, channel-based price differentiation is feasible, and this, together with the evidence
that price differentiation increases profits (Khan & Jain, 2003), should encourage companies to
pursue a price differentiation strategy whenever possible.
However, practitioners often argue for consistent prices across distribution channels to maintain
a strong brand—and because varying prices may lead to customers’ confusion, anger, irritation,
and perceptions of price unfairness (Asheraft, 2001; Neslin et al., 2006). As previous research
has shown that unfair price perceptions decrease purchase intentions (e.g., Campbell, 1999),
practitioners advocate “channel price integrity”.
In a similar vein, existing studies empirically analyzing price dispersion in online and offline
environments fail to acknowledge or find empirical evidence for channel-based price
differentiation by multi-channel retailers. In their approach, Pan et al. (2002) follow anecdotal
evidence and make the assumption that to preserve channel integrity, the multi-channel retailer
sets the same price in its channels. Ancarani and Shankar (2004) report that although prices for
some multi-channel retailers differ across channels, on average the differences are not
significant; as a result, they proceed with their analysis assuming equal prices. Similarly, Tang
and Xing (2001) argue that multi-channel retailers may wish to charge the same prices across
different channels to prevent destructive competition and conflict between them. Finally,
Sullivan and Thomas (2004), who analyze channel choice in multi-channel environments, find
consistent prices across channels in their data set, implying that multi-channel retailers prefer to
promote the uniformity of their channels rather than to engage in channel-based price
This literature review leads to the conclusion that, given the differences in channel
characteristics, channel-based price differentiation could in theory be an appealing and feasible
pricing strategy, but existing empirical studies fail to find any evidence for its occurrence in
CONDITIONS FOR SUCCESSFUL PRICE DIFFERENTIATION
Various studies theoretically analyze drivers of a company’s decision to engage in price
differentiation. For example, Anderson and Dana (2009) derive conditions for profitable price
differentiation that generalize existing results in the literature and apply to various forms of self-
selection price differentiation. The authors show that price differentiation is profitable if the ratio
of the marginal social value from an increase in quality to the total social value of the good
increases with consumers’ willingness to pay (Anderson & Dana, 2009).
Iyer and Seetharaman (2001), who use survey data to investigate gasoline stations and their
incentives to price differentiate, find that a larger income spread in the market leads to a greater
likelihood of gasoline stations’ price differentiating. Additionally, characteristics of the
individual retailers (such as branded stations, pay-at-pump facilities, and convenience stores)
influence the probability of engaging in price differentiation.
The key conditions for successful price differentiation have to do with market, retailer, and
product characteristics. We derive a conceptual model based on microeconomic theory and form
expectations about the impact of these characteristics on the extent of channel-based price
differentiation (see Figure 1).
Competition. Even when consumers have heterogeneous tastes, not all companies have the same
incentives or opportunities to engage in price differentiation. Microeconomic theory argues that a
company must have market power in the sense of having the ability to set prices above marginal
cost (Phlips, 1989). In the purely competitive market, where price equals marginal cost, every
company has to accept the same market price. Because any attempt to increase the price results
in the loss of all customers to the company’s competitors, price differentiation is prevented under
perfect competition (Varian, 1989). In contrast, monopolistic markets give companies the
opportunity to exercise strong market power, which allows companies to increase prices without
losing their customers (Varian, 1989). While researchers now recognize that duopolists and
oligopolists may also price differentiate, they still agree that price differentiation requires some
level of market power (Phlips, 1989). Therefore, it can be expected that the extent of price
differentiation will be higher for a lower level of competition.
Factors influencing occurrence and extent of channel-based price differentiation
Offline reach. Another important requirement for a company if it wishes to be able to price
differentiate is the ability to separate markets (Phlips, 1989). Physical distance is often used to
prevent consumers from escaping a market with higher prices by purchasing the product in a
market where the price is low (Phlips, 1989). For example, buyers who are far away from the
low-price market have to pay the high price at the market that they are close to because the
transaction costs related to purchasing from the low-price market are too high. These transaction
costs prevent spatial demand shift and contribute to successful price differentiation (Varian,
The number of offline branches that the multi-channel retailer operates (i.e., his offline reach)
influences a consumer’s opportunity to conduct purchasing through offline channels, with a
higher number decreasing the cost of switching to an offline channel. As a result, multi-channel
retailers that operate only a few offline branches may be able to separate online and offline
channels well and therefore be more likely to engage in channel-based price differentiation.
Likewise, multi-channel retailers that operate many offline branches allow their consumers to
easily switch channels, making channel-based price differentiation unfeasible. On this basis,
fewer offline branches should lead to a higher extent of price differentiation.
Online reach. As with offline reach, online reach is likely to influence the extent of channel-
based price differentiation. Whereas the online channel was only used by a few consumers at its
inception, it has experienced a huge increase over the years (online total sales in the US equaled
$28,299 million in 1999 and $108,324 million in 2006). As consumers become more familiar
with the online environment, transaction costs associated with switching to the online channel
decrease, and markets (here distribution channels) are no longer well separated. In a similar vein,
Zettelmeyer (2000) recognizes that multi-channel pricing strategies are likely to depend on the
development of the number of potential consumers who are willing to visit the company’s
website to make a purchase (i.e., online reach). In his theoretical model, he shows that if online
reach is low, the prices advertised through online and offline channels are likely to differ. With
an increasing online reach, competitive factors will lead multi-channel retailers to charge the
same prices through online and offline channels (Zettelmeyer, 2000). Therefore, companies with
higher online reach should be less likely to engage in channel-based price differentiation.
Number of distribution channels. There are many situations in which companies forgo the
opportunity to engage in price differentiation even when the standard requirements, such as
heterogeneous consumer tastes or market power, are satisfied. One explanation for such a
decision is the high cost of engaging and managing price differentiation strategies (Anderson &
Simester, 2001). In the context of multi-channel retailers, coordination costs increase as the
number of distribution channels increases. If a retailer operates only two distribution channels,
the coordination of channel-based price differentiation imposes less cost than in the case of more
distribution channels. We therefore expect that the probability of engaging in channel-based
price differentiation will decrease with the number of channels the retailer operates.
Size of the company. Based on their analytical model, Anderson and Dana (2009) argue that a
firm that faces a lower cost level is more likely to engage in price differentiation. Among various
factors that can drive the cost structure, company size has been argued to play an important role
(Shepard, 1991). Big companies (i.e., those with high volume or revenues), due to their superior
technology, efficient organization and/or cheaper purchases, enjoy economies of scale and
experience that decrease average total costs (Tellis, 1986). As a result, big companies have more
opportunities with regard to pricing products than do small companies, and we expect that they
are thus more likely to engage in channel-based price differentiation.
Product type. The nature of the product is also likely to influence the decision to engage in
channel-based price differentiation. If the product is appropriate for resale, then consumers
facing a lower price may resell the product for a lower amount to consumers facing a higher
price, which would jeopardize the profits to be accrued from price differentiation. Economic and
marketing theories distinguish between goods and services with the common agreement that
services are less appropriate for resale. At the same time, goods can be separated into two
groups—durables and non-durables—with the latter also being less appropriate for resale. As a
result, one can expect a higher extent of price differentiation for services than for goods and,
within the category of goods, a higher extent of price differentiation for non-durables than for
durables (Phlips, 1989; Varian, 1989; Zettelmeyer, 2000).
Other product characteristics are also likely to play a role in the context of channel-based price
differentiation. Because online and offline channels differ in their ability to convey information
about various product attributes (Alba et al., 1997; Shankar, Smith, & Rangaswamy, 2003), they
are not equally suitable for distributing all products. Indeed, consumers have been shown to
differ in their channel preferences depending on the type of product purchased (Kacen et al.,
2003; Levin, Levin, & Health, 2003). Whereas products such as electronics, books, or travel
arrangements have been shown to be suitable for both online and offline channels, consumers
strongly prefer buying clothing through offline channels, where they can try pieces on and
physically examine their quality (Kacen et al., 2003; Levin et al., 2003). When both channels are
equally appropriate for selling a given product and thus are similarly valued by consumers, the
differences in price sensitivity and thus the likelihood of channel-based price differentiation
should be lower. In contrast, when one channel significantly outperforms another and the
channel valuations differ, these differences in valuation yield the possibility of price
Brand power. Market power can be achieved via advertisements or public relations activities that
help to build strong brands, which offer companies more options in terms of pricing strategies
(Leuthesser, 1988). Additionally, strong brands decrease consumers’ price sensitivity (e.g., Kalra
& Goodstein, 1998; Erdem, Swait, & Louviere, 2002), which yields further opportunities to
charge differential prices. In a similar vein, an empirical study of the gasoline market shows that
retailers with stronger brands are more likely to charge differential prices (Iyer & Seetharaman,
However, based on the information economics perspective on brand equity (Erdem & Swait,
1998), it can also be argued that brands need to send consistent and clear signals to achieve brand
power. Pricing a brand differently across channels might lead to confusion and decrease brand
power and thus may not be in the interest of manufacturers. Given that manufacturers with strong
brands can be assumed to have a higher level of power in the manufacturer-retailer relationship
and thus a higher chance of influencing retailers’ pricing strategies, it is difficult to predict the
impact of brand strength on the retailer’s likelihood of engaging in price differentiation.
To address the research questions posed in this paper, we conducted two empirical studies. The
aim of Study 1 was to analyze whether and to what extent multi-channel retailers engage in
channel-based price differentiation. Study 2 as a follow-up study additionally analyzes the
factors that influence the extent of channel-based price differentiation.
The data for Study 1 and Study 2 were collected in a major German city between June and July
2005, and March and May 2006, respectively. Study 1 contains price observations for 1,080
products sold by 54 multi-channel retailers, while Study 2 contains 1,662 products sold by 61
multi-channel retailers. For both studies, we used haphazard sampling within a dimensional
sampling procedure to ensure the accuracy of the sample frame. Here, we first decided upon
several company and product characteristics we wanted to have included in our sample (e.g.,
different company sizes, different product categories, products’ availability in both distribution
channels). We then selected corresponding samples based on convenience but still as randomly
as possible. The products for which the prices were gathered were identical, and prices in online
and offline distribution channels were checked on the same day. Prices were gathered during a
personal visit to the offline store and from the store website. The sample includes multi-channel
retailers from various industries ranging from apparel and car rental to telecommunications and
covers various product categories.
The prices for the 1,080 products in Study 1 ranged from €0.39 cents to €1,849 for the offline
channel and from €0.39 cents to €1,799 for the online channel. The prices for the 1,662 products
in Study 2 ranged from €0.59 cents to €5,500 for the offline channel and from €0.59 cents to
€5,500 for the online channel.
In our further analysis, we ignore transaction costs (i.e., costs associated with obtaining the
product) and focus on online and offline prices. Therefore, both the transportation charges in the
case of offline purchases (i.e., the cost of traveling from home to the store) and shipping costs in
case of online purchases are ignored. This approach is motivated by the results of Morwitz,
Greenleaf, and Johnson (1998), who show that partitioned prices (e.g., the separate presentation
of the product price and shipping costs) decreases consumers’ recalled total cost and increases
demand. This implies that consumers focus on the product price and at least partly ignore
shipping costs. Additionally, Brynjolfsson, and Smith (2000), who analyze price differences and
price dispersion between online and conventional retailers, show that the results are consistent
when transaction costs (i.e., transportation charges and shipping costs) are accounted for and
ignored. The choice to ignore shipping costs is also supported by the analysis of our data. The
correlation between shipping costs and the price difference (price online – price offline) is as low
as .10 (p < .05) in Study 1 and as low as .03 (n.s.) in Study 2. This indicates that shipping costs
do not drive the price gap or price differentiation. At the same time, the majority (93% in both
studies) of retailers use only one or two different levels for shipping costs, indicating that
shipping costs are hardly used by retailers to compensate for differences in prices charged online
versus offline. Table 1 shows examples of the occurrence and extent of channel-based price
differentiation in the data.
Examples of occurrence and extent of channel-based price differentiation
Relative extent of
(Canon Pixma MP780, Art-Nr.
(Tefal Raclette und Partygrill, RE450)
(Comme une evidence, Eau de
Parfum, 50 ml)
(Pringles, Paprika, 200-gr-package)
(Steppjacke, Size 38, Art.-Nr.
(Opel Astra 1.4, Compact,
comprehensive coverage, 1 day,
*Relative extent of price differentiation is defined as (price offline-price online)/price offline
269.00 282.45 Yes
69.99 69.99 No 0.00%
14.90 16.95 Yes 12.09%
Tengelmann 1.79 1.59 Yes -12.78%
Klingel 39.95 49.95 Yes 20.02%
Avis 101.15 74.27 Yes -36.91%
In Study 2, in addition to examining prices offered through online and offline channel, we
gathered further information about retailers and market characteristics to analyze the factors that
influence the decision to engage in channel-based price differentiation.
Many approaches to testing the proposed factors are feasible. The ideal approach would be to
exhaustively collect objective historical data on each of the factors and find measures that reflect
the factor’s performance. Unfortunately, not only is getting this information very hard, but
identifying measures that best capture the factors is also challenging. While some factors, such as
offline reach, are rather easy to measure, the measurement of others, such as competitive
intensity, is still the subject of extensive discussion in current research. In the following sections,
we briefly review the different ways of operationalizing the factors, as discussed in the literature,
and present our way of dealing with the problem. The proposed factor operationalization and
proxies should be kept in mind when interpreting the model’s results.
In principle, competitive intensity could be measured by counting competitors or calculating
concentration ratios. However, Porter (1980) and others have argued that true measures of
competition require the consideration of a number of other factors, such as the threat of the entry
of new competitors, the threat of substitutes, the bargaining power of buyers, the bargaining
power of suppliers, and the degree of rivalry between existing competitors. Given that these
factors are rather difficult to quantify, some researchers have attempted to sample executives’
perceptions to obtain a measure of competitive intensity (e.g., Lusch & Laczniak, 1987). Our
approach, in contrast, is based on easily accessible data; we measure the level of competition as
the number of websites that are similar to the website of a given multi-channel retailer. The
number of similar websites is reported by the Google search engine, which contains a feature that
finds and displays websites with a similar content. Given that our measure captures the number
of similar websites rather than the number of competitors a retailer faces, it should be noted that
we measure the online competitive intensity rather than the overall competitive intensity.
The measurement of online and offline reach and of the number of distribution channels is rather
straightforward. We use alexa.com to obtain information about the online reach of each multi-
channel retailer and define online reach as the percentage of global Internet users who visit a
given website. Based on the retailers’ websites, we also gathered information about the number
of offline branches and used this as a measure of offline reach. Similarly, we obtain information
about the number of distribution channels that the company operates.
In previous studies, company size has been measured by either the number of employees, the
total asset value, revenues, sales volume or an index rank like Fortune 500 (e.g., Belkaoui &
Karpik, 1989). Because our argument regarding incentives for price differentiation is based on
economies of scale, our measure of the size of a firm needs to capture the sales volume a
company generates. Given that the companies’ revenues were stated on all companies’ websites,
we use this measure as a proxy for company size.
Several methods of measuring brand power—or its equivalent, brand equity—have been
proposed in the literature (e.g., Aaker, 1991; Erdem & Swait, 1998; Keller, 1993; Swait, Erdem,
Louviere, & Dubelaar, 1993). Marketing research companies, on the other hand, have used
aspects like “familiarity” and “favorability” (Core Brand’s Corporate Branding Index) or dollar
value (Millward Brown’s BrandZ Top100) to measure brand power. In this paper, we make use
of the concept of brand visibility as a necessary condition for brand power (O’Shaughnessy &
O’Shaughnessy, 2003). We therefore use the number of hits for a given brand name reported by
the Google search engine as a proxy for brand power.
Table 2 reports the summary statistics for the factors used in Study 2, while Table 3 shows the
correlation coefficients between them and the relative price gap between online and offline
prices (=(price offline-price online)/price offline). Lastly, the products are classified into seven
categories: services (9.3% observations), clothing and accessories (38.3%), housewares (e.g.,
furniture) (11.1%), cosmetics (8.2%), electronics (20.6%), leisure items (e.g., books, DVDs)
(6.1%), and food (6.4%). The categories make it possible to distinguish between products and
services, durables (i.e., electronics) and non-durables (i.e., food), search goods (i.e., electronics)
and experience goods (i.e., cosmetics), and products that need physical examination (i.e.,
clothing) and products without such a need (i.e., leisure items).
Summary statistics for factors used in the conceptual framework
Factor Definition Source Mean
Online competition The number of websites that are
similar to the website of a given
The number of offline branches
Percentage of global internet users
who visit a given website
multiplied by 1000
The number of all distribution
channels a company operates
Yearly company revenue in 2005
The number of hits for a given
brand name reported by the
Google search engine
Nr of channels Company website 3.12 1.20
Brand hits (million)
Correlation between factors and absolute relative price gap
.083** .041 1
.167** .093** .226**
-.044 .007 .093**
.056* .040 -.016
-.178** .005 .066**
*Significant at p < .10, **Significant at p < .05, ***Significant at p < .01
Nr of channels
Brand hits (million)
Relative price gap
Descriptive Results of Study 1 and Study 2
The descriptive results of Study 1 and Study 2 are summarized in Table 4. Both studies show that
multi-channel retailers engage in channel-based price differentiation, the extent and direction of
which vary by retailer and product category. For 20.55% of the analyzed 1,080 products in Study
1 and for 34.40% of the 1,662 products in Study 2, different prices are charged through these two
channels. For the products with price differentiation, the price offline was higher in 73.42% of
the cases in Study 1 and in 62.98% of the cases in Study 2. The mean of the relative extent of the
price gap (defined as (price offline-price online)/price offline) equals .49% in Study 1 and .76%
in Study 2, implying that on average, online prices are in both studies lower than offline prices
(see also Figure 2 for a histogram of relative price gaps). This average relative difference
between offline and online prices is significantly different from 0 at the .10 level in Study 1 and
at the .05 level in Study 2. In both studies, the highest positive relative price gaps can be found
for consumer electronics such as remote controls (Study 1: €149.00 in the online channel versus
€299.00 in the offline channel) or memory sticks (Study 2: €33.00 online versus €69.99 offline).
In contrast to this, we find in both studies a high variation among products with the highest
negative price gaps. Examples for these products hereby range from eye creams (Study 1: €19.90
online versus €7.90 offline) to sneakers (Study 1: €159.90 online versus €79.95 offline) and from
laminating machines (Study 2: €49.99 online versus €19.00 offline) to canned food (Study 2:
€1.79 online versus €0.99 offline).
Summary of the descriptive results of the two studies
Study 1 Study 2
Correlation between shipping costs and price gap .10 .03
Mean relative extent of price gap .49% .76%
Mean absolute, relative extent of price gap 2.54% 5.51%
Percentage of products with price differentiation
Percentage of products with higher offline prices given price
Mean relative extent of price differentiation given price
Mean absolute, relative extent of price differentiation given
Percentage of retailers engaging in channel-based price
Percentage of retailers always charging higher prices offline if
engaging in price differentiation
Percentage of retailers always charging higher prices online if
engaging in price differentiation
Percentage of retailers following mixed strategy if engaging in
Percentage of retailer’s assortment exhibiting price
differentiation if retailer engages in price differentiation
The mean of the absolute relative price gap (|price offline - price online)/price offline|) equals
2.54% in Study 1 and 5.51% in Study 2 and is in both studies significantly different from 0 at the
.001 level. The mean of the relative extent of price differentiation if prices were different was
2.40% in Study 1 (p < .10) and 2.21% in Study 2 (p < .05), and the mean absolute relative extent
of price differentiation was 12.33% (p < .001) and 16.06% (p < .001), respectively.
Histograms of relative price gaps
Study 1 Study 2
The analysis at the retailer level shows that 29.63% of the retailers in Study 1 and 60.66% of the
retailers in Study 2 engaged in channel-based price differentiation. Among these retailers,
18.75% in Study 1 and 5.41% in Study 2 always charged higher prices through offline channels,
6.25% in Study 1 and 2.70% in Study 2 always charged higher prices through online channels,
and the remaining 75.00% in Study 1 and 91.89% in Study 2 followed a mixed strategy—that is,
these retailers charged higher prices both online and offline. For retailers that engage in channel-
based price differentiation, price differences were exercised on average for 69.38% of the
analyzed assortment in Study 1 and for 54.61% of the analyzed assortment in Study 2.
Summarizing, we find that retailers do indeed engage in price differentiation. The price
differences are rather consistent with the results of Kacen et al. (2003) and reflect the differences
in consumers’ willingness to pay between online and offline channels. However, these price
differences are rather low in comparison to other types of self-selection price differentiation,
such as quantity-based price differentiation (e.g., Khan & Jain, 2005) or quality-based price
differentiation (e.g., Leslie, 2004).
Because the two studies were conducted during different time periods (i.e., 2005 for Study 1 and
2006 for Study 2) one can compare their results to analyze the development of channel-based
price differentiation over time. Although care should be taken because both retailers and offered
products are not statistically based random samples and do not match across studies, the results
seem to point out that retailers increasingly engage in channel-based price differentiation. At the
same time, those retailers offering differentiated prices seem to move from a unifying price
differentiation strategy towards a mixed price differentiation strategy, where they make the price
differentiation decision on a product-by-product basis.
Factors Influencing the Extent of Channel-Based Price Differentiation
The aim of Study 2 is to analyze not only the occurrence of channel-based price differentiation
but also the factors that influence its extent. The analysis thus takes into account two choices that
a multi-channel retailer faces: first, he chooses whether to engage in price differentiation, and
second, if he exercises the price differentiation option, he decides its extent. We operationalize
the extent of price differentiation as the absolute relative difference between an offline and
online price for a given product (i.e., (|price offline - price online)/price offline|). A price gap
equal to 0 indicates no price differentiation, while a positive price gap indicates the occurrence of
price differentiation. Increasing price gaps imply an increasing extent of price differentiation.
To model the two decisions of a multi-channel retailer, a Tobit II model is used that consists of
two specifications: first, the probit model describes whether a dependent variable (i.e., price gap)
is zero or positive, and second, the truncated regression model is used to analyze positive values
of the dependent variable (Amemiya, 1984). This results in the following specification:
ij ij ij
ijij ijij ij
where yij is the absolute relative price gap between offline and online channels for retailer i and
product j. This observed absolute relative price gap yij equals 0 if the unobserved latent variable
ij y is smaller than or equal to 0 and is positive if the unobserved latent variable
ij y is larger than
0. Xij contains the explanatory variables as given by the factors’ operationalizations as well as by
product category dummies. The Tobit II model is very similar to the Tobit I model, but it is more
flexible because it allows for different effects of explanatory variables on the decision whether to
price differentiate and on the size of the absolute, relative price gap.
Factors influencing the willingness to engage in channel-based price differentiation
Absolute relative price
Nr of channels
*Significant at p < .10, **Significant at p < .05, ***Significant at p < .01
Table 5 presents the results of the Tobit II model. The results show that the level of online
competition has a significant negative influence on the occurrence and the extent of channel-
based price differentiation (-.0385 and -.0077, respectively, p < .01). This implies that high levels
of online competition decrease the probability that a multi-channel retailer will engage in
channel-based price differentiation and decrease the price differences between channels, a result
that meets our previously stated expectations. With respect to offline and online reach, no
significant effect is found for the former, and a significant negative effect is found for the latter
with regard to the probability of observing differential prices (-.0020, p < .01) and with regard to
the size of the price gap (-.0004, p < .01). Thus, in line with the prediction by Zettelmeyer
(2000), as consumers become more familiar with the online environment and the costs associated
with their switching to the online channel decrease, the likelihood of the existence of price
differentiation between the two channels decreases.
Also, the number of distribution channels that multi-channel retailers operate has been shown to
have a negative influence on the extent of channel-based price differentiation (-.1572 for the
probability of engaging in price differentiation, p < .01 and -.0184 for the price gap, p < .01),
implying that with a higher number of distribution channels, price differentiation across channels
becomes less likely due to the complexity of channel coordination. In line with our expectations
and the analytical findings of Anderson and Dana (2009), we show that company revenue, which
serves as a proxy for company size, has a significant positive effect on both price differentiation
occurrence and extent (7.98E-12 for the probability of engaging in price differentiation, p < .01
and 1.79E-12 for the price gap, p < .01). Both lower costs and greater experience with pricing
strategies are probably the drivers of this effect. We cannot find a significant effect of brand hits
on either the probability of engaging in price differentiation or the size of the price gap in the
Tobit II model. Using brand hits as a proxy for brand power, our results contrast with the results
of Iyer and Seetharaman (2003). This may be either driven by the fact that these authors focus on
retailer brands, whereas we focus on product brands, or by our measure of brand power, which
captures only the visibility of the brand rather than its capability of charging a price premium or
Our results also show that the product type influences the extent of price differentiation, which
meets our expectations. All of the product-type parameters are significant, and their ordering
reveals that the extent of channel-based price differentiation is highest in the case of services,
which are less subject to the jeopardy of reselling. Within goods, we also find a higher extent of
channel-based price differentiation for non-durables (food) than for durables (housewares),
indicating again that retailers engage less in price differentiation for products that are appropriate
for resale. Interestingly, the comparison of the size of the product-type parameters shows a lower
level of price differentiation for clothing than for entertainment (e.g.., books, DVD), which is
surprising, given that clothes in particular need to be physically examined before purchase and
are thus not equally suitable for sale online and offline. At the same time, we find higher levels
of price differentiation for electronics than for cosmetics, even though one would expect the
former product category to be equally appropriate for online and offline buying. The results of
ANOVA testing also support the effect of significant differences in the extent of price
differentiation across product types (p < .001), reflecting the retailers’ acknowledgement of
different levels of suitability among these product types for different kinds of sales. These results
could also explain the large group of retailers using a mixed price differentiation strategy that is
adjusted to the product variety offered.
In the next step, we focus on the direction of price differentiation and analyze the factors that
influence the retailer’s decision to charge higher prices online or higher prices offline compared
with charging the same prices in both channels. The results of the regression model with a
relative price gap ((offline price-online price)/(offline price)) as the base outcome can be found
in Table 6.
Factors influencing the size of the relative price gap
Nr of channels
*Significant at p < .10, ***Significant at p < .01
Relative price gap
The results show that higher online competition decreases the size of the relative price gap and
thus increases online prices relative to offline prices (-.0066, p < .01). This result is particularly
interesting because it suggests that retailers do not decrease their online profit margins, due to
higher competition online. In contrast, it seems that retailers instead benefit from a higher
number of similar websites, maybe because it increases consumers’ familiarity with online
purchasing using the given retailer format.
Whereas we could not find any impact of offline reach on the decision to engage in price
differentiation, the results of the regression now reveal that a greater offline reach increases the
relative price gap (4.01E-06, p < .01) and thus decreases online prices relative to offline prices. A
high number of offline branches allows consumers to easily switch from online channels to
offline channels. Therefore, a multi-channel retailer with many offline branches is not able to
successfully charge higher prices through his online channel. As a result, retailers are no longer
able to exploit the higher search costs offline that are associated with a lower offline reach. Even
though one would expect a similar mechanism to be at work in the case of online reach, we find
no significant impact of this factor on the size of the relative price gap. A measure for online
reach that is better comparable to the measure of offline reach might in the future shed more light
on this issue.
Furthermore, the results show that the number of channels has a positive influence on the size of
the relative price gap (.0120, p < .01) and thus decreases online price relative to offline price.
This might be due to the fact that most other channels (e.g., catalog or telephone sales) share
more characteristics with the online than with the offline channel, thus making the latter one
more subject to channel switching and thereby reducing the retailer’s ability to successfully
charge higher prices in the online channel.
Both company revenue and brand hits have a positive influence on the relative size of the price
gap (1.65E-12, p < .01 and 4.37E-11, p < .01, respectively). It is likely that larger companies
have a higher incentive to migrate their consumers to the online channel, as they are better able
to exploit the cost advantages associated with that channel. As a result, they may be more
motivated to charge higher prices offline. Further, companies may have an incentive to charge
lower prices online for brands with a high number of brand hits, because those brands’ prices can
be more easily compared to competitors’ prices online and may in addition serve as visible
signals of the retailer’s overall price positioning.
Finally, the ordering of product type parameters reveals that retailers are more likely to charge
lower prices online for search goods (electronics: .1410, p < .01) than for experience goods
(cosmetics: .1451, p < .01). This is rather surprising because theory suggests that particularly for
search goods, the quality-related information provided through the internet should decrease the
price sensitivity of shoppers who employ this channel (see Alba et al., 1997). It might be that
factors such as the urge to take immediate physical possession or the desire to see a
demonstration of an electronic product are responsible for this effect. Our results also show that
except for the category food, it is more likely that retailers charge higher prices offline.
SUMMARY AND CONCLUSIONS
This study analyzes the occurrence and factors influencing the decision to engage in channel-
based price differentiation by multi-channel retailers. The results, in contrast to those of various
studies assuming or observing consistent prices in online and offline channels (e.g., Ancarani &
Shankar, 2004; Pan et al., 2002; Tang & Xing, 2001), show that many multi-channel retailers do
engage in channel-based price differentiation, with some indication that this tendency increases
over time. However, we find that retailers still apply a consistent price strategy for the majority
of their products. For the products with price differences between the online and offline
channels, the price gap of 12-16% reflects in general the differences in consumer channel
valuation but is rather low compared to other types of price differentiation. The results altogether
indicate that channel-based price differentiation exists, but it seems that it still has a rather
limited practical relevance for retailers.
Our empirical analysis shows that not all companies have the same level of motivation to engage
in price differentiation. The results of this study regarding factors that influence the occurrence
and extent of channel-based price differentiation support the notion that retailers act in
accordance with standard microeconomic theory: We show, for example, that higher levels of
online competition and online reach decrease retailers’ incentive to engage in price
differentiation across channels and that price differentiation seems to be very valuable for
companies with high revenue. At the same time, the burden associated with the coordination of
effective multi-channel price differentiation still inhibits some retailers from fully exploiting the
opportunities that price differentiation strategies may offer. Even though we find for some
product categories a level of price differentiation that meets standard microeconomic
expectations (e.g., higher price differentiation for services and non-durables), it seems that
differences in the nature of the products have so far not been fully explored.
We further find that multi-channel retailers charge on average higher prices through the offline
channel, a practice that is probably driven by a higher perceived risk of the relatively new online
channel and/or company’s incentive to migrate consumers to the less costly online channel.
Additionally, retailers may follow different operational goals for offline and online channels and
use the latter for increasing their visibility.
The findings of our study show that besides using the Internet as an additional distribution
channel, companies can further explore this channel by engaging in channel-based price
differentiation. Assuming that multi-channel retailers behave rationally and aim to maximize
profit, our study provides requirements and company characteristics necessary to successfully
engage in channel-based price differentiation.
However, because a low online reach helps to separate markets and foster channel-based price
differentiation, the increasing popularity of the Internet as a marketplace for retailers leads to
fewer opportunities to use this channel for price differentiation. Nevertheless, the possibility
exists that with the increasing popularity of the online channel, companies may decrease the
number of offline branches and thus preserve their ability to engage in channel-based price
differentiation due to a lower offline reach.
Our findings indicate that a higher level of online competition online does not necessarily lead to
lower prices in the online channel, a result that is consistent with previous research (Clay et al.,
2002; Erevelles, Rolland, & Srinivasan, 2001). In contrast, it seems that retailers benefit from a
higher number of similar websites, which supports the notion that multi-channel retailers are not
necessarily price takers and do have the power to influence their prices (Goolsbee, 2001).
Limitations and Future Research
Because we only analyzed one region and one store per chain, we cannot exclude the possibility
of spatial price differentiation. Additionally, the choice of products and retailers through the non-
statistically based sampling of products limits our ability to generalize our findings over time or
geographically. At the same time, the factor operationalization, particularly with respect to the
measurement of competition and brand power, may not capture all relevant facets of the factors.
The lack of consistent and comparable measures for online and offline reach further complicate
the interpretation of our results. Future research should therefore try to replicate our study with
the help of a larger and more exhaustive data set.
Finally, we have not analyzed the profitability of channel-based price differentiation; therefore,
we refrain from providing any recommendations regarding the election of an optimal price
strategy. Future research might take an approach similar to that of Khan and Jain (2005) and
focus more on the profitability of channel-based price differentiation to provide
recommendations concerning optimal price strategy. Additionally, further investigation of the
consumer perspective may reveal insights concerning the optimal level of price differences
accepted by consumers for various product categories.
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