2. Review of Related Literature
This article focuses on two areas of literature: marketing and
economics. The first examines the influence of research costs on consumer
decision-making, particularly in the context of online sales. The second is
price discrimination at the second level, where monopolists determine their
product lines so that higher-value consumers choose higher-quality, more
expensive products themselves. We discuss each literature in turn.
Search Costs in Online Retail
Existing literature on the costs of research in online
environments generally explores the
5
effect of reducing friction between information on consumer
choice, well-being, and market structure. The first articles associated the
decrease in research costs with an increase in competition, especially on
prices (e.g. Bakos, 1997). Subsequent work has shown that online research may
be more expensive than originally thought, perhaps because online shoppers have
higher search costs than offline shoppers, and there may be considerable
heterogeneity in search costs among online shoppers (Brynjolfsson and Smith
2000).
Lynch and Ariely (2000) found conditions in which increased
transparency of online quality could reduce price competition, confirming that
«in a competitive environment, the strategy of maintaining certain high
search costs is likely to fail». Others have shown that some companies
have found it advantageous to artificially increase research costs to promote
price comparison. Ellison and Ellison (2009) show that firms in a market have
an incentive to mask actual price and quality information sold online to reduce
the tendency of search-sensitive consumers to comprehensively compare
prices.
Ellison and Ellison (2005) show, as examples of obfuscation,
how online retailers cost-effectively offer complex price menus, products that
appear to be grouped but are no hidden prices, descriptions complex products,
and other tactics designed «to allow the tendering process to take
sufficient time». They argue that many advances in search engine
technology that are supposed to facilitate the collection of information from
consumers have subsequently been accompanied by business investment to hinder
research.
Our work differs from these documents in that, rather than
analyzing the competitive incentives that drive firms to impede research, we
focus on the potential benefits of price discrimination within firms by
increasing the costs of doing business. research. Although previous work has
recognized the importance of heterogeneous research costs for consumers, none
of our knowledge has examined how this heterogeneity can be exploited by a
monopolistic enterprise in an online context.
Price Discrimination
Varian (1980) formulates a price discrimination plan that
allows a company to extract surpluses of uninformed consumers through high
prices and simultaneously sell to informed consumers through low prices using a
mixed price balance strategy. Recent models with heterogeneous research also
find equilibrium in which sellers adopt a mixed strategy
6
(Ratchford 2009). For example, Stahl (1996) finds a mixed
strategic balance that, when implemented by two retailers, creates a market
divide so that fully informed consumers always buy from the low-cost business
while Uninformed consumers stop at less than a complete search and end up
paying more.
Previous research has shown that differences in learning,
provider switching costs, and risk perception when shopping online lead to
heterogeneity in consumers' willingness to search (Ratchford 2009). These
differences in consumer propensity to search allow retailers to selectively
target price or product promotions with different margins among customers
(Kopalle et al., 2009). The majority of research cost literature assumes that
multiple firms have different levels of research costs (Ratchford, 2009) and
thus separate the market based on the heterogeneity of consumers who are
willing to pay for additional research costs. In this article, we focus on a
single multi-product company that exploits this heterogeneity of search costs
to separate the market to offer different segments to different products and
prices.
We identify ways for a company to increase its profits by
simultaneously offering high prices and low prices, the latter in the form of
discounts, to attract informed and uninformed consumers by using search costs
as a segmentation mechanism. As Ellison and Ellison (2009) predict about
obfuscation, we also expect this approach to increase increments. But unlike
their result, we expect the company to not only capture the fraction of
customers who are willing to incur research costs but also to realize higher
profits from customers who are unwilling to incur high search costs. Besides,
there is no explicit or implicit intention to confuse customers as to the
quality or actual price of an item in the online store. All items and prices
are easily accessible to all consumers on the same website.
Ngwe (2016) shows that fashion stores use travel costs, away
from shopping centers, as a way to encourage shoppers to choose to buy in
stores - which leads to high travel costs to obtain low prices, or buying in
regular downtown stores - resulting in lower travel costs but higher prices.
This form and other forms of deliberate addition of research frictions as a
tool of price discrimination are well known to brick and mortar store managers
and can lead to concrete action. Unfortunately, online retailers cannot charge
travel costs by physically placing their stores. The question is whether
managers of online stores can add non-traditional and «virtual»
search costs to the online shopping process of consumers to obtain the same
benefit of price
7
discrimination. To make matters worse, online store managers
often sell new items and liquidation items through the same channel. Also, it
is not certain that the increase in search costs for a multi-brand
non-proprietary online retailer may be a promising strategy when consumers can
buy cheap substitutes from a competitor within a few clicks from distance
only.
To practice price discrimination, companies must first be able
to identify and distinguish sensitive customers or prospects from the high
prices of low-price customers or prospects. The literature has generally
defined three sets of characteristics associated with price sensitivity: the
characteristics of a person, e.g., demographic characteristics, current
behavioral characteristics, e.g., shopping characteristics, and past purchase
characteristics. Besides, previous literature has shown that demographic
characteristics are primitives of behavioral and purchasing characteristics
(Kim, Srinivasan, and Wilcox 1999). From the variations in each of these
characteristics, companies have tried to offer different prices to different
price-sensitive customers.
Discrimination of prices by demographics: Previous
research has linked demographic variables such as age, sex, income and
geographic location, and consumer price sensitivity (Kim, Srinivasan and
Wilcox, 1999). Traditional retailers and other businesses have used this
knowledge to offer different prices based on demographic profiles. Supermarkets
offer special discounts to retirees, cinemas offer students lower prices,
grocery stores offer discounts depending on location, and educational
institutions tend to offer scholarships and discounts to low-income families.
And because many companies believe that men are more price-sensitive than women
in certain categories, market comparisons have revealed considerable price
differences between virtually identical products for men and women.
The use of price discrimination based on online demographics,
on the other hand, has had mixed success. One obvious reason is that once
consumers know who benefits from the rebate and who does not, they are incited
to misrepresent or hide their identities. As a result, it becomes difficult for
online businesses to distinguish between different demographic characteristics
without requiring proof of identity, which would hinder many clients.
Price discrimination through buying behavior:
Monitoring current buying behavior has also been a ubiquitous tool for
brick and mortar retailers to assess a customer's price sensitivity. The way
consumers browse, search, compare options, and negotiate prices has been
widely
8
observed and used by sellers of expensive products that
require more thought. These include consumer electronics, automobiles, and
homes.
The use of behavioral variables observed during online
shopping has had mixed results. On the one hand, and unlike demographic
variables, search, click, and online navigation patterns are easily observable
without the client having to reveal themselves. Hannak et al. (2014) showed
that among 16 retailers and online travel providers, half of them showed that
they offer different products and/or prices to online shoppers based on the
users' cookies, clicks recorded, whether they have logged in or not, and the
use of the mobile phone or PC to search for products and services. Yet, online,
there are some disadvantages of behavior-based price discrimination if
consumers discover the rules and can emulate the behavior that will allow them
to get the lowest price. Amazon discovered it in 2000 when it offered the same
DVD for fewer buyers who did not have a cookie in their browser, to offer new
customers a cheaper price (CNN 2005). This encouraged Amazon's customers to
delete their cookies or not to log in to the website to obtain lower prices. In
summary, while behavioral price discrimination may be more observable than
demographics or personal characteristics, it is also «playable»
because clients are encouraged to learn to behave in a way that will allow them
to get low prices.
Price discrimination based on purchase history: The
third category of variables that have been associated with price sensitivity is
related to previous purchases. Looking at five categories of groceries, Kim,
Srinivasan, and Wilcox (1999) found that the characteristics of a person's
purchase history, such as previous purchase frequency, quantity purchased,
loyalty, and incidence cheaper items, are strongly associated with individual
price sensitivity, far more than demographic characteristics. Offline grocery
retailers and non-food retailers have successfully used this method by tracking
past consumer purchases using loyalty cards (Lal and Bell 2003) and using this
information to selectively target coupons, the prototype price discrimination
method used. Online, pre-purchase couponing is also ubiquitous. It is
observable and difficult to be playable because it is unlikely that consumers
will buy items that are not their best options at the moment just so they can
get future discounts.
The practice of price discrimination by the history of online
shopping poses two major challenges. First, online retailers that tend not to
have frequent or repeat purchases, such as auto, furniture, or apparel
resellers, do not have good data to reliably estimate the sensitivity
9
of new customers to price. Second, while many industries such
as groceries and fast food tend to offer fidelity-based discounts, the more you
buy, the less expensive, some do the opposite. It has been shown that the
insurance industry generally charges higher prices, not lower, for customer
loyalty (Hughes 2008). The reasoning is that people who tend to stay with their
insurance company for many years are opposed to the stress and hassle of
shopping for better insurance quotes and are therefore less price sensitive.
Insurance companies take advantage of this to increase loyalty customers'
premiums year by year, a «loyalty tax» by charging more than new
customers (Bankrate 2016). In the latter case, higher prices for loyalty,
consumers are encouraged to move from one company to another. The reason most
consumers do not do this is probably because of the considerable effort they
put into doing it. In these cases, purchases that are infrequent or
discouraging redemption, price discrimination from past purchases can have
serious negative effects on firms.
In summary, there are three generalizable categories of
variables associated with an individual's price sensitivity, according to
previous literature. Furthermore, no variable is perfect for price
discrimination in the sense that there are tradeoffs inherent in observability
and «playability». In particular, some variables are better than
others for the implementation of price discrimination policies, depending on
the category of product or industry. Therefore, a good model for estimating
price sensitivity should incorporate as many variables as possible of the three
types, demographic, behavioral, and past purchases. In section 5, we try to
estimate a price sensitivity model specific to each person in our context,
taking into account the constraints linked to the data available to the
company. Previously, we develop the research plan used in this paper.
| 
