A Tale of Two Pricing Systems for Services
Kelly Lyons
Faculty of Information
University of Toronto
45 Willcocks St.
Toronto, ON, Canada M5S 1C7
Paul R. Messinger
School of Business
University of Alberta
Edmonton, AB, Canada T6G 2R6
paul.messinger@ualberta.ca
Phone: 416-946-3839
Fax: 416-978-8942
kelly.lyons@utoronto.ca
Run H. Niu
School of Business and Technology
Webster University
470 East Lockwood Avenue
St. Louis, MO 63119-3194 U.S.A
Eleni Stroulia
Department of Computing Science
University of Alberta
221 Athabasca Hall
Edmonton, AB, Canada T6G 2E8
runniu68@webster.edu
stroulia@cs.ualberta.ca
ABSTRACT
Due to advances in technology and the rapid growth of online service
offerings, various innovative web-based service models and delivery methods
have appeared – including several free services. It is not always clear whether and
how these emerging mechanisms for online service delivery will result in
profitable businesses. In this paper, with an eye towards beginning to understand
the issues involved, we present an analytical model of rational customer choice
between available service plans. In particular, our model predicts how a monopoly
service provider should devise its plans under this rational choice assumption, if it
understands such customer behavior. We then describe how this model would
need to be extended in order to reflect increasingly inexpensive and even free
service offerings.
Keywords: Business Models, Online Profit Models, Social Media, Free
Services, Freemium
1
A Tale of Two Pricing Systems for
Services
1. INTRODUCTION
Web-based service offerings are making inroads on more traditional servicedelivery mechanisms. The phenomenon raises questions about (a) how the
emerging mechanisms for online service delivery will result in viable and
profitable businesses, and (b) how the availability of web-based service offerings
will co-exist with traditional service-delivery models. In Lyons et. al. (2009), an
analysis of business models is presented resulting in a categorization of offerings
into four classes: (1) computational processing and database service offerings,
provided as traditional utilities; (2) content-based service offerings by providers
from the old media (gathered by news teams and shared through newswires) and
new media (gathered from the Internet or created by online communities); (3)
transactional service offerings for physical products and packaged software
information, or media products; and (4) brokerage or affiliate service offerings
that help bring partners together to make their own transactions or barter. For each
class of offering, a description of how value is exchanged in a variety of specific
instances is presented (see Table 1). The list of business models (column 2)
largely follows Rappa (2008). The fee-structures column describes ways to
monetize the value created by the business model; however, increasingly, value in
these business models is realized through non-monetary means. This demonstrates
how the typical roles of provider and customer are changing in the context of
emerging online service offerings and in light of a move from a goods-dominant
to a service-dominant world (Vargo and Lusch 2004). Additional third-party
entities are also key stakeholders in these offerings and co-creation of value takes
place among many actors in the online service offerings.
The analysis of Lyons, et al. (2009) considers the co-creation of value and
involvement of third parties (especially in the case of advertising) in the examined
business models but does not examine in depth the phenomenon of “free”
offerings. In many current on-line businesses, several users receive significant
services for free and the resulting business models rely on the fact that at least
some users (or some third parties) are willing to pay for service offerings, at least
in some circumstances. In his latest book, Anderson (2009) suggests that the new
model of doing business means giving away much of an offering for free by only
charging for a fraction of the value created by the offering. The prevalence of this
business model is made possible in on-line service offerings because, as Anderson
argues, “atoms cost money, but bits are free” (Anderson, 2009). Accordingly,
although users have been acculturated to paying substantial prices for physical
products, they often expect online service offerings to be available for very little
or free. This expectation is reinforced by many companies (Google, Facebook,
Twitter, Survey Monkey, LinkedIn, etc.) giving away much of their service
offerings – or at least starting their companies by doing so.
2
Table 1: Service Classes, Business models, and Fee Structures (Lyons et. al. 2009)
Service Class
Computational
processing and
database services
– offered as oldstyle utilities
Content providers
in the old media
(gathered by news
teams and shared
through wire
services) and new
media (gathered
from the Internet
or created by
online
communities)
Transactional
services for
physical products
and packaged
software
information, or
media products.
Brokerage or
affiliate models
that help bring
partners together
to make their own
transactions or
barter.
Fee Structures (with Examples)
Fee-for-Access: Various forms of
SaaS.
Fee-for-Service: Salesforce.com,
Concur Technologies, Digital Insight,
Digital River, Rightnow
Technologies, Rypple, Taleo,
Ultimate Software, WebEx,
WebSideStory, Workstream
Advertising Model: Third-Party
Fee-for-Service: Google, Yahoo,
(advertiser) pays $ to content provider; Standard newspapers such as the New
provider places advertising in media;
York Times. Applies for advertising,
end-user receives services for free and wherein
is exposed to advertising.
Subscription Model: User pays $ to
Fee-for-Access: Standard newspapers
provider; provider provides service to and cable TV. SaaS applications.
user.
Fixed Fees: World of Warcraft
Infomediary Model: Third-party
Fee-for-Service: Doubleclick, Cnet.
service provider pays $ to info
(the only difference from traditional
provider; info provider consolidates
media is the nature of the content).
list of service providers; user selects
service provider; third-party provides
service to user. (User may also cocreate the service and provide ratings
of service providers.)
Community Model: Provider makes
Free: Wikipedia, Facebook, Youtube,
available service to user; users create
Amazon customer review
content which attracts other users;
Fee-for-Access: Second Life,
third-party pays $ to provider
LinkedIn, Cyworld, ClubPenguin,
(advertising); user may pay $ to
ActiveWorlds, World of Warcraft
provider (subscription).
Fee-for-Service: Facebook Ad,
Youtube Ad, Second Life Land,
ActiveWorlds Land, Webkinz Toys
(Ancillary objects), World of
Warcraft merchant (in-world)
Merchant: User provides $ to
Fee-for-Service: Most standard
provider; provider makes available
eCommerce: 1800flowers.com,
products or services to user; provider
Apple’s iTunes Store, Borders.com,
may create service or procure products sears.com, runningroom.com.
or services from third parties for $.
Manufacturer (Direct): User pays $ Fee-for-Service: Ikea, SaaS.
to provider; provider sells product or
service to user
Brokerage: User pays $ to broker;
Free: FriendFeed.
broker facilitates match-up of users
Fee-for-Service: eBay (auctions),
and service providers (which may
expedia.com (travel), Comfree (realinvolve a service exchange).
estate). Often commission based.
Affiliate: Users click through to third- Fee-for-Access: Google Affiliate
party for service; third party pays $ to Network
provider; user pays $ to provider.
Fee-for-Service (per click): Amazon
Affiliate Program
Value exchange: Business Model
Utility Model: User pays $ to
provider; provider provides service to
user.
Indeed, there exists an interesting contrast between two competing views
of business models for service offerings: one, wherein businesses with some
monopoly power price their services to customers who are assumed to rationally
choose service offerings in a way that maximizes their utility and the other,
3
suggested by Anderson (2009), wherein many online service offerings are
provided free in order to engage customers who then may decide to pay for other
offerings. In this paper, we examine exactly this contrast. We first develop an
analytical model of service pricing under the assumption that customers, served
by monopoly service providers, make rational choices towards maximizing their
utility. We then examine this model in light of Anderson’s (2009) views to show
how such an analytical model would need to be extended to take these emerging
business models into account. In some cases, this requires sketching out, in
general terms, possible underpinnings of models of service industries that may
substantiate Anderson’s conclusions. In the process, we lay out two very different
perspectives on the pricing of services. Probably the truth lies somewhere in
between.
To follow this plan, in Section 2, we put forward a specific analytical
model for pricing services that supposes customer rationality, customer
willingness to pay for services, and a single monopoly profit-maximizing service
provider. This analytical model gives rise to a series of testable propositions. In
Section 3, we then critique the assumptions and conclusions of this analytical
model in light of Anderson’s (2009) views of online service industries as
exhibiting proclivities, or biases, towards providing free or low-priced services for
many customers, together with examples of on-line businesses that exemplify the
various points. Finally, in Section 4, we conclude with a summary of the
knowledge contributions of this work and discuss our plans for future research.
2. AN ANALYTICAL MODEL OF SERVICE
PRICING
Service providers can utilize many possible fee structures. We refer to a feestructure regime as a service plan (or plan, for short). When a user selects a plan,
a contractual relationship is established between the user and the service provider
that permits the user to obtain the provider’s services at an agreed upon fee
structure (often for a pre-specified length of time).
Inspection of Table 1 suggests that most plans charge users directly in one
of three forms: a one-time fixed fee, periodic access fees, or fee-for-service
schedules. There are other plans, which make a service free to the user, but charge
third parties, such as advertisers, for the right to access the user network. We
accordingly abstract the following essential features for modeling purposes: that a
provider can charge: (a) variable fees for particular services; (b) fixed fees for
particular service bundles; or (c) no user fees, but, instead, fees to third parties for
access to the system and to the service users.
We will examine such features of service plans from the perspective of
customers (i.e., users) and service providers. We ask two questions. First, how
should rational customers choose from among the available plans and, given a
selected plan, how much of each service should they utilize? Second, given the
answer to the first question, how should service providers design their service
offerings and fee-schedule plans? We examine these questions in the context of an
analytical model.
4
2.1 Problem Formulation
We begin by examining a model of a single service provider making
available one or more service plans. Customers then choose a plan (or no plan at
all) and how much of each service to consume under that plan. We assume that
the service provider knows the customer’s problem and optimally sets its plan(s)
according to how the customer will respond. We, thus, analyze this problem in
reverse order starting with the customer’s problem.
2.1a. Customer’s Choice of a Service Plan
We begin by supposing that customers can choose from among i  1, , I
available plans, each of which specifies a fee structure for k  1, , K services.
Each plan i specifies a fixed fee Fi that provides up to the quantity, sk ,i , service
units for each service k  1,
, K .1 An additional per unit charge of pk ,i will be
collected for each unit used above a threshold sk ,i for each service k  1,
,K .
We allow for another feature of the plan, imposing hi hours of time spent with
advertising or engaged in self-service activities (a form of service co-generation).
Customers are assumed to have a total of T hours of time which can be consumed
by spending hi hours watching advertising and/or engaging in self-service (the
assumptions apply equally well for both interpretations) or which can be applied
toward working at an hourly wage rate, w , to support consumption of other
services or goods. Table 2 summarizes our notation.
Table 2. Model Notation
Variable Description
Fixed fee for plan i
Fi
sk ,i
Number of units of service k included in plan i
pk ,i
Price per unit of service k under plan i applicable after threshold sk ,i
Hours used in self-service or watching advertising arising from plan i
hi
qk
Quantity of service k consumed
Total time the customer has to work, self service, or watch
advertisements
Customer’s hourly wage
Utility (and quantity) of everything else consumed (explained below).
T
w
x
Note that this is a general structure that includes many forms of pricing
that we commonly see in the marketplace. When the fixed fee Fi , thresholds sk ,i ,
k  1,
, K , and hi are all zero, we have standard per-unit pricing ( pk ,i is the
1
Note that, in addition to the base (e.g., monthly) fee, a further fixed fee could also be
charged for the cost of the initiation of the services (for example a one-time fee for a device or a
set-up or registration fee), which we may consider subsumed in Fi. One advantage of abstracting to
a one-period model is that the fixed fee can describe both a one-time initiation fee or a periodic
fixed access fee.
5
price for a unit of service k). When the per-unit charges pk ,i , k  1, , K , are
approach infinity (or are unreasonably high), we have pure bundled pricing (the
bundle (s1,i , , sK ,i ) is being sold for a fee of Fi ). When the thresholds sk ,i
approach infinity, we have “all you can eat” pricing (for the fixed fee Fi ). And
when imposing a time cost hi (together with a lower direct monetary cost), we
describe business models that use advertising or a form of self-service.
There are myriad examples of all of these forms. The general formulation
of fixed fees, thresholds, and per-unit charges above the thresholds applies for cell
phone services which typically require a monthly fee for a given number of calls
and text messages and with use above this number, unit prices come into play.
Electricity and other utilities constitute a degenerate case with mostly variable
fees. Cable TV and premium membership fees for virtual worlds such as Second
Life or World of Warcraft constitute a different degenerate case with only fixed
monthly access fees. Infomediaries, such as cnn.com or yahoo.com, and search
engines, such as Google, constitute examples of plans with services free to users,
but for which fees are charged to third-party advertisers and such that users may
be required to spend time watching advertising. Assembling IKEA furniture is an
example of co-generation of value from the service of the furniture, which also
consumes user time.
2.1b. Customers’ Choices of Quantities of Services Consumed
Next, we assume that after having committed to a particular plan, the
customer chooses the quantity used, q k , of each of the k  1,, K available
services.
Analysis of Customer’s Choice Problem: Following microeconomic theory
(for example, see Varian, 1992), we begin by assuming that a customer
maximizes his or her utility function U, which we assume takes the following
tractable form:
U  x  f (q1 ,
, qK ) ,
where q k is the quantity consumed of service k (k=1,2,...,K), f (q1 ,
(1)
, qK ) is the
utility derived from consuming quantities q1 , , qK of the focal services, and x
is the utility arising from all other consumption activities. Concerning x , we are
using a Hicksian composite (often referred to in product-dominant
microeconomics as an “outside good”) (Varian, 1992). We assume that x is
scaled in such a way that one unit of utility arises from consuming one unit of this
composite. Thus, x describes both the quantity consumed of this composite of
everything else and the utility derived from consuming this quantity. Furthermore,
we assume that utility x is scaled so that one unit of this composite has a price of
$1. Generally, the intuition behind Equation (1) is that overall utility is the sum of
the utility derived from consuming the various services associated with the chosen
plan plus some additional utility, which is garnered from all of the customer’s
other consumption activities unrelated to the services under examination.
6
We assume that the customer maximizes his/her utility by choosing a plan
i and quantities q1 , , qK of the focal services under this plan, subject to the
following budget constraint:
K
w(T  hi )  x  Fi   pk ,i max(qk  sk ,i , 0) ,
(2)
k 1
where T is the total amount of time the user has to either work or be occupied
with some form of self-service or advertising, and w is the user’s hourly wage.
Intuitively, the customer uses up hi hours associated with plan i (either with
advertising or self-service) and has T  hi hours to work, which, at wage rate w ,
yields an income of w(T  hi ) . The customer can spend this income to buy the
outside utility or to pay for service plan i, which involves the standard fee Fi plus
the cost of the extra units he or she consumes of each service, above the default
quantities included in plan i.
Now we can state the customer’s choice problem as follows (substituting
out x from Equations (1) and (2)):
max [ max [ U subject to Eq. (2)]]
i
q1 , , qK
K
= max [ max [ f (q1 ,
i
q1 , , qK
, qK )  wT  whi  Fi   pk ,i max(qk  sk ,i ,0) ]]
k 1
(3)
Lastly, we note that a utility-maximizing customer will only choose to
purchase the optimal plan i * (that maximizes (3)) if the utility from plan i * is at
least as large as the total cost of the plan, that is, if
K
f (q1 ,
, qK )  Fi*  whi*   pk ,i* max(qk  sk ,i* ,0)
(4)
k 1
Otherwise, the customer will choose no service plan at all. This follows from
examination of Equations (2) and (1). Intuitively, the right side of (4) is the total
cost to the customer of plan i * , which from Equation (2) is the amount, x , of the
outside composite consumption activity that must be given up to pay for plan i * .
From Equation (1), this is also the amount of outside utility that must be given up
for plan i * . If (4) does not hold, the amount of foregone outside utility exceeds
the amount of utility arising from the service plan i * , itself, and the customer will
choose not to purchase plan i * at all (or any other service plan, for that matter,
since i * is better than any other available plan for the customer). The relation (4)
is an example of a “participation constraint” for the customer, since the customer
will not participate in a plan at all unless (4) holds.
Overall, this is a useful formulation of the customer’s choice problem for
services with potential applicability in some service applications. We explore
some simple applications below.
Two Preliminary Results: We begin by noting that the above formulation
gives rise to the following two straightforward propositions.
7
*
*
Proposition 1: Suppose that plan i * is chosen and quantities q1 ,..., qk are
consumed by a customer. If the marginal utility of a service k is positive, then
qk*  sk ,i* . Proof: Inspection of (3).
Intuitively, this states that so long as customers continue to value
additional units of a service, they will endeavor to utilize at least the default
quantity stipulated by a chosen plan i * .
Proposition 2: If the only differences between two available plans are
fixed cost, Fi , and the imposition of time hi associated with self-service or
reading of advertisements, then a customer will choose the option that minimizes
his/her w hi  Fi . As a result, customers with low wage rates will be relatively
more willing to give up time hi in exchange for a lower fixed cost Fi and
customers with high wage rates will be less willing to do so. Proof: Inspection of
(3).
Intuitively, this states that customers who have a high value of their time
will be less willing to give up their time to engage in self-service co-generation or
to watch advertisements, and, instead, will be more willing to pay the fixed fee.
We now turn to more complex applications of this formulation.
2.2 Service Provider’s Problem
We consider the problem facing a single service provider operating a local
monopoly. We consider two scenarios involving selling to homogenous and
heterogeneous customers. When customers are homogenous, they all share the
same preferences, described by the same utility function. When customers are
heterogeneous, they have different preferences for the available services,
described by different utility functions. We begin with the case of homogenous
customers.
Homogenous customers: Without loss of generality, we consider a group
of homogenous customers as a single customer (i.e., we normalize the market size
– the number of customers – to one). Suppose the service provider incurs fixed
production costs FC to create and offer services to customers (this is distinct
from the fixed fee Fi that the service provider charges to customers for plan i). In
addition, the provider is assumed to incur a variable cost of ck , for each unit of
service k provided to customers. With these assumptions, we can write the profit
 of offering plan i as a function of parameters ( Fi , s1,i , , sK ,i , p1,i , , pK ,i ) , as
follows:2
2
In principle, the provider’s costs might be decreasing functions of hi when we describe self-
service, and the revenues might be increasing functions of hi (in some way) when we describe
advertising. We omit such considerations here, and leave these possibilities to future research. In
addition, for the remainder of this section (for homogenous and heterogeneous customers), we set
hi =0.
8
 ( Fi , s1,i ,
, sK ,i , p1,i ,
K
K
k 1
k 1
, pK ,i )  Fi   pk ,i max(qk ,i  sk ,i , 0)  FC   qk ck .
(5)
The service provider’s problem is to design and make available the plan
(or plans) that maximize the service provider’s profits. In the case of a monopoly
service provider facing homogeneous customers, the provider need only provide
one plan, since all customers can maximize their utility by choosing the same plan
(according to (3) above), in which case any other plans the provider might offer
would be moot. For this reason, in the case of homogeneous customers, we
suppose the provider offers a single plan, and we drop the subscript i. We describe
the provider profit-maximizing plan as the values of ( Fˆ , sˆ1 , , sˆK , pˆ1, , pˆ K ) that
solve the following problem:
Max
F , s1 , , sK , p1 , , pK

Max
F , s1 , , sK , p1 , , pK
K
K
k 1
k 1
[ F   pk max(qk  sk , 0)  FC   qk ck ] .
(6)
Now, note that any profit-maximizing plan for the service provider must
meet the customer’s participation constraint (4). Otherwise the service provider
would have no customers, which is generally not optimal. We further argue that a
monopoly service provider optimizing the problem (6) will raise the fees, such
that (4) holds as an equality, which we write as
K
f (q1 ,
, qK )  F   pk max(qk  sk ,0) .
(7)
k 1
(We note again that we are assuming hi =0 for this section. This generalizes when
hi is not zero, but the derivations are more complicated.) Substituting (7) into (6)
and suitably adjusting the domain of maximization yields:
Max   Max[ f ( q1 ,
q1 , , qK
q1 , , qK
K
, qK )  FC   qk ck ] .
(8)
k 1
Intuitively, in economic terms, (8) says that the monopoly service provider will
appropriate and maximize the total market surplus (customer utility less service
provider’s cost). To do this, the service provider needs to set a plan
( Fˆ , sˆ1, , sˆK , pˆ1, , pˆ K ) that induces the customer (via (3)) to consume quantities
( qˆ1 ,
, qˆK ) that maximize (8). Assuming an interior solution, the optimal
quantities ( qˆ1 , , qˆK ) from the service provider’s perspective are such that
f (qˆ1 ,
qˆk ,..., qˆ K )
 ck , k  1,
qk
,K .
(9)
For the monopoly service-provider problem, the following proposition establishes
two possible optimal plans.
Proposition 3. A monopoly service provider has two profit-maximizing plans (that
solve (6)). One plan described by (10) emphasizes fee-for-service equal to the
marginal cost of providing the service. (This plan assumes an interior solution.)
9
Another plan (bundled pricing) described by (11) emphasizes a fixed fee that
extracts all the customers’ possible surplus utility.
pˆ k  ck , k  1,
Plan 1
sˆk  0 , k  1,
Fˆ  f (qˆ1 ,
Plan 2
,K
(10)
,K
K
, qˆ K )   ck qˆk
k 1
pˆ k   , k  1,
,K
sˆk  qˆk , k  1,
,K
(11)
Fˆ  f (qˆ1 , , qˆK )
Proof. See Appendix A.
We can interpret these optimal plans as follows. For Plan 1, the service
provider does not offer any lump-sum service level; instead, it charges a unit price
equal to its marginal cost. Intuitively, by charging a unit price equal to its own
marginal costs, the service provider is able to align the customer’s incentives
perfectly with its own, and the customer chooses what is optimal for the service
provider. The fixed fee is then set to extract any remaining customer surplus (of
utility less variable fees paid). For Plan 2, the service provider uses lump-sum
service levels and very high additional charges that motivate the customer to
choose the quantity that the service provider wants (as a boundary solution).
Again the fixed fee is set to extract any remaining customer surplus and maximize
the monopoly profits.
Heterogeneous customers: In this scenario, we assume heterogeneous
customers can be divided into two market segments and customers in each
segment are homogeneous. We assume that there are two segments, 1 and 2, of
equal size, and we normalize the size of each market segment – the number of
customers in each group – to one. Without loss of generality, we assume the
difference between these two segments is in the value that customers in each
segment place on service k such that Segment 1 values service k more than
Segment
2;
that
are,
f1(q1, , qK )  f2 (q1, , qK )
f (q , , qk , , qK ) f 2 (q1 , , qk , , qK )
and 1 1
, where f1 (q1 , , qK ) is Segment

qk
qk
1’s utility function and f 2 (q1 , , qK ) is Segment 2’s utility function. (Note that we
did not have any subscript for the utility function in previous discussion because
we considered only one type of customer – one homogeneous market segment.)
According to principle-agent theory (Holmstrom, 1979; Eisenhardt, 1989),
the service provider will design two different payment plans, in order to
differentiate the two customer types and maximize its total profit. Assume that
Plan 1 is designed for Segment 1 and Plan 2 for Segment 2. Then two types of
constraints should apply for each customer segment in order for the plans to
differentiate these two segments.
First, individual rationality constraints (sometimes also called
participation constraints) provide that each segment has non-negative net utility
from buying Plans 1 and 2, respectively:
10
K
, qK 1 )  F1   pk ,1 max(qk1  sk ,1, 0)  0 , and
f1 (q11 ,
(Segment 1)
k 1
K
f 2 (q12 ,
, qK 2 )  F2   pk ,2 max(qk 2  sk ,2 ,0)  0 ,
(Segment 2)
k 1
where qkj is the quantity of service k that Segment j will choose to use when
choosing Plan j ( j  1, 2 ).
This constraint guarantees that participating in the most preferred plan is
better than buying no plan at all. (This is the same constraint we applied in our
analysis of homogeneous customers in Equation (4).)
Second, incentive compatibility constraints (Varian, 1992) provide that
Segment 1 prefers Plan 1 and Segment 2 prefers Plan 2. In particular, we have
K
f1 (q11 ,
, qK 1 )  F1   pk ,1 max(qk1  sk ,1 , 0)  f1 (q112 ,
f 2 (q12 ,
, qK 2 )  F2   pk ,2 max(qk 2  sk ,2 , 0)  f 2 (q121 ,
k 1
K
k 1
K
, qK 12 )  F2   pk ,2 max(qk12  sk ,2 , 0)
k 1
K
, qK 21 )  F1   pk ,1 max(qk 21  sk ,1 , 0)
k 1
where qk12 is the quantity of service k that Segment 1 will choose to use when
choosing Plan 2, and qk 21 is the quantity of service k that Segment 2 will choose
to use when choosing Plan 1.
The
service
provider
would
then
design
two
payment
plans
( F1 , pk ,1 , sk ,1 , F2 , pk ,2 , sk ,2 ) to differentiate the two segments that maximize its
profit as follows:
Max
F1 , pk ,1 , sk ,1 , F2 , pk ,2 , sk ,2
K
K
K
k 1
k 1
k 1
F1   pk ,1 max(qk1  sk ,1 , 0)  F2   pk ,2 max(qk 2  sk ,2 , 0)   (qk1  qk 2 )ck  FC
.
(12)
Here q k 1 , k  1, , K , are the utility-maximizing quantities for Segment 1 (and
qk 2 for Segment 2), assuming that individual rationality and incentive
compatibility constraints apply for both customer segments.
Now we can state the solution to the above problem as follows:
Proposition 4: A monopoly service provider has two profit-maximizing options
(that solve (12) and differentiate the two customer segments).

One option consists of plans (13) and (14) involving fixed fees for both
plans (bundled pricing). The other option consists of plans (15) and
(16) involving a fee-for-service equal to the marginal cost of providing
the service for Plan 1 and a fixed fee (bundled pricing) for Plan 2.

The service provider can use either option to extract all of Segment 2’s
possible surplus utility (note that Plan 2 is the same in the two
options). However, in order to differentiate the two segments, the
service provider cannot extract all of Segment 1’s possible surplus
11
utility.
The
service
provider
has
to
leave
M  f1 (qˆ12 , , qˆK 2 )  f 2 (qˆ12 , , qˆK 2 ) to Segment 1 in order to motivate
Segment 1 to choose Plan 1.
Option 1:
pˆ k ,1   , k  1,
Plan 1:
sˆk ,1  qˆk1 , k  1, , K
Fˆ1  f1 (qˆ11, , qˆK1 )  M
pˆ k ,2   , k  1,
Plan 2:
,K
(13)
,K
sˆk ,2  qˆk 2 , k  1, , K
(14)
Fˆ2  f 2 (qˆ12 , , qˆK 2 )
Option 2:
pˆ k ,1  ck , k  1,
Plan 1:
sˆk ,1  0 , k  1,
Fˆ1  f1 (qˆ11 ,
,K
(15)
,K
K
, qˆ K 1 )   ck qˆk1  M
k 1
pˆ k ,2   , k  1, , K
Plan 2:
sˆk ,2  qˆk 2 , k  1, , K
(16)
Fˆ2  f 2 (qˆ12 , , qˆK 2 )
where
qˆk1 ,..., qˆK1 )
f 2 (qˆ12 , qˆk 2 ,..., qˆK 2 )
 ck ,
 ck ,
qk1
qk 2
, qˆK 2 )  f 2 (qˆ12 , , qˆK 2 )
f1 (qˆ11 ,
M  f1 (qˆ12 ,
k  1,
, K and
Proof. See Appendix A.
We can interpret this result as follows. Note that since the main concern is
to motivate Segment 1 to choose Plan 1, it does not matter whether a fee-forservice or a fixed fee is emphasized in Plan 1. As long as Plan 2 is a fixed fee
payment option, the service provider can differentiate the two customer segments
using either option. Also it can be shown that it is not possible to obtain solutions
to the problem by replacing (14) and (16) with fee for service structures, because
Plans 1 and 2 of each option could no longer differentiate the two segments.
Therefore, the service provider should always offer the fixed-fee structure to the
segment with lower valuation. Both fixed fee and fee-for-service structures work
for the segment with higher valuation on the services.
12
The question remains, however, whether the service provider always
benefits from differentiating between the two segments. We have the following
proposition.
Proposition 5. (A) A monopoly service provider benefits from differentiating the
two segments only when the following condition holds:
f1 (qˆ11 ,
K
, qˆK1 )   ck qˆk1  f1 (qˆ12 ,
k 1
K
, qˆK 2 )   ck qˆk 2
(17)
k 1
and (B) when condition (17) holds, customers who value a service more are
willing to use more and pay more for the service when a monopoly service
provider differentiates its customers.
Proof. See Appendix A.
The left side of condition (17) is the gross market surplus of Segment 1
(customer utility less service provider’s variable cost) that the provider can extract
when it differentiates the two segments. The right side of condition (17) is the
gross market surplus of Segment 1 that the provider can extract when it does not
differentiate the two segments. The service provider benefits from the
differentiation only when it can extract more surplus, which is to say the left side
is no less than the right side of (17).
Overall, we presented an analytical model of rational customers, possibly
heterogeneous, each charged the value of the services they receive from monopoly
service providers in a static context. Pricing is done through fixed fees for lumpsum quantities, and variable “add-on” fees, and the service provider has the ability
to craft different plans targeted to different user segments. Customers have the
ability to choose between plans, giving them some power to avoid being
completely dictated to, but firms also have the ability to craft different plans
targeted at different market segments. Note, however that, in our analysis, we
have not modeled the competition among service providers. The impact of
competition on the consumer choice and the provider pricing options are not
discussed. The model development along these lines is recommended as a subject
for future research. In Section 3, we assess our analytical model in light of
Anderson’s views (Anderson, 2009). Then, in Section 4, we discuss possible
extensions to our model to include some special features of modern service
applications
3. ANDERSON’S VIEW OF “FREE” SERVICES
The problem of defining service plans that result in profitability for service
providers has grown increasingly complex in the presence of extreme
competition, low cost to market entry, growing social networks, and increased
opportunity for advertising-based revenues through larger audiences. As an
illustration, Twitter is a very popular web-based service offering that, after almost
four years of growth, has yet to define its model for profitability (Liedtke,
2009a).3 As we saw in Section 2, creating analytical models to help determine
3
More recently, Twitter announced a revenue-generating relationship that will give
Microsoft and Google the rights to index Twitter data (Liedtke, 2009b) with users continuing to
participate in the service for free.
13
profitable service plans that will attract heterogeneous, rational customers facing a
monopoly service provider in a static context is sufficiently complex. In this
section, we discuss the added complications brought about by a world of “Free”
offerings as described by Anderson (2009) – with extreme competition, increasing
reliance on advertising as a business model, and a dynamic environment made up
of user-generated content and growing social networks. These factors influence
our ability to understand and model users’ choices and service providers’
offerings.
Most of the business models listed in Table 1 rely on the fact that (some)
customers are willing to pay a fee for their service use. At the same time, we are
witnessing an increasing number of online web-based services becoming
available, in some (possibly limited) form, for free. In his book, Anderson (2009)
describes two trends: some free things are cross subsidized in the sense that “you
get one thing free if you buy another, or if you pay for a service” where others are
free because their cost, “based in silicon”, is falling fast. The economic
importance of free products and services lies in the fact that, from the customer’s
perspective, there is a huge psychological difference between very cheap and free:
a free service can become viral, in a way that seems impossible when it costs even
a cent. Such viral services enable, even cause, the creation of large ecosystems of
multiple parties, providers, customers and others, some of which do exchange
money, although not necessarily in exchange for a free service. Anderson (2009)
lists six business models involving the exchange of free products and services.
1) “Freemium” model: Content, services, and software are available in multiple
tiers of use, including a basic free tier. In this model, perhaps only 1% of the
customers pay; and, since the actual cost of the product/service is low, the
paying customers bring in enough to cover the costs for the 99% of customers
who use the free version. (The term freemium was coined by venture capitalist
Fred Wilson.)
2) Free-through-advertising model: Content, services and software are offered
for free because the advertisers (third parties) are willing to pay for access to
customer communities with distinct interests, exemplified by their use of the
offerings.
3) Traditional cross-subsidized model: Products and services are offered for free
and sold as loss-leaders because they entice customers to pay for something
else.
4) Low-cost model: Some things are free because the marginal cost of production
and distribution is zero. In these cases, the service or content may become a
marketing vehicle to promote the sale of something else (and the motivation of
distribution is nearly the same as the cross-subsidization model). This may be
the case when free music is distributed to encourage attendance to concerts by
the same artist, for example. If it turns out to be never possible to charge for
the content (in this case, the music), these activities may become hobbies for
content providers (as is the case for some musicians).
5) Labor-exchange model: Services become free because the customers, through
their use of the service, add value to a network of users, by adding content (as
with Facebook) or simply by expanding the viewer base. This may be
subsequently monetizable through different means such that the service
provider receives value from offering the service for free.
14
6) Gift-economy model: Some things become free because the providers gain
some non-monetary value out of the process. For example, Wikipedia
contributors provide their services as gifts to posterity presumably for the
intrinsic value of self-expression and making a contribution. Somewhat
similarly, developers of open-source software may gain satisfaction from the
reputation they build among their peers (in this case, they may be able to
monetize their reputation at a later time as well).
Many on-line service offerings today employ a combination of the above business
models. In Table 3, we associate the business models presented in Table 1 with
Anderson’s (2009) taxonomy described above. In order to understand how
businesses can adopt these new “free” business models and remain viable and
profitable, analyses, similar to those presented in Section 2, are needed that can
also incorporate the complexities inherent in the environment of “free” in which
these business models exist. This requires an understanding of the circumstances
in which customers are willing to pay, in each of these models. Anderson suggests
that customers are motivated to pay for an offering under the following
circumstances: (a) to save time, (b) to lower risk, (c) for things they love, (d) for
status, or (e) once they are hooked on the offering. In the remainder of this section
we present specific illustrative examples for each of the Anderson’s business
models and discuss how analytical models, such as the ones presented in Section
2, would need to be extended in order to be useful in setting pricing and profit
models in the complex web-based environment that Anderson (2009) describes.
Table 3. Anderson’s Taxonomy as related to Service Classes from Table 1
Service Class
Computational
processing and
database services –
offered as old-style
utilities
Relationship to Anderson’s
Taxonomy
Utility Model: User pays $ to provider; Anderson 1(Freemium): Can provide
provider provides service to user.
tiered levels of service
Value exchange: Business Model
Advertising Model: Third-Party
Anderson 2 (Advertising)
(advertiser) pays $ to content provider;
provider places advertising in media;
end-user receives services for free and is
exposed to advertising.
Subscription Model: User pays $ to
Anderson 1 (Freemium): Can provide
Content providers
provider; provider provides service to tiered levels of service
in the old media
user.
(gathered by news
Infomediary Model: Third-party
Anderson 3(Cross-subsidized)
teams and shared
service provider pays $ to info provider;
through wire
info provider consolidates list of service
services) and new
providers; user selects service provider;
media (gathered
third-party provides service to user.
from the Internet or
(User may also co-create the service and
created by online
provide ratings of service providers.)
communities)
Community Model: Provider makes
Anderson 6 (Gift-economy)
available service to user; users create
Anderson 5 (Labor exchange)
content which attracts other users; thirdparty pays $ to provider (advertising);
user may pay $ to provider
(subscription).
Transactional
Merchant: User provides $ to provider; Anderson 1 (Freemium): Can provide
services for
provider makes available products or
some services or products for free.
physical products services to user; provider may create
Anderson 4 (Low cost)
and packaged
service or procure products or services
15
software
information, or
media products.
from third parties for $.
Manufacturer (Direct): User pays $ to
provider; provider sells product or
service to user
Brokerage: User pays $ to broker;
Brokerage or
broker facilitates match-up of users and
affiliate models
service providers (which may involve a
that help bring
partners together to service exchange).
make their own
Affiliate: Users click through to thirdtransactions or
party for service; third party pays $ to
barter.
provider; user pays $ to provider.
Anderson 1 (Freemium): Can provide
some services or products for free.
Anderson 4 (Low cost)
Anderson 3 (Cross-subsidized)
Anderson 3 (Cross-subsidized)
3.1 The Freemium Model (Anderson 1)
In principle there are at least two useful metrics upon which the success of the
Freemium model may depend: (a) the actual cost of the service; and (b) the
relative sizes and usage patterns of the free and paying users. Related issues
critical to success concern the pattern of conversion from free to paying users, the
length of the users’ access to the service, and the usage patterns of the two
communities (free and paying users). As shown in Table 3, we can find examples
of the Freemium model in three of the four classes of online services identified in
Lyons et al. (2009).
Consider Skype for example, a recognized success of the Freemium
model. Thanks to their peer-to-peer infrastructure,4 the cost of their service is
quite low. Even more importantly, the ratio of the free Skype-to-Skype minutes
vs. paid SkypeOut minutes has been hovering between 7 and 8.5 while other
companies are lucky to get 20 or 100 (see http://skypejournal.com/2009/07/skypesets-new-performance-records.html).
Online game companies aim to structure their costs so they can break even
if as little as 5-10% of the users pay. More specifically, in Club Penguin 25% of
users pay a monthly fee ($5/mo per paying user); Habbo has 10% monthly paying
players (at $10.30/mo per paying user); Runescape reports 16.6% monthly users
pay (at $5/mo per paying user) and Puzzle Pirates has 22% monthly paying
players (at $7.95/mo per paying user). It is estimated that 5-10% of free Flickr
users convert to paid Flickr Pro and Ning reports that 3% of its 500,000 social
network creators pay for the premium version. In contrast, 2% of the casual
downloadable-game users pay, and many free trial web startups get about 3-5%
paying users and shareware software programs often see less than 0.5% of users
paying.
(for
background,
see
http://www.longtail.com/the_long_tail/2008/11/freemium-math-w.html)
At the other end of the spectrum, Intuit offers basic TurboTax Online free
for federal taxes, but charges for the state version. As a result 70% of users opt to
pay for that version, since most people have to pay both federal and state taxes.
Tax software is also an example where the development of the content/service is
rather expensive, since substantial accounting expertise is embedded in it. In these
cases, without a substantial conversion rate, Freemium becomes unviable as a
business model. This is likely the reason why Babbel and the Wall Street Journal
4
http://www.skype.com/help/guides/p2pexplained/
16
have
recently
turned
away
from
the
Freemium
model
(http://www.fastcompany.com/blog/chris-dannen/techwatch/can-freemiumwork?partner=rss). Babbel, a language-learning site, moved to a subscription
model, as it came out of beta with the intent of developing more and better
content, maintaining a better look-and-feel for the web site without unsightly ads,
and encouraging members to stick with the service and not move to free
competitors, once they have committed to becoming members. On the other hand,
the potential exclusion of free access through Google by the Wall Street Journal
(WSJ) online appears to be less well thought out. HitWise estimates that 25% of
WSJ online traffic is funneled through Google News, and that the number is
increasing. When they do not have a specific task in mind when visiting WSJ but
rather visit to learn about a topic, users are reliant on aggregators and portals like
Google News or Yahoo to let them know that news is breaking
(http://weblogs.hitwise.com/bill-tancer/2009/11/newscorp_googleless.html).
The analytical model in Section 2 considered that rational customers
choose a payment plan between a fixed fee and no charge for extra use (Merchant
model from Tables 1 and 3) and no fixed fee with a charge for each use (Utility
model from Tables 1 and 3). These plans resemble the Freemium model if the
fixed fee is set to zero and there is no charge for extras for some customers. The
analytical model in Section 2 would have to be extended to incorporate the fact
that a considerable majority would choose zero fixed fee and zero charge for
extras. Indeed, the model would have to explicitly incorporate the increased value
of the network to those who would be willing to pay for the premium version of
the service.
3.2 Free-through-Advertising Model (Anderson 2)
The advertising model is, likely, the most prevalent business model on the web. It
almost always accompanies the Freemium model with users of the free service
being subjected to advertisements so that the cost of their service usage to the
provider is covered, at least partially by the paying advertisers. Some common
forms of advertising include Yahoo's pay-per-pageview banners, Google's payper-click text ads, and Amazon's pay-per-transaction "affiliate ads." Also relevant
is paid inclusion in search results, paid listing in information services, lead
generation, and pay-per-connection means of monetization used by social
networks like Facebook.
The analytical model in Section 2 considers time spent viewing ads but
does not take into account partnerships with advertisers, the large numbers of
customers reached by the ads, the low cost of ads, and the fact that some
customers find the targeted ads useful. The model treats advertisements simply as
a “price” that the customers have to pay (by wasting time which could otherwise
have been spent earning some other utility). On the other hand, the advertising
business model treats them as a “value-producing service” for which the providers
can charge the advertisers. This distinction highlights the most interesting
limitation of the analytical model in Section 2, namely that it circumscribes its
analysis to a two party system, including providers and customers, where most of
the business models in Table 3 involve multiple types of partners. For the
advertising model, one should realize the benefits to advertisers of increased
market awareness, trial rates, and even repeat purchase behavior of consumers.
17
3.3 Traditional Cross-Subsidized Model (Anderson 3)
Web beacons (i.e., transparent 1x1 pixel images embedded in HTML documents
to track users’ visits through to a set of web sites) were an early mechanism for
establishing networks of affiliate service providers. Through the monitoring of
web users’ navigation patterns, the group of affiliates could point users to each
other’s web sites. Today there are more and more complex mechanisms to support
such service-provider consortia.
For example, Google’s AdSense product enables any web site to become
affiliates to other providers who wish to advertise their products and services.
With AdSense for Content, the affiliate can place text, image or link ads in its web
sites, provided they follow Google’s policies. With AdSense for Search, the
affiliate places a search box in their web site, which leads to a results page that
hosts more pay-per-click ads. Finally, with Google Referrals the affiliate refers
visitors to use a Google product, like AdSense, AdWords, the Google Toolbar and
other Google software.
The proliferation of content and services on the web has also brought
forward the need for aggregators (who collect content of interest to their users,
filtering out uninteresting information) and infomediaries (who match users and
content providers). NetZero was one early example of a third-party infomediary.
They offered 40 free hours of monthly Internet access to more than 8 million
customers, who were required to allow a special browser, the ZeroPort, to remain
on their screen while online. The ZeroPort displayed ads that, based on the
marketing information the customers provided to NetZero, were likely to interest
them. ZeroPort also allowed small businesses, subscribing to NetZero, to reach
local customers and view the daily results of their online ad campaigns.
The infomediary model is a specialization of the affiliate model, where the
original service provider is the infomediary who forwards the customer to select
affiliates. The analytical model presented in Section 2 assumes one provider only
and rational customers and would have to be extended to incorporate affiliates and
other third party participants. In order to properly model and analyze affiliate
relationships, one would have to examine users’ navigation patterns in order to
understand the potential value of an affiliate relation. Clearly, not all affiliations
will be productive; the “original” service provider and its affiliate cannot be direct
competitors, neither can they be completely unrelated. The nature of the relation
has to be synergistic in a way that makes users who visit the original provider
likely to visit the affiliate. Modeling complexities such as these is nontrivial.
3.4 Low-Cost Model (Anderson 4)
In some cases, on-line service offerings will charge users for the service but
provide low-cost offerings (such as screen savers, images, and other features) for
free to promote or help market the online service. For example, software
developers frequently offer a limited version of a new product for free, in order to
entice the trial users to buy it. The analytical model in Section 2 could capture the
choice of free service offerings by setting the price per unit to zero but cannot
currently model the return value of marketing and promotion.
18
3.5 Labor-Exchange Model (Anderson 5)
The proliferation of on-line communities for professional networking, gaming,
and web-based and virtual-world social networking has given rise to many
variants of the community model, and these communities, in turn, have made
possible an easy application of Anderson’s labor-exchange business model. The
presence of such communities also brings tremendous dynamics to the web
environment.
Twitter, although not yet profitable, is continuously increasing in
membership (thus, growing its information exchange community) and this has led
several
third
parties
to
monetize
its
community
(http://mashable.com/2009/03/23/twitter-business-model-2/). Mashable launched
the Twitter Brand Sponsors that syndicates a limited number of brands into the
Mashable sidebar, so that interested visitors can choose to connect with those
brands on Twitter (http://mashable.com/2009/03/05/twitter-brand-sponsors/). The
ad network Federated Media launched ExecTweets that aggregates Tweets from
business executives, so that Twitter users can follow threaded version of the
executives’ tweets. In the former case, it is the brands that pay Mashable in order
to be featured in its sidebar. ExecTweets is currently sponsored by Microsoft but
the intent is to have people who are interested in the executives’ opinions pay to
subscribe to the service. The community of Twitter has also been the subject of
substantial analysis by Sysomos (http://www.sysomos.com/insidetwitter/), a
leading Media Platform Analytics company, interested in mining tweets to extract
information relevant to businesses that are the Sysomos’ paying customers5.
Twitter itself appears to be considering the idea of charging users to read tweets of
key users (see http://www.techradar.com/news/internet/twitter-to-charge-forreading-tweets-next-year-654608?src=rss&attr=all
and
http://www.media.asia/DigitalMedia/newsarticle/2009_11/Twitter-Japan-tointroduce-payment-model/38057). It is likely that some tweets will be available
for free, with valuable content – images, video or original research – being
available only to paying subscribers. All of these examples demonstrate the
potential value generated by large communities and the exchange that takes place
within them, in terms of aggregation of interesting information, business
intelligence or, simply, original content. As Anderson (2009) notes, “In each case,
the act of using the service creates something of value, either improving the
service itself or creating information that can be useful somewhere else.”
Service offerings become attractive to users because of what other users
provide. The analytical model in Section 2 does not take this notion into account
when modeling a user’s utility simply as the sum of the utility gained by the
provider’s service and the external utility. Instead, the community business model
postulates that the simple existence of a community, possibly created around a
free service, may give rise to other utilities that can become the basis for other
service offerings. This scenario also implies the need for a model that extends the
one presented in Section 2 to also enable consideration of a multiparty system:
namely the provider of a (potentially free) service enabling the creation of a
5
In
July
2010,
Sysomos
was
acquired
by
Marketwire
http://www.marketwire.com/press-release/Marketwire-Acquires-Sysomos-1286185.htm.
-
19
community of customers whose collaboration enables new service offerings by
the original or new providers.
3.6 Gift Economy (Anderson 6)
In gift economies, valuable goods and services are exchanged without any explicit
immediate or future rewards. This is similar to the communities of information
exchange we are witnessing today where people share their expertise and
knowledge for free. Consider, for example, the case of web sites, where software
developers can post and answer questions. Information is a non-trivial good that
can be gifted at practically no cost. The free-software community is another
example of an information gift economy. Programmers make their source code
available, allowing anyone to copy and modify or improve the code. These
downstream developers, depending on the software license attached, may be
prevented from even monetizing their improvements or they may have to share
their gains with the developers of the original software. In any case, these
exchanges are diametrically opposed to market-based trades made in a market
economy,
as
described
by
Section
2.
(Also
see
http://en.wikipedia.org/wiki/Gift_economy; .../free-software community; and
…/source code).
Overall, the six business models described by Anderson (2009) and
presented in this section can only be profitable as long as some people (or
organizations) are willing to pay, at least in some circumstances. These models
are not such that most customers are the ones who pay. Generally, Anderson’s
(2009) perspective departs significantly from the dyadic view of services (serviceprovider/customer) described in Section 2 where the service provider receives fees
from customers who pay for the service plans they use.
4. CONCLUSIONS AND CONJECTURES
The analytical model presented in Section 2 describes how customers are willing
to pay for some services. The model also includes the tradeoff that customers
encounter between paying a lower price and having to spend more time (with
advertising and self-service). The model is applicable to the pricing activities of
traditional and some newer service offerings, such as cell-phones,
telecommunications generally, Internet connectivity, cable TV, residential
electricity, and water. The model, however, falls short in its ability to describe
many of the types of service offerings described by Anderson (2009) and
discussed in Section 3.
To more fully describe modern service applications (that are online,
collaborative including a community of users, and are often free) significant
extensions to the model presented in Section 2 are needed, including the following
features:
1. Extension 1: Heterogeneity of User Preferences: There would have to be
some explicit heterogeneity of preferences represented in the model, whereby
a majority of consumers would prefer the free service, but a minority of
consumers would be willing to pay fees for the premium version of the
service. This minority of people would be willing to pay to save time; to
reduce risk; for things they love; for status; and, once they are hooked,
according to Anderson (2009). The fees they pay would cover the costs of
20
providing the service and allow for a majority of users to access parts of the
service for free. Indeed, more complex versions of the model should
acknowledge the tremendous diversity of users with different needs, desires,
and usage patterns.
2. Extension 2: Inclusion of Third Parties: A key source of revenue to service
providers in many cases is from third-party entities, including advertisers and
providers of related services. The model presented in Section 2 assumes only a
provider/customer scenario where by extensions would have to recognize
salient features of advertising environments, including the numbers of
customers reached by ads, the low cost of ads, and the fact that some
customers find the targeted ads useful. The benefits to advertisers would need
to be represented in these models such as increased market awareness, trial
rates, and even repeat purchase behavior of customers. Third party revenues
could also arise from other affiliate relationships whereby clicks or referrals
generate revenues to the managers of the virtual community. Provisions for
each of the third-party entities would need to be included in the model.
3. Extension 3: Value Brought by Users: The service users, by virtue of the fact
that they use the service, can augment the content available to all the users.
This is a content externality distinct from the well-known network externality
(Katz & Shapiro, 1985). The network externality states that having more
passive users on a network enhances the value of the network because more
people can be reached on the network. The content externality that we
emphasize states that users are active in co-creating the content in the network,
and that having more users co-create the content leads to economies of scale
benefiting all community participants. Accordingly, the community business
model postulates that the simple existence of a community, possibly created
around a free service, may give rise to other utilities that can become the basis
for other service offerings. The complexity of modeling content and other
value brought by the community of users would need to be included in the
model.
Each of these proposed model extensions describe community cogeneration of value with a community facilitator sharing some (but not all) of the
value that the community creates. Such a perspective differs markedly from the
perspective of the dyadic view of the relationship between a service-provider and
the customer described in Section 2 wherein the service provider receives fees
from customers who pay for the service plans they use.
Future pricing models for online and other informational services, thus,
need different perspectives than past pricing models for products and more
traditional services. These future models should build upon collaborative cogeneration of value among a community of users and not just co-generation of
value between service providers and its customers. We need pricing models that
describe the economic relationships between the various participants in a
collaborative ecosystem, whereby pricing and property-rights are designed to
induce productive coordination of activities in ways that dramatically diverges
from pricing systems of neoclassical economics.
ACKNOWLEDGEMENTS
The authors acknowledge the constructive comments of participants at a
workshop held at the Centers for Advanced Studies Conference in October 2008
21
(CASCON, 2008), including input and examples from Paul Sorenson (University
of Alberta), Sasha Chua (IBM), Timo Ewalds (founder of Nexopia.com), Henry
Kim (York University), and Stephen Perelgut (IBM). The authors’ research has
been supported by NSERC, the IBM Centers for Advanced Studies, iCORE,
Alberta Advanced Education and Technology, the Social Science and Humanities
Council of Canada, and the University of Alberta School of Retailing. The authors
contributed equally to this paper.
REFERENCES
Anderson C (2009) Free: The future of a radical price. Hyperion, New York
Holmstrom B (1979) Moral Hazard and Observability. Bell Journal of Economics,
10:74-91
Eisenhardt K (1989) Agency theory: An assessment and review. Academy of
Management Review 14(1):57-74
Holmstrom B (1979) Moral Hazard and Observability. The Bell Journal of
Economics 10(1):74-91
Katz M L, Shapiro C (1985) Network Externalities, Competition, and
Compatibility. The American Economic Review, 75(3):424-440
Liedtke M (2009a) Something to Tweet About: Twitter Valued at $1B. Associated
Press
http://abcnews.go.com/Technology/AheadoftheCurve/wireStory?id=8673657.
Accessed 23 April 2010
Liedtke M (2009b) Google, Microsoft now mutual friends of Twitter. Associated
Press http://www.thestar.com/news/sciencetech/technology/twitter/article/714187-google-microsoft-now-mutual-friends-of-twitter. Accessed 23 April 2010
Lyons K, Playford C, Messinger P R, Niu R H, Stroulia E (2009) Business
Models in Emerging Online Services. In: Nelson M L, Shaw M J, Strader T J
(eds) Value Creation in E-Business Management, 15th Americas Conference on
Information Systems, AMCIS 2009, SIGeBIZ track, San Francisco, California,
August 2009, Selected Papers, pp 44-55
Rappa
M
(2008)
Business
Models
on
the
Web.
http://digitalenterprise.org/models/models.html. Accessed 23 April 2010
Vargo S L, Lusch R F (2004) Evolving to a New Dominant Logic for Marketing.
Journal of Marketing 68:1–17
Varian H R (1992) Microeconomic Analysis, 3rd edn. WW Norton and Co, New
York
22
APPENDIX A
Proof of Proposition 3: With Plan 1, substituting pˆ k  ck and sˆk  0 into
(3) yields a maximization problem that has first order conditions described by
(9). So Plan 1 provides an (interior) solution for (8). Then
Fˆ  f (qˆ1 ,
K
, qˆk )   ck qˆk also makes it so that (7) holds. Therefore, Plan 1
k 1
will also solve (6).
With Plan 2, if the marginal utility
f (qˆ1 ,
qˆk ,..., qˆ K )
is positive, then
qk
sˆk  qˆk and pˆ k   will guarantee a corner solution with optimal quantity
choices qˆk , k  1, , K , described by (8). Then Fˆ  f (qˆ1, , qˆk ) also makes
it so that (7) holds. Therefore, Plan 2 will also solve (6).
f 2 (qˆ12 ,
qˆk 2 ,..., qˆK 2 )
 ck and
qk 2
f1 (q1 ,
, qk
qk
f1 (qˆ11 ,
qˆk1 ,..., qˆK1 )
 ck ,
qk1
, qK ) f 2 (q1 , , qk , qK )
,

qk
Proof of Proposition 4: With Plan 1, because
sˆk ,1  qˆk1  sˆk ,2  qˆk 2 . And it is true that the lump-sum quantities in the two
options are the optimal quantities that the customers should choose no matter who
chooses which payment option. The reason is the really large additional unit
prices. So if customer 1 chooses option 2, he will choose sˆk ,2  qˆk 2 . And if
customer 2 chooses option 1, he or she will choose sˆk ,1  qˆk1 . If the service
provider wants to extract all the customer 1’s surplus, i.e. M  0 , customer 1
would choose option 2 instead of option 1 because customer 1 can get a positive
net utility from option 2, which is M  f1 (qˆ12 , , qˆK 2 )  f 2 (qˆ12 , , qˆK 2 ) . Thus, if
the service provider wants customer 1 to choose option 2, the service provider
needs to give up what customer 1 can obtain from choosing option 2. That is why
we have the M term in option 1. Customer 2 will not choose option 1 because he
or she will obtain negative net utility with option 1).
With Plan 2, since option 2 is the same as in Plan 1, the service provider
has to give up same amount in the fixed fee in option 1 to incent customer 1 to
choose option 1, which is M  f1 (qˆ12 , , qˆK 2 )  f 2 (qˆ12 , , qˆK 2 ) . All the rest of the
analysis in solution 1 applies to this solution.
Since the main concern is to incent customer 1 to choose option 1, it does
not matter if fee-for-service or a fixed fee is emphasized in option 1. As long as
option 2 is a fixed fee payment option, the service provider can differentiate the
two segments using either Plan 1 or Plan 2.
Proof of Proposition 5A: When the service provider incent customer 1 to
choose option 1 instead of option 2, it incurs an additional variable
K
K
k 1
k 1
cost,  ck qˆk1   ck qˆk 2 because customer 1 chooses the higher quantity qˆ k 1 . Thus,
the service provider wants to differentiate the two customers only when the gain
23
achieved from incenting customer 1 is no less than the cost of incenting, i.e.
f1 (qˆ11 ,
, qˆK1 )  f1 (qˆ12 ,
written as f1 (qˆ11 ,
K
K
k 1
k 1
, qˆK 2 )   ck qˆk1   ck qˆk 2 . The condition can also be
K
, qˆK1 )   ck qˆk1  f1 (qˆ12 ,
k 1
K
, qˆK 2 )   ck qˆk 2 . Then the left side
k 1
is the gross market surplus that the service provider receives from customer 1
when the two customers are differentiated and the right side is the gross market
surplus that the service provider receives when the two customers are not
differentiated.
Proof of 5B: With Plans 1 and 2, customer 1 (who values the services
more than customer 2) chooses quantity qˆ k 1 ; that is, more than what customer 2
, qˆK1 )  f1 (qˆ12 , , qˆK 2 )  f 2 (qˆ12 , , qˆK 2 ) in
both plans. Customer 2 pays f2 (qˆ12 , , qˆK 2 ) in both plans. Since
f1(qˆ11, , qˆK1 )  f1 (qˆ12 , , qˆK 2 )  f2 (qˆ12 , , qˆK 2 )  f2 (qˆ12 , , qˆK 2 ) , customer 1 pays
more than customer 2.
chooses, qˆk 2 . Customer 1 pays f1 (qˆ11,
24