WO2001057771A1 - System and method for assisting customers in choosing among a set of commodities using customer preferences - Google Patents

System and method for assisting customers in choosing among a set of commodities using customer preferences Download PDF

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Publication number
WO2001057771A1
WO2001057771A1 PCT/US2001/003659 US0103659W WO0157771A1 WO 2001057771 A1 WO2001057771 A1 WO 2001057771A1 US 0103659 W US0103659 W US 0103659W WO 0157771 A1 WO0157771 A1 WO 0157771A1
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Prior art keywords
customer
commodity
parameter
method
cost
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Application number
PCT/US2001/003659
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French (fr)
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WO2001057771A9 (en
Inventor
Scott Andrew Synder
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Omnichoice.Com
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Publication date
Priority to US49748300A priority Critical
Priority to US09/497,483 priority
Application filed by Omnichoice.Com filed Critical Omnichoice.Com
Publication of WO2001057771A1 publication Critical patent/WO2001057771A1/en
Publication of WO2001057771A9 publication Critical patent/WO2001057771A9/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/06Buying, selling or leasing transactions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking

Abstract

A system and method for assisting a customer in choosing among commodities (see Fig. 5) based on preferences of the customer [100] that includes identifying at least one first parameter associated with a commodity; associated at least one value to the at least one first parameter; calculating an estimated cost of the commodity based on features of the commodity that are desired by the customer and the customer's usage characteristics; obtaining from the customer a preference weighting on at least one second parameter; calculating an effective cost by adjusting the estimated cost based on the preference weighting and the at least one value assigned to the parameters; and presenting a list of commodities to the customer containing at least the commodity with the lowest effective cost.

Description

SYSTEM AND METHOD FOR ASSISTING CUSTOMERS IN CHOOSING AMONG A SET OF COMMODITIES USING CUSTOMER PREFERENCES

RELATED APPLICATIONS

This application is related to U.S. utility application Serial No. 09/497,483, filed February 4, 2000 which is herein incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to the, sale of commodities, such as products and

services, and more particularly, to a system and method for assisting customers in

choosing among a set of commodities using customer preferences.

DESCRIPTION OF THE RELATED ART

In current practice, the average customer has a difficult time sifting through the

enormous number of options for commodities, such as products and services, in order to

find the best selection. Moreover, the increasing number of vendors that sell similar

products and/or services adds to the difficulty. For example, in one state alone, customers

may face a choice between thousands of vendors with thousands of different plans for

telecommunication, entertainment (cable/satellite), and power services. Continued

deregulation is increasing the number of available choices, as well as customer confusion

associated with the new choices. For example, as barriers to entry for local phone service

and cable are reduced, competition between vendors for customers will further intensify as

the number of competitors increases. As a result of these numerous choices, customers face various challenges. First,

customers lack perfect information and are unaware of available choices. Second,

customers are barraged with cryptic, difficult to decipher information from vendors. The

perpetual flood of new choices that are offered for the purpose of attracting new customers

exacerbates this problem. Third, many customers do not make optimum choices from the

deals offered by the various vendors because the customers do not understand the choices

in general, the impact the choices may have, and the cost of the choices. Fourth,

significant time investment is required for customers to find all of the information they

need to compare offerings from different vendors. Usually, it takes more time and effort

than the average customer wants to exert. Finally, customers feel that they are at the

mercy of the large "monopolistic" merchants and service providers.

Additionally, vendors must contend with their own set of issues. Vendors spend significant amounts of money on educating customers, selling products, and offering

service plans to customers. Vendors want to obtain new customers at a reasonable cost

and minimize the impact of turnover on their business. For example, a telecommunication

service provider may spend between $100-$350 to acquire each new customer; however,

as competition increases, customer retention also is an important issue.

Many vendors rely on conducting their own internal research studies in order to

better understand their potential target audience. The marketplace is changing so rapidly

that it is difficult for vendors to keep pace. Vendors need to remain current with their

customer base and anticipate new services and products based on the changing needs of the customer. Several systems and methods have been developed over the years to solve the

above problems, but these systems and methods have many disadvantages. For example,

many merchants or third-party resellers have web sites that offer side-by-side comparisons

of products and/or services. One disadvantage of such a web site is that the burden is on

the customers to spend several hours to determine what is best based on their own

subjective assessment of the value associated with the various options. Many web sites

present only the "lowest cost" commodity, without considering customer preferences. If

the product or service does not meet their needs, frustrated customers will either return the

commodities or switch to different vendors.

Accordingly, an integrated system and method are needed to assist customers in

selecting between various competing products and services based on the customers'

preferences, as well as to assist vendors in obtaining and retaining customers.

SUMMARY OF THE INVENTION

Accordingly, the present invention is directed to a system and method that

substantially obviate one or more of the problems due to limitations and disadvantages of

the related art. ' '

An object of the present invention is to provide a system and method for assisting

the customer in finding the optimal package of products and/or services based on the

customer's preferences. Customers determine preferences and the relative weight of the

preferences, and the system and method of the present invention use this information to

generate the optimal commodity options that best meet the expressed preferences of the customers. As a result, customers save time and money and find the best product/service

that meets their needs.

Another object of the present invention is to provide a system and method for

assisting vendors that sell commodities in acquiring and retaining customers by directing

customers to the commodities at a lower cost to the vendor.

Additional features and advantages of the invention will be set forth in the

description, which follows, and will be apparent from the description, or may be learned

by practice of the invention. The objects and other advantages of the invent on will be

realized and attained by the structure particularly pointed out in the written description and

claims hereof as well as the appended drawings.

To achieve these and other advantages in accordance with the purpose of the

invention, as embodied and broadly described herein, the present invention provides a

method for assisting a customer in choosing between commodities based on preferences of

the customer that includes the steps of identifying at least one first parameter associated

with a commodity; associating at least one value to the at least one first parameter;

calculating an estimated cost of the commodity based on features of the commodity that

are desired by the customer; obtaining from the customer a preference weighting on at

least one second parameter; calculating an effective cost by adjusting the estimated cost

based on the preference weighting and the at least one value assigned to the parameters;

and presenting a list of commodities to the customer containing at least the commodity with the lowest effective cost. In another aspect, the present invention provides a system for assisting a customer

in choosing between commodities based on preferences of the customer that includes an

optimizer device that is connected to a network, wherein the customer uses a customer

device for connecting to the optimizer device via the network and sending preferences of

the customer to the optimizer device, and wherein the optimizer device includes at least

one database that contains information about at least one commodity, and at least one

utility function, and a processing component for presenting to the customer a list of

commodities containing at least one commodity based on the preferences and the utility

function.

It is to be understood that both the foregoing general description and the following

detailed description are exemplary and explanatory and are intended to provide further

explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a further

understanding of the invention and are incorporated in and constitute a part of this

specification, illustrate embodiments of the invention and together with the description

serve to explain the principles of the invention.

In the drawings:

Fig. 1 is an overall system block diagram of a preferred embodiment of the present

invention; Fig. 2 is a block diagram illustrating the components of the customer device 100

shown in Fig. 1;

Fig. 3 is a block diagram illustrating the components of the optimizer device 300

shown in Fig. 1; Fig. 4 is a block diagram illustrating an example of a system of the present

invention;

Fig. 5 is a block diagram illustrating a second example of a system of the present

invention;

Fig. 6 is a flow chart depicting one embodiment of an operation of the present

invention;

Fig. 7 is a flow chart depicting the process of deriving the utility functions of the

present invention;

Fig. 8 is a sample survey for use in calculating utility equations for an Internet

Service Provider; and

Fig. 9 is a flow chart depicting the effective cost calculation of the present

invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the preferred embodiments of the present

invention, examples of which are illustrated in the accompanying drawings. Wherever

possible, the same reference numbers will be used throughout the drawings to refer to the

same or like parts. The present invention provides an integrated system and method to assist a

customer in selecting among the various commodities based on customer preferences and

to assist vendors in obtaining and retaining customers. In particular, the system of the

present invention determines the optimal commodity choices for a customer based on

values that the customer assigns to key features, attributes, or performance characteristics

of the commodity that the customer is shopping for and the relative importance of those

features, attributes, or performance characteristics to the customer. As a result, the present

invention enables a customer to make a "best value" choice, rather than just selecting the

"lowest cost" choice. The system and method of the present invention are not limited to any particular

product or service. The selection of any type of commodity, including but not limited to

goods, products, services, and/or service plans, can be optimized by the system and

method of the present invention. The commodity includes commodities that sell for

money and/or commodities that are free.

The components of the system will be described now, followed by a description of the operation of the system.

With reference to Fig. 1, a preferred embodiment of the system in accordance with

the present invention includes an optimizer device 300 that is connected to a network 200.

A customer device 100 accesses the optimizer device 300 through the network 200.

Network 200 may be any type of computer network, such as the Internet, an Intranet, or an

Extranet, for example. Access devices, such as phone lines, cable lines, fiber optic cables,

or wireless communication systems may be used to access the network 200. One or more types of access devices may be used to connect to the network 200. For example, the customer device 100 may access the network 200 using a wireless communication system,

whereas the optimizer device 300 may. access the network 200 using fiber optic cables.

This and other networks and access device configurations will be known to those skilled in

the art, and are within the scope of this invention. The structure of the customer device

100 and the optimizer device 300 will be described next.

Customers in the system of the present invention may include, but are not limited

to, consumers, businesses, or government entities. The customer device 100 is used by a

customer to access the network 200. The customer device 100 may be a personal

computer, a handheld computer or any similar device known to those skilled in the art. As

shown in Fig. 2, the customer device 100 may include a browser 110, such as a world wide web browser; other software and data storage 120; at least one input device 130, such as a

keyboard or a mouse; at least one communications device 140, such as a modem; at least

one processor 150; memory 160, and at least one output device 170, such as a monitor; all

of which may communicate with each other, for example, via a communication bus 180.

The memory 160 may be Random Access Memory (RAM), Read Only Memory (ROM),

or both. Other customer devices and their components will be known to those skilled in the art, and are within the scope of the present invention.

The optimizer device 300 shown in Fig. 1 will be described now. As shown in Fig. 3, the optimizer device 300 may include a web server component 310, a processing

component 320, a customer database 330, a commodity database 340, and an optimization database 350. The optimizer device 300 also may include at least one administrative interface for administering the various components. Other devices and their components

will be known to those skilled in the art, and are within the scope of the present invention.

Some of the components shown in Fig. 3 will be described in detail along with the

description of the system's operation.

Each of the components of the optimizer device 300 will be described now. The

web server component 310 may be used to host a web site. The processing component

320 may include optimization and database interaction routines. The web server

component 310 and the processing component 320 may be used to obtain information

from the customer, such as name and preferences, which is then stored in the customer

database 330.

The commodity database 340 stores information on the vendors and commodities.

Vendors in the present invention may include, but are not limited to, merchants, service

providers, government entities, and non-profit organizations. Vendors may either sell the

commodities or provide these commodities free. The administrative interface may be used

to enter information about these vendors and their commodities into the commodity

database 340. The vendors and commodities may be indexed in the commodity database

340 by categorizing them into a category. For example, all vendors that sell automobiles

may be categorized into a vehicle category. The commodity database 340 may include

quality, features, and price information for a commodity. For example, if the commodity

is an Internet Service Provider (ISP) plan, the commodity database may include

information about ISPs such as geographic area indicating the area where the ISP provides

service, monthly cost, usage cost per hour, equipment cost, connection speed, disk space, number of email accounts, quality rating, ςontract length, and termination fee. The quality

rating may be a third party rating or a rating based on previously collected samples of

customer responses to questions asked on the web site.

The optimization database 350 stores utility functions, constants, supporting

statistics, and other optimization equations. The utility function may be an equation for

calculating a quantitative value, such as a dollar value, that a customer assigns to the key

parameters of a commodity. Key parameters may include, but are not limited to, features,

attributes, or performance characteristics of a commodity. For example, in the case of an

ISP, the optimization database 350 may contain utility functions for calculating the value

that the customer assigns to connection speed, amount of disk space, quality of the service

provider, number of email accounts, and service contract length and termination fees

associated with early termination. The utility functions are evaluated to obtain values that

quantitatively represent a cost or benefit of the key parameters to the customer.

The optimization database 350 also stores other optimization equations, such as

estimated cost equations and effective cost equations. Estimated cost equations represent

the cost of a commodity to a customer based on the customer's requirements or usage. For

example, in the case of an ISP, the estimated cost equation is based on usage of the

Internet service and will be explained with the description of the operation of the system.

The estimated cost equation may also include other costs, such as online billing costs if the

customer requests online billing. Effective cost equations, on the other hand, include the

values obtained from evaluating the utility functions, estimated cost equations, and any

other costs, such as amortization costs. If the utility function represents a benefit to the customer, then the value is subtracted from the estimated cost. On the other hand, the

value is added to the estimated cost if it represents a cost or burden to the customer. These equations and utility function will be explained in detail with the description of the

operation of the system. Two examples of how the system of the present invention may be implemented

will be described now by referring to Figs. 4 and 5. As shown in Figs. 4 and 5, a customer

may use a desktop or laptop computer as customer device 100. This computer may

contain all the components shown in Fig. 2. At the other end, an application server may be

used as the optimizer device 300. This server may include the web server component 310,

processing component 320, customer database 330, commodity database 340, and optimization database 350. Optimizer device 300 and customer device 100 are connected

to each other via the Network 200, such as the Internet. The customer may use the

communications device 140 in his computer to connect to the Internet and access the web

site hosted by the web server component 310 on the application server using the browser

110 and standard Internet protocols .

Fig. 5 shows a second implementation and is similar to Fig. 4 with the exception of

the optimizer device 300. In Fig. 5, the optimizer device 300 consists of three servers, instead of one. Moreover, these three servers may be connected to each other, for example

in a Local Area Network (LAN). More servers assist in load balancing and keep

customers from getting frustrated. The web server component 310 may run on Server 1

and the other components of the optimizer device 300, such as the customer database 330, commodity database 340, and optimization database 350, may run on Servers 2 or 3. Depending on the amount of traffic to the web site, more servers may be added if needed.

The present invention is not limited to the above examples. Other implementation

configurations will be known to those skilled in the art, and are within the scope of the

present invention.

The operation of the system will be described now with reference to Figs. 6-9. In

step 805 of Fig. 6, the customer uses the customer device 100 to visit the web site hosted

by the web server component 310. For example, the customer may use the browser 110,

such as Netscape Navigator, to visit the web site. Next, the customer selects the desired .

commodity category, as indicated by step 810. For example, if the customer desires to

purchase a camera, the customer selects the camera category. Next, the customer is asked

to enter account information, as indicated by step 815. If the customer is a new user to the

system, the customer is asked to enter personal information, such as name, address, e-mail

address, and a password for future visits to the site, as indicated by step 817. Once the

customer provides this information, the system creates an account for the customer and the

system presents the customer with questions relating to the selected commodity category,

as indicated by step 820. Alternatively, if the customer already has an account with the

system, the system asks the customer for a user name and password at step 819. The

account information may be stored in the customer database 330. Other customer

authentication schemes known to those skilled in the art may be used and are within the

scope of the present invention.

Once the customer enters the correct user name and password, the system presents

the customer with questions relating to the selected commodity category, as indicated by step 820. The customer is presented with a set of questions relating to the commodity

category that was selected by the customer in step 810. In particular, the system asks for

the customer's hard requirements, preferences, and preference weightings. Hard

requirements may include requirements that the customer absolutely needs in a commodity and/or geographic area information. For exainple, if the customer is shopping for a four-

wheel drive vehicle, the customer may indicate that as a hard requirement. Preferences

may include the customer's preferences about certain features of the commodity.

Preference weightings may be the relative importance of these preferences against each

other. For example, if the customer is shopping for a new ISP plan, the system may

request the name and plan information of the customer's current service provider and a

current estimated monthly bill. The system may also ask the customer to rate the current

service provider. This rating may be used to calculate future quality ratings associated

with an ISP. The system may also request the customer's estimated approximate usage per

month, the current connection speed (CSref), the desired number of e-mail accounts

(EMref), the desired amount of disk storage space (DSref), and the desired contract length

(CLref). The system may also ask the customer for preference weightings for connection speed, disk storage space, number of e-mail accounts, quality, and price. Furthermore, the

customer may be asked to weigh the importance in percentages. In an alternative

embodiment, the customer may be forced to assign percentages so that the percentages add

to a hundred percent. For example, a customer may enter his importance ratings as connection speed (30%), disk space (10%), number of e-mail accounts (10%), quality

(20%) and price (30%). Additionally, the customer may choose to make connection speed, disk storage space, number of e-mail accounts, quality, and/or price hard requirements.

The geographic area is an automatic hard requirement in the case of an ISP because some

ISPs only provide service in certain areas.

In an alternative embodiment, some of the required information, such as

geographic location or preference information may be retrieved from the customer

database 330. This retrieved information may be the information that the customer

provided to the system during previous visits or during the account creation step.

Moreover, the customer may be presented with this retrieved information and may be

given the choice to amend this information rather than having to enter everything again.

Next, as shown in steps 840, 845, 850, and 855, the system queries the commodity database to find the eligible commodities that meet the customer's hard requirements that

were entered in step 825. For example, if a customer is shopping for four-wheel drive

vehicles, the system will query the commodity database for four-wheel drive vehicles. As

another example, if a customer shopping for an ISP plan has a hard requirement of at least

two e-mail accounts, only ISP plans serving the customer's geographic area and providing

at least two e-mail accounts are considered by the system to be "eligible". Geographic

area can be determined through Zipcodes, area codes and exchanges, or any other method known to those skilled in the art. If a particular commodity does not match the hard

requirements, the commodity is discarded as a choice for the customer. Alternatively, if a

particular commodity meets the customer's hard requirements, the system identifies the commodity as eligible. As an example, consider a customer located in the 610 area code and 644 exchange

area shopping for a new ISP plan. This customer requires that the ISP plan provide him with at least two e-mail accounts. Table 1 illustrates sample data from the commodity

database before the query is executed. After executing the query, the optimizer will

discard Plan 1, even though it is eligible geographically, because it does not offer at least

two e-mail accounts. Plan 4 is discarded because it is not in the customer's geographic

area. The shaded columns in Table 2 illustrate that these choices are ineligible.

Figure imgf000016_0001

Table 1

Figure imgf000016_0002
Figure imgf000017_0001

Table 2 Next, in steps 860 and 865, the system retrieves the estimated cost equations from

the optimization database and calculates the estimated costs for all the eligible

commodities. For example, in the case of the ISP plans, the effective cost calculation may

be expressed in terms of the estimated monthly cost (EMC):

EMC = Monthly cost + Usage Cost * Estimated Usage.

In the equation above, the Monthly cost is the recurring monthly charge in dollars

associated with a particular ISP plan; Usage Cost is the cost per time of use in dollars per

hour, and Estimated Usage is the customer's estimate of the amount of time that will be

spent using an ISP's services during a month.

For the three eligible plans listed in Table 2, the EMC can be calculated by using

the estimated usage information that the customer entered in step 825 and by using the

monthly cost and usage cost that were retrieved from the commodity database in step 865.

Assuming that the customer entered 30 hours/month in step 825, the EMCs for the

eligible plans listed in Table 2 are:

EMC (plan2)= $19.95

EMC (plan3)= $39.99 EMC (plan5)= $45.99

Next, the estimated cost is adjusted by the utility functions and other cost equations

stored in the optimization database 350. The process of calculating the utility functions

and associated constants will be described now with reference to Fig. 7. The process

begins with the identification of the key parameters associated with a commodity category,

as shown in step 610. These key parameters are parameters that affect the customer's

decision about a commodity and may include features, performance, or quality

characteristics of a particular commodity. For example, in the case of an ISP plan, the key

parameters are connection speed; disk space (which the customer, for example, may use

for a web site); the quality of the connection; number of e-mail accounts; and the contract

length and the termination fee associated with such a plan. These parameters may be

identified based on engineering judgment or may be based on a survey of random

customers.

Next, in step 620, the range for each parameter is identified. For example, in the

case of connection speed of an ISP plan, the range may be from less than 33kpbs to

1500kpbs. Once the ranges are identified, the utility functions may be calculated by using regression analysis, engineering judgement, or a combination of both, as shown in steps 630, 635, 640, 645, and 650. If regression analysis is used, a random set of customers may

be sampled to obtain a quantitative value, usually a dollar value, associated with that

particular parameter, as shown in step 635. For example, in the case of an ISP plan, a

survey like the one shown in Fig. 8 may be sent to ISP customers. The dollar values may be then used to calculate a best-fit utility function via regression analysis, as shown in step 645. Alternatively, the utility function may be calculated using engineering judgment, or a

combination of regression analysis and engineering judgment, as shown in step 640. For example, engineering judgement may be used to adjust the utility function obtained

through regression analysis. If needed, steps 630, 635, 640, and 645 may be repeated to

calculate the utility functions for all of the key parameters associated with each

commodity, as shown in step 650. The utility functions may be calculated for a

generalized sample group or for specific sample groups based on demographics, for

example age, income, household size, and spending. Moreover, the spending may be

based on the estimated costs. For example, in the case of an ISP plan, there may be five key parameters as

identified above, and thus, five utility functions. These five utility functions are: a. U(connection speed) = Kcs*Pcs*ln(CS/CSref)

b. U(disk space) = Kds*Pds*ln(DS/DSref)

c. U(quality) = Kq*Pq*ln(Q/Qref)

d. U(email) = Kem*Pem*ln(EM/EMref)

e. U(termination fee/contract length) = Ktf*TF*(CL-CLref)/CL

if CL>CLref, otherwise = 0 In these five utility functions, Kcs, Kdsj Kq, Kem, and Ktf are constants that will

be described in step 665; Pcs, Pds, Pq, and Pern represent the preference weightings and will be described in step 665; CS, DS, Q, EM, and CL represent connection speed, disk

space, quality, email, and termination fee, respectively, for a specific Internet Service

Provider plan and are stored in the commodity database 340; CSref, DSref, Qref, EMref, and CLref represent the values entered by the customer in step 825, or predefined, for

example as average values (such as QRef). These values entered by the customer may be

used to normalize the parameters, as shown in the utility functions above.

In step 655, an independence check is done on each utility function versus the other

utility functions for a given commodity using correlation, which may include regressing

against the other utility functions. If correlation is present, the redundant utility term is

either omitted from the equation or an interaction term of the form K3*Ul(x)*U2(x) is

added to account for the relationship, as shown in step 660. After the independence check

is completed, the constants for each utility function for which the customer provides a

preference weighting, such as Kcs, Kds, Kq, and Kem, are calculated by dividing the

original regression constants by the expected value of the preference weighting range, as

shown in step 665. For example, in the case of an ISP plan, the original regression

constants were 7.6 for connection speed, 0.9 for disk space, 3.64 for quality, 2.2 for e-mail, and 2.43 for termination fee/contract length. Moreover, the customer is asked to provide a

preference weighting over a weighting range of zero to hundred for five parameters: price,

quality, connection speed, disk space, and number of e-mail accounts. Thus, given equal

importance of all parameters, the expected value of the preference weighting for each

parameter is twenty. Accordingly, the constants, Kcs, Kds, Kq, and Kem, are calculated

by dividing the original regression constants by twenty. Ktf is not adjusted since the

customer does not input a preference weighting for the termination fee. Instead, the

customer inputs the acceptable value of the contract length in months, which is used as the reference contract length, CLref. Accordingly, in the ISP case, the constants are: Kcs =

0.38 , Kds = 0.045, Kq = 0.182, Kem = 0.11, and Ktf = 2.43.

Finally, the utility functions, along with the constants and the sample data used for

regression analysis, are stored in the optimization database 350, as shown in step 670.

Other methods of calculating utility functions will be known to those skilled in the art and

are within the scope of the present invention.

As mentioned in the foregoing description, the estimated cost calculated in step

860 is adjusted by the values obtained from evaluating the utility functions that relate to

the commodity that the customer desires to purchase. The value is subtracted from the

estimated cost if it represents a benefit to the customer. On the other hand, the value is

added to the estimated cost if it represents a cost or burden to the customer. Fig. 9 will be

used to describe this adjustment process. In step 910, the utility functions and effective

cost equations are retrieved from the optimization database 350. These equations are

evaluated based on the customer inputs as indicated by branch A in step 920. For

example, in the case of the ISP plan, the utility functions, U(connection speed), U(disk

space), U(quality), U(email), and U(termination fee), are evaluated. The resulting utility

function values and other related costs are added or subtracted from the estimated cost to

get the effective cost. The effective cost equations, in most cases, represent the sum of the

utility function values, estimated costs, and any other costs, such as amortized fixed costs.

For example, the effective cost equation for an ISP is:

Effective Cost = EMC + Amortized fixed Costs - U(connection speed) - U(disk

space) - U(quality) - U(email) + U(termination fee).

The Amortized fixed costs are equal to (Activation Fee + Equipment Cost)/Contract

Length (CL Ref). The amortized fixed costs and the value of the termination fee utility

function are added to the Effective Cost because these represent a cost or burden to the

customer, and the others are subtracted from frie Effective Cost because they represent a

benefit to the customer. Using the above equation, the effective costs for the eligible

commodities are Plan 2 is $18.6, Plan 3 is $32.5, and Plan 5 is $48.4.

Once the effective cost is calculated, the commodities are ranked based on

effective cost, as indicated by step 955. The highest ranked commodities are presented to

the customer, as indicated by step 965. For example, Plan 2 will be the highest ranked

plan in the ISP case. Next, the customer selects the commodity that the customer wants to

purchase and the system processes the request, as indicated by step 965. Now, the

customer has the option of either ending the session, such as by exiting the browser, or can

repeat this process for another commodity category, as indicated by steps 970, 975, and

branch C.

Although the present invention was described with an example of an ISP plan, the

present invention is not limited for use with an ISP. A person skilled in the art will know

how to modify the present invention for use with other products and/or services.

The present invention assists both customers and vendors. Customers obtain the

commodity that best meets their personal preferences, while the vendors educate

',1 customers about their commodities. Moreover, customers who use the system of the

present invention to select a vendor are likely to stay with this vendor because the commodity selection and thus, the vendor selection, is based on the customer's

preferences.

While the invention has been described in detail and with reference to specific

embodiments thereof, it will be apparent to one skilled in the art that various changes and

modifications can be made therein without departing from the spirit or scope thereof.

Thus, it is intended that the present invention covers the modifications and variations of

this invention provided they come within the scope of the appended claims and their

equivalents.

Claims

WHAT IS CLAIMED IS:
1. A method for assisting a customer in choosing among commodities based
on preferences of the customer, the method comprising the steps of:
identifying at least one first parameter associated with a commodity;
associating at least one value to the at least one first parameter;
calculating an estimated cost of the commodity based on features of the commodity
that are desired by the customer;
obtaining from the customer a preference weighting on at least one second
parameter;
calculating an effective cost by adjusting the estimated cost based on the preference
weighting and the at least one value assigned to the parameters; and
presenting a list of commodities to the customer containing at least the commodity
with the lowest effective cost.
2. The method of claim 1 , wherein the step of associating at least one value to
the at least one first parameter includes the" steps of:
setting a range for the at least one first parameter;
sampling a random set of customers over the range; and
determining a best fit utility function using regression analysis on data received as
a result of sampling.
3. The method of claim 1 , wherein the step of assigning at least one value to
the at least one first parameter includes the step of determining a utility function based on
engineering judgment.
4. The method of claim 3, wherein the utility function is evaluated to obtain
the at least one value, wherein the value represents a cost or benefit of the parameter to the
customer.
5. The method of claim 4, wherein the value is subtracted from the estimated
cost if the value represents a benefit to the customer or the value is added to the estimated
cost if the value represents a cost to the customer.
6. The method of claim 4, further comprising the steps of:
visiting a web site by the customer;' sending the preferences of the customer to the web site; and
selecting at least one commodity from the list of commodities for purchase.
7. The method of claim 6, further comprising the step of adding an amortized
fixed cost to the estimated cost.
8. The method of claim 1 , wherein the step of identifying at least one first
parameter associated with a commodity includes the step of identifying the parameter that
affects a customer's decision.
9. The method of claim 4, wherein the utility function is stored in a first
database, information about the commodity is stored in a second database, and information
about the customer is stored in a third database.
10. The method of claim 1 , wherein the commodity is selected from a group
consisting of products and services.
11. The method of claim 1 , wherein the parameter is a feature, an attribute, or a
performance characteristic associated with the commodity.
12. The method of claim 1 , wherein the at least one second parameter is the
same as the at least one first parameter.
13. A system for assisting a customer in choosing between commodities based on preferences of the customer, comprising:
an optimizer device that is connected to a network; and a customer device for connecting to the optimizer device via the network and
sending preferences of the customer to the optimizer device,
wherein the optimizer device includes at least one database that contains
information about at least one commodity, and at least one utility function, and a
processing component for presenting to the customer a list of commodities containing at
least one commodity based on the preferences and the utility function.
14. The system of claim 13, wherein the optimizer device includes a web server
component for hosting a web site and the customer uses the customer device to visit the
web site.
15. The system of claim 14, wherein the utility function is associated with a
parameter of the commodity and is evaluated to obtain the at least one value, wherein the
value represents a cost or benefit of the parameter to the customer.
16. The system of claim 15, wherein the preferences of the customer include
the preference weighting of customer on at least one second parameter associated with the
commodity.
17. The system of claim 16, wherein the at least one first parameter and at least
one second parameter are the same.
PCT/US2001/003659 2000-02-04 2001-02-05 System and method for assisting customers in choosing among a set of commodities using customer preferences WO2001057771A1 (en)

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