U.S. patent application number 14/043242 was filed with the patent office on 2014-01-30 for system, method and computer program product for predicting value of lead.
This patent application is currently assigned to TrueCar, Inc.. Invention is credited to William Lepler, Jason McBride, Oded Noy, Scott Painter.
Application Number | 20140032272 14/043242 |
Document ID | / |
Family ID | 44188634 |
Filed Date | 2014-01-30 |
United States Patent
Application |
20140032272 |
Kind Code |
A1 |
Noy; Oded ; et al. |
January 30, 2014 |
SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR PREDICTING VALUE OF
LEAD
Abstract
Embodiments disclosed herein provide a solution in determining a
lead value and making an introduction accordingly. In some
embodiments, in response to a consumer's search request for a
retail item within a geographical area, a decision system may
obtain from a local database a list of dealers capable of
provisioning the retail item--such as a new or used vehicle--at
various locations within the geographical area. For each dealer,
the system may calculate a dealer score across a plurality of tests
and set a dollar value to an introduction utilizing the dealer
score associated therewith. The performance measures of the tests
may be normalized and adjusted utilizing a set of coefficients. The
list of dealers may be sorted per dollar value of introduction and
presented to the consumer. To provide more accurate dealer
evaluations, the system may periodically reset the set of
coefficients using sales data.
Inventors: |
Noy; Oded; (Mar Vista,
CA) ; McBride; Jason; (Santa Monica, CA) ;
Lepler; William; (Sherman Oaks, CA) ; Painter;
Scott; (Bel Air, CA) |
Assignee: |
TrueCar, Inc.
Santa Monica
CA
|
Family ID: |
44188634 |
Appl. No.: |
14/043242 |
Filed: |
October 1, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12655462 |
Dec 30, 2009 |
8589250 |
|
|
14043242 |
|
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Current U.S.
Class: |
705/7.34 |
Current CPC
Class: |
G06Q 30/0282 20130101;
G06Q 30/02 20130101; G06Q 30/0639 20130101; G06Q 30/0205
20130101 |
Class at
Publication: |
705/7.34 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method of determining a value of a lead and making an
introduction based thereon, comprising: receiving at a server
computer hosting a Web site a request from a browser application
running on a client device of a consumer, wherein the request
contains information about a retail item and a geographical area;
extracting the information about the retail item and the
geographical area from the request; obtaining from a database
coupled to the server computer a list of dealers capable of
provisioning the retail item at various locations within the
geographical area; determining a value of making an introduction
between each of the dealers and the consumer; sorting the list of
dealers per their corresponding, individual value of introduction;
and presenting the sorted list of dealers to the consumer via the
client device.
2. The method according to claim 1, further comprising: for each of
the dealers, calculating a dealer score across a plurality of
tests, wherein the value of making an introduction between a dealer
and the consumer is determined utilizing the dealer score
associated with the dealer.
3. The method according to claim 2, wherein calculating a dealer
score across a plurality of tests further comprises normalizing
performance measures across the plurality of tests.
4. The method according to claim 3, further comprising testing
multiple coefficients for each of the dealers.
5. The method according to claim 4, further comprising periodically
resetting the coefficients.
6. The method according to claim 1, further comprising periodically
re-evaluating the list of dealers utilizing data from previous
sales associated therewith.
7. The method according to claim 1, wherein the retail item is a
new or used vehicle and wherein the information about the retail
item comprises make and model of the new or used vehicle.
8. A computer program product comprising at least one
computer-readable storage medium storing computer instructions
translatable by a processor to perform: receiving at a server
computer hosting a Web site a request from a browser application
running on a client device of a consumer, wherein the request
contains information about a retail item and a geographical area;
extracting the information about the retail item and the
geographical area from the request; obtaining from a database
coupled to the server computer a list of dealers capable of
provisioning the retail item at various locations within the
geographical area; determining a value of making an introduction
between each of the dealers and the consumer; sorting the list of
dealers per their corresponding, individual value of introduction;
and presenting the sorted list of dealers to the consumer via the
client device.
9. The computer program product of claim 8, wherein the computer
instructions are further translatable by the processor to perform:
for each of the dealers, calculating a dealer score across a
plurality of tests, wherein the value of making an introduction
between a dealer and the consumer is determined utilizing the
dealer score associated with the dealer.
10. The computer program product of claim 9, wherein the computer
instructions are further translatable by the processor to perform:
normalizing performance measures across the plurality of tests.
11. The computer program product of claim 10, wherein the computer
instructions are further translatable by the processor to perform:
testing multiple coefficients for each of the dealers.
12. The computer program product of claim 11, wherein the computer
instructions are further translatable by the processor to perform:
periodically resetting the coefficients.
13. The computer program product of claim 8, wherein the computer
instructions are further translatable by the processor to perform:
periodically re-evaluating the list of dealers utilizing data from
previous sales associated therewith.
14. A system for making an introduction on a lead-by-lead basis,
comprising: a processor; and at least one computer-readable storage
medium storing computer instructions translatable by the processor
to perform: receiving at a server computer hosting a Web site a
request from a browser application running on a client device of a
consumer, wherein the request contains information about a retail
item and a geographical area; extracting the information about the
retail item and the geographical area from the request; obtaining
from a database coupled to the server computer a list of dealers
capable of provisioning the retail item at various locations within
the geographical area; determining a value of making an
introduction between each of the dealers and the consumer; sorting
the list of dealers per their corresponding, individual value of
introduction; and presenting the sorted list of dealers to the
consumer via the client device.
15. The system of claim 14, wherein the computer instructions are
further translatable by the processor to perform: for each of the
dealers, calculating a dealer score across a plurality of tests,
wherein the value of making an introduction between a dealer and
the consumer is determined utilizing the dealer score associated
with the dealer.
16. The system of claim 15, wherein the computer instructions are
further translatable by the processor to perform: normalizing
performance measures across the plurality of tests.
17. The system of claim 16, wherein the computer instructions are
further translatable by the processor to perform: testing multiple
coefficients for each of the dealers.
18. The system of claim 17, wherein the computer instructions are
further translatable by the processor to perform: periodically
resetting the coefficients.
19. The system of claim 14, wherein the computer instructions are
further translatable by the processor to perform: periodically
re-evaluating the list of dealers utilizing data from previous
sales associated therewith.
20. The system of claim 14, wherein the retail item is a new or
used vehicle and wherein the information about the retail item
comprises make and model of the new or used vehicle.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of, and claims a benefit
of priority under 35 U.S.C. 120 of the filing date of U.S. patent
application Ser. No. 12/655,462, by inventors Noy et al., entitled
"System, Method and Computer Program Product for Predicting Value
of Lead" filed on Dec. 30, 2009, which is hereby expressly
incorporated by reference for all purposes.
TECHNICAL FIELD OF THE DISCLOSURE
[0002] The present disclosure relates generally to predicting the
value of a lead and, more particularly, to making introduction
between a buyer and a seller based on the predicted value of the
lead. Even more particularly, the present disclosure is related to
optimizing the introduction based on the likelihood of the buyer to
purchase a thing of value such as an automobile from the
seller.
BACKGROUND OF THE DISCLOSURE
[0003] Geographic proximity is no longer the primary driver of auto
purchases. In recent years, virtual dealerships have sprung up all
over the Internet. When consumers go online to buy a car, there are
usually multiple dealers that can sell a car to them. Intermediary
consumer-oriented service providers typically have several
automotive dealers in their system to which they can introduce to a
customer.
[0004] Examples of the types of introductions may include an
introduction for in-network dealers and an introduction for
non-network dealers, and so on. In-network dealers may be those
that have agreed to be in the intermediary service provider's
network. For example, in-network dealers may agree to pay the
intermediary service provider a fee for an introduction to a
customer that ended up purchasing a vehicle, after the purchase is
made. This is sometimes referred to as Pay-Per-Sale. Currently,
there is not a standardized way to operate in this marketplace.
Customers may visit multiple online solution providers, including
automotive research sites, lead generation providers, etc., for
their vehicle purchasing needs.
[0005] Existing solutions are believed to be lacking or have
drawbacks in at least the following main areas: (1) leads are
generally purchased in bulk where all leads in the same group or
category have the same price; 2) the determining factors on which
dealers should be presented to which customers are generally based
on a simple set of considerations; and (3) little or no access to
historical information across a wide consumer base. As more and
more consumers now surf the Internet to find deals, there is always
room for improvement.
SUMMARY OF THE DISCLOSURE
[0006] Embodiments disclosed herein can provide a predictive value
for each introduction between a potential buyer and a dealer within
a dealer network. For illustrative purposes, embodiments disclosed
herein describe a car dealer network where various types of
vehicles may be available for purchase and/or lease. Those skilled
in the art can appreciate that embodiments disclosed herein may be
readily adapted for other types of dealer networks, including, but
not limited to, a boat dealer network, a high end kitchen appliance
dealer network, a bicycle dealer network, a recreational vehicle
network, etc.
[0007] Some embodiments disclosed herein may enable an intermediary
online solution provider to make a meaningful introduction that
likely turns into a sale, on an item-by-item basis, between a
potential customer and a dealer. A non-exhaustive list of factors,
such as those listed below, may influence how a meaningful
introduction can be made: [0008] Which, if any, dealer(s) in the
area should be introduced to a particular customer? [0009] If
multiple dealers should be introduced to a customer, at what order
should the dealers be introduced? [0010] Where multiple types of
introduction are available, which dealer should be introduced in
which form?
[0011] To address these issues, some embodiments disclosed herein
may be implemented as a publicly-accessible Web site having
suitable software running on one or more server machines for
determining which dealer or dealers to introduce to a potential
buyer, based on a predictive value of such an introduction within a
dealer network.
[0012] More specifically, a Web site implementing an embodiment
disclosed herein may comprise the following functions, some of
which may be optional:
[0013] 1) Recommendation Engine.
[0014] In some embodiments, the following variables may be used to
generate a dealer recommendation to a potential customer: [0015]
Price compression [0016] Price distribution [0017] Dealer
location
[0018] In one embodiment, a dealer recommendation method may
comprise 1) collecting data on price compression, price
distribution, and dealer location; 2) applying weighting to each
variable; and 3) tuning the variables using one or more
machine-learning techniques. In some embodiments, the data
collected may include historical values over a certain period of
time at or around the dealer location. In some embodiments, the
weighting applied to each variable is tunable and/or user
definable.
[0019] 2) Variable-Cost of Leads.
[0020] In some embodiments, every lead may have its own price. Some
embodiments disclosed herein may determine a lead purchase price on
a lead-by-lead basis utilizing a plurality of factors, including,
but not limited to, the purchase price set by a buyer. Some
embodiments may utilize a statistical analysis by a buyer of
information about the underlying lead itself to determine the
statistical likelihood of a sale actually occurring based on a
particular lead.
[0021] In one embodiment, the statistical analysis by a buyer of
information may be performed using the aforementioned dealer
recommendation engine to recommend the lead to a particular car
dealer and using the historical close rates on leads recommended
using the dealer recommendation engine.
[0022] 3) What to Display and Optimized Order of Display.
[0023] Determine which dealers should be displayed to the
buyer--with the highest likelihood to submit a lead, and then
purchase a car from the presented dealer. For example, rather than
using a single and simpler rubric such as price or distance, some
embodiments may use predictive data to determine the optimal order
of presentation of dealers to a potential car buyer. This results
in a better conversion of potential car buyers to leads and
ultimately a higher monetization of the vehicle inquiry. Some
embodiments may also present a blend of dealerships from a dealer
network and lead generation dealerships based on price. The cohort
of dealers may self-reinforce the sale to the user by skillfully
presenting comparisons to make the ultimate choice easier
[0024] 4) Performance-Based Lead Sales.
[0025] Predict the likely cash value of a single selection point
(i.e., a single online customer looking to buy a single item). As
each lead may be priced individually, dealers may bid on the chance
of being introduced to such a customer. This
introduction-by-introduction basis allows the intermediary solution
provider to place each individual introduction in an open market
competitive environment. For example, some embodiments may
calculate and offer dynamic bids on leads in the open market based
on a potential car buyer's location and vehicle request. By
contrast, the traditional approach for lead sellers is to offer a
single, static price for a lead. Conventional lead generation
systems usually provide lead pricing for a category of leads.
[0026] These, and other, aspects of the disclosure will be better
appreciated and understood when considered in conjunction with the
following description and the accompanying drawings. It should be
understood, however, that the following description, while
indicating various embodiments of the disclosure and numerous
specific details thereof, is given by way of illustration and not
of limitation. Many substitutions, modifications, additions and/or
rearrangements may be made within the scope of the disclosure
without departing from the spirit thereof, and the disclosure
includes all such substitutions, modifications, additions and/or
rearrangements.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The drawings accompanying and forming part of this
specification are included to depict certain aspects of the
disclosure. It should be noted that the features illustrated in the
drawings are not necessarily drawn to scale. A more complete
understanding of the disclosure and the advantages thereof may be
acquired by referring to the following description, taken in
conjunction with the accompanying drawings in which like reference
numbers indicate like features and wherein:
[0028] FIG. 1 is a simplified diagrammatic representation of one
example embodiment of a system for predicting the value of a
lead;
[0029] FIG. 2 is a simplified diagrammatic representation of one
example network architecture in which embodiments disclosed herein
may be implemented;
[0030] FIG. 3 is a diagrammatic representation of one example
embodiment of system interaction;
[0031] FIG. 4 is a diagrammatic representation of one example
embodiment of a method of fine-tuning a plurality of tests utilized
in evaluating dealers known to an intermediary online solution
provider;
[0032] FIG. 5 is a diagrammatic representation of one example
embodiment of a method of evaluating dealers within a range defined
by a potential buyer;
[0033] FIG. 6 is a diagrammatic representation of one example
embodiment of a method of generating a list of dealers and setting
a dollar value for each introduction thereof; and
[0034] FIG. 7 is a diagrammatic representation of one example
embodiment of a system for transacting leads with third parties on
an item-by-item basis in an open market.
DETAILED DESCRIPTION
[0035] The disclosure and the various features and advantageous
details thereof are explained more fully with reference to the
non-limiting embodiments that are illustrated in the accompanying
drawings and detailed in the following description. Descriptions of
well-known hardware and software components, programming languages
and programming techniques are omitted so as not to unnecessarily
obscure the disclosure in detail. Skilled artisans should
understand, however, that the detailed description and the specific
examples, while disclosing preferred embodiments, are given by way
of illustration only and not by way of limitation. Various
substitutions, modifications, additions or rearrangements within
the scope of the underlying inventive concept(s) will become
apparent to those skilled in the art after reading this
disclosure.
[0036] Software implementing embodiments disclosed herein may be
implemented in suitable computer-executable instructions that may
reside on a computer-readable storage medium. Within this
disclosure, the term "computer-readable storage medium" encompasses
all types of data storage medium that can be read by a processor.
Examples of computer-readable storage media can include random
access memories, read-only memories, hard drives, data cartridges,
magnetic tapes, floppy diskettes, flash memory drives, optical data
storage devices, compact-disc read-only memories, and other
appropriate computer memories and data storage devices.
[0037] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having," or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, product, article, or apparatus that comprises a
list of elements is not necessarily limited only those elements but
may include other elements not expressly listed or inherent to such
process, process, article, or apparatus. Further, unless expressly
stated to the contrary, "or" refers to an inclusive or and not to
an exclusive or. For example, a condition A or B is satisfied by
any one of the following: A is true (or present) and B is false (or
not present), A is false (or not present) and B is true (or
present), and both A and B are true (or present).
[0038] Additionally, any examples or illustrations given herein are
not to be regarded in any way as restrictions on, limits to, or
express definitions of, any term or terms with which they are
utilized. Instead these examples or illustrations are to be
regarded as being described with respect to one particular
embodiment and as illustrative only. Those of ordinary skill in the
art will appreciate that any term or terms with which these
examples or illustrations are utilized encompass other embodiments
as well as implementations and adaptations thereof which may or may
not be given therewith or elsewhere in the specification and all
such embodiments are intended to be included within the scope of
that term or terms. Language designating such non-limiting examples
and illustrations includes, but is not limited to: "for example,"
"for instance," "e.g.," "in one embodiment," and the like.
[0039] FIG. 1 is a simplified diagrammatic representation of
example system 100 comprising enterprise computing environment or
network 130 of an online solution provider Zag. As illustrated in
FIG. 1, computer user or consumer 110 may interact with Web site
140 to conduct their car research and perhaps purchase a new or
used vehicle through Web site 140. In one embodiment, the user's
car buying process may begin when the user directs a browser
application running on the user's computer to send a request over
network 120 to Web site 140. The user's request may be processed
through decision system 160 coupled to Web site 140.
[0040] In some embodiments, decision system 160 may be capable of
determining the user's likelihood to buy and the dollar value of
certain dealers known to decision system 160. In some embodiments,
information about dealers known to decision system 160 is stored on
database 150 coupled to decision system 160 as shown in FIG. 1.
[0041] In some embodiments, the decision system may be implemented
as a recommendation engine capable of determining a list of dealers
to present to the user based on the user's likelihood to buy and
the dollar value of the dealers presented. In one embodiment, the
list of dealers may be displayed to the user via a user
interface.
[0042] In some embodiments, calculations by decision system 160 may
be based on information from a plurality of system components,
including data from sales matching system 170, a list of available
dealers and their performance history from database 150 and/or
dealers 180, and individual bids offered by Lead Buyer Aggregators
190. Examples of specific calculations by decision system 160 are
described below with reference to FIGS. 3-6.
[0043] FIG. 2 is a simplified diagrammatic representation of one
example network architecture 200 in which embodiments disclosed
herein may be implemented. For simplification, a single client
computer and a single server computer are shown in FIG. 2,
representing an example hardware configuration of data processing
systems capable of bi-directionally communicating with each other
over a public network such as the Internet. Those skilled in the
art will appreciate that enterprise computing environment 130 may
comprise multiple server computers and multiple client computers
may be bi-directionally coupled to Web site 140 over network 120.
Web site 140 may be hosted by server computer 210 in enterprise
computing environment 130.
[0044] Client computer 110 can include central processing unit
("CPU") 111, read-only memory ("ROM") 113, random access memory
("RAM") 115, hard drive ("HD") or storage memory 117, and
input/output device(s) ("I/O") 119. I/O 119 can include a keyboard,
monitor, printer, and/or electronic pointing device. Example of I/O
119 may include mouse, trackball, stylist, or the like. Client
computer 110 can include a desktop computer, a laptop computer, a
personal digital assistant, a cellular phone, or nearly any device
capable of communicating over a network. Server computer 210 may
have similar hardware components including CPU 211, ROM 213, RAM
215, HD 217, and I/O 219.
[0045] Each computer shown in FIG. 2 is an example of a data
processing system. ROM 113 and 213, RAM 115 and 215, HD 117 and
217, and database 150 can include media that can be read by CPU 111
and/or 211. Therefore, these types of computer memories include
computer-readable storage media. These memories may be internal or
external to computers 110 and/or 210.
[0046] Portions of the methods described herein may be implemented
in suitable software code that may reside within ROM 213, RAM 215,
HD 217, database 150, or a combination thereof. In some
embodiments, computer instructions implementing an embodiment
disclosed herein may be stored on a DASD array, magnetic tape,
floppy diskette, optical storage device, or other appropriate
computer-readable storage medium or storage device. A computer
program product implementing an embodiment disclosed herein may
therefore comprise one or more computer-readable storage media
storing computer instructions translatable by CPU 211 to perform an
embodiment of a method disclosed herein.
[0047] In an illustrative embodiment, the computer instructions may
be lines of compiled C.sup.++, Java, or other language code. Other
architectures may be used. For example, the functions of server
computer 210 may be distributed and performed by multiple computers
in enterprise computing environment 130. Accordingly, each of the
computer-readable storage media storing computer instructions
implementing an embodiment disclosed herein may reside on or
accessible by one or more computers in enterprise computing
environment 130.
[0048] In some embodiments, the various software components and
subcomponents, including Web site 140, database 150, decision
system 160, and sales matching system 170, may reside on a single
server computer or on any combination of separate server computers.
In some embodiments, some or all of the software components may
reside on the same server computer.
[0049] FIG. 3 is a diagrammatic representation of one example
embodiment of system interaction. In embodiments disclosed herein,
decision system 160 may interact with a plurality of components to
determine the best response to a particular user's request so that
a meaningful introduction can be made and likely be turned into a
sale. In one embodiment, these interactions may be housed in
database 150 coupled to decision system 160.
[0050] In one embodiment, decision system 160 is engaged when
consumer 110 visits Web site 140 and conducts a search with a set
of search criteria. Examples of search criteria may include zip
code, a vehicle make, year, and model, etc.
[0051] As a specific example, in one embodiment, consumer 110 may
provide Web site 140 with a specific zip code and a particular
vehicle make and model. In one embodiment, decision system 160 may
search database 150 and determine a dealer from which this specific
user is most likely to buy and returns a price that the user most
likely will pay for the specified vehicle. This is known as upfront
pricing. Advantages of upfront pricing may be found in an article
by Scott Painter, "Car Sales Lead Generation: Broken for Consumers,
Broken for Dealers," E-Commerce Times, Sep. 15, 2008, 4 pages, the
entire content of which is incorporated herein by reference.
[0052] In some embodiments, once consumer 110 selects a vehicle,
indicating their intention to buy, decision system 160 may operate
to determine which dealers to display by identifying in-network
dealers 180 near consumer 110. In one embodiment, decision system
160 may operate to determine which dealers may buy this particular
lead associated with consumer 110. In one embodiment, decision
system 160 may operate to determine how much lead aggregators 190
may pay for this particular lead.
[0053] In some embodiments, if consumer 110 selects one or more
dealers, decision system 160 may operate to submit consumer 110's
lead to those dealers 180 or lead aggregators 190. In one
embodiment, sales matching system 170 may operate to match
submitted leads to sales reported by dealers via DMS Sales Data
files. In one embodiment, if consumer 110 buys a car, sales
matching system 170 may operate to match that sale to a lead and
update the stats stored in database 150 for use by decision system
160 in subsequent calculations.
[0054] FIG. 4 is a diagrammatic representation of one example of a
method of fine-tuning a plurality of tests utilized in evaluating
dealers known to an intermediary online solution provider. In some
embodiments, method 400 may comprise receiving sales data 420 at
sales matching system 170 from dealer 180. As described above,
sales matching system 170 may receive sales data 420 from dealer
180 after a purchase from dealer 180 is made by consumer 110. Sales
matching system 170 may update database 150 with sales data 420
received from dealer 180 or may provide sales data 420 received
from dealer 180 to decision system 160. In some embodiments,
decision system 160 may periodically set and reset a set of
coefficient weights utilizing sales data 420. This process can be
useful as well as practical for some applications. For example, in
order to make accurate predictions on the value of the leads on an
introduction-by-introduction basis, it may be desirable to have all
the statistical data initially. However, this is not a requirement.
A system implementing an embodiment disclosed herein can begin with
a set of historical statistical data and improves/learns over time
by manipulating coefficient weights in view of sales data 420. As
will be described below with reference to FIG. 5, this set of
coefficient weights can then be utilized to continually evaluate
in-network dealers 180.
[0055] For example, in one embodiment, decision system 160 may set
and reset coefficient weights using the results of normalization,
the close rate for in-network dealers, and the dollar value derived
from selling leads to dealers. In one embodiment, this periodic
recalculation analyzes sales data from sales matching system 170,
stored leads from database 150, and DMS sales data from dealers 180
and resets coefficient weights accordingly. In one embodiment, this
process is done automatically and continuously.
[0056] In one embodiment, this periodic recalculation of
coefficient weights represents a learning loop for decision system
160 in evaluating in-network dealers 180 over time. In some
embodiments, decision system 160 may operate to review the
coefficients on a monthly basis. In some embodiments, decision
system 160 may operate to re-evaluate dealers 180 on a daily basis.
As a specific example, a system implementing an embodiment
disclosed herein may have 2,400 dealers, all of which would be
re-evaluated nightly and the system would be updated with the
latest data accordingly. In some embodiments, a dealer or
dealership refers to an entity capable of provisioning a retail
item such as a particular new car of a certain make, model, and
year--physical inventory of the same is not required. One example
could be that a buyer is interested in purchasing a new mid-size
sedan with a customized sports package by special order. Automotive
dealerships typically do not keep a physical inventory of all the
retail items that they can provision for their customers. In
embodiments disclosed herein, it is not necessary to review against
items in each dealership's physical inventory and the physical
inventory at a particular dealership has no effect on the
evaluation of that dealership.
[0057] FIG. 5 is a diagrammatic representation of one example of a
method of evaluating dealers within a range defined by a potential
buyer. As illustrated in FIG. 5, in one embodiment, system 500 may
evaluate all the dealers available for a given user's search
through a set of tests 530. In this example, suppose an address or
a zip code provided by consumer 110 defines geographical area or
range 510 and dealers 501, 503, 505, 507, and 509 from in-network
dealers 180 are located at various physical locations within area
510. Using a number of coefficients 535, represented by a.sub.1,
a.sub.2, a.sub.3, a.sub.4, . . . , system 500 may normalize a
dealer's performance measures and variables to make more meaningful
comparisons for a given search request. The coefficients can be
modified and the effect of these changes can be measured as system
500 learns to assign optimal weights to coefficients 535. Example
calculations for normalization and coefficients are described below
in further detail with reference to FIG. 6. These coefficients may
differ from implementation to implantation. For example, in one
market, the coefficients may represent considerations such as
Distance, Price Savings, Sales to a particular Zip code, and the
Close Rate of a particular dealer. In another market, the
coefficients may represent a different set of considerations. As
another example, system 500 may test different sets of coefficients
representing different sets of considerations for the same market.
The coefficients may also differ for various reasons. For example,
system 500 may test different coefficients for different makes of
vehicle. Additional conditions may also affect how system 500 tests
the coefficients and/or what coefficients are tested.
[0058] In some embodiments, the system may consider primary
variables and secondary factors. An example of a primary variable
may be the physical distance between the user and a particular
dealership (i.e., the location of a vehicle desired by the user).
In one embodiment, system 500 may perform the dealer evaluation
utilizing the following variables: [0059] Variable 1: Physical
distance between a user and a dealership where the desired vehicle
is located. [0060] Variable 2: Historical sales in the last 60
days. [0061] Variable 3: Dealer price offset (user's savings
offered on a partner site). [0062] Variable 4: All sales to buyers
within 5 miles of the search zip code from this dealer. This may
include direct sales and those made through one or more
intermediaries. [0063] Variable 5: Average daily internally
generated leads within a distance range. [0064] Variable 6: New car
sales to dealer. This may include direct sales and those made
through one or more intermediaries. [0065] Variable 7: Potential
net revenue from revenue sharing with one or more partner
sites.
[0066] In one embodiment, variables 1 through 3 may be
characterized as primary variables and variables 4 through 7 may be
characterized as secondary factors. Other variables and/or factors
may also be included.
[0067] FIG. 6 is a diagrammatic representation of one example of a
method for determining a value of an introduction. Method or flow
600 may be used by decision system 160 in response to consumer
110's search request for a Zip Code, Vehicle Make, and Vehicle
Model combination. In some embodiments, decision flow 600 may send
data to lead buyers 190 (step 602) and wait for a response (step
620). In some embodiments, decision flow 600 may send data to lead
buyers 190 (step 602) and get all possible dealers within area 510
from database 150 (step 604). Decision flow 600 may normalize each
dealer within area 510 across tests 530 (step 606) and test
multiple coefficients for each dealer (step 608). Decision flow 600
may then sort a list of dealers to which consumer 110 is to be
introduced (step 610) and set, for example, a dollar value for each
introduction (step 612). Decision flow 600 may blend lists, for
example, by dollar value per introduction (step 614). In one
embodiment, the lists may blend with a response from step 620.
[0068] The specific set of tests performed by the underlying system
on a set of in-network dealers within a range defined by a user may
vary from implementation to implementation. In some embodiments,
the user is provided with a set of choices via a user interface
implementing an embodiment disclosed herein. Each choice may
represent a particular combination of variables. For example, one
choice may include all makes, territories and program affinity
groups (PAGS) and one choice may be based on a specific make,
model, and trim package.
[0069] In embodiments disclosed herein, each introduction has a
value associated therewith. In one embodiment, this value is a
dollar value. The value of a particular introduction is determined
based in part on a particular dealer's score. In embodiments
disclosed herein, each dealer within a search range may be scored
utilizing the sum of multiplying each coefficient with its
normalized value such that:
[0070] Dealer score=N.sub.1.times.C.sub.1+N.sub.2.times.C.sub.2+ .
. . +N.sub.x.times.C.sub.x, where N.sub.x represents the normalized
value between 0 and 1 per test and C.sub.x represents the
coefficient for that market/make (or other parameter)
combination.
[0071] In this case, the dollar value for an introduction is then
the product of the dealer score, the score confidence, and the
dollar per car sold:
[0072] Dollar value for an introduction=dealer score.times.score
confidence.times.dollar per car sold.
[0073] Example Calculations
[0074] As a specific example, all variables are normalized with the
highest member of the cohort (comparison group of dealers)
receiving 1 and the lowest receiving 0. In one embodiment,
coefficients for the variables may be derived by taking each
variable score on a dealer basis and weighting according to the
following scheme:
average ( variable 1 - 7 variable i * 1 for lead variable i * 3 for
sales variable i * 0 for no lead ) = raw .beta. . ##EQU00001##
[0075] In one embodiment, coefficients may be derived and
normalized by:
average ( raw .beta. x ) min ( raw .beta. 1 : raw .beta. 7 ) =
.beta. x . ##EQU00002##
[0076] In one embodiment, algorithm score may be calculated as
follows:
.gamma. ( dealer score ) = i = 7 i = 1 ( .beta. i * .alpha. 1 ) .
##EQU00003##
[0077] FIG. 7 is a diagrammatic representation of one example
embodiment of a system for transacting leads with third parties on
an item-by-item basis in an open market. In Open Market model 700,
decision system 160 may be contacted by third party Web site 780 as
a result of a consumer search via Web site 780 for a Zip Code,
Vehicle Make, and Vehicle Model combination. In this example,
decision system 160 may return a response to third party Web site
780 with information on dealer coverage. If dealer coverage exists,
decision system 160 may return dealer information, vehicle price,
and the lead's dollar value.
[0078] Although the present disclosure has been described in detail
herein with reference to the illustrative embodiments, it should be
understood that the description is by way of example only and is
not to be construed in a limiting sense. It is to be further
understood, therefore, that numerous changes in the details of the
embodiments disclosed herein and additional embodiments will be
apparent to, and may be made by, persons of ordinary skill in the
art having reference to this description. Accordingly, the scope of
the present disclosure should be determined by the following claims
and their legal equivalents.
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