U.S. patent application number 14/291185 was filed with the patent office on 2014-09-18 for facilitating purchase of excess items.
This patent application is currently assigned to Amazon Technologies, Inc.. The applicant listed for this patent is Amazon Technologies, Inc.. Invention is credited to Michael M. George, Gustavo Eduardo Lopez, Steven T. Rabuchin, David C. Yanacek, Brandon H. Yarbrough.
Application Number | 20140278877 14/291185 |
Document ID | / |
Family ID | 50896881 |
Filed Date | 2014-09-18 |
United States Patent
Application |
20140278877 |
Kind Code |
A1 |
Yanacek; David C. ; et
al. |
September 18, 2014 |
Facilitating Purchase of Excess Items
Abstract
Disclosed are various embodiments for facilitating the purchase
of excess items. In one embodiment, customer information and
inventory data are retrieved from a merchant client. A
determination is made on whether excess items exist. A list of
target purchasers is generated based on data associated with a user
account, and offers are generated and sent by electronic
communication to the list of target purchasers.
Inventors: |
Yanacek; David C.; (Seattle,
WA) ; Lopez; Gustavo Eduardo; (Seattle, WA) ;
Rabuchin; Steven T.; (Kirkland, WA) ; George; Michael
M.; (Mercer Island, WA) ; Yarbrough; Brandon H.;
(Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc. |
Reno |
NV |
US |
|
|
Assignee: |
Amazon Technologies, Inc.
Reno
NV
|
Family ID: |
50896881 |
Appl. No.: |
14/291185 |
Filed: |
May 30, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13227587 |
Sep 8, 2011 |
8756100 |
|
|
14291185 |
|
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Current U.S.
Class: |
705/14.24 ;
705/14.39; 705/14.66 |
Current CPC
Class: |
G06Q 30/0223 20130101;
G06Q 30/0239 20130101; G06Q 30/0255 20130101; G06Q 30/0269
20130101 |
Class at
Publication: |
705/14.24 ;
705/14.66; 705/14.39 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A non-transitory computer-readable medium embodying at least one
program executable in a computing device, the at least one program
comprising: code that identifies a purchaser of a ticket for an
event; code that obtains a plurality of contacts associated with
the purchaser, the plurality of contacts being obtained from a
third party social networking site; code that determines at least
one target purchaser based at least in part on the plurality of
contacts; and code that transmits a respective offer to the at
least one target purchaser, the offer being for purchasing from an
excess supply of tickets associated with the event through an
electronic commerce system.
2. The non-transitory computer-readable medium of claim 1, wherein
the respective offer is presented to the at least one target
purchaser via an advertisement embodied in a third party user
interface.
3. The non-transitory computer-readable medium of claim 1, wherein
the purchaser is a first purchaser of a plurality of purchasers
that purchased at least one ticket for the event, and wherein the
code that determines the at least one target purchaser is
configured to remove duplicates between the plurality of contacts
and the plurality of purchasers to determine the at least one
target purchaser.
4. The non-transitory computer-readable medium of claim 1, wherein
the code that determines the at least one target purchaser is
further configured to determine the at least one target purchaser
based at least in part on at least one of: past purchase history,
search queries, subscriptions, multimedia playlists, wish lists, or
product views.
5. The non-transitory computer-readable medium of claim 1, wherein
the respective offer includes a price for purchasing from the
excess supply of tickets that is less than an amount paid by the
purchaser for the ticket.
6. A method, comprising: determining, by at least one computing
device, a plurality of initial purchasers of an item; obtaining, by
the at least one computing device, a plurality of contacts
associated with the plurality of initial purchasers, the plurality
of contacts being obtained from a third party social networking
site; determining, by the at least one computing device, a
plurality of target purchasers based at least in part on the
plurality of contacts; and generating, by the at least one
computing device, an offer listing for purchasing from an excess
inventory of the item.
7. The method of claim 6, wherein determining the plurality of
target purchasers comprises removing, by the at least one computing
device, duplicates between the plurality of contacts and the
plurality of initial purchasers to determine the plurality of
target purchasers.
8. The method of claim 6, wherein determining the plurality of
target purchasers further comprises: retrieving, by the at least
one computing device, a set of criteria; and determining, by the at
least one computing device, the plurality of target purchasers
based at least in part on the set of criteria; wherein the set of
criteria comprises at least one of: past purchase history, search
queries, subscriptions, multimedia playlists, wish lists, or
product views.
9. The method of claim 8, wherein the set of criteria is weighted
according to purchases by the plurality of target purchasers of
past offer listings.
10. The method of claim 6, wherein the plurality of initial
purchasers purchased the item at a first offer price, and the offer
listing for purchasing from an excess of the item comprises a
second offer price, the second offer price being a portion of the
first offer price.
11. The method of claim 6, wherein the item relates to a ticket for
an event.
12. The method of claim 6, further comprising providing, by the at
least one computing device, the offer listing to the plurality of
target purchasers.
13. The method of claim 12, wherein the offer listing is provided
to the plurality of target purchasers via an advertisement in a
respective third party network page rendered on a client device
associated with individual ones of the plurality of the target
purchasers.
14. A system, comprising: at least one computing device; and an
item recommendation application executable in the at least one
computing device, the item recommendation application comprising:
logic that determines a purchaser of an item; logic that obtains a
plurality of contacts associated with the purchaser, the plurality
of contacts obtained from a third party social networking site;
logic that determines a target purchaser for purchasing from an
excess inventory of the item based at least in part on the
plurality of contacts; and logic that provides an offer listing to
the target purchaser, the offer listing providing a recommendation
referencing the excess inventory of the item.
15. The system of claim 14, wherein the purchaser purchased the
item at a first price, and the item recommendation application
further comprises logic that determines a second price for
purchasing from the excess inventory of the item according to
demand for surplus of the item, the offer listing including the
second price.
16. The system of claim 14, wherein the purchaser is a first
purchaser of a plurality of purchasers, and wherein the target
purchaser is mutually exclusive of the plurality of purchasers.
17. The system of claim 14, wherein the item comprises a ticket
associated with an event.
18. The system of claim 14, wherein the logic that determines the
target purchaser for purchasing from an excess inventory of the
item is further based at least in part on at least one of: past
purchase history, search queries, subscriptions, multimedia
playlists, wish lists, product views, or data stored on the at
least one computing device.
19. The system of claim 14, wherein the logic that provides the
offer listing to the at least one target purchaser is configured to
provide the offer listing through at least one of: a user
interface, an electronic mail message, a text message, a quick
response (QR) code, or an advertisement.
20. The system of claim 19, wherein the advertisement is embodied
in a third party user interface rendered on a client device
associated with the target purchaser.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, and claims priority
to, co-pending U.S. patent application entitled "FACILITATING
PURCHASE OF EXCESS ITEMS," filed on Sep. 8, 2011, and assigned
application Ser. No. 13/227,587, which is incorporated herein by
reference in its entirety.
BACKGROUND
[0002] Tickets for such events as concerts and sporting events may
be sold through different channels such as online ticket merchants
or by telephone orders via a ticket sales representative. Many
times, event tickets go unsold when venues and ticket merchants are
unable to find demand for the available supply of tickets.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] Many aspects of the present disclosure can be better
understood with reference to the following drawings. The components
in the drawings are not necessarily to scale, emphasis instead
being placed upon clearly illustrating the principles of the
disclosure. Moreover, in the drawings, like reference numerals
designate corresponding parts throughout the several views.
[0004] FIG. 1 is a drawing of a networked environment according to
various embodiments of the present disclosure.
[0005] FIGS. 2A and 2B are example user interfaces depicting
electronic communications with offers to target purchasers
regarding excess tickets.
[0006] FIG. 3 is a flowchart that provides one example of the
operation of a portion of the merchant interface in FIG. 1
according to various embodiments.
[0007] FIG. 4 is a flowchart that provides one example of the
operation of a portion of the analytics engine in FIG. 1 according
to various embodiments.
[0008] FIG. 5 is a flowchart that provides one example of the
operation of a portion of the item recommendation application in
FIG. 1 according to various embodiments.
[0009] FIG. 6 is a schematic block diagram that provides one
example illustration of a computing device employed in the
networked environment of FIG. 1 according to various embodiments of
the present disclosure.
DETAILED DESCRIPTION
[0010] The present disclosure relates to facilitating the purchase
of excess items by providing offers to purchase such items as
tickets from excess inventory. Tickets for such events as concerts
and sporting events may be sold through different channels such as
online ticket merchants or by telephone order via a ticket sales
representative. Many times, however, event tickets go unsold when
venues and ticket merchants are unable to find demand for the
available supply of tickets. While ticket merchants may offer the
excess items (e.g., tickets) at a discounted price, this can affect
future customer demand as initial customers who purchased the
tickets at full price become aware of the discounted rates offered
later.
[0011] In accordance with various embodiments, a determination is
made relating to a list of purchasers who purchased an item at full
price. An item may refer to tickets for attending an event (e.g., a
sporting event) or products (e.g., books) that may be offered for
sale, purchase, rental, lease, download, and/or any other form of
consumption as may be appreciated. A list of prospective purchasers
is generated based on activities of the purchasers. Such activities
may correspond, for example, to past purchase history, search
queries relating to network pages of merchants, subscriptions, etc.
In one embodiment, an analytics engine facilitates deduplication of
the list of prospective purchasers with respect to a list of
initial purchasers. In particular, a target list of purchasers is
derived based on the prospective purchasers where the target list
of purchasers does not overlap with the list of purchasers who
purchased the items at full price. The excess items are then
offered to the target purchasers. In some embodiments, an
electronic commerce system may facilitate purchase of the items
offered to the target purchasers. In the following discussion, a
general description of the system and its components is provided,
followed by a discussion of the operation of the same.
[0012] With reference to FIG. 1, shown is a networked environment
100 according to various embodiments. The networked environment 100
includes one or more computing devices 103 in data communication
with one or more clients 106 by way of a network 109. The network
109 includes, for example, the Internet, intranets, extranets, wide
area networks (WANs), local area networks (LANs), wired networks,
wireless networks, or other suitable networks, etc., or any
combination of two or more such networks.
[0013] The computing device 103 may comprise, for example, a server
computer or any other system providing computing capability.
Alternatively, a plurality of computing devices 103 may be employed
that are arranged, for example, in one or more server banks or
computer banks or other arrangements. For example, a plurality of
computing devices 103 together may comprise a cloud computing
resource, a grid computing resource, and/or any other distributed
computing arrangement. Such computing devices 103 may be located in
a single installation or may be distributed among many different
geographical locations. For purposes of convenience, the computing
device 103 is referred to herein in the singular. Even though the
computing device 103 is referred to in the singular, it is
understood that a plurality of computing devices 103 may be
employed in the various arrangements as described above.
[0014] Various applications and/or other functionality may be
executed in the computing device 103 according to various
embodiments. Also, various data is stored in a data store 112 that
is accessible to the computing device 103. The data store 112 may
be representative of a plurality of data stores 112 as can be
appreciated. The data stored in the data store 112, for example, is
associated with the operation of the various applications and/or
functional entities described below.
[0015] The components executed on the computing device 103, for
example, include a networked storage system 115, a merchant
interface 118, an electronic commerce system 121, an item
recommendation application 124, and other applications, services,
processes, systems, engines, or functionality not discussed in
detail herein.
[0016] The networked storage system 115 is executed to maintain a
file hierarchy of files and folders in networked or metadata-based
file systems for users. The networked storage system 115 may be
regarded as maintaining a file system for each of the users of the
system. To this end, the networked storage system 115 may support
various file-related operations such as, for example, creating
files, deleting files, modifying files, setting permissions for
files, downloading files, and/or other operations. The networked
storage system 115 may be configured to maintain a record of file
activity, e.g., uploading of files, downloading of files, deletion
of files, preview of files, etc. The networked storage system 115
may be configured to serve up data addressed by uniform resource
locator (URL) via hypertext transfer protocol (HTTP).
[0017] The merchant interface 118 is executed to facilitate
electronic communications with one or more merchant clients 107
that provide tickets and other items to customers. The merchant
interface 118 may receive, for example, customer lists 150 relating
to initial customers who have already purchased tickets for an
event at full price. The electronic commerce system 121 is executed
to facilitate the online purchase of such items as tickets over the
network 109. The electronic commerce system 121 also performs
various backend functions associated with the online presence of a
merchant in order to facilitate the online purchase of media items.
For example, the electronic commerce system 121 may generate
network pages or portions thereof that are provided to clients 106
for the purposes of selecting tickets and/or other items for
purchase.
[0018] The item recommendation application 124 is executed to
generate offer listings in the form of electronic communications
with recommendations for the purchase of tickets and other items.
Such recommendations may be generated based on the user's
activities as well as other behavior of the friends or other
individuals associated with a user as will be described. The item
recommendation application 124 interfaces with the electronic
commerce system 121 to provide network pages 169 or other
electronic communication to one or more clients 106 to facilitate
selection and purchase of tickets and other items.
[0019] The data stored in the data store 112 includes one or more
user accounts 125. Associated with each user account 125, for
example, is various data associated with a respective user such as
subscriptions 127, purchase history 130, wish lists 133, browse
history 136, user profile data 145, search queries 147, social
networking data 148, among other types of data associated with a
respective user. The social networking data 148 may include, for
example, a listing of friends 149 of the user. Such friends 149 may
comprise individuals with whom a user shares an interpersonal
connection such as friendship, spousal relationships, being related
by blood relationship, and other types of interpersonal
connections.
[0020] The data associated with each user account 125 may also
include a storage bucket 153 for storing files 156 and associated
metadata 167. The subscriptions 127 associated with each user
account 125 may correspond to magazines, newspapers, newsletters,
etc. The purchase history 130 of a user may include a log or record
of item purchases or consumption associated with a user through the
electronic commerce system 121. The purchase history 130 may also
include a log or record of past offerings provided by the item
recommendation application 124 that have been redeemed by a user.
The browse history 136 includes data relating to a history of
browsing for items by a user through the electronic commerce system
121 and/or other systems, where such items may have been purchased
by the user.
[0021] The user profile data 145 may include information about
users with which the file systems in the networked storage system
115 are associated. The user profile data 145 may also include
information about user accounts with the electronic commerce system
121. Such information may comprise, for example, name, shipping
address, payment instruments, and other information. Account
settings may be employed to specify and track various settings and
other functionality with respect to a given account such as
passwords, security credentials, file management permissions,
storage quotas and limitations, authorized access applications,
billing information, and/or other data. The user profile data 145
may also include profile or demographic data associated with users
in addition to other profile information such as favorite team(s),
music preference, favorite musician(s), and so on.
[0022] The social networking data 148 may indicate one or more
social networks that are frequented by users associated with the
user accounts 125. Non-limiting examples of social networks include
Facebook.RTM., Twitter.RTM., MySpace.RTM., and others. The friends
149 include those individuals who may or may not hold their own
user account 125 with whom a given user has an interpersonal
relationship. The friends 149 may be identified by interfacing with
a given social network such as Facebook.RTM., Twitter.RTM.,
MySpace.RTM., and others. For example, such social network sites
may specify Applications Programming Interfaces (API's) to which
the item recommendation application 124 may send an API call to
request information about the friends of a user on such social
networks. Alternatively, one or more user interfaces may be
generated in the form of network pages or other content that
facilitate a user specification of their friends 149 by
facilitating a search for such individuals in the user accounts
125.
[0023] Assuming a listing of friends is obtained from a third party
social networking site, the item recommendation application 124 may
also employ various approaches to associating previously existing
user accounts 125 with the friends 149 discovered for a given user.
To this end, a user account 125 may be confirmed to belong to a
friend 149 of a given user by sending further API calls to the
social networking site to verify that the user account correlates
to a friend 149 of the respective user on the social networking
site. This may especially be necessary where a friend of an
individual has a common name.
[0024] The analytics engine 126 in the item recommendation
application 124 is executed to analyze the various data described
above to identify prospective purchasers for excess tickets and
other items. As a non-limiting example, the analytics engine 126
may analyze the purchase history 130 of a user in addition to
search queries 147 performed relative to a network page of a
merchant to identify an affinity between the user and events of
interest. Based on this, the item recommendation application 124
provides an offer listing in the form of one or more network pages
169, email(s), or other electronic communication to the client 106,
where the offer listing relates to excess tickets associated with
the identified events of interest.
[0025] The analytics engine 126 is further configured to generate a
list of target purchasers based on the list of prospective
purchasers where the target purchasers do not include purchasers
specified in the customer list 150 received from the merchant
client 107. The item recommendation application 124 provides an
offer listing with recommendations to the list of target
purchasers, thereby providing the target purchasers an opportunity
to purchase excess tickets or other items. The offer listings may
be in the form of a text message, a quick response (QR) code, an
advertisement, a network page, and so on.
[0026] When embedded in network pages, the offer listings provided
to clients 106 may include such offer listing source data as
hypertext markup language (HTML), extensible markup language (XML),
extensible HTML (XHTML), mathematical markup language (MathML),
scalable vector graphics (SVG), cascading style sheets (CSS),
images, audio, video, graphics, text, and/or any other data that
may be used in serving up or generating the offer listings. In some
embodiments, the offer listing source data may be distributed
across multiple data stores. The analytics engine 126 may further
track statistics on how many recommended offers for excess tickets
are purchased by target purchasers. Based on such statistics, the
analytics engine 126 assigns weighting factors to the various data
analyzed to derive prospective purchasers.
[0027] The client 106 is representative of a plurality of client
devices that may be coupled to the network 109. The client 106 may
comprise, for example, a processor-based system such as a computer
system. Such a computer system may be embodied in the form of a
desktop computer, a laptop computer, personal digital assistants,
cellular telephones, smartphones, set-top boxes, music players, web
pads, tablet computer systems, game consoles, electronic book
readers, or other devices with like capability. The client 106 may
include a display 157. The display 157 may comprise, for example,
one or more devices such as cathode ray tubes (CRTs), liquid
crystal display (LCD) screens, gas plasma-based flat panel
displays, LCD projectors, or other types of display devices,
etc.
[0028] The client 106 may be configured to execute various
applications such as a browser 160 and/or other applications. The
browser 160 may be executed in a client 106, for example, to access
and render network pages, such as web pages, or other network
content served up by the computing device 103 and/or other servers,
thereby rendering a network page 166 on the display 157. The
network page 166 may comprise, for example, items relating to a
recommendation provided by the item recommendation application 124
indicating that excess tickets are available to be purchased. The
client 106 may be configured to execute applications beyond the
browser 160 such as, for example, email applications, instant
message applications, and/or other applications.
[0029] The merchant client 107 is representative of a plurality of
client devices utilized by merchants, ticket distributors,
stadium/venue operators, etc. that may be coupled to the network
109. The merchant client 107 may comprise, for example, a
processor-based system such as a computer system. Such a computer
system may be embodied in the form of a desktop computer, a laptop
computer, personal digital assistants, cellular telephones,
smartphones, set-top boxes, music players, web pads, tablet
computer systems, game consoles, electronic book readers, or other
devices with like capability. The merchant client 107 may include a
data store 132 that includes customer lists 150 specifying
customers who have already purchased tickets and other items. The
customer lists 150 may include such information as account numbers,
email addresses, and other identifiers associated with customers.
The data store 132 may also include inventory data 151 for tracking
the availability of excess tickets. The computing device 103 is in
data communication with the merchant client 107 through the
merchant interface 118. The analytics engine 126 in the item
recommendation application 124 retrieves the customer list 150 and
inventory data 151 from merchant clients 107.
[0030] According to various embodiments, the item recommendation
application 124 is configured to generate recommendations for the
purchase of excess items through the electronic commerce system
121. Various mechanisms may be employed by the item recommendation
application 124 to generate a listing of target purchasers, where
the target purchasers are provided an opportunity to purchase
excess tickets.
[0031] Shown in FIG. 2A is an example user interface depicting an
electronic communication 203, such as an email, with an offer to a
target purchaser regarding excess tickets. In one embodiment, the
item recommendation application 124 (FIG. 1) provides an offer
listing sent to a client 106 (FIG. 1). In this non-limiting
example, the electronic communication 203 is an email communication
transmitted from the item recommendation application 124 to a
client 106 that includes information 206 relating to tickets for a
sporting event. The information 206 includes such details as the
teams that will be playing, the date, time, and location of the
sporting event. The target purchaser is able to perform a search
among the excess tickets still available.
[0032] In one embodiment, the item recommendation application 124
may also provide a price for the ticket(s) where price may be a
discounted price relative to the price the tickets were originally
on sale for. While the example in FIG. 2 depicts an email
communication 203 transmitted by the item recommendation
application 124, the offers to target purchasers may be transmitted
in other forms of electronic communication including, for example,
one or more network pages comprising web pages, which are rendered
by a browser 160 (FIG. 1) in the client 106, text messages, QR
codes and so on.
[0033] Referring next to FIG. 2B, shown is an example of a user
interface depicting an electronic communication 203 with an offer
to a target purchaser regarding excess tickets. The user interface
210 may be rendered by the browser 160 (FIG. 1), a media player, a
mobile application, or another application. The user interface 210
includes a listing 213 of media files, which may comprise files 156
(FIG. 1) stored in the data store 112 (FIG. 1).
[0034] A feed panel 219 may be rendered alongside the listing 213
or in another location in conjunction with the listing 213. The
feed panel 219 is utilized for conveying offers 212 to target
purchasers who may be interested in purchasing tickets that are
available. In the non-limiting example of FIG. 2B, the analytics
engine 126 (FIG. 1) determines that the user may be interested in
attending an upcoming concert corresponding to a particular artist
based on the user's music collection stored in the storage bucket
153 (FIG. 1). The item recommendation application 124 (FIG. 1)
generates the offer 212 shown in the feed panel 219 that notifies
the user that tickets are available. A link may be provided for the
target user to order tickets for the upcoming concert event.
[0035] Referring next to FIG. 3, shown is a flowchart that provides
one example of the operation of a portion of the merchant interface
118 according to various embodiments. It is understood that the
flowchart of FIG. 3 provides merely an example of the many
different types of functional arrangements that may be employed to
implement the operation of the portion of the merchant interface
118 as described herein. As an alternative, the flowchart of FIG. 3
may be viewed as depicting an example of steps of a method
implemented in the computing device 103 (FIG. 1) according to one
or more embodiments. The merchant interface 118 is executed in the
computing device 103 and interfaces with the merchant client 107
(FIG. 1) over the network 109 (FIG. 1).
[0036] Beginning with box 303, the merchant interface 118 retrieves
inventory data 151 (FIG. 1) from the merchant client 107. The
inventory data 151 may specify, for example, the total number of
tickets for a particular event in addition to the number of excess
tickets, if any, that are unsold. The inventory data 151 may also
comprise ticket information relating to what seats have been sold
and what seats are still available. In box 306, the merchant
interface 118 retrieves a customer list 150 (FIG. 1) comprising a
list of initial purchasers who purchased tickets at full price. In
box 309, if no excess tickets are available, the merchant interface
118 proceeds to box 312 and deletes the inventory data 151 and
customer list 150 retrieved from the merchant client 107. If excess
tickets are available, then the merchant interface 118 proceeds to
box 315 and determines if a list of target purchasers are available
at the merchant client 107. Such target purchasers may represent,
for example, prime or select customers who purchase tickets on a
regular basis from the merchant.
[0037] If a list of target purchasers is not available then the
merchant interface 118 proceeds to box 318 and a list of target
purchasers is generated, as described below. If a list of target
purchasers is available from the merchant, the merchant interface
118 proceeds to box 321, where offer listings are generated for the
list of target purchasers to provide the target purchasers an
opportunity to purchase the excess tickets. Thereafter the merchant
interface 118 ends as shown.
[0038] Referring next to FIG. 4, shown is a flowchart that provides
one example of the operation of a portion of the analytics engine
126 according to various embodiments. It is understood that the
flowchart of FIG. 4 provides merely an example of the many
different types of functional arrangements that may be employed to
implement the operation of the portion of the analytics engine 126
as described herein. As an alternative, the flowchart of FIG. 4 may
be viewed as depicting an example of steps of a method implemented
in the computing device 103 (FIG. 1) according to one or more
embodiments.
[0039] Beginning with box 403, the analytics engine 126 retrieves a
list of initial purchasers. As described above, the merchant
interface 118 (FIG. 1) may interface with merchant clients 107
(FIG. 1) to retrieve a customer list 150 (FIG. 1) with the initial
purchasers. The analytics engine 126 may then obtain the customer
list 150 from the merchant interface 118.
[0040] In box 406, the analytics engine 126 identifies relevant
user data to be used in deriving a list of prospective purchasers.
As described earlier, the data stored in the data store 112 (FIG.
1) includes one or more user accounts 125 (FIG. 1). Associated with
each user account 125 (FIG. 1), for example, is various data
associated with a respective user such as subscriptions 127 (FIG.
1), purchase history 130 (FIG. 1), wish lists 133 (FIG. 1), browse
history 136 (FIG. 1), user profile data 145 (FIG. 1), search
queries 147 (FIG. 1), social networking data 148 (FIG. 1), among
other types of data associated with a respective user. The data
associated with each user account 125 may also include a storage
bucket 153 (FIG. 1) for storing files 156 (FIG. 1) and associated
metadata 167 (FIG. 1).
[0041] All or a subset of the data above may be relevant in
deriving a list of prospective purchasers. As a non-limiting
example, the analytics engine 126 may have retrieved a list of
initial purchasers who purchased tickets to a Seattle Seahawks.RTM.
game to be played in two days. Based on the nature of the event
(i.e., a football game), the analytics engine 126 may determine
that such data as subscriptions 127 (for example, to sports news
publications), search queries 147, and purchase history 130
(relating to, for example, sports team merchandise) are highly
relevant in deriving a list of prospective purchasers. Other pieces
of information such as the geographical location of the user
specified in the user profile data 145 may also be relevant.
[0042] In box 409, the analytics engine 126 assigns weighting
factors to the relevant data. As a non-limiting example, the
analytics engine 126 may assign a relatively high weighting factor
to purchase history 130 as the purchase history 130 may reflect
past purchases of tickets for other sporting events. The analytics
engine 126 may also assign a relatively high weighting factor
because previously applying the purchase history 130 as a criteria
resulted in a target purchaser purchasing a ticket. In this regard,
the analytics engine 126 may be configured to evaluate trends
relating to successful strategies and store such information for
future use in applying criteria. In box 412, the analytics engine
126 derives a list of prospective purchasers based on the weighted
user data.
[0043] In box 415, the analytics engine 126 performs deduplication
of the list of prospective purchasers with respect to the list of
initial purchasers. In box 418, offer listings relating to the
excess supply of tickets are generated. Thereafter the analytics
engine 126 ends as shown.
[0044] Referring next to FIG. 5, shown is a flowchart that provides
one example of the operation of a portion of the item
recommendation application 124 according to various embodiments. It
is understood that the flowchart of FIG. 5 provides merely an
example of the many different types of functional arrangements that
may be employed to implement the operation of the portion of the
item recommendation application 124 as described herein. As an
alternative, the flowchart of FIG. 5 may be viewed as depicting an
example of steps of a method implemented in the computing device
103 (FIG. 1) according to one or more embodiments.
[0045] Beginning in box 503, the item recommendation application
124 retrieves the list of target purchasers derived by the
analytics engine 126 (FIG. 1). In box 506, the item recommendation
application 124 retrieves contact information for each of the
target purchasers from the data store 112 (FIG. 1). Such contact
information as email addresses may be stored as part of the user
profile data 145 (FIG. 1).
[0046] In box 509, offer listings are generated by the item
recommendation application 124 and sent to clients 106 (FIG. 1) by
email or other means of electronic communications. Other means of
communicating with target purchasers may be found, for example, in
the user profile data 145 (FIG. 1) or social networking data 148
(FIG. 1). In box 512, the generated offer listings with
recommendations for purchasing excess items are sent to the target
purchasers. Various social network sites may specify Applications
Programming Interfaces (API's) to which the item recommendation
application 124 may send an API call to transmit information
relating to offers to purchase excess tickets.
[0047] The item recommendation application 124 may also generate an
advertisement, for example, that is displayed when a user accesses
their social networking profile or other third party site. As
another means of electronic communication, the item recommendation
application 124 may also send a link via email to the client 106
where the link corresponds to a network page 169 (FIG. 1) rendered
on the display 157 (FIG. 1) at the client 106.
[0048] In box 515, the item recommendation application 124 may be
further configured to track statistics relating to whether offers
are redeemed. As a non-limiting example, the item recommendation
application 124 may log or record that a particular target
purchaser redeemed an offer to purchase tickets for a concert event
relating to a particular musician as such data may become useful in
generating future offer listings. The item recommendation
application 124 may also be configured to track statistics
regarding target purchasers who unsubscribe to receiving offers. In
box 518, the item recommendation application 124 may store the
statistics as part of the user data stored in the data store 112 in
the computing device 103. Thereafter, the item recommendation
application 124 ends as shown.
[0049] With reference to FIG. 6, shown is a schematic block diagram
of the computing device 103 according to an embodiment of the
present disclosure. The computing device 103 includes at least one
processor circuit, for example, having a processor 603 and a memory
606, both of which are coupled to a local interface 609. To this
end, the computing device 103 may comprise, for example, at least
one server computer or like device. The local interface 609 may
comprise, for example, a data bus with an accompanying
address/control bus or other bus structure as can be
appreciated.
[0050] Stored in the memory 606 are both data and several
components that are executable by the processor 603. In particular,
stored in the memory 606 and executable by the processor 603 are
the networked storage system 115, the merchant interface 118, the
item recommendation application 124, the analytics engine 126, and
potentially other applications. Also stored in the memory 606 may
be a data store 112 and other data. In addition, an operating
system may be stored in the memory 606 and executable by the
processor 603.
[0051] It is understood that there may be other applications that
are stored in the memory 606 and are executable by the processor
603 as can be appreciated. Where any component discussed herein is
implemented in the form of software, any one of a number of
programming languages may be employed such as, for example, C, C++,
C#, Objective C, Java.RTM., JavaScript.RTM., Perl, PHP, Visual
Basic.RTM., Python.RTM., Ruby, Delphi.RTM., Flash.RTM., or other
programming languages.
[0052] A number of software components are stored in the memory 606
and are executable by the processor 603. In this respect, the term
"executable" means a program file that is in a form that can
ultimately be run by the processor 603. Examples of executable
programs may be, for example, a compiled program that can be
translated into machine code in a format that can be loaded into a
random access portion of the memory 606 and run by the processor
603, source code that may be expressed in proper format such as
object code that is capable of being loaded into a random access
portion of the memory 606 and executed by the processor 603, or
source code that may be interpreted by another executable program
to generate instructions in a random access portion of the memory
606 to be executed by the processor 603, etc. An executable program
may be stored in any portion or component of the memory 606
including, for example, random access memory (RAM), read-only
memory (ROM), hard drive, solid-state drive, USB flash drive,
memory card, optical disc such as compact disc (CD) or digital
versatile disc (DVD), floppy disk, magnetic tape, or other memory
components.
[0053] The memory 606 is defined herein as including both volatile
and nonvolatile memory and data storage components. Volatile
components are those that do not retain data values upon loss of
power. Nonvolatile components are those that retain data upon a
loss of power. Thus, the memory 606 may comprise, for example,
random access memory (RAM), read-only memory (ROM), hard disk
drives, solid-state drives, USB flash drives, memory cards accessed
via a memory card reader, floppy disks accessed via an associated
floppy disk drive, optical discs accessed via an optical disc
drive, magnetic tapes accessed via an appropriate tape drive,
and/or other memory components, or a combination of any two or more
of these memory components. In addition, the RAM may comprise, for
example, static random access memory (SRAM), dynamic random access
memory (DRAM), or magnetic random access memory (MRAM) and other
such devices. The ROM may comprise, for example, a programmable
read-only memory (PROM), an erasable programmable read-only memory
(EPROM), an electrically erasable programmable read-only memory
(EEPROM), or other like memory device.
[0054] Also, the processor 603 may represent multiple processors
603 and the memory 606 may represent multiple memories 606 that
operate in parallel processing circuits, respectively. In such a
case, the local interface 609 may be an appropriate network that
facilitates communication between any two of the multiple
processors 603, between any processor 603 and any of the memories
606, or between any two of the memories 606, etc. The local
interface 609 may comprise additional systems designed to
coordinate this communication, including, for example, performing
load balancing. The processor 603 may be of electrical or of some
other available construction.
[0055] Although the networked storage system 115, the merchant
interface 118, the item recommendation application 124, the
analytics engine 126, and other various systems described herein
may be embodied in software or code executed by general purpose
hardware as discussed above, as an alternative the same may also be
embodied in dedicated hardware or a combination of software/general
purpose hardware and dedicated hardware. If embodied in dedicated
hardware, each can be implemented as a circuit or state machine
that employs any one of or a combination of a number of
technologies. These technologies may include, but are not limited
to, discrete logic circuits having logic gates for implementing
various logic functions upon an application of one or more data
signals, application specific integrated circuits having
appropriate logic gates, or other components, etc. Such
technologies are generally well known by those skilled in the art
and, consequently, are not described in detail herein.
[0056] The flowcharts of FIGS. 3-5 show examples of functionality
of an implementation of portions of the merchant interface 118,
analytics engine 126, and the item recommendation application 124.
If embodied in software, each block may represent a module,
segment, or portion of code that comprises program instructions to
implement the specified logical function(s). The program
instructions may be embodied in the form of source code that
comprises human-readable statements written in a programming
language or machine code that comprises numerical instructions
recognizable by a suitable execution system such as a processor 603
in a computer system or other system. The machine code may be
converted from the source code, etc. If embodied in hardware, each
block may represent a circuit or a number of interconnected
circuits to implement the specified logical function(s).
[0057] Although the flowcharts of FIGS. 3-5 show a specific order
of execution, it is understood that the order of execution may
differ from that which is depicted. For example, the order of
execution of two or more blocks may be scrambled relative to the
order shown. Also, two or more blocks shown in succession in FIGS.
3-5 may be executed concurrently or with partial concurrence.
Further, in some embodiments, one or more of the blocks shown in
FIGS. 3-5 may be skipped or omitted. In addition, any number of
counters, state variables, warning semaphores, or messages might be
added to the logical flow described herein, for purposes of
enhanced utility, accounting, performance measurement, or providing
troubleshooting aids, etc. It is understood that all such
variations are within the scope of the present disclosure.
[0058] Also, any logic or application described herein, including
the networked storage system 115, the merchant interface 118, the
item recommendation application 124, the analytics engine 126, that
comprises software or code can be embodied in any non-transitory
computer-readable medium for use by or in connection with an
instruction execution system such as, for example, a processor in a
computer system or other system. In this sense, each may comprise,
for example, statements including instructions and declarations
that can be fetched from the computer-readable medium and executed
by the instruction execution system. In the context of the present
disclosure, a "computer-readable medium" can be any medium that can
contain, store, or maintain the logic or application described
herein for use by or in connection with the instruction execution
system. The computer-readable medium can comprise any one of many
physical media such as, for example, magnetic, optical, or
semiconductor media. More specific examples of a suitable
computer-readable medium would include, but are not limited to,
magnetic tapes, magnetic floppy diskettes, magnetic hard drives,
memory cards, solid-state drives, USB flash drives, or optical
discs. Also, the computer-readable medium may be a random access
memory (RAM) including, for example, static random access memory
(SRAM) and dynamic random access memory (DRAM), or magnetic random
access memory (MRAM). In addition, the computer-readable medium may
be a read-only memory (ROM), a programmable read-only memory
(PROM), an erasable programmable read-only memory (EPROM), an
electrically erasable programmable read-only memory (EEPROM), or
other type of memory device.
[0059] It should be emphasized that the above-described embodiments
of the present disclosure are merely possible examples of
implementations set forth for a clear understanding of the
principles of the disclosure. Many variations and modifications may
be made to the above-described embodiment(s) without departing
substantially from the spirit and principles of the disclosure. All
such modifications and variations are intended to be included
herein within the scope of this disclosure and protected by the
following claims.
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