U.S. patent application number 15/951816 was filed with the patent office on 2019-10-17 for systems for determining customer interest in goods.
This patent application is currently assigned to Capital One Services, LLC. The applicant listed for this patent is Capital One Services, LLC. Invention is credited to Salik SHAH.
Application Number | 20190318395 15/951816 |
Document ID | / |
Family ID | 68063896 |
Filed Date | 2019-10-17 |
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United States Patent
Application |
20190318395 |
Kind Code |
A1 |
SHAH; Salik |
October 17, 2019 |
SYSTEMS FOR DETERMINING CUSTOMER INTEREST IN GOODS
Abstract
A system for determining customer interest in goods includes one
or more memory devices storing instructions and one or more
processors configured to execute the instructions. The processors
are configured to receive customer location data from a smart
device associated with a customer indicating the customer is within
a retail venue of a retailer and to monitor, based on the customer
location data, a current location of the customer within the retail
venue. The processors are further configured to receive goods
location data indicating locations of goods for sale within the
retail venue and determine that the customer is interested in a
particular good for sale within the retail venue based on the
current customer location remaining in proximity to the location of
the particular good for a predetermined period of time. The
processors also conduct a search of pricing of the particular good
at one or more other retailers and send a price comparison to the
customer.
Inventors: |
SHAH; Salik; (Washington DC,
DC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Capital One Services, LLC |
Mclean |
VA |
US |
|
|
Assignee: |
Capital One Services, LLC
Mclean
VA
|
Family ID: |
68063896 |
Appl. No.: |
15/951816 |
Filed: |
April 12, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0201 20130101;
G06Q 30/0281 20130101; H04W 4/35 20180201; H04W 4/029 20180201 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; H04W 4/35 20060101 H04W004/35; H04W 4/029 20060101
H04W004/029 |
Claims
1. A system for determining customer interest in goods, comprising:
one or more memory devices storing instructions; and one or more
processors configured to execute the instructions to: receive
customer location data from a smart device associated with a
customer indicating the customer is within a retail venue of a
retailer; monitor, based on the customer location data, a current
location of the customer within the retail venue; receive goods
location data from a plurality of transmitter devices indicating
locations of goods for sale within the retail venue; monitor, based
on the goods location data, a current location of the goods within
the retail venue; determine that the customer is interested in a
particular good for sale within the retail venue based on one of
the current customer location remaining in proximity to the current
location of the particular good for a predetermined period of time,
or a listing of the particular good within proximity to the
customer on a customer account; store each determination that the
customer is interested in the particular good for sale within the
retail venue; generate a profile of shopping behavior of the
customer based on the stored interest determinations; conduct a
search of pricing of the particular good at one or more other
retailers; send a price comparison to the customer for the
particular good based on results of the price search; and generate
a model of the customer interest in a plurality of goods at the
retail venues.
2. (canceled)
3. The system of claim 1, the one or more processors being further
configured to: receive location data of other customers within the
retail venue; determine interests of the other customers in goods
at the retail venue; store the interest determinations of the other
customers; and generate a profile of shopping behavior of each of
the other customers.
4. The system of claim 3, the one or more processors being further
configured to: compile the generated shopping behavior profiles to
generate a model of aggregate customer interest in a plurality of
products at the retail venue.
5. The system of claim 1, the one or more processors being further
configured to: receive information indicating whether the customer
purchases the particular good after receiving the price
comparison.
6. The system of claim 5, wherein if the customer does not purchase
the particular good after receiving the price comparison, the one
or more processors are further configured to send to the customer
smart device a discounted price for the good.
7. The system of claim 5, if the customer does not purchase the
particular good after receiving the price comparison, the one or
more processors are further configured to determine whether to
adjust a price of the particular good.
8. The system of claim 1, the one or more processors being further
configured to: determine whether the customer decided to purchase
the good based on the monitored location of the customer and the
location of the good.
9. The system of claim 8, the one or more processors being further
configured to: generate a model that analyzes the customer shopping
behavior profile and purchase determination.
10. The system of claim 9, the one or more processors being further
configured to: send, based on the model analysis, to the customer
smart device a discounted price for the good.
11. The system of claim 10, the one or more processors being
further configured to: receive information indicating whether the
customer purchases the particular good after receiving the
discounted price; and update the customer shopping behavior
profile.
12. The system of claim 1, wherein the customer includes a
plurality of customers and the retail venue includes a plurality of
retail venues, the one or more processors being further configured
to: receive customer location data from smart devices associated
with the plurality of customers at the plurality of retail venues;
monitor, based on the customer location data, a current location of
the customer within the retail venues; receive goods location data
indicating locations of goods for sale in the retail venues;
monitor, based on the goods location data, a current location of
the goods within the retail venues; determine interests of the
customers in the goods in the retail venues based on one of the
locations of the customers remaining in proximity to the current
locations of the goods in the retail venues for a predetermined
period of time, or listings of the particular goods within
proximity to the customers on the customer account; store each
determination that each of the customers is interested in a good
for sale in one of the retail venues; and generate a shopping
behavior profile of each of the customers based on the stored
interest determinations.
13. The system of claim 12, the one or more processors being
further configured to: compile the generated shopping behavior
profiles for the customers to generate a model of aggregate
customer interest in a plurality of goods at the retail venues.
14. The system of claim 13, wherein the plurality of retail venues
are affiliated with a common business entity.
15. The system of claim 14, the one or more processors being
further configured to determine whether each of the customers
decides to purchase the goods based on the monitored locations of
the customers and the locations of the goods.
16. The system of claim 15, wherein generating a model of aggregate
customer interest includes receiving and analyzing the determined
decisions to purchase the goods by the customers.
17. The system of claim 16, the one or more processors being
further configured to receive information indicating whether the
customers purchase the particular good after receiving the price
comparison.
18. The system of claim 17, wherein if any one of the customers
does not purchase the particular good after receiving the price
comparison, the one or more processors are further configured to
send to the smart device of the non-purchasing customer a
discounted price for the good.
19. The system of claim 18, wherein the offered discounted price is
adjusted based on the generated model analysis.
20. The system of claim 18, if any one of the customers does not
purchase the particular good after receiving the price comparison,
the one or more processors are further configured to determine
whether to adjust the price of the particular good at the retail
venue, or all affiliated retail venues.
Description
TECHNICAL FIELD
[0001] The present disclosure generally relates to a system for
determining customer interest in goods.
BACKGROUND
[0002] Reliably and seamlessly price checking goods within retail
venues is a burdensome task for customers purchasing multiple, or
even unique, goods.
[0003] As one example, a customer at a brick-and-mortar store
location may select upwards of 30 items, and place them into a
single shopping cart. The customer may wish to conduct an
item-by-item price check. However, in order to do so, the customer
must manually run the price comparisons, either with a search
engine or a smart device application, or automatically with a
scanning system, and hope the information is accurate. Such a
shopping experience can lead to errors, such as purchasing the
wrong good or amount of goods based on a believed deal.
[0004] As another example, a particular brick-and-mortar store
location may lose customers, who were once very loyal, to other
retail venues offering more competitive pricing. The other venues
can be found online, at other physical locations, or both. The
customers may prefer shopping at the particular brick-and-mortar
store location but price comparisons for like items they find on
the internet, or from a shopping application, uncover competing
prices that are too hard to pass up. The store owner is unaware of
the competing pricing and never has an opportunity to offer a
responsive discount in order to retain the customers.
[0005] Moreover, while some computerized solutions exist for
tracking customer proximity to goods, and offering discounts, such
solutions typically stop there. This is inefficient and does not
collect and utilize data for the benefit of both the store owner
and the customer.
[0006] The present disclosure provides systems and devices to solve
these and other problems.
SUMMARY
[0007] In the following description, certain aspects and
embodiments of the present disclosure will become evident. It
should be understood that the disclosure, in its broadest sense,
could be practiced without having one or more features of these
aspects and embodiments. Specifically, it should also be understood
that these aspects and embodiments are merely exemplary. Moreover,
although disclosed embodiments are discussed in the context of a
processor bracket and, it is to be understood that the disclosed
embodiments are not limited to any particular industry.
[0008] Disclosed embodiments include a system for determining
customer interest in goods. The system comprises one or more memory
devices storing instructions and one or more processors configured
to execute the instructions. The processors are configured to
receive customer location data from a smart device associated with
a customer indicating the customer is within a retail venue of a
retailer and to monitor, based on the customer location data, a
current location of the customer within the retail venue. The
processors are further configured to receive goods location data
indicating locations of goods for sale within the retail venue and
determine that the customer is interested in a particular good for
sale within the retail venue based on the current customer location
remaining in proximity to the location of the particular good for a
predetermined period of time. The processors also conduct a search
of pricing of the particular good at one or more other retailers
and send a price comparison to the customer for the particular good
based on results of the price search.
[0009] It is to be understood that both the foregoing general
description and the following detailed description are exemplary
and explanatory only, and are not restrictive of the disclosed
embodiments, as claimed.
BRIEF DESCRIPTION OF DRAWINGS
[0010] The accompanying drawings, which are incorporated in and
constitute a part of the specification, illustrate several
embodiments and, together with the description, serve to explain
the disclosed principles. In the drawings:
[0011] FIG. 1A is a block diagram of an exemplary system,
consistent with disclosed embodiments.
[0012] FIG. 1B is a diagram of an exemplary electronic system,
consistent with disclosed embodiments.
[0013] FIG. 1C is a diagram of an exemplary electronic system,
consistent with disclosed embodiments.
[0014] FIG. 1D is a diagram of an exemplary electronic system,
consistent with disclosed embodiments.
[0015] FIG. 2A is a diagram of an exemplary retail venue consistent
with disclosed embodiments.
[0016] FIG. 2B is a block diagram of an exemplary system,
consistent with disclosed embodiments.
[0017] FIG. 3 is a flowchart of an exemplary process for modeling
and analyzing customer shopping behavior and purchase
determinations.
[0018] FIG. 4 is a flowchart of an exemplary process for
determining a customer interest in goods and sending a price
comparison to the customer.
[0019] FIG. 5 is a flowchart of an exemplary process for
determining a customer interest in goods, sending a price
comparison, and updating the customer profile based on purchase
behavior.
[0020] FIG. 6 is a flowchart of an exemplary process for
determining multiple customers interest in goods for multiple
affiliated venues.
DETAILED DESCRIPTION
[0021] An initial overview of proximity detection technology is
provided immediately below and then specific exemplary embodiments
of systems and methods for determining customer interest in goods
are described in further detail later. The initial overview is
intended to aid in understanding some of the useful technology
relevant to systems and methods disclosed herein, but it is not
intended to limit the scope of the claimed subject matter.
[0022] One means of proximity detection technology is via
communication either between two devices or communication gathered
on a network encompassing two devices. Wireless communication is
more typical due to the nature and intentions associated with
proximity detection (i.e., wired communication likely provides some
indication of proximity already). The wireless communication of
proximity based content enables a user associated with a user
device to send or receive content, via a user device, when the user
device is within a limited proximity of a second device associated
with a location or object (e.g., a good for sale). The content may
be related to or associated with the location or object. Also, the
sending or receiving of the content may be triggered by the user
entering a limited proximity of the location or the object.
[0023] Wireless communication is any form of communication between
two devices where some point of communication does not require a
physical wired connection. Some wireless communication is based on
radio frequencies, but wireless communication is not limited to the
radio frequencies.
[0024] In one example, wireless communication and proximity
detection can be accomplished with a user's mobile computing device
(e.g., a smartphone). While the mobile computing device is
described herein as being mobile, the mobile computing device may
be a fixed device. The mobile computing device can be a handheld
computing device, a wearable computing device, a portable
multimedia device, a smartphone, a tablet computing device, a
laptop computer, a smart watch, an embedded computing device, or
similar device. An embedded computing device is a computing device
that is inlayed in a selected object such as a vehicle, a watch, a
key fob, a ring, a key card, a token, a poker chip, a souvenir, a
necklace amulet, and so forth. A computing device may be embedded
in substantially any type of object. The mobile computing device
can be a device that is user owned, rented, leased, associated
with, or otherwise in the possession of the user.
[0025] The wireless communication can be between the user's mobile
device and a second proximity device, such as a tag, that is
associated with or near the object/good. Like the user device, the
tag can be fixed or mobile. The tag may be another mobile computing
device or another device. The tag may be owned by the user or
another entity.
[0026] The location proximity based content that is communicated
between the user device and tag, or routed through a network, may
include content that is locally stored on each device, content that
is received through a wired or wireless network from a remote
storage device, or a combination thereof. The communicated content
may be generated by the user or another entity either locally or
remotely, and in advance or contemporaneously with the sending of
the content.
[0027] Reference will now be made in detail to exemplary
embodiments, examples of which are illustrated in the accompanying
drawings and disclosed herein. Wherever convenient, the same
reference numbers will be used throughout the drawings to refer to
the same or like parts.
[0028] The disclosed embodiments are directed to systems and
methods for determining customer interest in goods for sale. While
some computerized solutions exist for tracking customer proximity
to goods, and offering discounts, such solutions typically stop
there. This is inefficient and does not collect and utilize data
for the benefit of both the store owner and the customer.
Furthermore, none of the other solutions utilize machine learning
to properly stock a retail venue, or model purchasing behavior on
micro and macro levels for individual or multiple customers and
locations. And there is no system for combining such data to
determine customer interest in goods by analyzing their proximity
to a good, their past purchasing behaviors and interests, and
market pricing trends to further determine a competitive price
adjustment.
[0029] There exists substantial untapped consumer data sources that
can be utilized to provide improved services for prospective
customers. One such area of underutilized data is in determining
customer interest in goods. In particular, customer interest could
be determined based on their proximity, and duration of proximity,
to specific goods in a physical retail venue, as well as the
association of those specific goods to other goods in the retail
venue. To make this determination, a system for determining
customer interest would need to collect or receive input data
regarding the location and movement of the goods, as well as the
location and movement of the customer. Once the system collects or
receives data from which to determine a customer interested in a
particular good, then the system could further utilize that data to
provide an improved service, such as facilitating price comparison
or offering reduced pricing for the particular good.
[0030] The following description provides examples of systems and
methods for determining customer interest in goods. The arrangement
of components shown in the figures is not intended to limit the
disclosed embodiments, as the components used in the disclosed
systems may vary.
[0031] FIG. 1A depicts an illustrative system 100 for determining a
customer interest in goods in accordance with aspects of an
embodiment of the present disclosure. System 100 includes a
customer smart device 110, which can be any user device discussed
above, in wireless communication with a monitor device 120 which is
in further communication with a tag device 130 that indicates a
physical location of a good for sale within a retail venue. As
discussed above, the means of communication between devices 110,
120, and 130 can vary and the particular combination can also vary
such that device 110 may communicate directly with device tag 130
and vice versa. Monitor device 120 further communicates with a
network 140. It will also be understood that devices 110, 120, and
130 may also communicate directly with network 140 or through
network 140. Customer smart device 110, monitor device 120, tag
device 130, and network 140 further communicate with a storage
device 150. Storage device 150 stores an information model 160 and
a customer profile 170.
[0032] Through these illustrative components, system 100 collects
and utilizes data for the benefit of both the store owner and the
customer. For instance, by collecting location and proximity data
with devices 110, 120, and 130, storage device 150 can further
analyze customer interests in prospective goods with model 160. A
store owner may further use model 160 analysis and historical data
stored in customer profile 170 to offer a competitive service,
through price adjustments, product placements, product pairings,
etc., for the customer. The customer, for benefiting from this
beneficial experience, will in turn stay loyal to the store
owner.
[0033] FIG. 1B illustrates an exemplary configuration of smart
device 110, consistent with disclosed embodiments. Variations of
smart device 110 may be used to implement portions or all of each
of the devices of system 100, such as monitor device 120, good tag
130, and storage device 150. Likewise, even though FIG. 1B depicts
smart device 110, it is understood that devices 120, 130 and 150
may implement portions illustrated by exemplary smart device 110.
As shown, smart device 110 includes a display 111, an input/output
("I/O") device 112, one or more processors 113, and a memory 114
having stored therein one or more program applications 115, such as
an account app 116, and data 117. Smart device also includes an
antenna 118 and one or more sensors 119. One or more of display
111, I/O devices 112, processor(s) 113, memory 114, antenna 118, or
sensor(s) 119 may be connected to one or more of the other devices
depicted in FIG. 1B. Such connections may be accomplished using a
bus or other interconnecting device(s).
[0034] Processor 113 may be one or more known processing devices,
such as a microprocessor from the Pentium.TM. or Atom.TM. families
manufactured by Intel.TM., the Turion.TM. family manufactured by
AMD.TM., the Exynos.TM. family manufactured by Samsung.TM., or the
Snapdragon.TM. family manufactured by Qualcomm.TM.. Processor 113
may constitute a single core or multiple core processors that
executes parallel processes simultaneously. For example, processor
113 may be a single core processor configured with virtual
processing technologies. In certain embodiments, processor 113 may
use logical processors to simultaneously execute and control
multiple processes. Processor 113 may implement virtual machine
technologies, or other known technologies to provide the ability to
execute, control, run, manipulate, store, etc., multiple software
processes, applications, programs, etc. In another embodiment,
processor 113 may include a multiple-core processor arrangement
(e.g., dual, quad core, etc.) configured to provide parallel
processing functionalities to allow smart device 110 to execute
multiple processes simultaneously. One of ordinary skill in the art
would understand that other types of processor arrangements could
be implemented that provide for the capabilities disclosed
herein.
[0035] I/O devices 112 may include one or more devices that
customer smart device 110 to receive input from a customer and
provide feedback to the customer. I/O devices 112 may include, for
example, one or more buttons, switches, speakers, microphones, or
touchscreen panels. In some embodiments, I/O devices 112 may be
manipulated by the customer 105 to input information into smart
device 110.
[0036] Memory 114 may be a volatile or non-volatile, magnetic,
semiconductor, tape, optical, removable, non-removable, or other
type of storage device or tangible (i.e., non-transitory)
computer-readable medium that stores one or more program
applications 115 such as account app 116, and data 117. Data 117
may include, for example, customer personal information, account
information, and display settings and preferences. In some
embodiments, account information may include items such as, for
example, an alphanumeric account number, account label, account
balance, account issuance date, account expiration date, account
issuer identification, a government ID number, a room number, a
room passcode, and any other necessary information associated with
a customer and/or an account associated with a customer, depending
on the needs of the customer, entities associated with network 140,
and/or entities associated with system 100.
[0037] Program applications 115 may include operating systems (not
shown) that perform known operating system functions when executed
by one or more processors. By way of example, the operating systems
may include Microsoft Windows.TM., Unix.TM., Linux.TM., Apple.TM.,
or Android.TM. operating systems, Personal Digital Assistant (PDA)
type operating systems, such as Microsoft CE.TM., or other types of
operating systems. Accordingly, disclosed embodiments may operate
and function with computer systems running any type of operating
system. Smart device 110 may also include communication software
that, when executed by processor 113, provides communications with
network 140, such as Web browser software, tablet, or smart hand
held device networking software, etc. Smart device 110 may be a
device that executes mobile applications for performing operations
consistent with disclosed embodiments, such as a tablet, mobile
device, or smart wearable device.
[0038] Program applications 115 may include account app 116, such
as an account app for activating, setting up, and configuring a
customer access to communication with devices 120, 130, and 150
through the customer account. In some embodiments, account app 116
may include instructions which cause processor 111 to connect to
monitor device 120, good tag 130, and/or storage device 150 via
network 140.
[0039] Smart device 110 may also store data 117 in memory 114
relevant to the examples described herein for system 100. One such
example is the storage of device 110 location proximity to goods
data, obtained from sensors 119, for smart device 110, or
alternatively, received from monitor device 120, and/or tag device
130. Data 117 may contain any data discussed above relating to the
wireless communication of proximity based determinations. The data
117 may be further associated with information for a particular
customer or multiple customers.
[0040] Sensors 119 may include one or more devices capable of
sensing the environment around smart device 110 and/or movement of
smart device 110. In some embodiments, sensors 119 may include, for
example, an accelerometer, a shock sensor, a gyroscope, a position
sensor, a microphone, an ambient light sensor, a temperature
sensor, and/or a conductivity sensor. In addition, sensors 119 may
include devices for detecting location, such as, a Global
Positioning System (GPS), a radio frequency triangulation system
based on cellular or other such wireless communication and/or other
means for determining device 110 location.
[0041] Antenna 118 may include one or more devices capable of
communicating wirelessly. As per the discussion above, one such
example is an antenna wirelessly communicating with network 140 via
cellular data or Wi-Fi. Antenna 118 may further communicate with
monitor device 120, tag device 130, or directly with storage device
150 through any wired and wireless means.
[0042] FIG. 1C shows an exemplary tag device 130 consistent with
disclosed embodiments. Tag device 130 may include components that
may execute one or more processes to determine proximity and
location via a processor 131. Device 130 may further communicate
with monitoring device 120 via near-field communication (NFC),
Wi-Fi, Bluetooth, cellular, and/or other such forms of wireless
communication discussed herein. In certain embodiments, tag device
130 may include a power supply, such as a rechargeable battery,
configured to provide electrical power to one or more components of
tag device 130, such as processer 131, a memory 132, and a
communication device 133. Alternatively, device 130 may not include
a power supply and, rather, communicate through passive RFID or
other non-powered tag technology. In this non-powered instance, tag
device 130 may only transmit data when it receives ambient energy
transmitted by smart device 110 (e.g., emitting a signal after
receiving energy from radio waves generated by smart device 110).
Thus, in embodiments where tag device 130 is a non-powered tag,
device 130 may receive electromagnetic energy from smart device 110
and use that energy to transmit data stored in tag device 130. Tag
devices 130 in some embodiments, may be attached to or otherwise
associated with goods. Each tag device 130 may include a unique
identifier and/or other information identifying an item to which a
tag is attached. In some embodiments, tag devices 130 may be
implemented as Bluetooth Low Energy (BLE) tags. Tag devices 130 may
also include sensors such as temperature sensors, weight sensors,
motion sensors, location sensors, proximity sensors,
accelerometers, or the like.
[0043] In some embodiments, tag devices 130 may be further
associated with goods located at specific locations throughout
retail venue 100. Tag devices 130 may further communicate with
monitoring device 120, network 140, and storage device 150. Network
140 and/or storage device 150 can store the mapped specific
locations of tag devices 130. In addition, the retail venue itself
can be mapped, stored on network 140 or in storage device 150, such
that system 100 provides directions to customer smart device 110.
The directions may be to tag devices 130 of interest, or to general
features of the retail venue (such as exits, checkouts, changing
rooms, bathrooms, etc.). In addition, the mapped locations may be
based on tag devices 130, or alternatively, to locations the goods
themselves. The system 100 can locate smart device 110 within the
retail venue, through network 140 or monitoring device 120, and
network 140 (and/or monitoring device 120) can further monitor
smart device 110 location relative to tag devices 130. This tag
device 130 mapping data may be further associated with the
communicated data from tag devices 130 to monitor their locations,
and in turn, further used to determine tag devices 130 proximity to
smart device 110. As smart device 110 comes within proximity to tag
devices 130, system 100 can provide smart device 110 with good's
information associated with tag device 130. And as the smart device
110 moves about the retail venue, the system 100 can provide
updated tag device 130 information to smart device 110 based on
their respective proximities.
[0044] Returning to FIG. 1A, network 140 may comprise any type of
computer networking arrangement used to exchange data. For example,
network 140 may be the Internet, a private data network, virtual
private network using a public network, and/or other suitable
connection(s) that enables system 100 to send and receive
information between the components of system 100. Network 140 may
also include a public switched telephone network ("PSTN") and/or a
wireless network such as a cellular network, WiFi network, or other
known wireless network capable of bidirectional data transmission.
Network 140 may also comprise any local computer networking used to
exchange data in a localized area, such as WiFi,
Bluetooth.TM.Ethernet, Radio Frequency, and other suitable network
connections that enable components of system 100 to interact with
one another.
[0045] FIG. 1D shows an exemplary configuration of storage device
150 consistent with disclosed embodiments. Variations of exemplary
device 150 may be used to implement portions or all of devices of
system 100, such as smart device 110, monitor device 120, tag
device 130, and network 140. Likewise, even though FIG. 1D depicts
storage device 150, it is understood that devices 110, 120, and 130
may implement portions illustrated by exemplary storage device 150.
In one embodiment, storage device 150 may optionally include one or
more processors 151, one or more input/output (I/O) devices 152,
and one or more memories 153. In some embodiments, device 150 may
take the form of a server, general purpose computer, mainframe
computer, or the like. In some embodiments, device 150 may take the
form of a mobile computing device such as a smartphone, tablet,
laptop computer, or the like.
[0046] Alternatively, device 150 may be configured as a particular
apparatus, embedded system, dedicated circuit, or the like, based
on the storage, execution, and/or implementation of the software
instructions that perform one or more operations consistent with
the disclosed embodiments.
[0047] Processor(s) 151 may include one or more known processing
devices, such as mobile device microprocessors, desktop
microprocessors, server microprocessors, or the like. The disclosed
embodiments are not limited to a particular type of processor.
[0048] I/O devices 152 may be one or more devices configured to
allow data to be received and/or transmitted by device 150. I/O
devices 152 may include one or more digital and/or analog devices
that allow storage device 150 to communicate with other machines
and devices, such as other components and devices of system 100.
For example, I/O devices 152 may include a screen for displaying
messages to a user (such as a customer or retail venue manager).
I/O devices 152 may also include one or more digital and/or analog
devices that allow a user to interact with system 100, such as a
touch-sensitive area, keyboard, buttons, or microphones. I/O
devices 152 may also include other components known in the art for
interacting with a user. I/O devices 152 may also include one or
more hardware/software components for communicating with other
components of system 100. For example, I/O devices 152 may include
a wired network adapter, a wireless network adapter, a cellular
network adapter, or the like. Such network components enable device
150 to communicate with other devices of system 100 to send and
receive data.
[0049] Memory 153 may include one or more storage devices
configured to store instructions usable by processor 151 to perform
functions related to the disclosed embodiments. For example, memory
153 may be configured with one or more software instructions, such
as one or more program applications 154 that perform one or more
operations when executed by processor 151. The disclosed
embodiments are not limited to separate programs or computers
configured to perform dedicated tasks. For example, memory 153 may
include a single program or multiple programs that perform the
functions of mobile device 110, good monitor device 120, or tag
device 130. Memory 153 may also store data 155 that is used by the
one or more applications 154.
[0050] In certain embodiments, memory 153 may store software
executable by processor 151 to perform one or more methods, such as
the methods represented by the flowcharts depicted in FIGS. 3-6
and/or the methods associated with user interface (e.g., display
111) discussed above with reference to FIG. 1B. In one example,
memory 153 may store one or more program applications 154.
Applications 154 stored in memory 153, and executed by processor
151, may include a venue app that causes processor 151 to execute
one or more processes related to financial services provided to
customers including, but not limited to, processing credit and
debit card transactions, checking transactions, processing payments
for goods, price checking goods, analyzing customer purchasing
behavior and adjusting good pricing based on the analysis, and/or
adjusting good pricing. In some examples, program applications 154
may be stored in an external storage device, such as a cloud server
located outside of network 140, and processor 151 may retrieve and
execute the externally stored programs 154.
[0051] Storage Device 150 may be used to store data 155 relevant to
examples described herein for system 100. One such example is the
storage of location proximity data received from smart device 110,
monitor device 120, or tag device 130. Data 155 may contain any
data discussed above relating to the wireless communication of
proximity based determinations. In addition, data 155 may contain
customer profile 170 data such as purchasing behavior
determinations, previous purchasing patterns, inventory listing of
goods for sale, goods price, price comparison data, previous
offered discounts for goods. The data 155 associated with
particular customer or retail venue may also contain associated
information for customers or retail venues. Data 155 may further
include data unique for each good tag, as well as any information
relative to any particular good. Data 155 may also include model
160 determinations and analysis.
[0052] Storage device 150 may include at least one database 156.
Database 156 may be a volatile or non-volatile, magnetic,
semiconductor, tape, optical, removable, nonremovable, or other
type of storage device or tangible (i.e., non-transitory) computer
readable medium. For example, database 156 may include at least one
of a hard drive, a flash drive, a memory, a Compact Disc (CD), a
Digital Video Disc (DVD), or a Blu-ray.TM. disc.
[0053] Database 156 may store data, such as data 155 that may be
used by processor 151 for performing methods and processes
associated with disclosed examples. Data stored in database 156 may
include any suitable data, such as information relating to a
customer, and/or a retail venue, information relating to
transactions, and information model 160 and/or customer profile
170, data relating to the customer determinations, or modeled
purchasing behavior. Although shown as a separate unit in FIG. 1D,
it is understood that database 156 may be part of memory 153, or an
external storage device located outside of system 100. At least one
of memory 153, and/or database 156 may store data and instructions
used to perform one or more features of the disclosed examples. At
least one of memory 153, and/or database 156 may also include any
combination of one or more databases controlled by memory
controller devices (e.g., server(s), etc.) or software, such as
document management systems, Microsoft SQL databases, Share Point
databases, Oracle.TM. databases, Sybase.TM. databases, or other
relational databases. Storage device 150 may also be
communicatively connected to one or more remote memory devices
(e.g., databases (not shown)) through network 140, or a different
network. The remote memory devices may be configured to store
information and may be accessed and/or managed by system 100.
Systems and methods consistent with disclosed examples, however,
are not limited to separate databases or even to the use of a
database.
[0054] The components of device 150 may be implemented in hardware,
software, or a combination of both hardware and software, as will
be apparent to those skilled in the art. For example, although one
or more components of device 150 may be implemented as computer
processing instructions, all or a portion of the functionality of
device 150 may be implemented instead in dedicated electronics
hardware.
[0055] Storage device 150 also stores model 160 and customer
profile 170. Through processor(s) 151, storage device 150 runs
model 160 for performing methods and processes associated with
disclosed examples described more fully below. Model 160 may
analyze received data 155 for customers including, but not limited
to, processed transactions, checked transactions, checked goods
prices at third party retailer, processed payments for goods,
purchasing customer behavior, and/or adjusted good pricing. In some
examples, model 160 may be stored in an external storage device,
such as a cloud server located outside of network 140 and storage
device 150, and processor 151 may execute the model 160
remotely.
[0056] Customer profile 170 is a subset of data 155 stored in
device 150 and analyzed by model 160. Data 155 is further
associated with multiple customers and each respective customer has
a customer profile that contains their associated purchasing
behavior determinations and analysis.
[0057] FIG. 2A illustrates an exemplary retail venue 200 system.
Upon entry into retail venue 200, a customer 205 moves about the
physical premises. Customer 205 has smart device 110 that
communicates with monitoring device 120 and tag devices 130a-n,
associated with respective goods 232a-n, as well as storage device
150. Smart device 110 transmits data 206 within venue 200 and
accesses network 140. Tag devices 130a-n transmit data 208a-n to
monitoring device 120, and/or smart device 110, and/or via network
140 to storage device 150. Monitoring device 120 receives data 209
from smart device 110 and tag devices 130a-n and further routes the
data to storage device 150. Communication between smart device 110,
monitoring device 120, and tag devices 130 may occur through
various means. Some forms of communication, as already discussed,
are near-field communication (NFC), Wi-Fi, Bluetooth, cellular,
and/or other such forms of wireless communication discussed herein.
In certain embodiments, smart device 110 and/or tag device 130 may
include a power supply, such as a battery, configured to provide
electrical power to one or more components of smart device 110
and/or tag device 130, such as processer 113/131, a memory 114/132,
and a communication device 118/133. Alternatively, 130 may not
include a power supply and, rather, communicate through passive
RFID or other non-powered tag technology. In this non-powered
instance, tag device 130 may only transmit data when it receives
ambient energy transmitted by another device, such as smart device
110 or monitoring device 120(e.g., tag device 130 emitting a signal
after receiving energy from radio waves generated by smart device
110 or monitoring device 120). Thus, in embodiments where device
130 is non-powered, tag device 130 may receive electromagnetic
energy from another device and use that energy to transmit data
stored within. Alternatively, smart device 110 and/or tag device
130 may store location proximity data within an internal memory
component (i.e., memory 114 and/or 132), or the devices 110 and/or
130 may continuously transmit their location data to monitoring
device 120.
[0058] FIG. 2A depicts a wired connection between monitoring device
120 and storage device 150, but it is further understood that this
connection is possible either through wired or wireless
communication means as discussed throughout here.
[0059] A person of ordinary skill will now understand that the
retail venue 200 system and good 232a-n placement throughout the
retail venue can be altered to better suit the store owner and
customer. For instance, based on collected data and/or model 160,
popular items may be relocated near the check-out area or by the
entrance to catch the attention of customer 205. Alternatively,
goods 232a-n can be placed near each other based on past determined
interests in prospective goods.
[0060] In particular, FIG. 2A illustrates product tag device 130a
located on a venue shelf adjacent to good 232a (i.e., a TV set in
FIG. 2A). Tag device 130a is configured to transmit signal 208a
with enough power so that signal 208a is detectable within venue
200, and in particular, by monitoring device 120. Customer 205
carries smart device 110, which is configured to receive signal
208a from tag device 130a and/or transmit its own signal 206, along
with collected proximity data to tag devices 130a-n, which is
further detected by monitoring device 120 or routed to network 140
(and back to storage device 250) by other means. It is further
understood that smart device 110 may receive and transmit signals,
and it may also by-pass monitoring device 120. Signals 206 and
208a-n are collected by storage device 150, either through network
140 or monitoring device 120, and customer profile 170 further
determines customer 205 locations relative to goods tags
130a-n.
[0061] FIG. 2B shows network 140 may be further associated with an
affiliated retailer network 240 including retail venues 200a-200e.
Storage devices associated with venues 200a-200e, connected through
affiliated retailer network 240, collect multiple customer profiles
170 for multiple customers 205 at venue 200a, as well as, similar
data from venues 200b-e. Affiliated retailer network 240 may
further gather model analysis from each of the venues 200a-e and
store the gathered analysis at any storage device 150 in the
affiliated venues 200a-e. Affiliated retailer network 240 further
provides communication between venues 200a-e through network
140.
[0062] FIG. 3 is a is a flow chart of an exemplary process for
modeling, to build model 160, collected data from venues 200a-e,
tag devices 130a-n, and smart devices 110. The process begins by
collecting input 320 such as customers' accounts 302, customers'
interests 304, customers' location data 306, goods location data
308, and customers' purchase history 310. As discussed above,
customers' profiles 302 contain data collected by monitoring
devices 120 and storage devices 150 across respective venues
200a-e. The customers' profiles may contain the above mentioned
data collected at step 320, such as customers' accounts 302,
customers' interests 304, customer location data 306 in the retail
venues, associated goods data 308 within proximity to each
customer, and customers' purchase history 310 at each venue 200a-e,
but the customers' profiles generally include the determined
analysis of customers' interests in purchasing select goods, as
determined by model 160. A customer's account 302 may be a customer
configured profile affiliated with the venues 200a-e. The customer
may further configure account 302 to provide secure access to the
customer's purchasing information and shipping information. And the
customer may further customize a shopping list on account 302. Each
customer's interests 304 may be a collection of pre-selected
interests of the customer from either smart device 110 or customer
account 302. For example, customer 205 may update its account 302
with fruit produce brand preferences and this information may be
further routed via network 140 to model 300 (at step 320) to
determine future interests. Alternatively, customer 205, via smart
device 210 or similar device, may notify retail venue 200, via
network 140, of an intended shopping list from account 302, and
customer interest 304 information containing the shopping list from
account 302 will also transmitted to model 300. Location data 306
and 308 will continuously be monitored, while the respective smart
device 110 and good tag devices 130a-n, are within retail venues
200a-e. This location data contains proximity data and time
durations. For instance, this location data may contain proximity
distance data between smart device 110 and good tag devices 130a-n,
as well as, time duration data indicating how long device 110 and
good tag devices 130a-n were within proximity to each other. Model
300 also receives, at step 320, customers' purchase history 310
from venues 200a-e. Purchase history data may include information
for every good purchase in venues 200a-e, by customer 205, as well
as the pricing for each good, the discounted offers for each good,
the price checked comparison for each good, the adjusted prices for
each good, and even the rejected goods customer 205 decided not to
purchase (after specifically being offered a discount or after it
was determined customer 205 would purchase the good).
[0063] Next, at step 330, model 300 is updated with the newly
received data from step 320. Steps 320 and 330 are performed in
real time and model 300 continuously receives data and updates
itself based on the new data. Then at step 340, model 300 analyzes
the received data 320 to determine micro and macro purchasing
patterns for specific customers and the collective customers for
all venues 200a-e. Model 300 may employ various machine learning
techniques to analyze the collected data 320. Examples of machine
learning techniques include decision tree learning, association
rule learning, artificial neural networks, inductive logic
programming, support vector machines, clustering, Bayesian
networking, reinforcement learning, representation learning,
similarity and metric learning, spare dictionary learning,
rule-based machine learning, etc. For example, at step 340, model
300 may analyze the proximity data and time duration data received
in step 320 and determine that customer 205 is interested in
certain goods because device 110 was within proximity to good tag
devices 130a-n for a set amount of time (e.g., three minutes). And
as model 300 learns, from above techniques, this time duration
trigger may adjust such that customer 205's proximity to goods for
less than three minutes may also indicate an interest in the
goods.
[0064] A person of ordinary skill will now understand that through
these modeling steps, system 100 further facilitates the goal of
tracking customer proximity to goods and offering an improved
retail shopping experience. By utilizing customer and good location
data, and machine learning, model 300 may further assist the store
owner by providing analytics to properly stock the retail venue,
and track purchasing trends at micro and macro levels. The
analytics can determine accurate shopping trends to enable the
retail venue owner to negotiate favorable purchases, on the supply
side, and in return, offer favorable retail pricing on the demand
side.
[0065] FIG. 4 is a flowchart of an exemplary process for
determining customer 205 interest in goods 232a-n in retail venue
200, any one of goods 232a-n more generally referred to therein as
goods 232. The process begins at step 401, where monitoring device
120 enters scanning mode, whereby it detects and receives customer
205 location data from venue 200, either by direct communication
between smart device 110 and device 120 or through network 140. At
step 402, monitoring device 120 continuously receives data from
smart device 110 and monitors the customer 205 (associated with
device 110) locations within venue 200. In addition, monitoring
device 120 continuously scans for signals from tag devices 130a-n
as well.
[0066] At step 403, monitoring device 120 receives associated goods
232 locations from tag devices 130a-n within venue 200. Monitoring
device may scan for tag devices 130a-n in particular zones within
venue 200. In some embodiments, tag devices 130a-n may begin
transmitting data in step contemporaneously with step 403 when
smart device 110 is detected within proximity. For example, where
device 130 is implemented as a Bluetooth Low Energy tag, tag device
130 may transmit data at the end of a time interval (e.g., such as
every 500 ms). In embodiments where tag device 130 is a powered
tag, step 403 may represent a periodic sending of data by tag
device 130. In other embodiments, such as those where passive RFID
or other non-powered tags are used, tag device 130 may only
transmit data when it receives ambient energy transmitted by smart
device 110 (e.g., emitting a signal after receiving energy from
radio waves generated by smart device 110). Thus, in embodiments
where tag device 130 is a non-powered tag, step 403 may represent
device 130 receiving electromagnetic energy from smart device 110
and using that energy to transmit data stored in tag device
130.
[0067] In step 404, system 100 determines customer 205 interest in
goods 232 by analyzing the received location data of goods 232 and
customer 205 location data, and as a duration of customer 205
lingering in proximity to goods 232. System 100 may analyze the
received data and deduce customer interest by triggers other than
proximity and duration, for instance, such as noting the particular
good is listed on the account of customer 205.
[0068] At step 405, system 100 conducts a price search for goods
232 determined to be of interest to customer 205 in step 404. Prior
to customer 205 checking out, system 100 will check for lower
prices of goods, either in goods held by customer 205, or in goods
determined to be of interest to customer 205. System 100 will
compare the current pricing at retail venue 200 against other
non-affiliated retail venues elsewhere, either physically nearby or
online. System 100 will further notify customer 205 of the results
of this price comparison, at step 406, via network 140 and smart
device 110.
[0069] FIG. 5 is a flowchart of an exemplary process for
determining the interest of customer 205 in goods 232a-n with tag
devices 130a-n and smart device 110 in retail venue 200. The
process begins with step 501, where monitoring device 120 enters
scanning mode, by detecting transmitted signals 206 and 208, and
receives location data of customer 205 from venue 200, either by
direct communication between smart device 110 and device 120 or
through network 140. At step 502, monitoring device 120
continuously receives data from smart device 110 and monitors
customer 205 (associated with device 110) locations within venue
200. In addition, monitoring device 120 continuously scans for
signals from tag devices 130a-n.
[0070] At step 503, monitoring device 120 receives associated goods
(232a-n) locations from tag devices 130a-n within venue 200.
Monitoring device may scan for tag devices 130a-n in particular
zones within venue 200. In some embodiments tag devices 130a-n may
begin transmitting data in step contemporaneously with step 503
when smart device 110 is detected within proximity. For example,
where device 130 is implemented as a Bluetooth Low Energy tag, tag
device 130 may transmit data at the end of a time interval (e.g.,
such as every 500 ms). In embodiments where tag device 130 is a
powered tag, step 503 may represent a periodic sending of data by
tag device 130. In other embodiments, such as those where passive
RFID or other non-powered tags are used, tag device 130 may only
transmit data when it receives ambient energy transmitted by smart
device 110 (e.g., emitting a signal after receiving energy from
radio waves generated by smart device 110). Thus, in embodiments
where tag device 130 is a non-powered tag, step 503 may represent
tag device 130 receiving electromagnetic energy from smart device
110 and using that energy to transmit data stored in tag device
130.
[0071] In step 504, system 100 determines customer 205 interest in
goods 232 by analyzing the received location data of goods 232 and
customer 205 location data, and a duration of customer 205
lingering in proximity to goods 232. System 100 may analyze the
received data and deduce customer interest by triggers other than
proximity and duration, for instance, such as noting the particular
good is listed on the account of customer 205. At step 505, system
100 stores step 504 determinations for customer 205 in storage
device 150. Based on the stored determinations and received data
from steps 501-504, system 100 generates customer profile 170 in
step 506. The generated profile 170 generally contains information
used to deduce the customer 205 shopping behavior. For instance,
and as discussed above with reference to FIG. 3, the generated
profile 170 may contain customer interests, customer location data
in the retail venue, associated goods data within proximity to
customer, and customer purchase history at venue 200, but profile
170 generally includes of the determined analysis of customer's
interest in purchasing select goods.
[0072] At step 507, system 100 generates model 160 with profile
170, locations, and shopping behavior data for customer 205. Like
model 300, the generated model 160 from step 507 will analyze the
shopping trend, behavior, and purchase determinations of customer
205.
[0073] At step 508, system 100 conducts a price search for the
goods 232 in which it was determined that the customer 205 has
interest at step 504. Prior to customer 205 checking out, system
100 will check for lower prices of goods, either in goods held by
customer 205, or in goods determined to be of interest to customer
205. System 100 will compare the current pricing at retail venue
200 against other non-affiliated venues elsewhere, either
physically or online.
[0074] System 100 will further notify customer 205 of the results
of this price comparison, at step 509, via networks 140 and smart
device 210.
[0075] At step 510, system 100 receives indication whether or not
customer 205 purchased goods 232. Not only will system 100 receive
indication of all actual purchased goods by customer 205, but it
will also receive indication whether customer 205 purchased goods
subject to the step 509 price comparison. Monitoring device 120
further communicates, to storage device 150, the location data from
steps 501-503 and received financial transaction data such that
processor(s) 151 further deduces what goods 232a-n were purchased
by customer 205. If it is further determined that the good(s)
subject to the step 509 price comparison was not purchased, then
system 100 further determines price adjustments at step 520. For
example, if system 100 determines that customer 205 continues to be
interested in good 232 but fails to purchase good 232 after
multiple trips to venue 200, then system may determine a new
favorable price for good 232 to incentivize future purchase. The
price adjustment at step 520 may be for just a particular goods
232, or for collective goods 232a-n, in the form of a future rebate
or price reduction offer.
[0076] System 100 then updates the customer purchase behavior
profile at step 530. If system 100 receives indication that
customer 205 purchased good 232 then at step 530, system 100
updates the customer purchase behavior profile. Alternatively, if
system 100 receives indication that customer 205 did not purchase
good 232, even after receiving a future rebate or price reduction
offer at step 520, then at step 530, system 100 updates the
customer purchase behavior profile.
[0077] FIG. 6 is a flowchart of an exemplary process for
determining the interest of multiple customers 205 in goods 232a-n
associated with good tag devices 130a-n across affiliated network
140, where each of these multiple customers 205 has one of smart
devices 110 at retail venues 200a-200e. The process begins at step
601, where monitoring device 120 at each retail venue 200a-200e
enters scanning mode, detecting transmitted data signals 206 and
208, and receives location data of customer 205 from venues 200a-e
via either direct communication between smart devices 110 and each
monitoring device 120 or through network 140. At step 602,
monitoring device 120 continuously receives data from multiple
smart devices 110 and monitors locations of multiple customers 205
(associated with devices 110) within venues 200a-e. In addition,
monitoring device 120 continuously scans for signals from tag
devices 130a-e.
[0078] At step 603, monitoring device 120 receives associated goods
(232a-n) locations from tag devices 130a-n within venues 200a-e.
Monitoring device 120 may scan for tag devices 130a-n in particular
zones within venues 200a-e. In some embodiments tag devices 130a-n
may begin transmitting data in step contemporaneously with step 603
when smart device 110 is detected within proximity. For example,
where tag device 130 is implemented as a Bluetooth Low Energy tag,
tag device 130 may transmit data at the end of a time interval
(e.g., such as every 500 ms). In embodiments where tag device 130
is a powered tag, step 603 may represent a periodic sending of data
by tag device 130. In other embodiments, such as those where
passive RFID or other non-powered tags are used, tag device 130 may
only transmit data when it receives ambient energy transmitted by
smart device 110 (e.g., emitting a signal after receiving energy
from radio waves generated by smart device 110). Thus, in
embodiments where device 130 is a non-powered tag, step 603 may
represent tag device 130 receiving electromagnetic energy from
smart device 110 and using that energy to transmit data stored in
device 130.
[0079] In step 604, system 100 determines customer 205 interest in
goods 232 by analyzing the received location of goods data 232 and
customer 205 location data, and a duration of customer 205
lingering in proximity to good 232. System 100 may analyze the
received data and deduce customer interest by triggers other than
proximity and duration, for instance, such as noting the particular
good is listed on the account of customer 205. At step 605, system
100 stores step 604 determinations for customer 205 in storage
device 150. Based on the stored determinations and received data
from steps 601-604, system 100 generates customers profiles 170 for
each respective customer in step 606. The generated profiles 170
generally contain information used to deduce the particular
customer 205 shopping behavior. For instance, and as discussed
above with reference to FIG. 3, the generated profiles may contain
customer interests, customer location data in the retail venues,
associated goods data within proximity to customer, and customers
purchase history at each venue 200a-e, but generally consist of the
determined analysis of customer's interest in purchasing select
goods.
[0080] At step 607, storage device 250 compiles the generated
profiles 170. Step 607 may occur on a micro level for each venue
200a-e or on a macro level for all venues. Likewise, step 607 may
occur only for a specific customer 205 or group of customers. At
step 608, system 100 generates a model with the profiles,
locations, and shopping behaviors data for customers 205. Like the
compiled profiles, one collective model may be generated for all
venues 200a-e and all customers, or specific models may be created
for specific ones of venues 200a-e and even one specific customer
205 Like model 300, the models generated in 608 will analyze the
shopping trends, behavior, and purchase determinations of customers
205.
[0081] At step 609, system 100 conducts a price search for goods
232 in which it was determined that the customers 205 have interest
at step 604. Prior to each customer 205 checking out, system 100
will check for lower prices of goods, either in goods held by
customer 205, or in goods determined to be of interest to customer
205. System 100 will compare the current pricing at the retail
venue where customer 205 is located, e.g., retail venue 200a,
against other local retail venues 200b-e or non-affiliated retail
venues elsewhere, either physically nearby or online. System 100
will further notify customer 205 of the results of this price
comparison, at step 610, via network 140, and smart device 110.
[0082] At step 611, system 100 receives indication whether or not
customer 205 purchased goods 232. Not only will system 100 receive
indication of all actual purchased goods by customer 205, but it
will also receive indication whether customer 205 purchased goods
subject to the step 610 price comparison. Monitoring device 120
further communicates, to storage device 150, the location data from
steps 601-603 and received financial transaction data that
processor(s) 151 further deduces what goods 232a-n were purchased.
If it is further determined that the good(s) subject to the step
610 price comparison was not purchased, then system 100 further
determines price adjustments at step 620. For example, if multiple
customers 205 decide not to purchase a particular good 232 after
price comparison step 610, then system 100 may determine that a
more competitive price is required. Alternatively, if system 100
determines that a particular one of customers 205 continues to be
interested in good 232 but fails to purchase good 232 after
multiple trips to venue 200, then system 100 may determine a new
favorable price for good 232 to incentivize future purchase. These
price adjustment determinations may be across all retail venues
200a-e, or at just one retail venue 200. The price adjustment at
step 620 may be for just the particular good 232 and further just
for the particular customer 205 in the form of a future rebate or
price reduction offer.
[0083] System 100 then updates the customer purchase behavior
profile at step 630. If system 100 receives indication that
customer 205 purchased good 232, then at step 630, system 100
updates the customer purchase behavior profile. Alternatively, if
system 100 receives indication that customer 205 did not purchase
good 232, even after receiving a future rebate or price reduction
offer at step 520, then at step 530, system 100 updates the
customer purchase behavior profile Like profile compiling step 607
and model generation step 608, this updated profile step 630 may be
conducted for a particular customer 205 or multiple customers 205,
and/or for just one venue 200a or multiple venues 200b-e. At step
631, based on the updated model and customer purchasing profiles,
system 100 further adjusts goods 232 to remain competitive with
third party retail venues presented during the price comparison in
step 609 and to further follow purchasing trends at micro retail
venue and customer levels, as well as the collective macro level
purchasing trends across all affiliated retail venues 200a-n.
[0084] While illustrative embodiments have been described herein,
the scope thereof includes any and all embodiments having
equivalent elements, modifications, omissions, combinations (e.g.,
of aspects across various embodiments), adaptations and/or
alterations as would be appreciated by those in the art based on
the present disclosure. For example, the number and orientation of
components shown in the exemplary systems may be modified. Thus,
the foregoing description has been presented for purposes of
illustration only. It is not exhaustive and is not limiting to the
precise forms or embodiments disclosed. Modifications and
adaptations will be apparent to those skilled in the art from
consideration of the specification and practice of the disclosed
embodiments.
[0085] The elements in the claims are to be interpreted broadly
based on the language employed in the claims and not limited to
examples described in the present specification or during the
prosecution of the application, which examples are to be construed
as non-exclusive. It is intended, therefore, that the specification
and examples be considered as exemplary only, with a true scope and
spirit being indicated by the following claims and their full scope
of equivalents.
* * * * *