U.S. patent application number 17/163362 was filed with the patent office on 2022-08-04 for methods and apparatus for providing context aware personalized in-store customer experience.
The applicant listed for this patent is Walmart Apollo, LLC. Invention is credited to Kannan Achan, Vidya Sagar Kalidindi, Shirpaa Manoharan, Kaushiki Nag, Rahul Ramkumar.
Application Number | 20220245685 17/163362 |
Document ID | / |
Family ID | 1000005399174 |
Filed Date | 2022-08-04 |
United States Patent
Application |
20220245685 |
Kind Code |
A1 |
Nag; Kaushiki ; et
al. |
August 4, 2022 |
METHODS AND APPARATUS FOR PROVIDING CONTEXT AWARE PERSONALIZED
IN-STORE CUSTOMER EXPERIENCE
Abstract
The disclosed subject matter relates to a system and method for
personalizing customer experience at a retailer's physical location
in order to increase sales and customer satisfaction. The
personalization is based upon classification of customer's online
interaction with the retailer. Upon detecting the customer's
presence at the retailer's physical location, data of the
customer's online interactions is retrieved and classified based on
the type of online interactions and temporal characteristics. Push
content is transmitted to the customer, the push content being
based upon at least the classification and data associated with
retailer's physical location.
Inventors: |
Nag; Kaushiki; (Sunnyvale,
CA) ; Achan; Kannan; (Saratoga, CA) ;
Manoharan; Shirpaa; (Sunnyvale, CA) ; Kalidindi;
Vidya Sagar; (Milpitas, CA) ; Ramkumar; Rahul;
(Santa Clara, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Walmart Apollo, LLC |
Bentonville |
AR |
US |
|
|
Family ID: |
1000005399174 |
Appl. No.: |
17/163362 |
Filed: |
January 30, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/55 20220501;
G06F 16/285 20190101; H04L 67/52 20220501; G06Q 30/0625 20130101;
G06Q 10/087 20130101; G06Q 30/0633 20130101; G06F 16/9535 20190101;
G06Q 30/0631 20130101; G06Q 30/0205 20130101; G06F 16/9537
20190101; G06Q 30/0281 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 10/08 20060101 G06Q010/08; G06Q 30/06 20060101
G06Q030/06; G06F 16/28 20060101 G06F016/28; G06F 16/9537 20060101
G06F016/9537; G06F 16/9535 20060101 G06F016/9535; H04L 29/08
20060101 H04L029/08 |
Claims
1. A system for personalizing customer experience at a retailer's
physical location based upon classification of prior online
interaction with the retailer, comprising: a customer location
beacon; a mobile device associated with the customer; a database; a
communication system; a computing device operably connected to the
database, customer location beacon and the communication system,
the computing device configured to: receive a notification of the
customer's presence at the retailer's physical location from the
customer location beacon, access the database for the customer's
online interactions with or available to the retailer; classify the
customer's online interactions into one of a plurality of
classifications; access the database for retailer information
associated with the physical location; select push content based
upon at least the classification and the retailer information; and
transmit the selected push content to the mobile device.
2. The system of claim 1, wherein the computing device is further
configure to determine whether there are online interactions by the
customer are within a predetermined amount of time prior to the
customer's present at the physical location as a basis to classify
the customer's online interactions.
3. The system of claim 2, wherein the online interactions are
selected from the group consisting of search, add to cart, purchase
and views.
4. The system of claim 2, wherein the predetermined amount of time
is selected from the group consisting of less than or equal to a
week, less than or equal to ten days, and less than or equal to a
month.
5. The system of claim 1, wherein the computing device is further
configured to determine whether the customer initiated an in-store
search for a product to classify the customer's interactions.
6. The system of claim 1, wherein the computing device is further
configured to determine if the customer has an online account
associated with the retailer to classify the customer's
interactions.
7. The system of claim 2, wherein the retailer information
comprises information selected from the group consisting of current
inventory, product information, inventory layout, promotions, local
preferences and sales trends associated with the retailer's
physical location.
8. The system of claim 2, wherein the computing device is further
configured to select the push content based on an object of the
online interactions for a first classification, wherein the push
content includes the object of the online interactions.
9. The system of claim 2, wherein the computing device is further
configured to select the push content based on an object of the
online interactions, wherein the push content includes a product
complimentary to the object of the online interactions.
10. A method of personalizing customer experience at a retailer's
physical location based upon classification of customer's online
interaction, comprising: determining the customer's presence at the
retailer's physical location, accessing data of the customer's
online interactions with or available to the retailer; classifying
the customer's online interactions into one of a plurality of
classifications; accessing retailer information associated with the
physical location; selecting push content based upon at least the
classification and the retailer information; and transmitting the
selected push content to the customer.
11. The Method of claim 10, wherein the step of classifying
includes determining there are online interactions by the customer
within a predetermine amount of time prior to the customer's
present at the physical location.
12. The method of claim 11, wherein the online interactions are
selected from the group consisting of search, add to cart, purchase
and views.
13. The method of claim 11, wherein the predetermined amount of
time is selected from the group consisting of less than or equal to
a week, less than or equal to ten days, and less than or equal to a
month.
14. The method of claim 10, wherein the step of classifying
includes determining the customer initiated an in-store search for
a product and selecting the push content based on the object of the
in-store search.
15. The method of claim 10, wherein the step of classifying
includes determining the customer has an online account associated
with the retailer and selecting the push content based on the
retailer information.
16. The method of claim 11, wherein the retailer information
comprises information selected from the group consisting of
consisting of current inventory, product information, inventory
layout, promotions, local preferences and sales trends associated
with the retailer's physical location.
17. The method of claim 11, wherein the step of selecting push
content is further based on an object of the online
interactions.
18. The method of claim 17, wherein the push content includes the
object of the online interactions.
19. The method of claim 17, wherein the push content includes a
product complimentary to the object of the online interactions.
20. A non-transitory computer readable medium having instructions
stored thereon, wherein the instructions, when executed by at least
one processor, cause a device to perform operations comprising:
determining the customer's presence at the retailer's physical
location; accessing data of the customer's online interactions with
or available to the retailer; classifying the customer's online
interactions into one of a plurality of classifications; accessing
retailer information associated with the physical location;
selecting push content based upon at least the classification and
the retailer information; and transmit the selected push content to
the customer; wherein the selection of the push content is based on
the object of the customer's online interaction in a first
classification, the selection of push content is based on features
from the retailer information in a second classification and the
selection of push content is based on the object of an in-store
search in a third classification.
Description
TECHNICAL FIELD
[0001] The disclosed subject matter relates generally to
personalizing an in-store experience based on the type of web based
interactions of the customer.
BACKGROUND
[0002] Commercial websites and applications often provide
recommendations to their users. These recommendations include
content related to the current web page or application being
accessed by the user (e.g., related news articles), products
related to the product in the user's shopping cart (e.g., the user
purchases shoes). If so, it may include recommendations or
advertisements/advertisements related to the current web page being
accessed by the user (e.g. sock recommendations) and/or the product
in the user's shopping cart (e.g. shoes). Product and offer
recommendations may also be submitted to email communications sent
to the user. Personalized, relevant, and appropriate
recommendations can help increase user traffic, sales, and/or
revenue, and are therefore a key component of commercial websites
and applications.
[0003] Operators of commercial websites however fail to recognize
that the information gathered online may be advantageously tailored
to personalize an in-store visit as well. Specifically, the
information gathered from the user's interaction with the web page
or app, may be used with data related to a specific store, or area
of which the user is determined to be within proximity to. In other
words, some of the same information which enables webpages to make
recommendations to the user, may also be used to influence in-store
behavior of the user, taking account of the temporal relationship
between the gathered information and the users in-store visit. For
example, the recommendation for purchase of socks described above
would advantageously be tied to the inventory of the current store
in which the user is located, or the same recommendation could be
altered if the recommended pair of socks is not in stock at the
particular store.
[0004] The temporal relationship of the prior information gathered
from the user and the current in-store visit is another parameter
that may be used to guide or personalize the user's in-store
experience.
SUMMARY
[0005] The embodiments described herein are directed to a system
and method for personalizing a customer's in-store experience.
Personalizing the customer's in-store experience is advantageous in
that it has been shown to be more influential that generic
marketing and thus increased sales and allows among other things
shaping customer traffic to aisles for targeted items and
local/seasonal/geo-based customer centric targeting. In addition to
or instead of the advantages presented herein, persons of ordinary
skill in the art would recognize and appreciate other advantages as
well.
[0006] In accordance with various embodiments, exemplary systems
may be implemented in any suitable hardware or hardware and
software, such as in any suitable computing device.
[0007] In some embodiments, the system includes a customer location
beacon; a mobile device associated with the customer; a database; a
communication system; and a computing device connected to the
database, customer location beacon and the communication system.
The computing device is configured to receive a notification of the
customer's presence at the retailer's physical location from the
customer location beacon, and triggered by the notification access
the database(s) for the customer's prior or current online
interactions with or available to the retailer. The computing
device classifies the customer's online interactions into one of
several of classifications, based in part on the temporal
characteristic of those interactions. The computing device in these
embodiments is also configured to access the database for retailer
information associated with the visited store or retailer's
physical location, and select push content based upon the
classification and the retailer information associated with the
visited store; and transmit the selected push content to the
customer's phone or other mobile device.
[0008] In other embodiments, a method is provided that personalizes
customer experience at a retailer's physical location based upon
classification of customer's prior and current online interactions.
The method including determining the customer's presence at the
retailer's physical location, in response accessing the customer's
online interactions with or available to the retailer and
classifying the customer's recent online interactions into one of a
plurality of classifications. The method also includes accessing
retailer information associated with the specific physical
location; selecting push content based upon the classification and
the retailer information, and transmitting the push content to the
customer's phone.
[0009] In yet other embodiments, a non-transitory computer readable
medium having instructions stored thereon is provided. The
instructions, when executed by at least one processor, cause a
device to perform operations including determining the customer's
presence at the retailer's physical location, accessing data of the
customer's recent online interactions and retailer information
associated with the retailer's physical location and classifying
the customer's online interactions. The instructions also include
selecting push content based upon the classification of the
customer's interactions and the retailer information; and
transmission of the selected push content to the customer's mobile
device. The push content is also selected based on the object of
the customer's online interaction given a first classification,
selected based on features from the retailer information given a
second classification and selected based on the object of an
in-store search in a third classification.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The features and advantages of the present disclosures will
be more fully disclosed in, or rendered obvious by the following
detailed descriptions of example embodiments. The detailed
descriptions of the example embodiments are to be considered
together with the accompanying drawings wherein like numbers refer
to like parts and further wherein:
[0011] FIG. 1 is a block diagram of communication network used to
personalize customer experience in accordance with some
embodiments;
[0012] FIG. 2 is a block diagram of the in-store experience
personalization computing device of the communication system of
FIG. 1 in accordance with some embodiments;
[0013] FIG. 3 is a diagram of a system for personalizing a
customer's in-store experience in accordance with embodiments of
the disclosed subject matter;
[0014] FIG. 4 is a flowchart of operations carried out by the
in-store experience personalization computing device in accordance
with embodiments of the disclosed subject matter: and,
[0015] FIG. 5 is a flowchart of a method for personalizing customer
experience at a retailer's physical location based upon
classification of customer's online interaction with the retailer
in accordance with embodiments of the disclosed subject matter.
DETAILED DESCRIPTION
[0016] The description of the preferred embodiments is intended to
be read in connection with the accompanying drawings, which are to
be considered part of the entire written description of these
disclosures. While the present disclosure is susceptible to various
modifications and alternative forms, specific embodiments are shown
by way of example in the drawings and will be described in detail
herein. The objectives and advantages of the claimed subject matter
will become more apparent from the following detailed description
of these exemplary embodiments in connection with the accompanying
drawings.
[0017] It should be understood, however, that the present
disclosure is not intended to be limited to the particular forms
disclosed. Rather, the present disclosure covers all modifications,
equivalents, and alternatives that fall within the spirit and scope
of these exemplary embodiments. The terms "couple," "coupled,"
"operatively coupled," "operatively connected," and the like should
be broadly understood to refer to connecting devices or components
together either mechanically, electrically, wired, wirelessly, or
otherwise, such that the connection allows the pertinent devices or
components to operate (e.g., communicate) with each other as
intended by virtue of that relationship.
[0018] Turning to the drawings, FIG. 1 illustrates a block diagram
of a communication system 100 that includes an in-store experience
personalization computing device 102 (e.g., a server, such as an
application server), a web server 104, and database 116, and
multiple customer computing devices 110, 112, 114 operatively
coupled over network 118.
[0019] An in-store experience personalization computing device 102,
server 104, and multiple customer computing devices 110, 112, 114
can each be any suitable computing device that includes any
hardware or hardware and software combination for processing and
handling information. For example, each can include one or more
processors, one or more field-programmable gate arrays (FPGAs), one
or more application-specific integrated circuits (ASICs), one or
more state machines, digital circuitry, or any other suitable
circuitry. In addition, each can transmit data to, and receive data
from, or through the communication network 118.
[0020] In some examples, the in-store experience personalization
computing device 102 can be a computer, a workstation, a laptop, a
server such as a cloud-based server, or any other suitable device.
In some examples, each of multiple customer computing devices 110,
112, 114 can be a cellular phone, a smart phone, a tablet, a
personal assistant device, a voice assistant device, a digital
assistant, a laptop, a computer, or any other suitable device. In
some examples, in-store experience personalization computing device
102, and web server 104 are operated by a retailer, and multiple
customer computing devices 112, 114 are operated by customers of
the retailer.
[0021] Although FIG. 1 illustrates three customer computing devices
110, 112, 114, the communication system 100 used for in-store
personalization can include any number of customer computing
devices 110, 112, 114. Similarly, the communication system 100 can
include any number of workstation(s) (not shown), in-store
experience personalization computing devices 102, web servers 104,
and databases 116.
[0022] The in-store experience personalization computing device 102
is operable to communicate with database 116 directly or over
communication network 118. For example, in-store experience
personalization computing device 102 can store data to, and read
data from, database 116. Database 116 may be remote storage
devices, such as a cloud-based server, a disk (e.g., a hard disk),
a memory device on another application server, a networked
computer, or any other suitable remote storage. Although shown
remote to the in-store experience personalization computing device
102, in some examples, database 116 may be a local storage device,
such as a hard drive, a non-volatile memory, or a USB stick. The
in-store experience personalization computing device 102 may store
data from workstations or the web server 104 in database 116. In
some examples, storage devices store instructions that, when
executed by in-store experience personalization computing device
102, allow intent free answering computing device 102 to determine
one or more s results in response to a user query.
[0023] Communication network 118 can be a WiFi.RTM. network, a
cellular network such as a 3GPP.RTM. network, a Bluetooth.RTM.
network, a satellite network, a wireless local area network (LAN),
a network utilizing radio-frequency (RF) communication protocols, a
Near Field Communication (NFC) network, a wireless Metropolitan
Area Network (MAN) connecting multiple wireless LANs, a wide area
network (WAN), or any other suitable network. Communication network
118 can provide access to, for example, the Internet.
[0024] FIG. 2 illustrates the in-store experience personalization
computing device 102 of FIG. 1. The in-store experience
personalization computing device 102 may include one or more
processors 201, working memory 202, one or more input/output
devices 203, instruction memory 207, a transceiver 204, one or more
communication ports 207, and a display 206, all operatively coupled
to one or more data buses 208. Data buses 208 allow for
communication among the various devices. Data buses 208 can include
wired, or wireless, communication channels.
[0025] Processors 201 can include one or more distinct processors,
each having one or more processing cores. Each of the distinct
processors can have the same or different structure. Processors 201
can include one or more central processing units (CPUs), one or
more graphics processing units (GPUs), application specific
integrated circuits (ASICs), digital signal processors (DSPs), and
the like.
[0026] Processors 201 can be configured to perform a certain
function or operation by executing code, stored on instruction
memory 207, embodying the function or operation. For example,
processors 201 can be configured to perform one or more of any
function, method, or operation disclosed herein.
[0027] Instruction memory 207 can store instructions that can be
accessed (e.g., read) and executed by processors 201. For example,
instruction memory 207 can be a non-transitory, computer-readable
storage medium such as a read-only memory (ROM), an electrically
erasable programmable read-only memory (EEPROM), flash memory, a
removable disk, CD-ROM, any non-volatile memory, or any other
suitable memory.
[0028] Processors 201 can store data to, and read data from,
working memory 202. For example, processors 201 can store a working
set of instructions to working memory 202, such as instructions
loaded from instruction memory 207. Processors 201 can also use
working memory 202 to store dynamic data created during the
operation of intent free answering computing device 102. Working
memory 202 can be a random access memory (RAM) such as a static
random access memory (SRAM) or dynamic random access memory (DRAM),
or any other suitable memory.
[0029] Input-output devices 203 can include any suitable device
that allows for data input or output. For example, input-output
devices 203 can include one or more of a keyboard, a touchpad, a
mouse, a stylus, a touchscreen, a physical button, a speaker, a
microphone, or any other suitable input or output device.
[0030] Communication port(s) 209 can include, for example, a serial
port such as a universal asynchronous receiver/transmitter (UART)
connection, a Universal Serial Bus (USB) connection, or any other
suitable communication port or connection. In some examples,
communication port(s) 209 allows for the programming of executable
instructions in instruction memory 207. In some examples,
communication port(s) 209 allow for the transfer (e.g., uploading
or downloading) of data, such as machine learning algorithm
training data.
[0031] Display 206 can display user interface 205. User interfaces
205 can enable user interaction with in-store experience
personalization computing device 102. In some examples, a user can
interact with user interface 205 by engaging input-output devices
203. In some examples, display 206 can be a touchscreen, where user
interface 205 is displayed by the touchscreen.
[0032] Transceiver 204 allows for communication with a network,
such as the communication network 118 of FIG. 1. For example, if
communication network 118 of FIG. 1 is a cellular network,
transceiver 204 is configured to allow communications with the
cellular network. In some examples, transceiver 204 is selected
based on the type of communication network 118 in-store experience
personalization computing device 102 will be operating in.
Processor(s) 201 is operable to receive data from, or send data to,
a network, such as communication network 118 of FIG. 1, via
transceiver 204.
[0033] FIG. 3 illustrates a diagram 300 of a system for
personalizing a customer's in-store experience. The in-store
experience personalization computing device 302 is operably
connected to data stores (databases) 315 and 316, these data stores
are shown separately for illustration only to reflect different
categories of data accessible by the in-store experience
personalization computing device 302, however it is also envisioned
that the data stores may be unitary. Data stores include real time
and historical customer on-line interactions 315 and retailer
information 316.
[0034] Store beacon 320 detects the presence of a customer at the
retailer's physical location. The store beacons interact with
customer's mobile device to determine its proximity to the
retailer's physical location in order to determine the customer's
presence. Location data from the customer may be determined by
information provided to the beacon via a retailer app, connection
to local network, or a VLR (visiting location register).
[0035] The in-store experience personalization computing device 302
is also operably connected to a notification system 330. The
notification system 330 communicates through the retailer app on
the user's mobile device (phone) 312, or other application, such as
SMS, Social Media or other communication platform. The notification
system 330 receives push notifications from the in-store experience
personalization computing device 302 and relays the notifications
to the customer's mobile device 312.
[0036] The flowchart of FIG. 4 illustrates several paths for the
personalization of the customer's in-store experience, each
dependent upon the customer's current or prior online interactions.
In one example, the customer has searched and browsed online for
items related to a type of product (e.g. a bread machine) five day
prior to the visit. The customer however in this example did not
purchase the product. Upon visiting the retailer's physical
location, specifically store X as shown in Block 401, the store
location beacons are fired. Customer information and retailer
information is retrieved from database 116 as shown in Block 403.
The customer information is associated with the customers past
session data over a predetermined time, (e.g. in the past 7 days)
is fetched from the database 116. Other customer information may
also be retrieved, such as customer brand affinity and price
conciseness. The retailer information associated with the physical
location is also retrieved from the database 116, this information
may include inventory, product descriptions, product locations,
store layout, local product preferences, and other features
associated with store X.
[0037] As shown in decision block 405, it is determined whether the
customers has recent online activity within a predetermined time
period. In the present example as noted above, the customer
conducted an online search for a product 5 days prior, and thus in
Block 407, the system 300 selects a product(s) or type of
products(s) based on the object of the recent online activity
(interactions). The predetermined period may be a week, ten days,
two weeks or other appropriate period and may be adjustable with
respect to the date(e.g. shorter period at holidays), as well as
with respect to past customer shopping patterns (e.g. visits store
X once a month, and thus a 29 day period may be more advantageous)
The product(s) selection may be combined with product location
(aisle information for store X) as shown in Block 409 and the push
notification is then sent to the customer as shown in Block 450. In
the case when the customer after the search purchased a bread
machine in the recent online interaction, the system advantageously
may select related complimentary items for inclusion in the push
notification, such as a bread slicer, or ingredients for making
bread. The push notifications may take the form of a pop up, SMS,
email, or phone call. The push notification may include several
products (items) available at the store and ordered by relevance,
along with the aisle location within the store to guide the
customer.
[0038] In another example, the customer visits store Y and performs
an in-store search for a product (e.g. humidifier) using the
retailer's application as shown in Block 421. Receiving an API call
from the application, the in-store experience personalization
computing device 102, accessing the customer's interaction history
and retailer information, selects product(s) as shown in Block 423
related to the subject of the in-store search, irrespective of the
recent online interactions considered in the prior example. Based
on the retrieved information, (e.g. customers price consciousness,
popularity of specific products, store inventory, store sales etc.)
the in-store experience personalization computing device 102
selects a product(s) (e.g. a popular brand of humidifier). The
product(s) selection may be combined with product location (aisle
information for store X) as shown in Block 409 and the push
notification is then sent to the customer as shown in Block
450.
[0039] In another example, a customer visits the retailer's
physical location, however the customer has not had any significant
activity over the prior predetermined period, (e.g. two weeks), but
has regular online activity over a second longer predetermined
period (e.g. 2 years). Upon the visit to store X as shown in Block
401, the store location beacons are fired and the customer
information and retailer information is retrieved from database 116
as shown in Block 403.
[0040] As shown in decision block 405, it is determined whether the
customers has recent online activity within a predetermined time
period. In this example as noted above, the customer has not had a
recent online interaction within the predetermined period. In
decision block 415 it is determined whether the customer has any
online interaction over the past two years, as a result of a
positive determination, products associated with features of store
X are selected for the push notification as shown in Block 417. The
features of store X may include items trending at that store,
inventory, discounts, promotions etc. The product(s) selection may
be combined with product location (aisle information for store X)
as shown in Block 409 and the push notification is then sent to the
customer as shown in Block 450.
[0041] In FIG. 5, the steps involved in personalizing the in-store
experience based on online interaction is illustrated. The
customer's presence at one of the retailer's physical locations is
detected in Block 502, preferably by the store location beacon 320.
The in-store experience personalization computing device 102
receives an indication of the customer and the store visited, in
response, data related to customer online interactions with the
retailer is retrieved as shown in Block 504, based upon this
information, the customer's online interactions are classified into
one of a plurality of classifications as shown in Block 506. A
first classification for online interactions within a prior first
predetermined period, (e.g. within seven days, ten days, two weeks,
a month), a second classification being within a prior second
longer predetermined period, (e.g. within the last year or two
years, or any interactions) and a third classification being a real
time (i.e. during the customers visit) in-store search by the
customer. Other classifications or sub classifications, based upon
the type of interactions are also envisioned, such as customer
clicks, views, add to cart, and/or purchases, or combinations
thereof. The first classification in some embodiments results in
the product (i.e. object) of the recent interactions being the
basis for the products selected for the push notification, the
second classification results in the features of the specific store
being the basis for the products selected for the push notification
and the third classification results in the object of the in-store
search being the basis.
[0042] The in-store experience personalization computing device 102
also accesses retailer information associated with the particular
store (physical location) visited in Block 508, as noted this
information may include items trending at that store, inventory,
out of stock items, discounts, promotions, product information,
product location and local preferences, among others. The retailer
information may be stored centrally for a plurality of stores or
may be resident in each individual store. Based upon both the
classification of the user's prior interactions and the retailer
information particular to the visited store, products and/or
services available at the retailer's physical location are selected
as shown in Block 510. The selections may be ranked by relevance
using additional customer historic information, as well as the
retailer information, and may be combined with product location
(aisle information) and transmitted to the customer's mobile device
312 as a push notification as shown in Block 512. The push
notification advantageously enables the retailer at least in some
situations to provide the customer with recommendations strongly
related to their prior or current online interactions along with
aisle navigation information directing the customer to the products
relevant while they are actually in the store. The push
notifications generated using the disclosed subject matter also
allows for consideration of the customer's historic tendencies
(brand infinity, price tolerance, etc.) to be utilized in marketing
the customer while present in the store, irrespective of the
customer's intent for visiting the retailer's physical
location.
[0043] Although the methods described above are with reference to
the illustrated flowcharts, it will be appreciated that many other
ways of performing the acts associated with the methods can be
used. For example, the order of some operations may be changed, and
some of the operations described may be optional.
[0044] In addition, the methods and system described herein can be
at least partially embodied in the form of computer-implemented
processes and apparatus for practicing those processes. The
disclosed methods may also be at least partially embodied in the
form of tangible, non-transitory machine-readable storage media
encoded with computer program code. For example, the steps of the
methods can be embodied in hardware, in executable instructions
executed by a processor (e.g., software), or a combination of the
two. The media may include, for example, RAMs, ROMs, CD-ROMs,
DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or any other
non-transitory machine-readable storage medium. When the computer
program code is loaded into and executed by a computer, the
computer becomes an apparatus for practicing the method. The
methods may also be at least partially embodied in the form of a
computer into which computer program code is loaded or executed,
such that, the computer becomes a special purpose computer for
practicing the methods. When implemented on a general-purpose
processor, the computer program code segments configure the
processor to create specific logic circuits. The methods may
alternatively be at least partially embodied in application
specific integrated circuits for performing the methods.
[0045] While the disclosed subject matter is described using
customer data obtained from online interactions with the retailer
associated with the visited store, it is also envisioned that
customer data from other sources, for instance interactions on
search engines, or with other retailer sites would also be useful
in practicing the personalization of the customer's in-store
experience. For example, if the customer visiting the retailer's
store, had searched in the prior five days on a generic search
engine for a bread machine, or had visited a webpage for bread
machines from manufacturer or other seller and that data was
available to the retailer, the data could be used in the same
manner as the described internal data.
[0046] The foregoing is provided for purposes of illustrating,
explaining, and describing embodiments of these disclosures.
Modifications and adaptations to these embodiments will be apparent
to those skilled in the art and may be made without departing from
the scope or spirit of these disclosures.
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