U.S. patent application number 15/177178 was filed with the patent office on 2016-12-15 for method and system for providing business intelligence based on user behavior.
The applicant listed for this patent is Clickagy, LLC. Invention is credited to Harry Russell Maugans, III.
Application Number | 20160364736 15/177178 |
Document ID | / |
Family ID | 57515867 |
Filed Date | 2016-12-15 |
United States Patent
Application |
20160364736 |
Kind Code |
A1 |
Maugans, III; Harry
Russell |
December 15, 2016 |
METHOD AND SYSTEM FOR PROVIDING BUSINESS INTELLIGENCE BASED ON USER
BEHAVIOR
Abstract
Disclosed is a computer implemented method of providing business
intelligence based on user behavior. The method may include a step
of receiving a user identifier associated with the user from a
requesting entity, such as a server computer. Further, the method
may include a step of identifying an anonymous identifier
corresponding to the user identifier. Additionally, the method may
include a step of retrieving anonymous user behavior data based on
the anonymous identifier. Furthermore, the method may include a
step of transmitting the anonymous user behavior data to the
requesting entity. Accordingly, the anonymous user behavior data
may be used by the requesting entity to, for example, to enrich
data, such as CRM data, of the user with the anonymous user
behavior data.
Inventors: |
Maugans, III; Harry Russell;
(Alpharetta, GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Clickagy, LLC |
Alpharetta |
GA |
US |
|
|
Family ID: |
57515867 |
Appl. No.: |
15/177178 |
Filed: |
June 8, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62173071 |
Jun 9, 2015 |
|
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|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0275 20130101;
G06Q 30/0202 20130101; H04L 63/0407 20130101; G06F 21/316 20130101;
H04L 67/22 20130101; H04L 67/306 20130101; G06F 21/6263 20130101;
H04L 67/02 20130101; G06Q 30/0201 20130101; G06Q 30/0269 20130101;
G06F 16/9566 20190101; G06F 2221/2101 20130101; G06F 16/9535
20190101; G06F 40/279 20200101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06F 21/62 20060101 G06F021/62; H04L 29/08 20060101
H04L029/08 |
Claims
1. A method of providing business intelligence based on user
behavior, wherein the method is a computer implemented method, the
method comprising: a. receiving a user identifier associated with a
user from a requesting entity; b. identifying an anonymous
identifier corresponding to the user identifier; c. retrieving
anonymous user behavior data based on the anonymous identifier; and
d. transmitting the anonymous user behavior data to the requesting
entity.
2. The method of claim 1, wherein the requesting entity is
configured for: a. receiving the anonymous user behavior data
corresponding to the user; and b. appending the anonymous behavior
data to data associated with the user stored in a database.
3. The method of claim 1, wherein the requesting entity is a server
computer, wherein the data associated with the user is comprised in
a Customer Relationship Management (CRM) database executable on the
server computer.
4. The method of claim 1 further comprising: a. identifying at
least one of a product and a service used by the user based on the
user behavior data; b. identifying at least one of an interested
product and an interested service associated with the user based on
the user behavior data; and c. predicting a churn based on a
comparison of the at least one of a product and a service with at
least one of the interested product and the interested service.
5. The method of claim 4 further comprising: a. receiving a request
for a churn prediction from the requesting entity; and b.
transmitting a churn prediction based on the predicting.
6. The method of claim 5, wherein the churn prediction comprises a
risk value indicating a likelihood of the user to churn towards at
least one of the interested product and the interested service.
7. The method of claim 5, wherein the churn prediction comprises
indication of at least one of the interested product and the
interested service.
8. The method of claim 1 further comprising an indication of at
least one keyword, wherein the anonymous user behavior data
comprises an affinity value corresponding to the at least one
keyword.
9. The method of claim 1, wherein the anonymous identifier is
identified based on operating a one-way hash function on the user
identifier.
10. The method of claim 1, wherein retrieving the anonymous user
behavior data comprises retrieving data from a plurality of cookies
corresponding to a plurality of websites, wherein each cookie
comprises at least a portion of the anonymous user behavior data,
wherein each cookie is associated with the anonymous
identifier.
11. The method of claim 1, wherein the anonymous user behavior data
is based on online activity of user.
12. The method of claim 11, wherein the anonymous user behavior
data comprises contextual data corresponding to the online
activity, wherein the contextual data corresponds to at least one
user device used by the user to perform the online activity.
13. The method of claim 12, wherein the contextual data comprises
device data representing the at least one user device.
14. The method of claim 13, wherein the device data comprises at
least one of a device identifier associated with a user device, a
network identifier associated with a communication network used for
performing the online activity, an Operating System (OS) identifier
of an OS installed on the user device and a browser identifier of a
browser installed on the user device.
15. The method of claim 12, wherein the contextual data comprises
sensor data representing state of the at least one user device
during performance of the online activity.
16. The method of claim 1, wherein the anonymous user behavior data
comprises at least one of demographic data and psychographic data
of the user.
17. The method of claim 1, wherein the anonymous user behavior data
comprises at least one interest of the user.
18. The method of claim 17, wherein the anonymous user behavior
data comprises a plurality of keywords representing the at least
one interest and a plurality of affinity values corresponding to
the plurality of keywords.
19. A method of providing business intelligence based on user
behavior, wherein the method is a computer implemented method, the
method comprising: a. receiving anonymous user behavior data
corresponding to a user; b. comparing the anonymous user behavior
data with data of known users; and c. associating the anonymous
user behavior data with a known user based on a result of the
comparing.
20. A system for providing business intelligence based on user
behavior, the system comprising: a. a communication module
configured to: i. receive a user identifier associated with the
user from a requesting entity; and ii. transmit anonymous user
behavior data to the requesting entity; b. a processing module
coupled to the communication module, wherein the processing module
is configured to identify the anonymous identifier corresponding to
the user identifier; and c. a storage module coupled to the
processing module, wherein the storage module is configured to
retrieve anonymous user behavior data based on an anonymous
identifier.
Description
RELATED APPLICATIONS
[0001] Under provisions of 35 U.S.C. .sctn.119(e), the Applicant
claims the benefit of U.S. provisional application No. 62/173,071,
filed Jun. 9, 2015, which is incorporated herein by reference.
[0002] The following related U.S. patent applications, filed on
even date herewith in the name of Clickagy, LLC, assigned to the
assignee of the present application, are hereby incorporated by
reference: [0003] Attorney Docket No. E279P.001US01, entitled
"METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR CREATING A PROFILE
OF A USER BASED ON USER BEHAVIOR;" [0004] Attorney Docket No.
E279P.001US03, entitled "METHOD AND SYSTEM FOR CREATING AN AUDIENCE
LIST BASED ON USER BEHAVIOR DATA;" and [0005] Attorney Docket No.
E279P.001US04, entitled "METHOD AND SYSTEM FOR INFLUENCING AUCTION
BASED ADVERTISING OPPORTUNITIES BASED ON USER CHARACTERISTICS."
[0006] It is intended that each of the referenced applications may
be applicable to the concepts and embodiments disclosed herein,
even if such concepts and embodiments are disclosed in the
referenced applications with different limitations and
configurations and described using different examples and
terminology.
FIELD OF DISCLOSURE
[0007] The present disclosure generally relates to providing
business intelligence based on user behavior. More specifically,
the present disclosure relates to a method and system for enriching
data of users, such as CRM data, with anonymous user behavior
data.
BACKGROUND
[0008] Individuals and companies often use data derived from the
Internet to optimize business strategies. For example, data derived
from the Internet may be used to study demographics,
psychographics, market behavior, competitor affinity, targeted
marketing, and expanding markets. For example, companies often use
market data to best market their products and services. Moreover,
companies often use targeted marketing to specific individuals to
try to improve marketing effectiveness.
[0009] When consumers visit a website, the pages they visit, the
amount of time they view each page, the links they click on, the
searches they make and the things that they interact with, allow
sites to collect that data, and other factors, create a `profile`
that links to that visitor's web browser. As a result, companies
can use this data to create defined audience segments based upon
visitors that have similar profiles. When visitors return to a
specific site or a network of sites using the same web browser,
those profiles can be used to allow advertisers to position their
online ads in front of those visitors who exhibit a greater level
of interest and intent for the products and services being offered.
On the theory that properly targeted ads will fetch more consumer
interest, the publisher (or seller) can charge a premium for these
ads over random advertising or ads based on the context of a
site.
[0010] Behavioral marketing can be used on its own or in
conjunction with other forms of targeting based on factors like
geography, demographics or contextual web page content. While there
is an abundance of data from global Internet use, much of the data
is unavailable due to privacy laws. The information that is
available is often too general to be useful and does not provide
adequate resolution.
BRIEF OVERVIEW
[0011] A business intelligence provisioning platform may be
provided. This brief overview is provided to introduce a selection
of concepts in a simplified form that are further described below
in the Detailed Description. This brief overview is not intended to
identify key features or essential features of the claimed subject
matter. Nor is this brief overview intended to be used to limit the
claimed subject matter's scope.
[0012] According to some embodiments, one objective of the business
intelligence platform (also reference to as "the platform") to
provide anonymous user behavior data to requesting entities, such
as a server computer.
[0013] Accordingly, the platform may be configured to collect
anonymous user behavior data of users from a variety of sources.
For example, as users access webpages on different websites, the
web servers hosting the websites may collect user behavior data,
for instance, using cookies. Further, the platform may be
configured to communicate with each webserver to obtain the user
behavior data. However, in some instances, each webserver may be
configured to render the user behavior data anonymous in order to
protect the privacy of the users. Accordingly, the platform may
receive the anonymous user behavior data from each webserver.
Further, using a technique, such as for example, cookie syncing,
the platform may be configured to aggregate anonymous user behavior
data corresponding to a particular user across different
sources.
[0014] For instance, each webserver may be configured to generate
an anonymous identifier corresponding to a user by performing a one
way mapping, such as for example, one-way hashing of a user
identifier, such as an email address. Accordingly, by employing a
common one way mapping, anonymous user behavior data generated at
different websites may be correlated and aggregated as anonymous
user behavior data related to a particular user.
[0015] Further, in order to provision the anonymous user behavior
data, the platform may be configured to receive a request for
anonymous user behavior data corresponding to a user. Further, the
request may include a user identifier. Additionally, in some
instances, the request may include a plurality of user identifiers
corresponding to a plurality of users. Upon receiving the request,
the platform may identify a corresponding anonymous identifier
using the one way mapping. Accordingly, based on the anonymous
identifier, corresponding anonymous user behavior data may be
identified and retrieved from a database included in the platform.
Subsequently, the platform may transmit the anonymous user behavior
data to the requesting entity.
[0016] In some instances, the requesting entity may include a
Customer Relationship Management (CRM) database. Further, the CRM
database may include specific data associated with the user, such
as for example, first name, last name, phone number, postal
address, products purchased, services subscribed to and so on. In
some instances, the data included in the CRM data may be obtained
from offline sources such as, for example, a brick and mortar store
where a product was purchased.
[0017] Accordingly, upon receiving the anonymous user behavior data
of the user, the requesting entity may enrich the CRM database with
the anonymous user behavior data. For instance, the anonymous user
behavior data may include keywords indicating interests of the
user, such as, for example, brands, products and services.
Additionally, the anonymous user behavior data may include affinity
values corresponding to the keywords. An affinity value of a
keyword may indicate a relative measure of the user's interest with
regard to the keyword. Accordingly, the CRM database may be
populated with rich data of users providing greater business
intelligence and insights for the users of the CRM database.
[0018] Further, in some embodiments, the platform may be configured
to match specific data of a user, for example, data available in
the CRM database with the anonymous user behavior data. In other
words, the platform may be configured to correlate data from the
CRM database with the anonymous user behavior data in order to
identify an association between data of a user in the CRM database
and anonymous user behavior data corresponding to the user.
Accordingly, in an instance, the platform may receive at least a
portion of data from the CRM database. For example, the CRM
database may include demographic data of the user, such as age,
location, educational qualifications, employment details and so on.
Further, the anonymous user behavior data may also include
corresponding demographic data for each user. Accordingly, by
correlating demographic data in the CRM database with the
demographic data included in the anonymous user behavior data, the
platform may be able to build an association between data in CRM
database and the anonymous user behavior data. Accordingly,
anonymous user behavior data corresponding to a particular user
and/or a group of users may be identified and provisioned.
[0019] Further, in some embodiments, by comparatively analyzing
specific data of users with the anonymous user behavior data,
customer churn may be predicted. For example, based on specific
data available in the CRM database, a product and/or a service used
by the user may be identified. Further, based on the anonymous user
behavior data, an interested product and/or an interested service
may be identified. Subsequently, by comparing data of the product
and/or the service with that of the interested product and/or the
interested service the customer churn may be predicted.
[0020] Alternatively and/or additionally, in some embodiments, the
platform may also be configured to predict churn. Accordingly, in
some instances, the platform may receive a request for churn
prediction. Further, the platform may be configured to analyze the
anonymous user behavior data to identify an interested product
and/or an interested service. Furthermore, the anonymous user
behavior data may include contextual data such as, for example,
data indicative of the user device used to access webpages.
Accordingly, by comparing the contextual data with the data
indicative of the interested product and/or the interested service,
the platform may be able to predict churn. For instance, the
anonymous user behavior data may indicate that an android
smartphone was used to access webpages about iPhone on several
websites. This may indicate a strong interest of the user towards
iPhone. Accordingly, the platform may predict, with a measure of
likelihood, churn of the user from the android device to iPhone.
Such churn prediction may enable an operator the requesting entity,
such as mobile device manufacturer, to identify a business
opportunity and take actions, such as, for example, providing
targeted advertisements of iPhone to the user that have a higher
rate of conversion into sale.
[0021] Both the foregoing brief overview and the following detailed
description provide examples and are explanatory only. Accordingly,
the foregoing brief overview and the following detailed description
should not be considered to be restrictive. Further, features or
variations may be provided in addition to those set forth herein.
For example, embodiments may be directed to various feature
combinations and sub-combinations described in the detailed
description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate various
embodiments of the present disclosure. The drawings contain
representations of various trademarks and copyrights owned by the
Applicants. In addition, the drawings may contain other marks owned
by third parties and are being used for illustrative purposes only.
All rights to various trademarks and copyrights represented herein,
except those belonging to their respective owners, are vested in
and the property of the Applicants. The Applicants retain and
reserve all rights in their trademarks and copyrights included
herein, and grant permission to reproduce the material only in
connection with reproduction of the granted patent and for no other
purpose.
[0023] Furthermore, the drawings may contain text or captions that
may explain certain embodiments of the present disclosure. This
text is included for illustrative, non-limiting, explanatory
purposes of certain embodiments detailed in the present disclosure.
In the drawings:
[0024] FIG. 1 illustrates a block diagram of an operating
environment consistent with the present disclosure;
[0025] FIG. 2 is a flow chart of a method for creating a user
profile based on user behavior according to some embodiments;
[0026] FIG. 3 a flow chart of a method of providing anonymous user
behavior data according to some embodiments;
[0027] FIG. 4 a flow chart of a method of predicting churn based on
anonymous user behavior data according to some embodiments;
[0028] FIG. 5 a flow chart of a method of correlating anonymous
user behavior data with data associated with known users according
to some embodiments;
[0029] FIG. 6 a flow chart of a method of providing anonymous user
behavior data according to some embodiments;
[0030] FIG. 7 illustrates exemplary business intelligence related
to a user provided based on anonymous user behavior data according
to some embodiments;
[0031] FIG. 8 illustrates an online user behavior of a user based
on which a user profile may be created in accordance with some
embodiments;
[0032] FIG. 9 illustrates an exemplary comprehensive user browsing
data based on which a user profile may be created in accordance
with some embodiments;
[0033] FIG. 10 illustrates Natural Language Processing performed on
data extracted from webpages visited by a user based on which a
user profile may be created in accordance with some embodiments;
and
[0034] FIG. 11 is a block diagram of a system including a computing
device for performing the methods of FIG. 2 to FIG. 6.
DETAILED DESCRIPTION
[0035] As a preliminary matter, it will readily be understood by
one having ordinary skill in the relevant art that the present
disclosure has broad utility and application. As should be
understood, any embodiment may incorporate only one or a plurality
of the above-disclosed aspects of the disclosure and may further
incorporate only one or a plurality of the above-disclosed
features. Furthermore, any embodiment discussed and identified as
being "preferred" is considered to be part of a best mode
contemplated for carrying out the embodiments of the present
disclosure. Other embodiments also may be discussed for additional
illustrative purposes in providing a full and enabling disclosure.
As should be understood, any embodiment may incorporate only one or
a plurality of the above-disclosed aspects of the display and may
further incorporate only one or a plurality of the above-disclosed
features. Moreover, many embodiments, such as adaptations,
variations, modifications, and equivalent arrangements, will be
implicitly disclosed by the embodiments described herein and fall
within the scope of the present disclosure.
[0036] Accordingly, while embodiments are described herein in
detail in relation to one or more embodiments, it is to be
understood that this disclosure is illustrative and exemplary of
the present disclosure, and are made merely for the purposes of
providing a full and enabling disclosure. The detailed disclosure
herein of one or more embodiments is not intended, nor is to be
construed, to limit the scope of patent protection afforded in any
claim of a patent issuing here from, which scope is to be defined
by the claims and the equivalents thereof. It is not intended that
the scope of patent protection be defined by reading into any claim
a limitation found herein that does not explicitly appear in the
claim itself.
[0037] Thus, for example, any sequence(s) and/or temporal order of
steps of various processes or methods that are described herein are
illustrative and not restrictive. Accordingly, it should be
understood that, although steps of various processes or methods may
be shown and described as being in a sequence or temporal order,
the steps of any such processes or methods are not limited to being
carried out in any particular sequence or order, absent an
indication otherwise. Indeed, the steps in such processes or
methods generally may be carried out in various different sequences
and orders while still falling within the scope of the present
invention. Accordingly, it is intended that the scope of patent
protection is to be defined by the issued claim(s) rather than the
description set forth herein.
[0038] Additionally, it is important to note that each term used
herein refers to that which an ordinary artisan would understand
such term to mean based on the contextual use of such term herein.
To the extent that the meaning of a term used herein--as understood
by the ordinary artisan based on the contextual use of such
term--differs in any way from any particular dictionary definition
of such term, it is intended that the meaning of the term as
understood by the ordinary artisan should prevail.
[0039] Regarding applicability of 35 U.S.C. .sctn.112, 6, no claim
element is intended to be read in accordance with this statutory
provision unless the explicit phrase "means for" or "step for" is
actually used in such claim element, whereupon this statutory
provision is intended to apply in the interpretation of such claim
element.
[0040] Furthermore, it is important to note that, as used herein,
"a" and "an" each generally denotes "at least one," but does not
exclude a plurality unless the contextual use dictates otherwise.
When used herein to join a list of items, "or" denotes "at least
one of the items," but does not exclude a plurality of items of the
list. Finally, when used herein to join a list of items, "and"
denotes "all of the items of the list."
[0041] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar elements. While many embodiments of
the disclosure may be described, modifications, adaptations, and
other implementations are possible. For example, substitutions,
additions, or modifications may be made to the elements illustrated
in the drawings, and the methods described herein may be modified
by substituting, reordering, or adding stages to the disclosed
methods. Accordingly, the following detailed description does not
limit the disclosure. Instead, the proper scope of the disclosure
is defined by the appended claims. The present disclosure contains
headers. It should be understood that these headers are used as
references and are not to be construed as limiting upon the
subjected matter disclosed under the header.
[0042] The present disclosure includes many aspects and features.
Moreover, while many aspects and features relate to, and are
described in, the context of data mining for marketing purposes,
embodiments of the present disclosure are not limited to use only
in this context. For example, the platform may be used to study
demographics, psychographics, market behavior, competitor affinity,
and expanding markets.
I. PLATFORM OVERVIEW
[0043] Consistent with embodiments of the present disclosure, a
business intelligence provisioning platform may be provided. This
overview is provided to introduce a selection of concepts in a
simplified form that are further described below. This overview is
not intended to identify key features or essential features of the
claimed subject matter. Nor is this overview intended to be used to
limit the claimed subject matter's scope.
[0044] A platform consistent with embodiments of the present
disclosure may be used by individuals or companies to determine,
with relative accuracy, statistics about individuals using the
Internet and groups of such individuals. Such statistics may be
used by the platform to predict, for example, but not limited to,
an individual or group of individuals' personal and commercial
behavior. As a non-limiting, illustrative example, the platform may
be used by a washing machine company to, for example, determine
which individuals are likely to be purchasing a new washing
machine, and which brands they are most likely to purchase based on
webpages that they visit.
[0045] Embodiments of the present disclosure may operate in a
plurality of different environments. For example, in a first
aspect, the platform may receive notice that an individual has
visited a webpage. Then, the platform may crawl that page to gather
raw data from the page. For example, the platform may use various
algorithms, including, but not limited to, for example, natural
language processing (NLP) and digital signal processing
(audio/image/video data) to search the web page for key words or
phrases.
[0046] Still consistent with embodiments of the present disclosure,
the platform may receive raw data as it tracks individuals
throughout, for example, an ad network or collection of ad
networks. Tracking may include, for example, but not be limited to,
a crawling of each visited webpage so as to create a profile for
the page. As will be further detailed below, the profile may be
generated by, for example, the aforementioned algorithms used to
gather raw data for the page.
[0047] Accordingly, in some embodiments, interaction of a user with
a plurality of servers, such as for example, content servers, ad
servers and so on may be monitored. For instance, when the user
visits a webpage provided by a server, a tracking cookie may be
instantiated in order to save information regarding the user and/or
the user's interaction with the webpage. For instance, the tracking
cookie may be instantiated at the server side and may include
information such as a timestamp corresponding to the user's
visiting of the webpage and one or more identifiers associated with
the user. The one or more identifiers may be for example, a network
identifier such as an Internet Protocol (IP) number and/or a MAC
number, a device identifier such as an IMEI number, a software
environment identifier, such as OS name, browser name etc., user
identifiers such as email address, first name, last name, middle
name, postal address etc. and values of contextual variables such
as GPS location of the device used to access the webpage, sensor
readings of the device while accessing the webpage and so on.
[0048] In some embodiments, the one or more identifiers, such as
the IMEI number, may uniquely identify the user while preserving
anonymity of the user.
[0049] In other embodiments, the one or more identifiers may be
subjected to encryption or a one way hashing in order to render the
one or more identifiers unreadable to other users while maintaining
the ability of the one or more identifiers to uniquely identify the
user. For example, in some instances, tracking cookie may be
instantiated on a client side, where the tracking cookie may reside
on a user device, such as a smartphone or a laptop computer.
Accordingly, any information collected by the tracking cookie may
remain accessible in human readable form only within the user
device. However, prior to transmitting the tracking cookie to the
server side, the information collected may be subjected to hashing.
Accordingly, in some embodiments, information about the user in
human readable form may not be available at the server side. Thus,
users may be ensured of preserving their privacy.
[0050] Further, in some embodiments, each of the plurality of
servers may adopt a common hashing algorithm such that each of the
plurality of servers may compute a common hash value for the one or
more identifiers. Accordingly, when information in the tracking
cookies from each of the plurality of servers is transmitted to the
platform, the information collected by multiple tracking cookies
may be identified as being associated with the same user based on
the common hash value. Such a technique may allow tracking the user
across multiple servers accessed by the user through a common user
device.
[0051] In yet further embodiments of the present disclosure, the
raw data may be from purchased data acquired by data aggregators.
The raw data may include, for example, a plurality of device
specific information (e.g., device serial number, IP address, and
the like) along with a listing of websites accessed by the device.
The platform may be enabled to identify a plurality of devices
associated with a single individual and, subsequently, associated
the data aggregated and processed for each device to a single
individual profile.
[0052] For instance, in some embodiments, where the user may access
the same and/or different servers through multiple user devices, a
correlation of the information collected by the multiple cookies
may be performed in order to track the user. For instance, each of
the multiple tracking cookies may not include all of the one or
more identifiers. For example, the user may access a webpage of a
server using a smartphone, while the user may access a webpage of
another server using a laptop computer at work. Further, the laptop
computer may include additional restrictions that forbid the
tracking cookie from collecting some of the one or more
identifiers. However, at least some of the information collected by
the multiple cookies may still be common. Accordingly, by
correlating information across the multiple tracking cookies, it
may be ascertained that the multiple tracking cookies are
associated with the same user. Further, in some embodiments, a
threshold of correlation value may be established. Accordingly, the
multiple tracking cookies may be determined to be associated with
the user only if a correlation value exceeds the threshold.
[0053] The platform may then apply the aforementioned algorithms to
process the websites accessed by the devices and, in this way,
profile the websites as will be detailed below. The profiled
website may then be used to characterize an individual who has been
detected to access the profiled website. Moreover, and as will be
further detailed below, the characterized individual data may then
be grouped along with other individuals' data assessed by the
platform in a plurality of ways including, but not limited to,
geographic, household, workplace, interests, affinities, gender,
age, and the like.
[0054] It should be understood that each individual analyzed by the
platform of the present disclosure may be weighted with an
`affinity` of relationship to a particular category. For example,
for those individuals who have visited websites profiled to be more
`female` friendly may be determined, by the platform, to be most
likely a `female` based on, either solely or at least in part, the
individuals web-traffic of profiled webpages associated with the
individuals tracked device.
[0055] As yet a further example, the platform may identify
individuals that visit webpages that include the words "cell phone"
and determine that the individuals may be more likely to be
shopping for cell phones. Further, by counting the number of times
the individuals visit webpages that have predominately iPhones
versus webpages that have predominately Android phones, the
likelihood that such individuals prefer one phone to the other may
be assessed. The platform may group like users to create useful
statistical data. For example, the platform may create groups of
people that are most likely willing to purchase a specific product
(e.g., cell phones, or, more specifically, Android
smartphones).
[0056] Embodiments of the platform may further be used to enable a
platform user (e.g., mobile telecommunications company) to better
understand its target market. Accordingly, data that has been
acquired, aggregated, and processed by the platform may be provided
to the user. For example an application program interface (API) may
provide statistics about single individuals (e.g., likelihood that
an individual prefers Android phones to iPhones), or groups of
individuals (e.g., which individuals prefer Android phones to
iPhones). Such statistics may be provided in, for example, lists,
charts, and graphs. Further, searchable and sortable raw data may
be provided. In some embodiments, the data may be provided to
licensed users. For example, users that have identified data such
as, for example, AT&T, which has a list of known individuals,
may use the data to, for example, further market to their known
list of individuals or predict churn.
[0057] In some embodiments, the processed data may be provided to
the user as a plug-in. For example, if an individual logs into a
website for the first time (e.g., Home Depot), the website owner
may be able to customize the display for the first-time individual.
In other embodiments, the platform may integrate with a customer
relationship module (CRM). In this way, the CRM may be
automatically updated with processed data for individuals in the
CRM.
[0058] Both the foregoing overview and the following detailed
description provide examples and are explanatory only. Accordingly,
the foregoing overview and the following detailed description
should not be considered to be restrictive. Further, features or
variations may be provided in addition to those set forth herein.
For example, embodiments may be directed to various feature
combinations and sub-combinations described in the detailed
description.
II. PLATFORM CONFIGURATION
[0059] FIG. 1 illustrates one possible operating environment
through which a platform consistent with embodiments of the present
disclosure may be provided. By way of non-limiting example, a
platform 100 may be hosted on a centralized server 110, such as,
for example, a cloud computing service. A user 105 may access
platform 100 through a software application. The software
application may be embodied as, for example, but not be limited to,
a website, a web application, a desktop application, and a mobile
application compatible with a computing device 1100. One possible
embodiment of the software application may be provided by Clickagy,
LLC.
[0060] As will be detailed with reference to FIG. 11 below, the
computing device through which the platform may be accessed may
comprise, but not be limited to, for example, a desktop computer,
laptop, a tablet, or mobile telecommunications device. Though the
present disclosure is written with reference to a mobile
telecommunications device, it should be understood that any
computing device may be employed to provide the various embodiments
disclosed herein.
[0061] A user 105 may provide input parameters to the platform. For
example, input parameters may be certain device IDs. As another
example, input parameters may include individuals living in
Atlanta, Ga. Input parameters may be passed to server 110. Server
110 may further be connected to various databases, such as, for
example, purchased data 120, tracking data 125 and CRM data 130. In
some embodiments, the CRM may be associated with the user. For
example, user's CRM database may interface with the platform.
[0062] Information relevant to individuals associated with the
input parameters, such as, for example, which websites they
visited, may be sent to web crawler 115. Web crawler 115 may search
webpages and online documents visited by individuals being tracked
and gather data associated with the searched webpages and online
documents. For example, web crawler 115 may utilize natural
language processing and audio, video and image processing to gather
information for websites. Web crawler 115 may further perform
algorithms and build profiles based on webpages and online
documents being searched, such as, for example, constructing
`affinities` for websites (further discussed below). Information
and website and online document profiles being tracked may be
passed back to server 110. Server 110 may further construct
profiles for individuals being tracked and groups of individuals
being tracked. The individual and group profiles as well as further
data (e.g. personally identifiable information (PPI), non-PPI,
de-identified data and website/individual/group affinity) may be
returned to user 105.
[0063] User 105 may then use the returned data. For example, user
105 may merge the individual and group profiles with their own
data. In some embodiments, user 105 may license the data to other
individuals or companies. In further embodiments, user 105 may
receive data in a visual form, such as, for example, on a dashboard
containing tables, graphs, and charts summarizing the data. In some
embodiments, received data may be integrated with a user CRM
database. Further, in some embodiments, the received data may be
utilized by an API. For example, a plug-in may utilize the received
data for identifying individuals (and their associated information,
affinities and preferences) that visit a user's website for the
first time.
III. PLATFORM OPERATION
[0064] FIG. 2 to FIG. 6 are flow charts setting forth the general
stages involved in methods 200 to 600 consistent with some
embodiments of the disclosure. Methods 200 to 600 may be
implemented using a computing device 1100 as described in more
detail below with respect to FIG. 11.
[0065] Although methods 200 to 600 have been described to be
performed by platform 100, it should be understood that computing
device 1100 may be used to perform the various stages of methods
200 to 600. Furthermore, in some embodiments, different operations
may be performed by different networked elements in operative
communication with computing device 1100. For example, server 110
may be employed in the performance of some or all of the stages in
methods 200 to 600. Moreover, server 110 may be configured much
like computing device 1100. Furthermore, in some embodiments, some
of the methods 200 to 600 may be performed by a requesting entity
in communication with the platform 100, such as a CRM database
including CRM data 130.
[0066] Although the stages illustrated by the flow charts are
disclosed in a particular order, it should be understood that the
order is disclosed for illustrative purposes only. Stages may be
combined, separated, reordered, and various intermediary stages may
exist. Accordingly, it should be understood that the various stages
illustrated within the flow chart may be, in various embodiments,
performed in arrangements that differ from the ones illustrated.
Moreover, various stages may be added or removed from the flow
charts without altering or deterring from the fundamental scope of
the depicted methods and systems disclosed herein. Ways to
implement the stages of methods 200 to 600 will be described in
greater detail below.
[0067] Method 200 may begin at starting block 205 and proceed to
stage 210 where platform 100 may receive data from an individual's
internet use. For example, the platform may receive information
about a webpage that the individual visited or a Microsoft Word
document or PDF that an individual downloaded. Information may
include the URL of the webpage. Further information may be
received, including IP address of the individual, search history of
the individual, and geolocation of the individual.
[0068] From stage 210, where platform 100 receives data from an
individual's Internet use, method 200 may advance to stage 220
where platform 100 may further gather information associated with
the individual's Internet use. For example, the platform may crawl
the webpage that the individual visited. For example, the platform
may search for specific key words or phrases. In some embodiments,
if the webpage has already been crawled, the webpage may be
skipped.
[0069] During the crawl, the platform may perform, for example,
natural language processing (NLP) to further process the context of
the words and phrases in the text. In addition, the platform may
utilize image recognition, audio recognition, and/or video
recognition to gather data about the individual's Internet use. For
example, images may be scanned with optical character recognition
(OCR). The OCR scanning may generate words or phrases for
characterizing the webpage. Further, image recognition software may
be used to characterize the webpage. For example, artificial
intelligence (AI) software may be used to determine whether an
image is showing for example, a dog or a tree. Audio files from the
webpage may be scanned, using, for example, voice recognition
software, to further provide information to characterize the
webpage. Video files from a page may be converted to a series of
images from periodic individual frames and scanned in the same
manner as an image. In addition, the audio associated with the
video may be scanned to provide data about the webpage. Likewise,
text from the webpage may also be extracted and analyzed based on
NLP. The combination of text, image, audio and video recognition
may provide a human-style "view" of what the webpage provides. The
human-style "view" may enable the platform to optimize
characterization of the webpage.
[0070] Information that is acquired from the crawl may further be
associated with how recently such information was associated with
the webpage (e.g., newer information may be given a higher
relevance than older information). The platform may receive further
information, for example, that is purchased from various data
aggregators (e.g., aggregators that track specific IDs.) In
addition, information may be tracked from an existing individual
base. For example, if the individual clicks ("I Agree") on certain
terms and conditions, the platform may place a tracking cookie on
the individual's device to further gather information. In some
embodiments, stages 210 and 220 may comprise 207, where platform
100 receives general data. The general data may include, for
example, data from webpages (e.g., text, image, audio, and video
data associated with the webpage) and data from individuals (e.g.,
which websites the individuals have visited, information from the
individuals' social media profiles, and the like).
[0071] Once platform 100 further gathers information associated
with the individual's Internet use in stage 220, method 200 may
continue to stage 230 where platform 100 may analyze the
information. In some embodiments, the platform may perform natural
language processing (NLP) as well as image, audio and video
recognition to analyze the information. For example, the platform
may use specific keywords and phrases, as well as keywords
associated with image, video and audio files, found on each webpage
and attach a plurality of `affinities` to each page. For example,
for a news article about iPhones, the platform may return hundreds
of `keywords`, including "Apple" with 94% affinity, "cell phone"
with 81% affinity, and "screen" with 52% affinity. The platform may
then interpret the information based on the individual's Internet
use to create a profile associated with the affinities.
[0072] For example, an individual may visit a number of webpages
that have high affinity for keywords like "truck", "football", and
"Scotch". Such an individual may be statistically more likely to be
a male. As another example, another individual may visit a number
of webpages that have high affinity for keywords like "nail
polish", "Midol", and "Pinterest." Such an individual may be
statistically more likely to be female. Such statistical
predictions may be associated with a confidence level. Further,
statistical predictions may be made for an abundance of other
characteristics, such as, for example, but not limited to, age,
marital status, parental status, approximate household income,
industry of employment, sport preference, automobile preference,
and phone preference.
[0073] After platform 100 analyzes the information for each
individual in stage 230, method 200 may proceed to stage 240 where
platform 100 may group users based on certain characteristics. For
example, individuals likely to be of a certain characteristic, such
as, for example, gender, age, marital status, parental status,
approximate household income, and industry of employment, may be
grouped together. Additionally, individuals may be grouped together
based on their preferences, such as, for example, sport preference,
automobile preference, and phone preference.
[0074] Further, in some embodiments, individuals may be grouped.
For example, using logic functions (e.g., AND, OR, and NOT),
individuals of a specific type may be grouped and sorted.
Accordingly, a group profile corresponding to a plurality of users
may be created. Further, the group profile may include a plurality
of keywords and a corresponding plurality of group affinity values.
A group affinity value of a keyword may be based on aggregation of
affinity values of the keyword associated with the plurality of
users. For instance, as shown, the plurality of users may
correspond to a group of website visitors, list of email
recipients, marketing audience, paying customers and so on.
[0075] Further, each user may be associated with a user profile
comprising keywords, corresponding affinity values and one or more
other characteristics such as demographic characteristics.
Additionally, in some embodiments, one or more of keywords,
corresponding affinity values and demographic characteristics may
be determined based on analysis of the webpages visited by the
users.
[0076] According to some embodiments, a method 300 of providing
business intelligence based on user behavior may be provided as
illustrated in FIG. 3. The method 300 may be a computer implemented
method. Accordingly, one or more steps of the method 300 may be
performed automatically by a computer.
[0077] The user behavior may include for example, online activity
performed by the user such as viewing webpages, online shopping,
downloading content from the internet, uploading content to the
internet and interacting with a desktop application and/or a mobile
application. An exemplary online user behavior data based on which
a user profile may be created is illustrated in FIG. 8.
[0078] The method may include a step 310 of receiving a user
identifier associated with a user from a requesting entity. In
general, the user identifier may be data of any form that may
uniquely identify a user. For example, the user identifier may be a
text string such as, a name, a phone number, an email address, an
IMEI number, and IP number, a device serial number and so on.
Alternatively, the user identifier may also include a biometric
feature of the user, such as a voice sample, fingerprint and so
on.
[0079] In some instances, the user identifier may be such that the
user's personal details may not be identifiable by general public
based only on the user identifier. For instance, an IMEI number of
a smartphone purchased by a user may be associated with personal
details about the user. However, such information is kept private
by the manufacturer/distributor of the smartphone. Accordingly,
mere possession of the IMEI number by another party may not
compromise the privacy of the user. As another example, a personal
email address of the user may also be such that the user's personal
details may not be identifiable from the personal email address.
For example, the personal email address may not be correlatable
with personal information of the user available from other data
sources, in case the personal information does not include the
personal email address.
[0080] However, in some other instances, the user identifier may be
such that the user's personal details may be identifiable from the
user identifier itself and/or by querying other data sources. For
instance, the user identifier may include the first name and last
name of the user. As another example, the user identifier may
include the official email address of the user. Further, personal
details of the user may be publicly available along with the
official email address on a company profile page of the user.
Accordingly, in some instances, possession of the user identifier
may enable one to determine the personal details of the user by
querying the company profile page.
[0081] Further, in some embodiments, the requesting entity may be a
server computer. Additionally, in some embodiments, the data
associated with the user may be included in a Customer Relationship
Management (CRM) database executable on the server computer. For
instance, the server computer may be operated by companies who may
want access to the anonymous user behavior data in order to
understand interests of users so that, for example, more relevant
and targeted marketing may be performed.
[0082] Further, the method 300 may include a step 320 of
identifying an anonymous identifier corresponding to the user
identifier. The anonymous identifier may in general be such that
personal details of the user may not obtained from only the
anonymous identifier. In other words, the anonymous identifier may,
in some instances, enable the platform or any other computer system
to uniquely identify the user among other users. However, no
personal details of the user may be derivable based only on the
anonymous identifier.
[0083] Further, in some embodiments, the anonymous identifier may
be identified based on operating a one-way hash function on the
user identifier. Accordingly, the user identifier may not be
recoverable from the anonymous identifier due to the nature of the
one-way hash function. Consequently, possession of the anonymous
identifier in itself may not compromise privacy of the user.
[0084] Further, the method 300 may include a step 330 of retrieving
anonymous user behavior data based on the anonymous identifier. For
instance, the platform may include a database including anonymous
user behavior data corresponding to a plurality of users.
Accordingly, the database may be queried with the anonymous
identifier as a key in order to retrieve the anonymous user
behavior data of the user.
[0085] Further, in some embodiments, retrieving the anonymous user
behavior data may include retrieving data from a plurality of
cookies corresponding to a plurality of websites. For example, the
method may include communicating with a plurality of webservers in
order to receive data from cookies corresponding to the user from
each of the plurality of webservers. Further, the database included
in the platform may be populated with the data from the
cookies.
[0086] Furthermore, in some embodiments, each cookie may include at
least a portion of the anonymous user behavior data. Furthermore,
in some embodiments, each cookie may be associated with the
anonymous identifier. As a result, data from the plurality of
cookies may be identified as corresponding to the same user.
[0087] Further, in some embodiments, the anonymous user behavior
data may be based on online activity of user. In general online
activity may be any activity performed by the user on a network,
such as for example, the Internet. For instance, the online
activity may include visiting a webpage, downloading and/or
uploading content from the internet and so on. Alternatively and/or
additionally, in some embodiments, the anonymous user behavior data
may be based on offline activity of the user, such as for example,
interactions of the user with applications executing on a user
device. Accordingly, data associated with the interactions may be
captured and included in the anonymous user behavior data.
[0088] Further, in some embodiments, the anonymous user behavior
data may include contextual data corresponding to the online
activity. In general, the contextual data may represent a state of
one or more of the user, a user device, an environment of the user
and/or the user device, data generated and/or captured by other
devices in the vicinity of the user and/or the user device and so
on.
[0089] Further, in some embodiments, the contextual data may
correspond to one or more user devices used by the user to perform
the online activity. For instance, the user may use a variety of
user devices such as, for example, laptop computer, desktop
computer, tablet computer, smartphone and so on to visit webpages
over a period of time and at different places such as home, office,
restaurants, on-road and so on. Accordingly, contextual data
corresponding to one or more of the user devices may be captured
and associated with the anonymous user behavior data of the
user.
[0090] Further, in some embodiments, the contextual data may
include device data representing the at least one user device. In
general, the device data may include any data captured by the user
device or any other device in communication with the user device.
For example, the device data may include information regarding the
hardware and/or the software configuration of the user device.
Further, the device data may also include a state of operation of
the user device. The state of operation may include for example,
applications currently executing on the user device, amount of
processor availability, amount of available storage, battery level
and so on.
[0091] Further, in some embodiments, the device data may include
one or more of a device identifier associated with a user device, a
network identifier associated with a communication network used for
performing the online activity, an Operating System (OS) identifier
of an OS installed on the user device and a browser identifier of a
browser installed on the user device. In some cases, the device
data may be such that a corresponding user may be uniquely
identifiable. For example, an IMEI number or a static IP address
may uniquely identify a user. In other cases, the device data may
be such that a small group of similar users may be identifiable.
For instance, a combination of geolocation, device type, OS,
browser type may enable identification of a set of similar users
who may be proximally located. Accordingly, knowledge of such users
may enable companies to target marketing campaigns, such as
distributing flyers, in the location focusing on the set of
users.
[0092] Further, in some embodiments, the contextual data may
include sensor data representing state of the at least one user
device during performance of the online activity. For instance,
sensor data from a location sensor such as a GPS receiver may be
captured while a user is visiting a webpage. Similarly, motion data
from an accelerometer may be captured to indicate whether the user
was in a state of rest or of motion while visiting a webpage.
Likewise, the sensor data may also include environmental
information such as temperature, pressure, and so on. Accordingly,
the sensor data may provide another dimension for categorizing the
anonymous user behavior data. For instance, such data may enable
identification of anonymous user behavior data of a very specific
set of users, for example, those traveling by a metro rail.
[0093] Further, in some embodiments, the anonymous user behavior
data may include at least one of demographic data and psychographic
data of the user.
[0094] Further, in some embodiments, the anonymous user behavior
data may include at least one interest of the user. For instance,
the at least one interest may be towards a topic, a subject, a
person, an event, a product/service and so on. In some embodiments,
the at least one interest may be inferred based on content searched
for and/or content consumed. For example, search keywords provided
by the user may be captured and one or more interests may be
inferred based on the search keywords. Similarly, keywords from
content, such as webpages relating to a particular topic, may be
captured and used to infer an interest of the user towards the
topic.
[0095] Further, in some embodiments, the anonymous user behavior
data may include a plurality of keywords representing the at least
one interest and a plurality of affinity values corresponding to
the plurality of keywords. For instance, an exemplary set of
keywords identified for a user based on the user's interaction with
various webpages is illustrated in FIG. 9. For example, based on
the user's visiting of a webpage related to sports news, the
keywords "Football" and "Basketball" may be identified and
associated with the user. Accordingly, the plurality of keywords
may indicate one or more topics of interest to the user. For
instance, the plurality of keywords may be extracted from webpages
visited by the user. Accordingly, the method 200 may be performed
to extract the plurality of keywords from webpages. Further, each
keyword may be associated with an affinity value that indicates a
relative importance of the keyword to the user. For example, the
user may have visited multiple webpages, each of which may include
a listing top 10 smartphones. Accordingly, multiple keywords
associated with the top 10 smartphones may be identified and
extracted. However, although keywords associated with all top 10
smartphones may be associated with the user, each keyword may be
assigned a different affinity value depending on an interest level
of the user towards a particular keyword. For example, a user may
have performed an interaction, such as clicking a link, on one or
more of the webpages indicative of an interest towards some of the
top 10 smartphones. Accordingly, the keywords associated with those
smartphones may be assigned relatively greater affinity values than
other keywords.
[0096] Additionally, in some embodiments, the keywords and
corresponding affinity values may be identified based on Natural
Language Processing (NLP) performed on content of the webpages
visited by the user. For instance, as illustrated in FIG. 10,
analyzing content of the webpage using, for example, NLP may result
in identification of a category of content, such as
"Entertainment". Further, NLP may also identify brand affinities of
the webpage, such as for example, "Star wars" that may provide a
greater contextual relevance and brand awareness to users.
Additionally, NLP may also include event detection involving
identification of specific time-sensitive triggers, such as for
example, an upcoming "New Movie". Further, NLP may also identify
important topics addressed in the content of the webpage and
associate those topics as concept tags with the webpage, such as
for example, "Cinema". Further, NLP may also include entity
extraction involving identifying relevant proper nouns like people
and/or brands.
[0097] Further, in some embodiments, the data associated with the
user may include data representing at least one of a product and a
service associated with the user. For instance, the user may have
purchased one or more products and subscribed to one or more
services. Accordingly, such data indicating the products and/or
services purchased by the user may be obtained from online and/or
offline stores where the purchases were made. For instance,
companies that sell products and/or services maintain such
information regarding products and/or services procured by each of
their customers in a CRM database. For example, the data may
include name of a product, model number of the product, year/month
of purchase, cost and so on. Further, in some instances, the data
may also include products and/or services towards which the user
may have expressed an explicit interest. For example, the user may
have enquired about a product and/or service through a
communication channel such as email, phone call etc. with a
company. Accordingly, such information about explicit interests may
be captured and stored in the CRM database.
[0098] Further, in some embodiments, the data associated with the
user may include offline data. For example, the offline data may be
obtained from other sources, such as brick and mortar stores.
Accordingly, for example, purchase information of the user related
to one or more products may be obtained. Similarly, offline data
may be obtained from businesses that conduct user surveys.
Accordingly, information regarding interests and lifestyle of the
user may be obtained.
[0099] Further, the method 300 may include a step 340 of
transmitting the anonymous user behavior data to the requesting
entity, such as the server computer including the CRM database.
Further, in some embodiments, the requesting entity may be
configured for receiving the anonymous user behavior data
corresponding to the user and appending the anonymous behavior data
to data associated with the user stored in the a database, such as
for example, the CRM database.
[0100] Accordingly, in some instances, the CRM database may be
supplemented with the anonymous user behavior data as exemplarily
illustrated in FIG. 7. As shown, initially, the CRM database may
include an email address of the user while other details about the
user may be absent (indicated by question marks). However, upon
performing the method 200, the CRM database may receive and store
keywords representing the user's interests towards various topics,
products, brands etc.
[0101] Further, in some embodiments, the method 200 may include a
step of receiving an indication of at least one keyword. For
example, a company operating the CRM database may transmit the user
identifier along with a set of keywords in order to understand the
user's affinity towards the set of keywords. Accordingly, the
anonymous user behavior data may include an affinity value
corresponding to the set of keywords as exemplarily illustrated in
FIG. 7. In addition, demographic data, such as for example, gender,
age, income, marital status etc. also may also be received and
stored in the CRM database.
[0102] Turning now to FIG. 4, a method 400 of predicting churn in
accordance with some embodiments is illustrated. A churn of a user
with respect to a product and/or service may involve procurement of
an alternative product and/or service by the user. In some cases,
the user may switch completely from the product and/or service to
the alternative. Accordingly, companies manufacturing and/or
selling the product and/or service may benefit from identifying
users who are likely to churn. Accordingly, the companies may take
one or more corrective actions in order to minimize or eliminate
the churn.
[0103] Further, prior to a user switching over to an alternative
product and/or service, the user may perform certain behavior such
as searching for alternative products and/or services and reviewing
information regarding particular alternative products and/or
services. Accordingly, user behavior data may potentially include
indicators of a likely churn of users from a product and/or a
service.
[0104] Accordingly, the method 400 may include a step 410 of
identifying at least one of a product and a service used by the
user based on the user behavior data. For example, when a user
visits a webpage hosted by a webserver, a cookie on the webserver
may capture device data corresponding to one or more user devices
used by the user to access the webpage. Based on the device data,
an indication of products and/or services currently used by the
user may be gleaned. For instance, the device data may indicate
that the user possesses an android smartphone and uses AT&T
internet service.
[0105] Additionally, in some embodiments, prior to performing step
410, the method may include a step of receiving a request for a
churn prediction from a requesting entity such as a webserver
including a CRM database.
[0106] Further, the method 400 may include a step 420 of
identifying at least one of an interested product and an interested
service associated with the user based on the user behavior data.
For instance, the user may perform searches for webpages related to
iPhone and visit the webpages. Accordingly, such user behavior may
be captured and an implicit and/or explicit interest of the user
towards a product and/or service, such as iPhone may be
identified.
[0107] Additionally, the method 400 may include a step 430 of
predicting a churn based on a comparison of the at least one of a
product and a service with at least one of the interested product
and the interested service. For example, the user behavior data may
indicate that the user currently possess an android phone while
expressing an implicit and/or an explicit interest towards iPhone.
Accordingly, a difference between the current product and the
interested product based on the comparison may indicate a
likelihood of churn. Further, in some embodiments, the churn
prediction may include a risk value indicating a likelihood of the
user to churn towards at least one of the interested product and
the interested service. The risk value may be determined, for
example, based on an affinity of the keyword representing the
interested product and/or the interested service.
[0108] Further, in some embodiments, the churn prediction may
include indication of at least one of the interested product and
the interested service. Continuing the preceding example, the churn
prediction may include indication of iPhone, and in some cases, a
risk value indicating a likelihood of the user switching from the
android phone to iPhone.
[0109] Accordingly, in some embodiments, the churn prediction may
be transmitted to the requesting entity, such as a server computer
including the CRM database. Accordingly, in some embodiments, the
CRM database may be further enriched by data indicative of churn
and a likelihood of churn corresponding to users for each product
and/or service.
[0110] FIG. 5 illustrates a flow chart of a method 500 of
correlating anonymous user behavior data with data associated with
known users according to some embodiments. Accordingly, the method
500 may include a step 510 of receiving anonymous user behavior
data corresponding to a user. For instance, the anonymous user
behavior data may include a combination of the device data and
demographic data. Further, the method 500 may include a step 520 of
comparing the anonymous user behavior data with data of known
users. For example, data of known users in the CRM database may
include demographic data along with device data. Accordingly, the
device data and demographic data included in the anonymous user
behavior data may be correlated with the corresponding data of each
of the known users in the CRM database.
[0111] Further, the method 500 may include a step 530 of
associating the anonymous user behavior data with a known user
based on a result of the comparing. For example, based on the
comparison, it may be determined that the device data and the
demographic data included in the anonymous user behavior data
matches with that of the user than with that of other users in the
CRM database. Accordingly, it may be determined that the anonymous
user behavior data represents behavior of the user. Subsequently,
the association may be stored in the CRM database for further
use.
[0112] FIG. 6 a flow chart of a method 600 of providing anonymous
user behavior data according to some embodiments. The method 600
may include a step 610 of receiving user data associated with a
user from a requesting entity, such as the server computer
including the CRM data. For example, the user data may include one
or more of, but is not limited to, demographic data, the device
data and so on. Further, the method 600 may include a step 610 of
comparing the user data with anonymous user behavior data of a
plurality of users. For instance, the platform may include a
database containing anonymous user behavior data of a plurality of
users. However, the specific user associated with a given anonymous
user behavior data may not be known to the platform. Accordingly,
the platform may compare the user data with anonymous user behavior
data of each user in the database in order to find a match.
Accordingly, the method 600 may include a step 630 of identifying
anonymous user behavior data of the user based on a result of the
comparing. Thus, the platform may be able to identify an
association between anonymous user behavior data of a user with
other data of the user, such as that available in a CRM
database.
IV. PLATFORM ARCHITECTURE
[0113] The user profile creation platform 100 may be embodied as,
for example, but not be limited to, a website, a web application, a
desktop application, and a mobile application compatible with a
computing device. The computing device may comprise, but not be
limited to, a desktop computer, laptop, a tablet, or mobile
telecommunications device. Moreover, platform 100 may be hosted on
a centralized server, such as, for example, a cloud computing
service. Although methods 200 to 600 have been described to be
performed by a computing device 1100, it should be understood that,
in some embodiments, different operations may be performed by
different networked elements in operative communication with
computing device 1100.
[0114] Embodiments of the present disclosure may comprise a system
having a memory storage and a processing unit. The processing unit
coupled to the memory storage, wherein the processing unit is
configured to perform the stages of methods 200 to 600.
[0115] FIG. 11 is a block diagram of a system including computing
device 1100. Consistent with an embodiment of the disclosure, the
aforementioned memory storage and processing unit may be
implemented in a computing device, such as computing device 1100 of
FIG. 11. Any suitable combination of hardware, software, or
firmware may be used to implement the memory storage and processing
unit. For example, the memory storage and processing unit may be
implemented with computing device 1100 or any of other computing
devices 1118, in combination with computing device 1100. The
aforementioned system, device, and processors are examples and
other systems, devices, and processors may comprise the
aforementioned memory storage and processing unit, consistent with
embodiments of the disclosure.
[0116] With reference to FIG. 11, a system consistent with an
embodiment of the disclosure may include a computing device, such
as computing device 1100. In a basic configuration, computing
device 1100 may include at least one processing unit 1102 and a
system memory 1104. Depending on the configuration and type of
computing device, system memory 1104 may comprise, but is not
limited to, volatile (e.g. random access memory (RAM)),
non-volatile (e.g. read-only memory (ROM)), flash memory, or any
combination. System memory 1104 may include operating system 1105,
one or more programming modules 1106, and may include a program
data 1107. Operating system 1105, for example, may be suitable for
controlling computing device 1100's operation. In one embodiment,
programming modules 1106 may include affinity calculating modules,
such as, for example, webpage affinity calculation application
1120. Furthermore, embodiments of the disclosure may be practiced
in conjunction with a graphics library, other operating systems, or
any other application program and is not limited to any particular
application or system. This basic configuration is illustrated in
FIG. 11 by those components within a dashed line 1108.
[0117] Computing device 1100 may have additional features or
functionality. For example, computing device 1100 may also include
additional data storage devices (removable and/or non-removable)
such as, for example, magnetic disks, optical disks, or tape. Such
additional storage is illustrated in FIG. 11 by a removable storage
1109 and a non-removable storage 1110. Computer storage media may
include volatile and nonvolatile, removable and non-removable media
implemented in any method or technology for storage of information,
such as computer readable instructions, data structures, program
modules, or other data. System memory 1104, removable storage 1109,
and non-removable storage 1110 are all computer storage media
examples (i.e., memory storage.) Computer storage media may
include, but is not limited to, RAM, ROM, electrically erasable
read-only memory (EEPROM), flash memory or other memory technology,
CD-ROM, digital versatile disks (DVD) or other optical storage,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other medium which can be used to
store information and which can be accessed by computing device
1100. Any such computer storage media may be part of device 1100.
Computing device 1100 may also have input device(s) 1112 such as a
keyboard, a mouse, a pen, a sound input device, a touch input
device, etc. Output device(s) 1114 such as a display, speakers, a
printer, etc. may also be included. The aforementioned devices are
examples and others may be used.
[0118] Computing device 1100 may also contain a communication
connection 1116 that may allow device 1100 to communicate with
other computing devices 1118, such as over a network in a
distributed computing environment, for example, an intranet or the
Internet. Communication connection 1116 is one example of
communication media. Communication media may typically be embodied
by computer readable instructions, data structures, program
modules, or other data in a modulated data signal, such as a
carrier wave or other transport mechanism, and includes any
information delivery media. The term "modulated data signal" may
describe a signal that has one or more characteristics set or
changed in such a manner as to encode information in the signal. By
way of example, and not limitation, communication media may include
wired media such as a wired network or direct-wired connection, and
wireless media such as acoustic, radio frequency (RF), infrared,
and other wireless media. The term computer readable media as used
herein may include both storage media and communication media.
[0119] As stated above, a number of program modules and data files
may be stored in system memory 1104, including operating system
1105. While executing on processing unit 1102, programming modules
1106 (e.g., platform application 1120) may perform processes
including, for example, one or more of methods 200 to 600's stages
as described above. The aforementioned process is an example, and
processing unit 1102 may perform other processes. Other programming
modules that may be used in accordance with embodiments of the
present disclosure may include electronic mail and contacts
applications, word processing applications, spreadsheet
applications, database applications, slide presentation
applications, drawing or computer-aided application programs,
etc.
[0120] Generally, consistent with embodiments of the disclosure,
program modules may include routines, programs, components, data
structures, and other types of structures that may perform
particular tasks or that may implement particular abstract data
types. Moreover, embodiments of the disclosure may be practiced
with other computer system configurations, including hand-held
devices, multiprocessor systems, microprocessor-based or
programmable consumer electronics, minicomputers, mainframe
computers, and the like. Embodiments of the disclosure may also be
practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network. In a distributed computing environment,
program modules may be located in both local and remote memory
storage devices.
[0121] Furthermore, embodiments of the disclosure may be practiced
in an electrical circuit comprising discrete electronic elements,
packaged or integrated electronic chips containing logic gates, a
circuit utilizing a microprocessor, or on a single chip containing
electronic elements or microprocessors. Embodiments of the
disclosure may also be practiced using other technologies capable
of performing logical operations such as, for example, AND, OR, and
NOT, including but not limited to mechanical, optical, fluidic, and
quantum technologies. In addition, embodiments of the disclosure
may be practiced within a general purpose computer or in any other
circuits or systems.
[0122] Embodiments of the disclosure, for example, may be
implemented as a computer process (method), a computing system, or
as an article of manufacture, such as a computer program product or
computer readable media. The computer program product may be a
computer storage media readable by a computer system and encoding a
computer program of instructions for executing a computer process.
The computer program product may also be a propagated signal on a
carrier readable by a computing system and encoding a computer
program of instructions for executing a computer process.
Accordingly, the present disclosure may be embodied in hardware
and/or in software (including firmware, resident software,
micro-code, etc.). In other words, embodiments of the present
disclosure may take the form of a computer program product on a
computer-usable or computer-readable storage medium having
computer-usable or computer-readable program code embodied in the
medium for use by or in connection with an instruction execution
system. A computer-usable or computer-readable medium may be any
medium that can contain, store, communicate, propagate, or
transport the program for use by or in connection with the
instruction execution system, apparatus, or device.
[0123] The computer-usable or computer-readable medium may be, for
example but not limited to, an electronic, magnetic, optical,
electromagnetic, infrared, or semiconductor system, apparatus,
device, or propagation medium. More specific computer-readable
medium examples (a non-exhaustive list), the computer-readable
medium may include the following: an electrical connection having
one or more wires, a portable computer diskette, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, and a
portable compact disc read-only memory (CD-ROM). Note that the
computer-usable or computer-readable medium could even be paper or
another suitable medium upon which the program is printed, as the
program can be electronically captured, via, for instance, optical
scanning of the paper or other medium, then compiled, interpreted,
or otherwise processed in a suitable manner, if necessary, and then
stored in a computer memory.
[0124] Embodiments of the present disclosure, for example, are
described above with reference to block diagrams and/or operational
illustrations of methods, systems, and computer program products
according to embodiments of the disclosure. The functions/acts
noted in the blocks may occur out of the order as shown in any
flowchart. For example, two blocks shown in succession may in fact
be executed substantially concurrently or the blocks may sometimes
be executed in the reverse order, depending upon the
functionality/acts involved.
[0125] While certain embodiments of the disclosure have been
described, other embodiments may exist. Furthermore, although
embodiments of the present disclosure have been described as being
associated with data stored in memory and other storage mediums,
data can also be stored on or read from other types of
computer-readable media, such as secondary storage devices, like
hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a
carrier wave from the Internet, or other forms of RAM or ROM.
Further, the disclosed methods' stages may be modified in any
manner, including by reordering stages and/or inserting or deleting
stages, without departing from the disclosure.
[0126] All rights including copyrights in the code included herein
are vested in and the property of the Applicant. The Applicant
retains and reserves all rights in the code included herein, and
grants permission to reproduce the material only in connection with
reproduction of the granted patent and for no other purpose.
V. ASPECTS
[0127] The application includes at least the following aspects:
[0128] Aspect 1. A method of providing business intelligence based
on user behavior, wherein the method is a computer implemented
method, the method comprising:
[0129] a. receiving a user identifier associated with a user from a
requesting entity;
[0130] b. identifying an anonymous identifier corresponding to the
user identifier;
[0131] c. retrieving anonymous user behavior data based on the
anonymous identifier; and
[0132] d. transmitting the anonymous user behavior data to the
requesting entity.
[0133] Aspect 2. The method of aspect 1, wherein the requesting
entity is configured for:
[0134] a. receiving the anonymous user behavior data corresponding
to the user; and
[0135] b. appending the anonymous behavior data to data associated
with the user stored in a database.
[0136] Aspect 3. The method of aspect 1, wherein the requesting
entity is a server computer, wherein the data associated with the
user is comprised in a Customer Relationship Management (CRM)
database executable on the server computer.
[0137] Aspect 4. The method of aspect 1 further comprising:
[0138] a. identifying at least one of a product and a service used
by the user based on the user behavior data;
[0139] b. identifying at least one of an interested product and an
interested service associated with the user based on the user
behavior data; and
[0140] c. predicting a churn based on a comparison of the at least
one of a product and a service with at least one of the interested
product and the interested service.
[0141] Aspect 5. The method of aspect 4 further comprising: [0142]
a. receiving a request for a churn prediction from the requesting
entity; and [0143] b. transmitting a churn prediction based on the
predicting.
[0144] Aspect 6. The method of aspect 5, wherein the churn
prediction comprises a risk value indicating a likelihood of the
user to churn towards at least one of the interested product and
the interested service.
[0145] Aspect 7. The method of aspect 5, wherein the churn
prediction comprises indication of at least one of the interested
product and the interested service.
[0146] Aspect 8. The method of aspect 1 further comprising an
indication of at least one keyword, wherein the anonymous user
behavior data comprises an affinity value corresponding to the at
least one keyword.
[0147] Aspect 9. The method of aspect 1, wherein the anonymous
identifier is identified based on operating a one-way hash function
on the user identifier.
[0148] Aspect 10. The method of aspect 1, wherein retrieving the
anonymous user behavior data comprises retrieving data from a
plurality of cookies corresponding to a plurality of websites,
wherein each cookie comprises at least a portion of the anonymous
user behavior data, wherein each cookie is associated with the
anonymous identifier.
[0149] Aspect 11. The method of aspect 1, wherein the anonymous
user behavior data is based on online activity of user.
[0150] Aspect 12. The method of aspect 11, wherein the anonymous
user behavior data comprises contextual data corresponding to the
online activity, wherein the contextual data corresponds to at
least one user device used by the user to perform the online
activity.
[0151] Aspect 13. The method of aspect 12, wherein the contextual
data comprises device data representing the at least one user
device.
[0152] Aspect 14. The method of aspect 13, wherein the device data
comprises at least one of a device identifier associated with a
user device, a network identifier associated with a communication
network used for performing the online activity, an Operating
System (OS) identifier of an OS installed on the user device and a
browser identifier of a browser installed on the user device.
[0153] Aspect 15. The method of aspect 12, wherein the contextual
data comprises sensor data representing state of the at least one
user device during performance of the online activity.
[0154] Aspect 16. The method of aspect 1, wherein the anonymous
user behavior data comprises at least one of demographic data and
psychographic data of the user.
[0155] Aspect 17. The method of aspect 1, wherein the anonymous
user behavior data comprises at least one interest of the user.
[0156] Aspect 18. The method of aspect 17, wherein the anonymous
user behavior data comprises a plurality of keywords representing
the at least one interest and a plurality of affinity values
corresponding to the plurality of keywords.
[0157] Aspect 19. The method of aspect 2, wherein the data
associated with the user comprises data representing at least one
of a product and a service associated with the user.
[0158] Aspect 20. The method of aspect 2, wherein the data
associated with the user comprises offline data.
[0159] Aspect 21. A method of providing business intelligence based
on user behavior, wherein the method is a computer implemented
method, the method comprising:
[0160] a. receiving anonymous user behavior data corresponding to a
user;
[0161] b. comparing the anonymous user behavior data with data of
known users; and
[0162] c. associating the anonymous user behavior data with a known
user based on a result of the comparing.
[0163] Aspect 22. The method of aspect 21, wherein the anonymous
user behavior data is based on online activity of user.
[0164] Aspect 23. The method of aspect 22, wherein the anonymous
user behavior data comprises contextual data corresponding to the
online activity, wherein the contextual data corresponds to at
least one user device used by the user to perform the online
activity.
[0165] Aspect 24. The method of aspect 23, wherein the contextual
data comprises device data representing the at least one user
device.
[0166] Aspect 25. The method of aspect 24, wherein the device data
comprises at least one of a device identifier associated with a
user device, a network identifier associated with a communication
network used for performing the online activity, an Operating
System (OS) identifier of an OS installed on the user device and a
browser identifier of a browser installed on the user device.
[0167] Aspect 26. The method of aspect 23, wherein the contextual
data comprises sensor data representing state of the at least one
user device during performance of the online activity.
[0168] Aspect 27. The method of aspect 21, wherein the anonymous
user behavior data comprises at least one of demographic data and
psychographic data of the user.
[0169] Aspect 28. The method of aspect 21, wherein the anonymous
user behavior data comprises at least one interest of the user.
[0170] Aspect 29. The method of aspect 28, wherein the anonymous
user behavior data comprises a plurality of keywords representing
the at least one interest and a plurality of affinity values
corresponding to the plurality of keywords.
[0171] Aspect 30. The method of aspect 29 further comprising:
[0172] a. receiving a user identifier associated with the user;
[0173] b. identifying an anonymous identifier corresponding to the
user identifier; and
[0174] c. retrieving the anonymous user behavior data based on the
anonymous identifier.
[0175] Aspect 31. The method of aspect 21, wherein data of known
users comprises data representing at least one of a product and a
service associated with known users.
[0176] Aspect 32. The method of aspect 31 further comprising
predicting a churn based on a comparison of the anonymous user
behavior data with data representing at least one of the product
and the service, wherein the anonymous user behavior data indicates
an interest of the user towards at least one of another product and
another service.
[0177] Aspect 33. The method of aspect 21 further comprising:
[0178] a. identifying at least one of a product and a service used
by the user based on the user behavior data;
[0179] b. identifying at least one of an interested product and an
interested service associated with the user based on the user
behavior data; and
[0180] c. predicting a churn based on a comparison of the at least
one of a product and a service with at least one of the interested
product and the interested service.
[0181] Aspect 34. The method of aspect 21, wherein the data of
known users is comprised in a Customer Relationship Management
(CRM) database.
[0182] Aspect 35. The method of aspect 22, wherein the anonymous
user behavior data comprises a plurality of Universal Resource
Locators (URLs) associated with webpages visited by the user and a
corresponding plurality of time values representing the times when
the webpages were visited.
[0183] Aspect 36. A method of providing business intelligence based
on user behavior, wherein the method is a computer implemented
method, the method comprising:
[0184] a. receiving user data associated with a user;
[0185] b. comparing user data with anonymous user behavior data of
a plurality of users; and
[0186] c. identifying anonymous user behavior data of the user
based on a result of the comparing.
[0187] Aspect 37. A system for providing business intelligence
based on user behavior, the system comprising:
[0188] a. a communication module configured to:
[0189] i. receive a user identifier associated with the user from a
requesting entity; and
[0190] ii. transmit anonymous user behavior data to the requesting
entity;
[0191] b. a processing module coupled to the communication module,
wherein the processing module is configured to identify the
anonymous identifier corresponding to the user identifier; and
[0192] c. a storage module coupled to the processing module,
wherein the storage module is configured to retrieve anonymous user
behavior data based on an anonymous identifier.
[0193] Aspect 38. The system of aspect 37, wherein the requesting
entity is configured to:
[0194] a. receive the anonymous user behavior data corresponding to
the user; and
[0195] b. append the anonymous behavior data to data associated
with the user stored in a database.
[0196] Aspect 39. The system of aspect 37, wherein the requesting
entity is a server computer, wherein the data associated with the
user is comprised in a Customer Relationship Management (CRM)
database executable on the server computer.
[0197] Aspect 40. The system of aspect 37, wherein the processing
module is further configured to:
[0198] a. identify at least one of a product and a service used by
the user based on the user behavior data;
[0199] b. identify at least one of an interested product and an
interested service associated with the user based on the user
behavior data; and
[0200] c. predict a churn based on a comparison of the at least one
of a product and a service with at least one of the interested
product and the interested service.
[0201] Aspect 41. The system of aspect 40, wherein the
communication module is further configured to:
[0202] a. receive a request for a churn prediction from the
requesting entity; and
[0203] b. transmit a churn prediction based on the predicting.
[0204] Aspect 42. The system of aspect 41, wherein the churn
prediction comprises a risk value indicating a likelihood of the
user to churn towards at least one of the interested product and
the interested service.
[0205] Aspect 43. The system of aspect 41, wherein the churn
prediction comprises indication of at least one of the interested
product and the interested service.
[0206] Aspect 44. The system of aspect 37, wherein the
communication module is further configured to receive an indication
of at least one keyword, wherein the anonymous user behavior data
comprises an affinity value corresponding to the at least one
keyword.
[0207] Aspect 45. The system of aspect 37, wherein the anonymous
identifier is identified based on operating a one-way hash function
on the user identifier.
[0208] Aspect 46. The system of aspect 37, wherein retrieving the
anonymous user behavior data comprises retrieving data from a
plurality of cookies corresponding to a plurality of websites,
wherein each cookie comprises at least a portion of the anonymous
user behavior data, wherein each cookie is associated with the
anonymous identifier.
[0209] Aspect 47. The system of aspect 37, wherein the anonymous
user behavior data is based on online activity of user.
[0210] Aspect 48. The system of aspect 47, wherein the anonymous
user behavior data comprises contextual data corresponding to the
online activity, wherein the contextual data corresponds to at
least one user device used by the user to perform the online
activity.
[0211] Aspect 49. The system of aspect 48, wherein the contextual
data comprises device data representing the at least one user
device.
[0212] Aspect 50. The system of aspect 49, wherein the device data
comprises at least one of a device identifier associated with a
user device, a network identifier associated with a communication
network used for performing the online activity, an Operating
System (OS) identifier of an OS installed on the user device and a
browser identifier of a browser installed on the user device.
[0213] Aspect 51. The system of aspect 48, wherein the contextual
data comprises sensor data representing state of the at least one
user device during performance of the online activity.
[0214] Aspect 52. The system of aspect 37, wherein the anonymous
user behavior data comprises at least one of demographic data and
psychographic data of the user.
[0215] Aspect 53. The system of aspect 37, wherein the anonymous
user behavior data comprises at least one interest of the user.
[0216] Aspect 54. The system of aspect 53, wherein the anonymous
user behavior data comprises a plurality of keywords representing
the at least one interest and a plurality of affinity values
corresponding to the plurality of keywords.
[0217] Aspect 55. The system of aspect 38, wherein the data
associated with the user comprises data representing at least one
of a product and a service associated with the user.
[0218] Aspect 56. The system of aspect 38, wherein the data
associated with the user comprises offline data.
VI. CLAIMS
[0219] While the specification includes examples, the disclosure's
scope is indicated by the following claims. Furthermore, while the
specification has been described in language specific to structural
features and/or methodological acts, the claims are not limited to
the features or acts described above. Rather, the specific features
and acts described above are disclosed as example for embodiments
of the disclosure.
[0220] Insofar as the description above and the accompanying
drawing disclose any additional subject matter that is not within
the scope of the claims below, the disclosures are not dedicated to
the public and the right to file one or more applications to claims
such additional disclosures is reserved.
* * * * *