U.S. patent application number 14/208572 was filed with the patent office on 2014-09-18 for matching social media user to marketing campaign.
This patent application is currently assigned to Comverse Ltd.. The applicant listed for this patent is Comverse Ltd.. Invention is credited to Amit Braytenbaum, Omer Uretzky.
Application Number | 20140278976 14/208572 |
Document ID | / |
Family ID | 51532251 |
Filed Date | 2014-09-18 |
United States Patent
Application |
20140278976 |
Kind Code |
A1 |
Braytenbaum; Amit ; et
al. |
September 18, 2014 |
MATCHING SOCIAL MEDIA USER TO MARKETING CAMPAIGN
Abstract
Disclosed are systems and methods that gather information in
levels of various categories about the "likes" of a user of a
social site and, by assigning a weight factor to each level of
category of "likes", a "match" score for the user is calculated.
The calculation of "match" scores can be performed for a plurality
of users to obtain a set of users who are potential customers of a
marketing campaign. The set of users whose "likes" are evaluated
can be compared to the "target" of an advertising campaign to
generate potential customers for that campaign; this serves to
improve the conversion rate for the advertising campaign.
Alternatively, the "like" profiles can be used to analyze the
advertising campaign portfolio of an entity and provide
recommendations as to those advertising campaigns likely to be of
interest to a particular set of users, based on calculated "like"
profiles.
Inventors: |
Braytenbaum; Amit; (Lehavim,
IL) ; Uretzky; Omer; (Ramat Hasharon, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Comverse Ltd. |
Tel Aviv |
|
IL |
|
|
Assignee: |
Comverse Ltd.
Tel Aviv
IL
|
Family ID: |
51532251 |
Appl. No.: |
14/208572 |
Filed: |
March 13, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61791042 |
Mar 15, 2013 |
|
|
|
61788969 |
Mar 15, 2013 |
|
|
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Current U.S.
Class: |
705/14.53 |
Current CPC
Class: |
G06Q 30/0255 20130101;
H04L 67/22 20130101; G06Q 50/01 20130101; G06F 16/24578
20190101 |
Class at
Publication: |
705/14.53 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/00 20060101 G06Q050/00 |
Claims
1. A method comprising: compiling data about "likes" of a user of a
social site in different defined levels of specificity of a
category of information; assigning a weight factor to each level of
specificity of the category of information, wherein the weight
factor increases when the specificity of the level increases;
multiplying the weight factor for each level of specificity of the
category of information to obtain a product for each level of
specificity of the category of information; and adding the products
obtained to derive a "like" profile for the user.
2. The method of claim 1, further comprising updating the "like"
profile of the user based upon the user's activity while logged
onto the social site.
3. The method of claim 1, wherein the updating the "like" profile
comprises: recognizing that the user of the social site is logging
onto the social site; accessing the social site concurrently with
the user logging on; monitoring "like" activity of the user while
the user is on the social site; gathering information concerning
the "like" activity of the user while the user is logged onto the
social site; and updating the "like" profile of the user using the
information gathered concerning the user activity while the user is
logged onto the social site.
4. The method of claim 1, further comprising: compiling "like"
profiles for a plurality of users; evaluating an advertising
campaign to determine the "like" profile of a user who may be a
target of the campaign; comparing the "like" profile of the
plurality of users to the advertising campaign
5. The method of claim 4, further comprising identifying a group of
members of the plurality of users who match the target of the
campaign.
6. An apparatus comprising: a processor; and a memory that contains
instructions that are readable by the processor and cause the
processor to: gather data about "likes" of a user of a social site
in different levels of specificity of a category of information;
assign a weight factor to each level of specificity of the category
of information, wherein the weight factor increases when the
specificity of the level increases; multiply the weight factor for
each level of specificity of the category of information to obtain
a product for each level of specificity of the category of
information; and add the products obtained to obtain a "like"
profile for the user for the category of information.
7. The apparatus of claim 6, wherein said instructions further
cause the processor to: recognize that a user of the social site is
logging onto the social site; access the social site concurrently
with the user logging on; monitor "like" activity of the user while
the user is on the social site; gather information concerning the
"like" activity of the user while the user is logged onto the
social site; and update the "like" profile of the user using the
information gathered concerning the user activity while the user is
logged onto the social site.
8. A storage device comprising instructions that are readable by a
processor and cause a processor to: recognize that a user of a
social site is logging onto the social site; access the social site
concurrently with the user logging on; monitor "like" activity of
the user while the user is on the social site; gather information
concerning the "like" activity of the user while the user is logged
onto the social site; and update the "like" profile of the user
using the information gathered concerning the user activity while
the user is logged onto the social site.
9. The storage device of claim 8, wherein the instructions further
cause the processor to: recognize that the user of the social site
is logging onto the social site; access the social site
concurrently with the user logging on; monitor "like" activity of
the user while the user is on the social site; gather information
concerning the "like" activity of the user while the user is logged
onto the social site; and update the "like" profile of the user
using the information gathered concerning the user activity while
the user is logged onto the social site.
10. The storage device of claim 9, wherein the "like" profile can
be used to analyze an advertising campaign portfolio of an entity
and provide recommendations to the advertising campaign.
Description
CROSS-REFERENCED APPLICATIONS
[0001] This application is related, and claims priority, to U.S.
Provisional Application Ser. No. 61/791,042, filed on Mar. 15, 2013
that is incorporated herein in its entirety by reference. This
application is also related, and claims priority to, U.S.
Provisional Application Ser. No. 61/788,969, filed on Mar. 15, 2013
that is also incorporated herein in its entirety by reference.
BACKGROUND OF THE DISCLOSURE
[0002] 1. Field of the Disclosure
[0003] The present disclosure relates generally to systems and
methods for determining and identifying individuals who may be
receptive to a marketing campaign. More particularly, the present
disclosure relates to systems and methods for determining and
identifying a social media user to a marketing campaign.
[0004] 2. Background of the Disclosure
[0005] Web or network-based applications having a social aspect are
increasing in both number and popularity. For example, websites
such as Facebook.RTM., Twitter.RTM., and LinkedIn.RTM., to name
just a few, are fast becoming some of the most visited and used
websites on the Internet. These sites provide channels of
communication, comment, viewpoint, etc., for all the users, as well
as for the people with who the user is in contact, or who are in
contact with the user, whether directly or indirectly. Although
each of these social sites is quite different, they share in common
some key concepts. For instance, each of these social sites allows
a user to define his or her relationship with other users, and in
some instances, other objects or entities. These relationships can
be defined as, e.g., "followers" on Twitter.RTM., "friends" on
Facebook.RTM. or "connections" on LinkedIn.RTM.. In addition, each
of these social sites allows users to provide comments concerning
the information displayed, or shown, by other users with who the
user is in contact, such as "likes" and/or "comments" relating to
postings, messages, viewpoints, reviews, pictures and the like.
[0006] Entities, such as advertisers, websites and other marketers,
are constantly revising and updating their marketing strategies in
an effort to have their advertising/marketing campaigns be more
effective. One measure of the effectiveness of an
advertising/marketing campaign is to measure the "conversion" rate
of the campaign. In general, conversion rate refers to the number
of customers obtained versus the number of potential customers
contacted/sent/exposed to an advertising/marketing campaign. Stated
otherwise, conversion rate is the percentage of potential customers
who take a desired action.
[0007] In the online world, the desired action can take many forms,
varying from site to site. Examples include sales of products,
membership registrations, newsletter subscriptions, software
downloads, or just about any activity beyond simple page browsing.
A high conversion rate depends on several factors, all of which
must be satisfactory to yield the desired results--the interest
level of the visitor, the attractiveness of the offer, and the ease
of the process. The interest level of the visitor is maximized by
matching the right visitor, the right place, and the right
time.
[0008] Traditional data collected and stored in entity information
systems in user profiles generally includes demographics, ARPU,
ordering habits, usage and so on. This data can be considered
"high" level data in that it touches upon potential customers based
on general characteristics, but it does not take into consideration
or analyze the specifics of a potential customer's likes, dislikes
and/or preferences.
[0009] Thus, there exists a need for systems and methods that can
identify potential customers with more likelihood that the
potential customer will have a level of interest matching a
particular advertising/marketing campaign. Conversely, a need
exists for identifying a particular advertising/marketing campaign
that will likely be more appealing and attractive to a set or
subset of potential customers.
[0010] These and other needs are met according to the present
disclosure, as will be more fully described in the paragraphs that
follow.
SUMMARY OF THE DISCLOSURE
[0011] The present disclosure provides improved conversion rates on
campaigns so that offers sent to prospects should be unique and
compelling. Targeting the most likely potential customers based on
relevant data that provides a more precise picture of the potential
customer's likes can identify those customers who are most likely
to be positively influenced by a particular advertising/marketing
campaign. The more specific, detailed and comprehensive the picture
that is able to be gained about the potential customer(s), the more
effective a personalized campaign will be. Enriching the profile of
potential customers with social information that includes likes,
interests, preferences, social activity and what friends are doing
can open up a new level of personalization.
[0012] The present disclosure provides a component to the profile
of potential customer(s) that is aimed at extending the
personalization capabilities of the advertising/marketing campaigns
of an entity such as an advertiser or website by leveraging data
collected from interacting with the user in the multiple social
media networks. Therefore, according to the present disclosure, it
is now possible to identify with more confidence potential
customers who are likely be attracted to a particular
advertisement.
[0013] The present disclosure also provides that an entity, such as
an advertiser or website or the like, will be able to select among
its portfolio of marketing campaigns to identify those that are
more likely to have impact upon a set of potential customers.
[0014] One embodiment of the present disclosure provides systems
and methods that identify a set of users that meet the
characteristics defined by the campaign (target audience). The
result of these methods and systems is a list of users ranked by
their level of appropriateness for the campaign. This information
will enable an entity, such as an advertiser or website or the
like, to distribute campaigns to the ideal target audience without
having to manually segment and analyze customer profiles. The
campaign owner will only have to define the attributes of the
campaign and their weights. The entity, such as an advertiser or
website or the like, will have the option to configure a threshold
score so that the system returns only users whose scores are above
the threshold.
[0015] Another embodiment of the present disclosure provides
systems and methods that identify for a specific user, a set of the
most appropriate campaigns in an entity's list of campaigns ranked
by the level of suitability to the user. This information will
enable an entity, such as an advertiser or website, to propose the
most beneficial offers to a customer without having to browse
through the full campaign catalog and estimate which campaign will
be optimal for the user's needs.
[0016] According to the present disclosure, each potential
customer-campaign relationship is represented by a score calculated
by the systems and methods of the present disclosure. The score is
derived by the quality of the match.
[0017] Also according to the present disclosure, each campaign
attribute is given a weight that represents the level of its
importance for the matching. Then, according to the present
disclosure, the attribute is matched with the user profile
characteristics (based on his/her interests, behavior, preferences,
demographics, and the like), and the result of the match is a final
score for the campaign that is the sum of all
campaign-attribute/user-characteristic scores.
[0018] Also according to the present disclosure, to make these
mechanisms applicable, the systems and methods also include the
option to distribute the campaigns through social channels, as well
as to track the success of the campaigns (by monitoring the
conversion rate). By using the systems and methods of the present
disclosure, the entity, such as an advertiser or website, can gain
several advantages, such as faster time to market of new campaigns,
greater effectiveness in targeting customers, simplified management
of campaign catalogue(s), and detailed tracking of campaign success
through social channels.
[0019] The present disclosure further provides a system and a
method that includes: gathering information in levels of various
categories about the "likes" of a user of a social site; assigning
a weight factor to each level of category of "likes"; multiplying
the weight factor for each level of category of "likes" of
information to obtain a product for each level of category of
information; and adding the products obtained to obtain a "match"
score for the user.
[0020] The present methods and systems further provide for
repeating the gathering, assigning, multiplying and adding for a
plurality of users. In this way, the systems and methods obtains a
set of users who are potential customers of the entity such as an
advertiser or website. The set of users can be ranked in order of
"match". However, unlike the high level analysis that is presently
undertaken such as by looking at demographics (as mentioned above),
the systems and methods of the present disclosure employ
multi-level analysis that looks at different levels of specificity
of the "likes" (or equivalent, depending on the particular social
site) of a potential customer to ultimately provide matches of
interest(s) that are close or identical to the "target" of the
advertising campaign(s) of the entity.
[0021] The method of the above embodiment can also include updating
the information of a user based upon the user's activity while
logged onto a social site that includes: recognizing that a user of
a social site is logging onto a social site; accessing the social
site concurrently with the user logging on; monitoring activity of
the user while the user is on the social site; gathering
information concerning the activity of the user while the user is
logged onto the social site; and updating the "likes" (or
equivalent, depending on the particular social site) of the user
using the information gathered concerning the user activity while
the user is logged onto the social site.
[0022] According to the present disclosure, there is also provided
an apparatus/system that performs the method including: a
processor; and a memory that contains instructions that are
readable by said processor and cause said processor to: gather
information in levels of various categories about the "likes" of a
user of a social site; assign a weight factor to each level of
category of "likes"; multiply the weight factor for each level of
category of "likes" of information to obtain a product for each
level of category of information; and add the products obtained to
obtain a "match" score for the user. The apparatus/system further
provide for repeating the instructions to gather, assign, multiply
and add for a plurality of users. In this way, the processor
apparatus/system of the present disclosure allow for obtaining a
set of users who are potential customers of the entity such as an
advertiser or website. The set of users can be ranked further in
order of "match". The apparatus/system can also include
instructions that cause the processor to: recognize that the user
of a social site is logging onto a social site; access the social
site concurrently with the user logging on; monitor activity of the
user while the user is on the social site; gather information
concerning the activity of the user while the user is logged onto
the social site; and update the absolute social score of the user
using the information gathered concerning the user activity while
the user is logged onto the social site.
[0023] According to the present disclosure, there is also provided
a storage device comprising instructions that are readable by a
processor and cause the processor to: gather information in levels
of various categories about the "likes" of the user of a social
site; assign a weight factor to each level of category of "likes";
multiply the weight factor for each level of category of "likes" of
information to obtain a product for each level of category of
information; and add the products obtained to obtain a "match"
score for the user. The apparatus/system further provide a storage
device comprising instructions that are readable by the processor
and cause the processor to gather, assign, multiply and add for a
plurality of users. In this way, the processor of the present
disclosure can obtain a set of users who are potential customers of
the entity such as an advertiser or website. The set of users can
also be ranked in order of "match" strength or closeness to the
particular advertising campaign. The apparatus/system can include
instructions that further cause the processor to: recognize that
the user of a social site is logging onto a social site; access the
social site concurrently with the user logging on; monitor activity
of the user while the user is on the social site; gather
information concerning the activity of the user while the user is
logged onto the social site; and update the absolute social score
of the user using the information gathered concerning the user
activity while the user is logged onto the social site.
[0024] According to the present disclosure, the "likes" (or
equivalent, depending upon the social site) of the user of a social
site are compiled and can be "matched" to an advertising campaign
of an entity in any one of a number of ways. For instance, an
entity can have a particular advertising campaign that it wishes to
promote and requests the identification of users of a social site
most likely to be positively influenced by the campaign. In this
instance, the methods and system of the present disclosure are
capable of comparing the attributes of the particular advertising
campaign to the compiled "likes" of a plurality of social site
users. Thereafter, a list of users of the social site best
"matching" the attributes of the particular advertising campaign
can be selected and targeted. Alternatively, an advertising
campaign portfolio of an entity can be analyzed using the methods
and systems of the present disclosure and the best "matching"
campaigns of the entity can be identified and selected for
presentation to the list of social site users best fitting the
particular advertising campaign.
[0025] The apparatus/system and methods described herein are
applicable to any social site for the gathering of information
concerning the activity and evaluation of one or more user's
"match" score and for determining the relative "match" score for a
plurality of users. The evaluation can be tailored to the needs or
interests of any entity having a desire to know which user(s) may
be interested or influenced with respect to any one or more of a
set of advertising campaigns offered by that entity.
[0026] All such embodiments as mentioned above are included within
the teachings of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] FIG. 1 is a block diagram of a system that employs the
techniques described herein;
[0028] FIG. 2 is a general hierarchy chart of a system and method
of the present disclosure;
[0029] FIG. 3 is an exemplary specific hierarchy chart of a system
and method of the present disclosure;
[0030] FIG. 4 is an exemplary comparative table between a user's
interest profile and an exemplary campaign profile;
[0031] FIG. 5 is an exemplary score calculation and match
calculation for the comparative tables of FIG. 4; and
[0032] FIG. 6 is a flow chart of a process that employs the
techniques described herein.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0033] Referring to the drawings and, in particular, FIG. 1, a
system generally represented by reference numeral 100 is shown that
employs the methods described herein. System 100 includes a server
105, a user device 135, a social network server 180, and a client
server 190, each of which is communicatively coupled to a network
170, e.g., the Internet. User device 135 is utilized by a user
101.
[0034] Server 105 includes a processor 110 and a memory 115.
Although server 105 is represented herein as a standalone device,
it is not limited to such, but instead can be coupled to other
devices (not shown) in a distributed processing system. Server 105
is also communicatively coupled to a database 125. Server 105 can
also operate to support performance of relevant operations of
system 100 in a "cloud computing" environment or within the context
of "software as a service" (SaaS). At least some operations of
server 105 can be performed by a group of computers (as examples of
machines including processors), these operations being accessible
via network 170 via one or more appropriate interfaces, e.g.,
application program interfaces (APIs).
[0035] Processor 110 is an electronic device configured of logic
circuitry that responds to and executes instructions. Memory 115 is
a tangible computer-readable storage device encoded with one or
more computer programs. In this regard, memory 115 stores data and
instructions readable and executable by processor 110 for
controlling the operation of processor 110. Memory 115 can be
implemented in a random access memory (RAM), a hard drive, a read
only memory (ROM), or a combination thereof. One component of
memory 115 is a program module 120.
[0036] User device 135 includes a user interface 140, a processor
150 and a memory 160. User 101 utilizes user device 135 to access
social network server 180, e.g., Facebook.RTM., Twitter.RTM.,
and/or LinkedIn.RTM., via network 170. User device 135 can be
implemented, for example, as a cell phone, a personal computer
(PC), a tablet PC, a set-top box (STB), a Personal Digital
Assistant (PDA), or any device capable of executing instructions,
sequential or otherwise, that specify actions to be taken by that
device.
[0037] User interface 140 includes a display 141 and a keyboard
142. Display 141 is a device by which system 100 presents
information in visual form to user 101. By keyboard 142, user 101
inputs information to user device 135, and to social network server
180 via network 170. User interface 140 also includes a cursor
control mechanism (not shown), such as a mouse, track-ball, joy
stick, or a touch-screen, that is compatible with display 141 that
allows user 101 to manipulate a cursor (not shown) on display 141
for communicating additional information and command selections to
user device 135 and social network server 180.
[0038] Processor 150 is an electronic device configured of logic
circuitry that responds to and executes instructions.
[0039] Memory 160 is a tangible computer-readable storage device
encoded with a computer program. In this regard, memory 160 stores
data and instructions readable and executable by processor 150 for
controlling the operation of processor 150. Memory 160 can be
implemented in a RAM, a hard drive, a ROM, or a combination
thereof. One component of memory 160 is a program module 161.
[0040] The term "module" is used herein to denote a functional
operation that can be embodied either as a stand-alone component or
as an integrated configuration of a plurality of subordinate
components. Thus, each of program modules 120 and 161 can be
implemented as a single module or as a plurality of modules that
operate in cooperation with one another. Moreover, although program
modules 120 and 161 are described herein as being installed in
memory 115 and memory 160, respectively, and therefore being
implemented in software, they could be implemented in any of
hardware, e.g., electronic circuitry, firmware, software, or a
combination thereof.
[0041] While program modules 120 and 161 are indicated as already
being loaded into memories 115 and 160, respectively, they can be
configured on a storage device 175 for subsequent loading into
memories 115 and 161. Storage device 175 is a tangible
computer-readable storage medium that stores program modules 120
and 161 thereon. Examples of storage device 175 include a compact
disk, a magnetic tape, a read only memory, an optical storage
media, a hard drive or a memory unit having multiple parallel hard
drives, and a universal serial bus (USB) flash drive.
Alternatively, storage device 175 can be a random access memory, or
other type of electronic storage device, located on a remote
storage system (not shown) and coupled to server 105 and user
device 135 via network 170.
[0042] Client server 190 accesses server 105 via a password or
other security mechanism to, in turn, access data from server 105
for reasons and methods described in more detail herein below.
[0043] In practice, system 100 will include participation by many
users (not shown) each of which employ a respective user device
(not shown) similar to that of user device 135 to utilize and
interact with social network server 180.
[0044] FIG. 2 shows a general hierarchy 200 of a system and method
of the present disclosure. In FIG. 2 there is an advertising
campaign chart 210 and a social user "likes" chart 260. Advertising
campaign 210 has various levels of "categories", designated as
level "1" 220, level "2" 230, level "n" 240 and level "n+1" 250.
Likewise, social user "likes" 260 has various levels of
"categories", designated as level "1" 270, level "2" 280, level "n"
290 and level "n+1" 295. The number of levels set forth in FIG. 2
has been kept to four (4) for simplicity sake, but many more levels
are possible depending upon the needs of any particular desired
degree of "match" between social user "likes" 260 and advertising
campaign 210. Similarly, each level 1, 2, n and n+1 is divided into
various subcategories. For example, advertising campaign 210 level
1 220 is divided into four (4) subcategories 221, 222, 223 and 224,
while social user "likes" 260 level 1 270 is divided into four (4)
subcategories 271 [Phil-not in FIG. 2--incorrectly states 221],
272, 273 and 274. Once again, for simplicity sake, the number of
subcategories in each level in FIG. 2 has been limited. Of course,
as will be apparent, the number of categories, i.e., level 1, level
2, level n and level n+1, and the number of subcategories can be as
many as desired in order to obtain the degree of specificity in the
"match" between advertising campaign 210 and social user "likes"
chart 260. Also in FIG. 2, each level, e.g., 220, 270 is designated
with a "weight" W.sup.1, W.sup.2, W.sup.n and W.sup.n+1 that will
be explained in more detail with respect to the Figures that
follow. In general, each of weights W.sup.1, W.sup.2, W.sup.n and
W.sup.n+1 increases in value as the respective level of category
with which the weight is associated increases in number. Stated
otherwise, the weight for category level n+1 greater that the
weight for category level n, which is greater than the weight for
category level 2, and so forth.
[0045] FIGS. 3-5 are a specific example 300 of a how a "match"
between social user "likes" 260 and advertising campaign 210 is
determined according to the present disclosure. In FIG. 3, level 1
220 of advertising campaign 210 is divided into subcategories,
including, "sports" 222 and "music" 223 [Phil-not in FIG.
3--incorrectly states 233]. Likewise, level 1 270 of social user
"likes" 260 is divided into subcategories, including "movies" 271,
"sports" 272, and "music" 273. For purposes of FIG. 3, further
subcategories of "music" 223, 273 will be discussed. First,
focusing on advertising campaign 210, subcategory "music" 223 is
further broken down into subcategories on level 2 230, including
"pop" 2321, which is then further broken down in level 3 240 into
subcategories including "musician" 241. Providing more specificity,
musician subcategory of level "n" of advertising campaign 210
includes "tickets to Lady Gaga" concert at level "n+1".
[0046] Turning to social user "likes" 260, in level 270 "music" 273
matches with the "music" 223 of advertising campaign 210. The
"match" between social user "likes" 260 and advertising campaign
210 in level 1 220, 270, is accorded a weight 1, W.sup.1. Moving to
level 2 280, among social user's "likes" chart 260 within "music"
273 in level 1 is "pop" 282 in level 2. In advertising campaign
210, the level 2 target is likewise "pop" 232. Thus, again, the
level 2 interests of social user "likes" c260 matches advertising
campaign 210. The "match" between social user 260 and advertising
campaign 210 in level 2 230, 280, is accorded a weight 2, W.sup.2,
that is greater than W.sup.1. The relative weights between levels
can be assigned in any particular circumstance depending up the
needs/desires of advertising campaign 210. Continuing, FIG. 3 shows
that in level 3, social user "likes" 260 and advertising campaign
210 again provide a "match" with "musician" 241, 291, which is
accorded a weight 3, W.sup.3. Continuing further, as shown in FIG.
3, in level 4 250, 295 there is no "match" between social user
"likes" 260 ("Madonna") and advertising campaign 210 ("Tickets to
Lady Gaga").
[0047] FIG. 4 shows a social user "likes" profile 400 in table
form. Social user "likes" profile 400 in FIG. 4 is more complete
than exemplified in FIG. 3. Advertising campaign profile 410 is set
forth well, with the ultimate target of the campaign to locate and
interest users in "Lady Gaga Concert Tickets". As can be seen from
FIG. 4, social user "likes" profile 400 matches advertising
campaign profile 410 in category levels 1, 2 and 3, but do not
match in category level 4. In more detail, FIG. 4 shows that social
user "likes" profile 400, in category level 1 270 ("music"),
matches three (3) times (guitar, U2 and Madonna) with advertising
campaign profile 410. Continuing, social user "likes" profile 400,
in category level 2 280 ("pop" (genre)) matches one (1) time with
advertising campaign profile 410, and in category level 3 290
("musician/band") matches one (1) time with advertising campaign
profile 410 (under the category level 2 match). Lastly, in category
level 4 295 social user "likes" profile 400 does not match with
advertising campaign profile 410.
[0048] FIG. 5 shows a "weighted" calculation for the matches
between social user "likes" profile 400 and advertising campaign
profile 410 of FIG. 4. In FIG. 5, the "weights" per category level
"match" are set forth in the table 510 [Phil-not shown in FIG. 5].
In table 510, the weight for a category level 220 match 501 is 1;
the weight for a category level 230 match 502 is 2; the weight for
a category level 240 match 503 is 5 and the weight for a category
level 2504 match 504 is 10. The individual calculated scores for
each category level match between advertising campaign profile 410
and social user "likes" profile 400 is set forth in score
consolidation table 520. The total score of matches for social user
"likes" profile 400 forth in FIG. 4 is "12". The matching and score
consolidation for any number of a plurality of users according to
the present disclosure can be calculated in accordance with the
description respect set forth in FIGS. 3-5.
[0049] User(s) 101 usually have information on two levels. The
first level is public information; information which is generally
available to everyone, such as a profile on Facebook.RTM. or
LinkedIn.RTM., and this information is available and can be
gathered whether or not the user(s) 101 are logged onto the site.
In some cases, the publicly available information is obtained by
querying a social site, for example, via an application programming
interface (API) request. For instance, social network services
provide various public interfaces (e.g., API's) through which
information can be obtained. Also, all user(s) 101 have an
electronic ID which, if available to server 105, allows server 105
to identify user(s) 101 moving from one social site to another, and
to determine if user(s) 101 can be considered a social leader on
more than one social site.
[0050] The second level is private information; information that
can only be seen by, e.g., "friends" or perhaps "friends of
friends". This information can only be gathered when user(s) 101
are actively logged onto the social site. As user(s) 101 log onto a
social site, social network server 180 notifies server 105 of
user(s) 101 activity. As with public information, server 105 can
query the social site via an API to obtain the private information
of user(s) 101 when any user 101 logs onto a social site and begin
a session. Server 105 monitors the activity of user(s) 101 while
user(s) 101 are logged onto social site. As user(s) 101 input
"like" information on the social site, server 105 gathers that
"like" information and updates user(s)' 101 "like" profile.
[0051] Thus, gathering public "like" information about user(s) 101
can be performed at any time by server 105 in a batch-type
collection manner. On the other hand, private "like" information is
gathered when user(s) 101 are logged onto a social site and perform
activities on the social site in a dynamic-type collection manner.
Once again, user(s) 101 "like" profile is updated as user(s) 101
perform activity when logged onto a social site.
[0052] The "like" profile for user(s) 101 is described in more
detail below.
[0053] Using the public and private information gathered by server
105, the present disclosure describes methods and systems for
quantitatively calculating the "like" profile of different user(s)
101 of social sites based on an analysis of various sources of
social information. In the following description, for purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the various aspects of
different embodiments of the present disclosure. It will be
evident, however, to one skilled in the art, that the present
disclosure can be practiced without all of the mentioned specific
details.
[0054] The operation of apparatus/system and the method of the
present disclosure will be described in more detail below, in
accordance with FIG. 6.
[0055] FIG. 6 shows a method 600 for gathering information
concerning user's 101 utilization of, and interaction with, social
network server (SNS) 180. While FIG. 6 shows the steps of method
600 being performed sequentially, it will be appreciated that
certain of the steps may be performed concurrently or continuously.
Method 600 commences with step 605.
[0056] In step 605, user 101 logs onto social network server 180
via network 170 using, e.g., user device 135 shown in FIG. 1. From
step 605, method 600 progresses to step 610.
[0057] In step 610, social network server 180, in a communication
via network 170, notifies server 105 that user 101 has logged onto
to social network server 180. From step 610, method 600 progresses
to step 615.
[0058] In step 615, as user 101 performs activities on social
network server 180, server 105 gathers information about user's
"likes" activity, including user's 101 "likes" activities made with
respect to "posts", "comments", others' "likes", as well as to
specific genres, entities, personalities, people and categories of
various types. In step 615, server 105 can continually update
user's 101 social score or, alternatively, server 105 can update
user's 101 social score at regular timed intervals or, still
alternatively, can update user's 101 social score only when user
101 logs off from social network server 180. From step 615, method
600 progresses to step 620.
[0059] In step 620, user 101 logs off the social site and server
105 no longer monitors and gathers information concerning user's
101 activities while logged onto the social site. From step 620,
method 600 progresses to step 625.
[0060] In step 625, server 105 updates user's 101 "likes" profile
and these updated social scores are stored in memory 115 and/or in
data base 125. From step 625, method 600 progresses to step
630.
[0061] In step 630, server 105 receives a query from client server
190 for a "like" profile analysis for comparison to a particular
advertising campaign of client server 190 based upon parameters
designated by the query sent from client server 190 to server 105.
The parameters can be parameters set by client server 190 from
previous queries or specific parameters can be included in the new
query. As mentioned above, the query from client server 190 can be
predicated upon any analysis of users' 101 "like" profile desired
by client server 190. From step 630, method 600 progresses to step
635.
[0062] In step 635, server 105 calculates users' 101 "like" profile
"match" score based upon parameters set forth in the query made by
client server 190. From step 635, method 600 progresses to step
640.
[0063] In step 640, based upon the query by client server 190 and
the "like" profile analysis performed by server 105, server 105
provides client server 190 with the identity of users 101 resulting
from the analysis of the "like" profile data of users 101 by client
server 105 in accordance with the query and parameters set by
client server 190.
[0064] Method 600 then ends.
[0065] As mentioned above, method 600 is applicable to the
situation where, based upon a compilation of users' 101 "like"
profiles, server 105 is able to evaluate the portfolio of
advertising campaigns of client server 190 and select the
advertising campaign most likely to yield positive results for the
advertiser. This methodology saves the advertiser from having to
parse through a library of its advertising campaign portfolios to
select the advertising campaign well-suited for a particular
population of users 101 having a specific "like" profile. Stated
another way, the methods and systems of the present disclosure
gather and analyze the "likes" of users 101 to a level of detail
and specificity not previously available. For example, as set forth
above with respect to FIGS. 3-5, an advertiser can have a promotion
for Lady Gaga Concert Tickets and would like to direct the
promotion at the most likely purchasers. The methods and systems of
the present disclosure are capable of providing an audience of
potential customers to that advertiser based upon the detailed
analysis of a population of users 101 "like" profiles. However,
rather than merely being interested in users 101 who may have the
specific interest in Lady Gaga, an advertiser may wish to broaden
the audience of potential customers to whom its advertisements are
directed. In such an instance, the advertiser may wish to direct
its advertising not only to individual users 101 who "like" Lady
Gaga, but to those who "like" other specific female artists or,
even slightly more broadly, to individual users who "like" female
pop singers. The methods and systems according to the present
disclosure allow for all such variations, as can be seen from the
foregoing examples.
[0066] Thus, the present systems and methods for determining and
identifying individuals who may be receptive to a marketing
campaign derives information that can be of particular interest to
and for use by entities, e.g., advertisers and websites, who can
then direct more precisely targeted advertising/marketing campaigns
or other information to the identified individuals. The
identification of potentially receptive individuals, whether
individually or in groups, is based on surveying the individual's
"likes" (e.g., on Facebook) or similar indicators that the
individual has an appreciation of certain subject matter, and
should allow a higher "conversion" rate with respect to the
advertising/marketing campaign, as well as allowing entities to
select the most appropriate advertising/marketing campaign(s) from
the entities' portfolio.
[0067] The terms "comprises" or "comprising" are to be interpreted
as specifying the presence of the stated features, integers, steps
or components, but not precluding the presence of one or more other
features, integers, steps or components or groups thereof.
[0068] It should be understood that various alternatives,
combinations and modifications could be devised by those skilled in
the art. For example, steps associated with the processes described
herein can be performed in any order, unless otherwise specified or
dictated by the steps themselves. The present disclosure is
intended to embrace all such alternatives, modifications and
variances that fall within the scope of the appended claims.
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