U.S. patent application number 14/086238 was filed with the patent office on 2014-05-22 for method and system for predictive marketing campigns based on users online behavior and profile.
The applicant listed for this patent is Insightera Ltd.. Invention is credited to Mickey ALON, Mike TELEM.
Application Number | 20140143012 14/086238 |
Document ID | / |
Family ID | 50728811 |
Filed Date | 2014-05-22 |
United States Patent
Application |
20140143012 |
Kind Code |
A1 |
ALON; Mickey ; et
al. |
May 22, 2014 |
METHOD AND SYSTEM FOR PREDICTIVE MARKETING CAMPIGNS BASED ON USERS
ONLINE BEHAVIOR AND PROFILE
Abstract
The present invention discloses a method for providing
prediction of content usage in a website. The method comprising the
steps of: real time monitoring traffic of visitors in a website,
tracking visitors that are visiting the monitored website to
identify one or more parameters relating to user profile,
navigation path or content usage, applying at least one statistical
algorithm on identified parameters, said algorithm is at least one
of: clustering algorithm, nearest neighbor algorithm or probability
algorithm and defining content replacement and recommendation for
visitors when visiting the monitored website according analysis
results of the at least one statistical algorithm.
Inventors: |
ALON; Mickey; (Herzlia,
IL) ; TELEM; Mike; (Giv'at Shmuel, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Insightera Ltd. |
Petah Tikva |
|
IL |
|
|
Family ID: |
50728811 |
Appl. No.: |
14/086238 |
Filed: |
November 21, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61728865 |
Nov 21, 2012 |
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Current U.S.
Class: |
705/7.29 |
Current CPC
Class: |
H04L 67/22 20130101;
G06F 40/14 20200101; G06N 7/005 20130101; G06Q 30/0201 20130101;
H04L 67/306 20130101; G06F 16/951 20190101; G06Q 30/0641 20130101;
G06Q 30/0202 20130101 |
Class at
Publication: |
705/7.29 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method for providing prediction of content usage in a website,
said method comprising the steps of: real time monitoring traffic
of visitors in a website; tracking visitors that are visiting the
monitored website to identify one or more parameters relating to
user profile, navigation path or content usage; applying at least
one statistical algorithm on identified parameters, said algorithm
is at least one of: clustering algorithm, nearest neighbor
algorithm or probability algorithm and defining content replacement
and recommendation for visitors when visiting the monitored website
according the statistical algorithm analysis results of the at
least one statistical algorithm.
2. The method of claim 1 clustering algorithm enable classifying
user into groups by analyzing of profile and navigation activities
parameters of the user.
3. The method of claim 1 wherein the probability algorithm enables
analyzing the monitored user behavior and identifying correlation
or association between visitors profile parameters, navigation path
and/or content usage for creating probability tree.
4. The method of claim 1 wherein the nearest neighbor algorithm
enable classifying visitors into groups by analyzing content usage
parameters of the user.
5. The method of claim 1 further comprising the step of scoring the
content items according to a probability tree created by the
probability algorithm.
6. The method of claim 1 further comprising the step of scoring the
content items according to Neighborhoods created by the nearest
neighbor algorithm.
7. The method of claim 1 further comprising the step of analyzing
organization behavior by Identifying function of each user in the
organization and classifying his behavior for generating marketing
scenarios based on analyzed organization behavioral.
8. The method of claim 1 further comprising the step of checking
user profiles and classified pattern behavior for identifying
correlation with existing clients in CRM database.
9. The method of claim 1 further comprising the step of
characterizing user marketing state in a sale scenario based on
comparing analysis results to predefined marketing templates.
10. The method of claim 1 further comprising the step of scoring
incoming leads based on CRM accounts similarity.
11. The method of claim 1 further comprising the step of providing
recommendation for further communication or actions to be taken
based on one of the following: neighborhood and/or clustering
groups or probability algorithm.
12. The method of claim 1 wherein the statistical algorithm
analysis results are cached using in-memory optimized matrix model,
which allow real-time interactions on big data.
13. The method of claim 1 wherein the content recommendation is
based on content items clustering algorithm which enable
classifying content into groups by analyzing plurality of
attributes of the visitors that consumed the content.
14. A system for providing prediction of content usage in a
website, said system comprised of: A tracking module for monitoring
traffic of visitors in a website and identifying one or more
parameters relating to user profile, navigation path or content
usage; statistical algorithm for analyzing identified parameters,
said algorithm is at least one of: clustering algorithm, nearest
neighbor algorithm or probability algorithm and prediction module
for defining content replacement and recommendation for visitors
when visiting the monitored website according to analysis results
of the at least one statistical algorithm.
15. The system of claim 14 wherein the clustering algorithm enables
classifying user into groups by analyzing of profile and navigation
activities parameters of the user.
16. The system of claim 14 wherein the probability algorithm
enables analyzing the monitored user behavior and identifying
correlation/association between visitors profile parameters,
navigation path and/or content usage for creating probability
tree.
17. The system of claim 14 wherein the nearby neighbor algorithm
enables classifying user into groups by analyzing content usage
parameters of the user.
18. The system of claim 14 wherein the prediction is further based
on scoring the content items according to probability tree created
by the probability algorithm.
19. The system of claim 14 wherein the statistical algorithm
further comprises the step of analyzing organization behavior by
Identifying function of each user in the organization and
classifying his behavior for generating marketing scenarios based
on analyzed organization behavioral.
20. The system of claim 14 wherein the prediction is further based
on checking user profiles and classified pattern behavior for
identifying existing clients in CRM database.
21. The system of claim 14 the statistical algorithm further
comprise characterizing user marketing state in a sale scenario
based on comparing analysis results to predefined marketing
templates.
22. The system of claim 14 wherein the prediction module is further
based on scoring incoming lead based on CRM accounts
similarity.
23. The system of claim 14 wherein the prediction module further
providing recommendation for further communication or actions to be
taken, based on neighborhood and/or clustering groups or
probability algorithm.
Description
TECHNICAL FIELD
[0001] The present invention relates to the field of marketing
prediction, and more particularly, to predicting content usage
based on statistically analyzing online visitor's behavior
patterns.
BACKGROUND ART
[0002] Current solutions for predicting content usage are based on
likelihood of a user to consume a content item based on statistical
usage the similar content items by other visitors ignoring the
knowledge which can be collected of user identification and
navigation behavior when browsing a particular website.
[0003] Known in the art predictive analytics utilizes machine
learning algorithms over big data, using common practices of
scoring, next best item and optimized email campaigns. The Big data
systems which can work with batch and asynchronous processing,
raise a challenge for providing predictive analytics data response
that would be ready for real-time interactions.
SUMMARY OF INVENTION
[0004] The present invention discloses a method for providing
prediction of content usage in a website. The method comprising the
steps of: capturing and monitoring real time behavior of visitors
in a website, tracking visitors that are visiting the monitored
website to identify one or more parameters relating to visitor
profile attributes, such as Geo location, navigation path or
content usage, applying at least one statistical algorithm on
identified parameters, said algorithm is at least one of:
clustering algorithm, nearest neighbor algorithm or probability
algorithm and defining content replacement and recommendation for
visitors when visiting the monitored website according to the
analysis results of the at least one statistical algorithm.
[0005] According to some embodiments of the present invention the
clustering algorithm enables classifying visitors into groups by
analyzing the profile and navigation activities parameters of the
user.
[0006] According to some embodiments of the present invention the
probability algorithm enable analyzing the monitored user behavior
and identifying correlation/association between visitors profile
parameters, navigation path and/or content usage for creating
probability tree.
[0007] According to some embodiments of the present invention the
nearest neighbor algorithm enable classifying visitors into groups
by analyzing content usage parameters of the user.
[0008] According to some embodiments of the present invention the
method further comprises the step of scoring the content items
according to a probability tree created by the probability
algorithm.
[0009] According to some embodiments of the present invention the
method further comprises the step of scoring the content items
according to Neighborhoods created by the nearest neighbor
algorithm
[0010] According to some embodiments of the present invention the
method further comprises the step of analyzing an organizations
behavior by Identifying function of each user in the organization
and classifying his behavior for generating marketing scenarios
based on analyzed organizational behavior.
[0011] According to some embodiments of the present invention the
method further comprises the step of checking user profiles and
classified pattern behavior for identifying correlation with
existing clients in CRM (Customer Relationship Management)
database.
[0012] According to some embodiments of the present invention the
method further comprises the step of characterizing a user's
marketing state based on comparing analysis results to predefined
marketing templates.
[0013] According to some embodiments of the present invention the
method further comprises the step of scoring incoming leads based
on CRM accounts similarity.
[0014] According to some embodiments of the present invention the
method further comprises the step of providing recommendation for
further communication/actions to be taken based on neighborhood
and/or clustering groups or probability algorithm.
[0015] The present invention discloses a system for providing
prediction of content usage in a website. The system is comprised
of: a tracking module for monitoring traffic of visitors in a
website and identifying one or more parameters relating to user
profile, navigation path or content usage, statistical algorithm
for analyzing identified parameters, said algorithm is at least one
of: clustering algorithm, nearest neighbor algorithm or probability
algorithm and prediction module for defining content replacement
and recommendation for visitors when visiting the monitored website
according to analysis results of the at least one statistical
algorithm.
[0016] These, additional, and/or other aspects and/or advantages of
the present invention are: set forth in the detailed description
which follows; possibly inferable from the detailed description;
and/or learnable by practice of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0017] FIG. 1 is a block diagram of a system for adjusting content
of webpages, according to some embodiments of the invention;
[0018] FIG. 2 is a flowchart illustrating a method of tracking
visitors, according to some embodiments of the invention;
[0019] FIG. 3 is a flowchart illustrating a method of generating
anonymous profile of a user, according to some embodiments of the
invention;
[0020] FIG. 4 is a flowchart illustrating a method of clustering
algorithm, according to some embodiments of the invention;
[0021] FIG. 5 is a flowchart illustrating a method of assigning
visitors to clustered groups , according to some embodiments of the
invention;
[0022] FIG. 6 is a flowchart illustrating a method of analyzing
organization behavior, according to some embodiments of the
invention;
[0023] FIG. 7A is a flowchart illustrating a method of near by
neighbor algorithm, according to some embodiments of the
invention;
[0024] FIG. 7B is a flowchart illustrating a method of CRM
Association module, according to some embodiments of the
invention;
[0025] FIG. 8A is a flowchart illustrating a method of probability
algorithm, according to some embodiments of the invention;
[0026] FIG. 8B is a flowchart illustrating a method of scoring
according to some embodiments of the invention; and
[0027] FIG. 9 is a flowchart illustrating a method of prediction
module, according to some embodiments of the invention;
MODES FOR CARRYING OUT THE INVENTION
[0028] In the following detailed description of various
embodiments, reference is made to the accompanying drawings that
form a part thereof, and in which are shown by way of illustration
specific embodiments in which the invention may be practiced. It is
understood that other embodiments may be utilized and structural
changes may be made without departing from the scope of the present
invention.
[0029] The term "big data" as used herein in this application, is
defined as a collection of data sets that is so large and complex
that it is not possible to handle with database management tools.
As per Gartner, "Big Data are high-volume, high-velocity, and/or
high-variety information assets that require new forms of
processing to enable enhanced decision making, insight discovery
and process optimization."
[0030] The term "anonymous profile" as used herein in this
application, is defined as a visitor of a website that didn't
identify by login process to the website.
[0031] The term "proprietary heuristics" as used herein in this
application, is defined as experienced techniques that were
developed by the applicant and are used when an exhaustive search
or computation is impractical.
[0032] The present invention aims for statistical analysis of
anonymous visitors behavior that navigate in an Internet website
including at least one of: clicks, visits, navigation pattern, Geo
location, role within the organization, association to an
organization, social network or industry. It is suggested,
according to the present invention, to enable evaluation process
for analyzing marketing status and predicting content usage,
providing content and recommendations based on the statistical
analysis, marketing scenarios and strategies, as identified
throughout the user navigation and content usage (i.e. behavior
pattern in the internet website and content consumption).
[0033] The prediction process provides a marketing strategy which
known as "prospect nurturing". This marketing strategy enables
serving personalized content to visitors and it is used today
mostly by email communication, and only for identified prospects
(i.e. visitors). The present invention allows real-time prospect
nurturing for anonymous visitors throughout their navigation in the
website. It may provide anonymous potential clients with marketing
information to bring them into or advance them in the sales'
cycle.
[0034] In other words, the present invention allows a real-time
auto-prediction and marketing evaluation process based on detection
of behavioral pattern, marketing scenarios and classification of
anonymous visitors (i.e. potential clients). Anonymous visitors are
normally at pre-lead state and nurturing them is called `seed
nurturing`.
[0035] In `seed nurturing` the classification of visitors is
performed by heuristics to find conversion patterns behavior (i.e.
conversion from an anonymous user to a business lead) that are
common between anonymous visitors. Conversion patterns are the
pattern behavior of anonymous visitors that are becoming business
leads after being exposed to specific marketing material. For
example, on his second visit to a web site an anonymous user sees a
link to a white paper, the user clicks on it and reads it, and
fills out a form requesting more details thus becomes a business
lead. Identification of such conversion patterns may be used for
verifying or updating the predefined rules in the engagement rules
of the web content adjustments.
[0036] FIG. 1 is a block diagram of a system for predicting content
usage and/or marketing status, according to some embodiments of the
invention.
[0037] According to some embodiments of the present invention,
anonymous application 100 aims to improve content consumption
prediction and providing recommendation of content or further
marketing activities for visitors that are coming via user
communication devices 105. Optionally improving the marketing
evaluation and prediction performed by identifying marketing status
and scenario, according to the viewer's behavior (i.e. navigation
path), activities associations and other parameters.
[0038] According to some embodiments of the present invention, the
anonymous application 100 may activate a tracking module 110 to
generate a profile of an anonymous user (i.e. viewer) by various
parameters and store it in a profile database 115 as will be
described in detail in FIG. 2.
[0039] According to some embodiments of the present invention, a
clustering module 120 may monitor viewers by various parameters and
cluster them into groups, as will be described in detail in FIG.
3.
[0040] The process of clustering viewers into groups involves
analysis of big data. In order to save time and computer resources,
proprietary heuristics are being implemented. These proprietary
heuristics are taking into account intersections of profiles of
visitors and industries. For example, a viewer that was clustered
into a group of venture capital industry may view content related
to currency rate and stocks.
[0041] According to some embodiments of the present invention, an
anonymous profile generating module 117 may handle data on each
viewer and assign each viewer to a group, as will be described in
detail in FIG. 4.
[0042] According to some embodiments of the present invention, an
assignment module 125 may handle a profile of a user and assign it
to a predefined group, as will be described in detail in FIG.
5.
[0043] According to some embodiments of the present invention, a
nearby neighbor module 140 may analyze behavior pattern of user's
content usage, as will be described in detail in FIG. 7A.
[0044] According to some embodiments of the present invention, a
CRM association module 145 may analyze behavior pattern of user's
for associating with CRM data, as will be described in detail in
FIG. 7b.
[0045] According to some embodiments of the present invention, a
probability module 160 may analyze behavior pattern of correlation
between visitors navigation content usage activates, as will be
described in detail in FIG. 8A.
[0046] According to some embodiments of the present invention, a
scoring module 165 may analyze behavior patterns of correlation
between visitors navigation and content usage activates, as will be
described in detail in FIG. 8B.
[0047] According to some embodiments of the present invention, a
prediction module 160 may operate to evaluate content usage of the
visitors, provide recommendation for further engagements based on
the marketing state and identifying market scenarios.
[0048] FIG. 2 is a flowchart illustrating a method of tracking
visitors, according to some embodiments of the invention.
[0049] According to some embodiments of the present invention,
tracking module 110 in FIG. 1 may monitor traffic in a specified
website (stage 210).
[0050] According to some embodiments of the present invention, user
communication device 105 in FIG. 1 of a viewer (i.e. user) that is
navigating in the monitored website may be tracked to identify
various parameters (stage 220) such as: (i) identifying geographic
origin of the tracked viewer (stage 230); For example, a viewer
coming from London and another viewer that is coming from New
Delhi. (ii) Identifying organization or private origin of the
tracked viewer (stage 240); In other words, checking if the viewer
is navigating from a working place or from a residential place
(iii) identifying social origin, of the tracked viewer, meaning
checking if the user was referred from a social website such as
Facebook.TM. (stage 250); (iv) identifying origin of website that
the user is coming from (stage 260) For example, search engines
like Google and Bing (v) identifying and checking actions of
visitors (i.e. viewers) in the monitored website (stage 270). For
example, search actions by keywords in the monitored website or
navigating in a specific section of the website such as careers and
openings, content selections and usage (consumption), content type,
history (number of visits), geo location and goals.
[0051] According to some embodiments of the present invention,
after performing various identifications, as mentioned above, the
tracking module 110 in FIG. 1 may audit all identified data related
to each tracked user and store it in a unique caching repository
for enabling real time statistics and data retrieval on future
engagements (stage 280).
[0052] FIG. 3 is a flowchart illustrating a method of generating
anonymous profile of a user, according to some embodiments of the
invention.
[0053] According to some embodiments of the present invention,
anonymous profile generation module 125 in FIG. 1 may receive data
for each user (stage 310) that was collected from tracking module
110. Next, behavior of each user in the website may be monitored
(stage 320).
[0054] According to some embodiments of the present invention,
period of time of exposure to webpages in the website may be
checked and correlated with content and profile of visitors (stage
330).
[0055] According to some embodiments of the present invention, the
monitored behavior may be analyzed (stage 340) and industry or geo
location of the user may be identified (stage 350).
[0056] According to some embodiments of the present invention, the
generation of user anonymous profile (step 360) is based on
analyzed behavior and the groups classification according user's
industry and organization association
[0057] FIG. 4 is a flowchart illustrating a method of clustering
algorithm, according to some embodiments of the invention.
[0058] According to some embodiments of the present invention,
clustering module 120 in FIG. 1 may monitor visitors that are
navigating in a specified website (stage 410). During the
monitoring, some or all of the following information is collected
from the monitored visitors: origin details, contact details,
navigation path (stage 410).
[0059] According to some embodiments of the present invention,
clustering module 120 is checking usage of visitors contact details
in the website via the website and other communication parties such
as email, etc. (stage 420). The clustering module may check
feedback and action of the visitors that are navigating in the
monitored website such as registering to the monitored website
(including its services) or initiation of contact via the website
by the user such as, sending an email or calling representatives of
the monitored website. Such information can be used to indicate on
successful matching between the visitors' profile and behavior and
the presented content adjusted by the application 100 in FIG.
1.
[0060] According to some embodiments of the present invention,
clustering module 120 in FIG. 1 may check visitors' login to the
website via a social network website such as Facebook.TM. (stage
430).
[0061] Finally, clustering module 120 in FIG. 1 may cluster
visitors by generation of groups using analysis of statistics of
the results of all checks and identifications as mentioned above
(step 440): clicks, visits, geo location, revenue, organization
size (optionally: navigation path, origin,
social/organization/industry, association, history (number of
visits), search terms, visitors' behavior including navigation
path, selections, keywords used in information searches, and user
feedback. The classification process may find correlation between
the different parameters which characterize the user profile and
its behavior for identifying groups of visitors which their
characteristics indicate of at least one common interest or common
behavior, such that the same content may be targeted to most
visitors of the group.
[0062] The generation of groups may be based on the analyzed
behavior using proprietary heuristics that were collected regarding
a user's behavior as described above.
[0063] The proprietary heuristics techniques are used to analyze
the user's grouping clustering data for reducing the scale of the
big data problem by cross analyzing the group clustering data
according to attributes (i.e. geo location, industry or
organization association) of the user. In other words, instead of
processing a large amount of data in case of a matching of a user
to a group it may require to process only reduced amount of data
records of group clustering data, using the heuristics related to
the geo location, industry or organization association which may
reduce usage of resources such as computer resources and time in
the process.
[0064] To provide quick response, i.e. in less than 50
milliseconds, the present application clusters big data based on
timeline of the navigation process, public digital organization
and/or social data and actual visit timestamp. Indexing the data
based on those parameters makes it possible to track trends, and
retrieve relevant data for personalization of "anonymous visitors"
while maintaining of a sustainable data model.
[0065] According to some embodiments of the present invention,
clustering module may store clustered groups in unique caching
repository for enabling real time statistics and data retrieval for
engagement.
[0066] The unique caching repository utilizes an in-memory
optimized matrix model, which allows real-time interactions on big
data. This model is optimized for the usage of each statistical
algorithms by implementing one of the following: high density
matrix which filters out the low relevancy recommendation mapping,
aggregated clustering data (hence eliminating duplicate content
items records) or caching next best offer based on visitor timeline
to enable real-time retrieval while the user navigates through the
website and/or filtering out, less relevant or deprecated/older
visitors.
[0067] This unique caching repository enables to optimize memory
footprint from Giga bytes on disk to megabytes in memory.
[0068] FIG. 5 is a flowchart illustrating a method of assigning
visitors to clustered groups, according to some embodiments of the
invention.
[0069] According to some embodiments of the present invention,
assignment module 125 may receive a profile of a user (stage 510)
and analyze it (stage 520). Next, assignment module 125 may assign
the user to a predefined group using the profile of the user and to
calculate correlation (stage 530). The present application suggests
a classification process, which utilizes correlation of time and IP
and name of an organization to identify visitors and their
clustered groups.
[0070] Finally, in case there is a specified amount of exceptional
visitors that are not assigned to the group, a training process of
clustering user profiles is reactivated (stage 540). The user
profiles and behavior may change over time; therefore accordingly
the group clustering has to be adapted to reflect the change. The
present invention provides a dynamic model by continuously
analyzing statistics regarding a user's profile and behavior in
comparison to the group clustering definition and identifying when
statistically the amount of exceptional visitors has exceeded a
predefined level. In this case, the training process is reactivated
for a predefined time period for redefining the group
clustering.
[0071] FIG. 6 is a flowchart illustrating a method of analyzing
organizational behavior, according to some embodiments of the
invention. The module analyzes anonymous profiles to identify
visitors associated with the same organization by checking the
identified origin of the user (step 610), contact details entered
by the visitors or communication message used by the user. The
activities of identified visitors of the same organization are
analyzed for identifying behavior patterns of an organization by
checking time period usage, association and type of activities of
the visitors (step 620). This analysis enables to identify
functions of each user in the organization and classifying his
behavior for analyzing marketing behavioral patterns of the
organization (step 630). Optionally based on analyzing marketing
behavioral pattern can be generated marketing scenarios templates
(steps 640 and 650).
[0072] FIG. 7A is a flowchart illustrating a method of nearest
neighbor algorithm, according to some embodiments of the invention.
The nearest neighbor algorithm include the following steps:
analyzing visitors actions sequence in the website such as the
sequence of content selection and usage (step 710), classifying
visitors actions by type to identify content consumption action
(step 720), analyzing actions in relation to their occurrence time
(step 730), for example if they occurred in the first visit of the
user or the second one and finally applying nearest neighbor
algorithm using collaborative filtering (step 740) for creating
nearby neighbor groups based on behavior including URLs and content
items (Asset) consumed by visitors. Optionally the creation
neighbor groups, is further depended on search terms, content
selections, content type, visits history (number of visits). The
created nearby neighbor groups are stored in unique caching
repository for enabling real time statistics and data retrieval for
engagement (step 750).
[0073] FIG. 7B is a flowchart illustrating a method of CRM
Association module, according to some embodiments of the invention.
The CRM Association module applies one of the following steps:
classifying behavior patterns of visitors to characterize their
intentions, needs and marketing state/status within a sales
scenario (step 770), such as awareness, interest, evaluation etc.
Based on user profiles, classified pattern behavior and marketing
state are identified returning clients in CRM database (step
780).
[0074] FIG. 8A is a flowchart illustrating a method of probability
algorithm, according to some embodiments of the invention. The
probability module applies at least one of the following steps:
analyzing navigation path of the visitors, content selection and
consumption (step 810), identifying statistical correlation or
association between successive action of navigation and content
selection and consumption (step 820) and accordingly build content
items (Assets) probability tree based on visitors action (click
stream) or identified correlation (step 830). The created
probability tree is stored in a unique caching repository for
enabling real time statistics and data retrieval for engagement
(step 840).
[0075] FIG. 8B is a flowchart illustrating a method of scoring
according to some embodiments of the invention. The scoring
algorithm includes at least one of the flowing steps: using
probability tree data for scoring content items, using statistics
of nearest neighbor algorithm for scoring content items and
integrating scores content items of the probability tree nearest
neighbor algorithm. Optionally the module may score incoming leads
based on CRM accounts similarity.
[0076] FIG. 9 is a flowchart illustrating a method of prediction
module, according to some embodiments of the invention.
[0077] The prediction module provides two types of recommendations:
[0078] "action" recommendation including future
communication/actions to be taken based on neighborhood and/or
clustering groups or probability tree.
[0079] According to further option the recommended actions are
based on marketing status/stage in the sale process which may be
predefined by using marketing scenarios or identified pattern of
actions(step 950) [0080] replacement and content recommendation
(which can be optionally based on automatic rules) for the specific
user, these recommendations are predicted using the results
analysis of clustering and/or neighborhood grouping and or
probability tree. Optionally the prediction is based on marketing
status in a sale or analyzed behavioral pattern.
[0081] According to other embodiments of the present invention the
content recommendation is based on content items (Assets)
clustering algorithm which enable classifying content into groups
by analyzing plurality of attributes of the visitors that consumed
the content (i.e. clicks, visits, Geo, location, industry, revenue,
organization size, organization revenue).
[0082] According to some embodiments of the present invention, the
module further comprises at least one of the following steps:
Optional analyzing marketing state of the visitors is a sale
process (step 910), optional, analyzing organization interaction
pattern between the organization organs (step 920), optional
comparing analysis results to predefined marketing scenarios
templates (step 930), optional Predicting/Estimating marketing
status/stage of user or organization within a sale scenario (step
940),
[0083] Optionally the module may check analysis results against CRM
data. The estimation may be used for providing recommendation for
further communications/actions to be taken based on estimated
marketing status/stage and predefined marketing scenarios.
[0084] Many alterations and modifications may be made by those
having ordinary skill in the art without departing from the spirit
and scope of the invention. Therefore, it must be understood that
the illustrated embodiment has been set forth only for the purposes
of example and that it should not be taken as limiting the
invention as defined by the following invention and it's various
embodiments.
[0085] Therefore, it must be understood that the illustrated
embodiment has been set forth only for the purposes of example and
that it should not be taken as limiting the invention as defined by
the following claims. For example, notwithstanding the fact that
the elements of a claim are set forth below in a certain
combination, it must be expressly understood that the invention
includes other combinations of fewer, more or different elements,
which are disclosed in above even when not initially claimed in
such combinations. A teaching that two elements are combined in a
claimed combination is further to be understood as also allowing
for a claimed combination in which the two elements are not
combined with each other, but may be used alone or combined in
other combinations. The excision of any disclosed element of the
invention is explicitly contemplated as within the scope of the
invention.
[0086] The words used in this specification to describe the
invention and its various embodiments are to be understood not only
in the sense of their commonly defined meanings, but to include by
special definition in this specification structure, material or
acts beyond the scope of the commonly defined meanings. Thus if an
element can be understood in the context of this specification as
including more than one meaning, then its use in a claim must be
understood as being generic to all possible meanings supported by
the specification and by the word itself.
[0087] The definitions of the words or elements of the following
claims are, therefore, defined in this specification to include not
only the combination of elements which are literally set forth, but
all equivalent structure, material or acts for performing
substantially the same function in substantially the same way to
obtain substantially the same result. In this sense it is therefore
contemplated that an equivalent substitution of two or more
elements may be made for any one of the elements in the claims
below or that a single element may be substituted for two or more
elements in a claim. Although elements may be described above as
acting in certain combinations and even initially claimed as such,
it is to be expressly understood that one or more elements from a
claimed combination can in some cases be excised from the
combination and that the claimed combination may be directed to a
sub-combination or variation of a sub-combination.
[0088] Insubstantial changes from the claimed subject matter as
viewed by a person with ordinary skill in the art, now known or
later devised, are expressly contemplated as being equivalently
within the scope of the claims. Therefore, obvious substitutions
now or later known to one with ordinary skill in the art are
defined to be within the scope of the defined elements.
[0089] The claims are thus to be understood to include what is
specifically illustrated and described above, what is conceptually
equivalent, what can be obviously substituted and also what
essentially incorporates the essential idea of the invention.
[0090] Although the invention has been described in detail,
nevertheless changes and modifications, which do not depart from
the teachings of the present invention, will be evident to those
skilled in the art. Such changes and modifications are deemed to
come within the purview of the present invention and the appended
claims.
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