U.S. patent application number 12/012493 was filed with the patent office on 2008-09-18 for use of behavioral portraits in web site analysis.
This patent application is currently assigned to 7 Billion People, Inc.. Invention is credited to Brian Gugliemetti, William Charles Minnis, Mark Nagaitis, Trevor Pokorney, Eric Schank.
Application Number | 20080228819 12/012493 |
Document ID | / |
Family ID | 39674733 |
Filed Date | 2008-09-18 |
United States Patent
Application |
20080228819 |
Kind Code |
A1 |
Minnis; William Charles ; et
al. |
September 18, 2008 |
Use of behavioral portraits in web site analysis
Abstract
A method is provided for determining a website user behavioral
portrait based on navigation on the website and dynamically
reconfiguring web pages based on those portraits. In accordance
with the method, data relating to the progress of a user through a
website is recorded, and an ongoing behavioral portrait of the user
is built based on the data. The portrait is then used to
dynamically reconfigure web content.
Inventors: |
Minnis; William Charles;
(Austin, TX) ; Nagaitis; Mark; (Austin, TX)
; Pokorney; Trevor; (Cedar Park, TX) ;
Gugliemetti; Brian; (Austin, TX) ; Schank; Eric;
(Austin, TX) |
Correspondence
Address: |
FORTKORT & HOUSTON P.C.
9442 N. CAPITAL OF TEXAS HIGHWAY, ARBORETUM PLAZA ONE, SUITE 500
AUSTIN
TX
78759
US
|
Assignee: |
7 Billion People, Inc.
|
Family ID: |
39674733 |
Appl. No.: |
12/012493 |
Filed: |
January 31, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60898807 |
Feb 1, 2007 |
|
|
|
Current U.S.
Class: |
1/1 ;
707/999.107; 707/E17.009 |
Current CPC
Class: |
H04L 67/22 20130101;
G06Q 30/02 20130101; G06Q 30/0255 20130101; G06Q 30/0641 20130101;
G06Q 30/0244 20130101; G06Q 30/0201 20130101; G06F 16/9535
20190101; G06Q 30/0269 20130101 |
Class at
Publication: |
707/104.1 ;
707/E17.009 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for analyzing a website, comprising: recording data
relating to the online behavior of a plurality of users through the
website; building a behavioral portrait for each of the plurality
of users based on the data; categorizing the behavioral portraits
of the plurality of users into a plurality of portrait types; and
analyzing the behavior of the plurality of users on the website as
a function of portrait type.
2. The method of claim 1, wherein the recorded data relates to
behavior selected from the group consisting of (a) items clicked on
by a user; (b) the number of times a user abandons the web site;
(c) the number of return visits a user makes to the website; (d)
the number of online purchases made by a user on the website; (e)
the average time a user spends on the web site; and (f) the number
of pages visited on the web site by a user.
3. The method of claim 2, wherein the recorded data is measured
over a fixed period of time.
4. The method of claim 1, wherein analyzing the behavior of the
plurality of users on the website as a function of portrait type
involves determining the relative frequency with which items are
clicked on by a user as a function of portrait type.
5. The method of claim 1, wherein analyzing the behavior of the
plurality of users on the website as a function of portrait type
involves determining the relative frequency with which users
abandon the website a function of portrait type.
6. The method of claim 1, wherein analyzing the behavior of the
plurality of users on the website as a function of portrait type
involves determining the relative frequency with which users make
return visits to the website a function of portrait type.
7. The method of claim 1, wherein analyzing the behavior of the
plurality of users on the website as a function of portrait type
involves determining the relative frequency of online purchases on
the website a function of portrait type.
8. The method of claim 1, wherein analyzing the behavior of the
plurality of users on the website as a function of portrait type
involves determining the average time users spend on the website a
function of portrait type.
9. The method of claim 1, wherein analyzing the behavior of the
plurality of users on the website as a function of portrait type
involves determining the average number of pages users visit on the
website a function of portrait type.
10. The method of claim 2, further comprising: outputting graphical
information representing the recorded data as a function of
portrait type.
11. A method for analyzing a web page, comprising: categorizing the
features appearing on the web page in terms of at least one
behavioral trait which selection of the feature would indicate; and
creating a graphical overlay which reflects the categorization of
the features.
12. The method of claim 11, wherein the graphical overlay comprises
a color-coded map.
13. The method of claim 12, further comprising: superimposing the
color-coded map over the web page.
14. The method of claim 11, further comprising: using the
categorization of features to create behavioral portraits of users
accessing the web page.
Description
FIELD OF THE DISCLOSURE
[0001] The present disclosure relates generally to methods for
customizing web page content, and more specifically, to methods for
generating user behavioral portraits based on web site navigation
and search behavior, and for dynamically reconfiguring web page
content based on such portraits to produce personalized web page
content.
BACKGROUND OF THE DISCLOSURE
[0002] As e-commerce has evolved into a widespread means of doing
business, online competition among merchants has increased
dramatically. Much of the attention in online marketing has been
directed towards placing advertisements for products or services as
close to a spending decision as possible, since this is often a
significant factor in an online merchant's likelihood of
success.
[0003] As a specific example, a car rental company might design
their website so that it is likely to turn up as a relevant hit
when a consumer uses a search engine to search the term "car
rental". The company might even purchase prioritization from one or
more businesses that manage popular search engines such as
YAHOO!.RTM. or GOOGLE.RTM. so that their web site will appear near
the top of the search results page whenever terms indicating an
interest in car rentals are input into the search engine. In some
cases, the company may even go the additional step of purchasing
banner ads or pop-ups that are triggered by relevant search
queries.
[0004] While the foregoing approach may be part of a sound online
marketing strategy, it suffers from the drawback that it relies
upon an overt manifestation of consumer interest to identify
potential purchasers of a product or service. Consequently, such an
approach may miss a significant number of sales opportunities,
simply because it identifies many potential purchasers of a product
or service well after a spending decision has been made. In the
interim, the consumer may have been exposed to a wide variety of
competing products and services.
[0005] Some of the more recent refinements in online marketing have
focused on placing products or services even closer to a spending
decision by looking for more subtle clues to a consumer's
interests. Referring back to the previous example, the car rental
company may place advertisements on web sites that help consumers
to purchase airline tickets, based on the realization that a
significant number of people who are purchasing airline tickets
will also require a rental car. However, while this type of
approach may also form part of a sound online strategy, it is
founded on correlations that may be weak. Hence, this type of
approach often yields a low success rate.
[0006] Other methods of online marketing have evolved which seek to
match advertising content to perspective purchasers based on
relevance determined from broad demographic information or consumer
purchase history. For example, some websites use pop-up ads and
banners whose content is selected based on the gender and age of a
consumer provided during web site registration, on information
gleaned from previous on-line purchases by the consumer, or on the
geographic region indicated, for example, by the user's IP
address.
[0007] However, methods which rely on data obtained from web site
registration are of limited utility, since many consumers are
hesitant to spend time on websites completing forms and profiles
for what is perceived to be of little benefit. Methods based on
broad demographic information frequently have a low success rate,
since they are necessarily based on broad generalizations which may
not apply to a given consumer. Methods based on purchase history
are prone to error, since simple product relationships based on
previous purchases can be misleading. Previous purchases may have
no bearing on the consumer's personal interests, as may be the case
if those purchases represent gifts purchased for others. Moreover,
even if the previous purchases were for the consumer's personal
enjoyment, those purchases may not represent the consumer's current
interests. For example, the fact that a consumer's browsing history
or previous purchases indicate a past interest in travel does not
mean that the consumer has a current interest in travel. The
consumer may have exhausted all of his vacation time, and is now
interested in goods and services commensurate with a regular work
schedule.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] In the following figures, like reference numerals indicate
like elements.
[0009] FIG. A1 is a flowchart illustrating some of the general
methodologies described herein.
[0010] FIG. A2 is an illustration of a network over which
behavioral portraits may be gathered in accordance with some of the
methodologies described herein.
[0011] FIG. A3 is an illustration of user actions which may be
analyzed in the building of a behavioral portrait in accordance
with some of the methodologies described herein.
[0012] FIG. A4 is an illustration of some of the questions which
can be answered with the information provided by some of the
systems and methodologies described herein, as compared to the
information gleaned by conventional methodologies.
[0013] FIG. B1 is an illustration of a particular, non-limiting
embodiment of a network equipped with a dedicated server appliance
for implementing some of the software and methodologies taught
herein.
[0014] FIG. B2 is an illustration of a particular, non-limiting
embodiment of a network equipped with a dedicated server appliance
for implementing some of the software and methodologies taught
herein.
[0015] FIG. B3 is an illustration of a particular, non-limiting
embodiment of a network equipped with a dedicated server appliance
for implementing some of the software and methodologies taught
herein.
[0016] FIG. B4 is an illustration of a particular, non-limiting
embodiment of a network architecture for implementing some of the
software and methodologies taught herein.
[0017] FIG. C1 is an illustration of a web page.
[0018] FIG. C2 is an illustration of the web page of FIG. C1 with a
color overlay illustrating the categorization of objects appearing
thereon.
[0019] FIG. D1 is a chart depicting the various levels of webpage
customization possible in accordance with some embodiments of the
methodologies described herein.
[0020] FIG. D2 is an illustration of a standard web page as
compared to a portrait enhanced web page of the type which may be
generated through the application of some of the methodologies
described herein.
[0021] FIG. D3 is an illustration depicting the web pages of FIG.
D2 in greater detail.
[0022] FIG. D4 is an illustration of some of the behavioral
characteristics of a particular portrait type.
[0023] FIG. D5 is an illustration of some of the behavioral
characteristics of a particular portrait type.
[0024] FIG. D6 is an illustration of a behavioral portrait
corresponding to the portrait type depicted in FIG. D4.
[0025] FIG. D7 is an illustration of a behavioral portrait
corresponding to the portrait type depicted in FIG. D5.
[0026] FIGS. D8 through D13 illustrate modifications to a web page
in light of the portrait depicted in FIG. D6.
[0027] FIGS. D14 through D19 illustrate modifications to a web page
in light of the portrait depicted in FIG. D7.
[0028] FIG. E1 is a flowchart illustrating some of the
methodologies described herein which utilize output analytics
derived from user behavioral portraits.
[0029] FIG. E2 is an illustration of some of the output analytics
which may be obtained from the methodology depicted in FIG. D1.
[0030] FIG. E3 is an illustration of some of the questions a
website owner or online marketer may obtain answers to with the
output analytics obtained from the methodology depicted in FIG.
E1.
[0031] FIG. E4 is a graph showing the percentage of visits to a web
site as a function of user behavioral portrait.
[0032] FIG. E5 is a graph showing the distribution of visits to a
website as a function of user behavioral portrait.
[0033] FIG. E6 is a graph showing the percentage of return visits
to a web site as a function of user behavioral portrait.
[0034] FIG. E7 is a graph showing the distribution of return visits
to a website as a function of user behavioral portrait.
[0035] FIG. E8 is a graph showing the percentage of closures or
abandonment of a web page as a function of user behavioral
portrait.
[0036] FIG. E9 is a graph showing the distribution of closures or
abandonment of a web page as a function of user behavioral
portrait.
[0037] FIG. E10 is a graph showing the distribution of the average
amount of time spent on a web site as a function of user behavioral
portrait.
[0038] FIG. E11 is a graph showing the distribution of the average
number of links selected on a web site as a function of user
behavioral portrait.
[0039] FIG. F1 is an illustration of a particular, non-limiting
embodiment of a customer passport which may be obtained in
accordance with some of the methodologies described herein.
[0040] FIG. F2 is a flowchart illustrating some of the
methodologies described herein which involve generation of a
customer passport.
SUMMARY OF THE DISCLOSURE
[0041] In one aspect, a method for dynamically reconfiguring web
pages based on user behavioral portraits is provided which
comprises (a) recording data relating to the behavior of a user on
a website; (b) building a behavioral portrait of the user based on
the data; and (c) dynamically reconfiguring web content based on
the behavioral portrait.
[0042] In another aspect, a method for developing user behavioral
portraits is provided which comprises (a) recording data relating
to the behavior of a user on a website; and (b) building a
behavioral portrait of the user based on the data.
[0043] In another aspect, a method for reconfiguring web pages is
provided which comprises (a) providing a behavioral portrait of a
user; and (b) dynamically reconfiguring web content based on the
behavioral portrait.
[0044] In a further aspect, a method for providing customized web
pages is provided which comprises (a) receiving a request for a web
page from a client associated with a user; (b) modifying the
requested web page in light of a behavioral portrait developed for
the user; and (c) providing the modified web page to the user in
place of the requested web page.
[0045] In still another aspect, a system for providing customized
web pages is provided which comprises (a) a first server adapted to
provide web pages to a client associated with a user and being
further adapted to receive input from the user when the user
accesses features on the web pages; (b) a software program adapted
to (i) receive user input from the first server, (ii) create a
behavioral portrait of the user based on the user input, and (iii)
dynamically update the behavioral portrait as additional user input
becomes available, thereby creating an updated behavioral portrait;
and (c) a second server adapted to alter the content of the web
pages displayed by said first server based on the updated
behavioral portrait.
[0046] In yet another aspect, a system for providing customized web
pages is provided which comprises (a) a server in communication
with a client over a network; and (b) a software program adapted to
develop a behavioral portrait of a user associated with the client
based on captured data relating to the online behavior of the
user.
[0047] In still a further aspect, a method for doing business is
provided which comprises (a) providing a computing device equipped
with a software program adapted to (i) receive user input from a
server, (ii) create a behavioral portrait of the user based on the
user input, and (iii) utilize the behavioral portrait to modify a
web page; (b) utilizing the computing device to produce modified
web content; (c) providing the modified web content to a web site
provider; and (d) charging the web site provider a fee based on the
modified web content displayed on the web site.
[0048] In another aspect, a method for doing business is provided
which comprises (a) providing a behavioral portrait of a user to a
third party entity, the behavioral portrait being based on user
input gathered during an online session that the user was involved
in; and (b) assessing a first fee to the third party based on the
third party's acceptance of the portrait.
[0049] In still another aspect, a method for doing business is
provided which comprises (a) creating a behavioral portrait of a
user based on user input gathered during an online session that the
user was involved in; (b) using the behavioral portrait to
determine a recommended manner of interacting with the user; and
(c) selling to another party a document containing the recommended
manner of interacting with the user.
[0050] In yet another aspect, a method of analyzing a website is
provided which comprises (a) recording data relating to the online
behavior of a plurality of users through the website; (b) building
a behavioral portrait for each of the plurality of users based on
the data; (c) categorizing the behavioral portraits of the
plurality of users into a plurality of portrait types; and (d)
analyzing the behavior of the plurality of users on the website as
a function of portrait type.
[0051] In still another aspect, a method for analyzing a web page
is provided which comprises (a) categorizing the features appearing
on the web page in terms of at least one behavioral trait which
selection of the feature would indicate; and (b) creating a
graphical overlay which reflects the categorization of the
features.
DETAILED DESCRIPTION
A. Overview
[0052] One major shortcoming of the above described marketing
methods, especially when they are applied in online marketing
contexts, is that they focus almost exclusively on identifying
potential purchasers of a product or service, rather than focusing
on the manner in which the product or service is being presented.
Consequently, these approaches fail to apply psychological
principles to the identification and accommodation of a consumer's
preferred purchase patterns. In the equivalent human-to-human
interaction attendant to a sales opportunity, body language,
interaction, dialog and physical indicators may all influence the
tone, form and content of any given conversation. These cues are
critical to the success of any face-to-face sales meeting, and a
skilled salesperson will utilize these cues to quickly adapt his
approach as necessary to maximize the likelihood of success.
[0053] By contrast, such psychological principles have not been
applied heretofore to equivalent web-based dialogs. In particular,
methods currently utilized for implementing online advertising and
selecting web content fail to ascertain the reason and motivation a
given online consumer has for closing a sale. These methods ignore
the manner in which a given consumer prefers to make decisions, and
fail to tailor the presentation of information to a consumer's
preferences (e.g., in accordance with how the consumer prefers to
have information presented to them).
[0054] Continuing the car rental example given above and applying
it to visitors to the website of a car rental company, a first
visitor to the website may be looking for a vehicle that offers
excitement and entertainment for a camping trip. A second visitor
may be looking for an image of success and style in a vehicle. A
third visitor may be looking for the safest vehicle. In addition,
each of these visitors may have their own unique approaches to
making a spending decision. For example, one visitor may prefer to
make a spending decision based largely on how others rate a
particular product offering, while another visitor may prefer to
make a spending decision after reviewing a detailed comparison of
product performance specifications. A website that fails to
promptly recognize each of these diverse motivations, and to
present appropriate content in an appropriate manner that is
suitably prioritized to reflect the user's preferred approach to
making a spending decision, may strike one or more of these users
as being not particularly relevant to that user's interests. As a
result, the user may navigate to a different, possibly competing
web site.
[0055] It has now been found that the above noted needs may be met
through systems, methods and software of the type described herein
which analyze an individual's online behavior so as to derive the
individual's behavioral portrait, and which use that behavioral
portrait to modify the manner in which information is presented to
the individual. In the context of a buying situation, a behavioral
portrait is the psychological profile of an individual as it
pertains to the individual's preferences in that situation,
including their preferred decision-making approach, their
motivation for making the purchase, and the manner in which they
prefer to have information presented to them.
[0056] FIG. A1 provides an overview of some of these systems and
methodologies. As seen therein, the starting point for many of
these systems and methodologies is the capture a1-1 of the web
behavior of one or more individuals. This behavior is then analyzed
and is used to develop a behavioral portrait a1-2 of the
individual. The resulting behavioral portrait may then be put to a
variety of end uses.
[0057] In some embodiments, the methodologies described herein, and
the systems and software which implement them, apply scientific and
psychological principles to improve the way that information is
presented to a website user a1-5. These methodologies may be used
to emphasize information which is particularly relevant to a user's
current needs and state of mind, and to de-emphasize information
which, although possibly important, may be ancillary to a
particular user's interests or decision making process.
Consequently, these methodologies may be utilized to customize
websites so that they appear to have been defined for a given
user's particular needs at a particular time. As a result,
navigation around the website will seem more natural for the user,
transaction closure procedures will be appropriately tailored so
that they are more appropriate for the situation and the user's
current state of mind, and marketing offers on the site will be
customized for user behavior as befits the time and context.
[0058] Preferably, the behavioral portrait is utilized to
dynamically (and possibly automatically) customize, reconfigure or
personalize web pages, web content and/or web sites so that the
resulting web pages are more conducive to an individual's state of
mind. As a result, the individual's experience in navigating a
website may be more rewarding. In an e-commerce setting, this may
result in a greater number of sales closures, and may have the
effect of improving website applicability, sales, and return rates,
while also providing specific valuable information which may be
utilized to differentiate a given website from competitive
offerings. Hence, in some embodiments, the methodologies described
herein may be utilized to provide dynamic and personalized web
content which is adapted to customer buying behaviors.
[0059] In other applications, an individual's behavioral portrait
(or information pertaining thereto) may be provided to human
salespeople, call centers, marketing teams and the like a1-3 for
use in determining how to interact with that individual. These
parties may use the behavioral portrait (or information pertaining
thereto) to better understand how to communicate with the
individual, how to handle objections the individual may have, and
how to close a sale with the individual.
[0060] In still other applications, analyses may be performed on
the manner in which individuals interact with a website or web page
as a function of their behavioral portrait a1-4. These analyses may
be utilized, for example, to refine a website or web page to make
it more attractive to a target set of users, to help a business
better understand its customer base and how to interact with that
customer base, and to identify problem areas with a website or web
page. In particular, these analyses may be utilized to help the
owner of a website or a web marketing program to determine how to
improve e-commerce closure rates, to understand how to improve
visitor return rates, to understand why customers are leaving the
website, to increase the effectiveness of web marketing programs
and offline programs, and to understand how best to communicate
with various customer segments.
[0061] As seen in FIG. A2, the capture of web behavior may be
accomplished through the use of software running on servers a2-3
which remotely and transparently monitor the online behavior of
users a2-1 on a web site. The pages on the website (or the
templates from which these pages are derived) may be provided with
appropriate JavaScript or other suitable web applications which
categorize and tag every relevant action that a user can take.
These actions may include the pages a user navigates, the items the
user clicks on, search terms entered by the user (including the
entry of narrowing searches), check out information, pages
abandoned by the user, selection of "top 10" items by the user, and
other such information. FIG. A3 depicts various actions a visitor
may take on a single web page and various paths the visitor may
take through a web site. These actions, and the paths taken to get
to a given action, may be used to build a psychological behavioral
portrait for that visitor.
[0062] FIG. A4 summarizes some of the information the foregoing
methodology provides and contrasts it to the information provided
by conventional methods (such as polls) utilized to gather
information about consumers. As seen therein, the information a4-7
gathered by conventional techniques typically includes relevance
information (information on pricing, promotions, purchase history,
products, or typical "crowd" behavior), demographics (including
information such as age, location, gender or income), or analytical
information (information such as a user's operating system, the
number of closures on a website, the number of visitors to the
website, the number of browsers who have abandoned the website, and
the browser types being utilized to browse the web site). While
such information may be useful, it provides very little information
about the consumer's personality or state of mind. Hence, while
this information may be used to identify potential customers, it
provides very little useful information about how and when to
interact with the customer.
[0063] By contrast, methodologies are possible in accordance with
the teachings herein which provide a variety of information very
specific to the customer and the customer's current state of mind.
This information is very useful in understanding how, and when, to
interact with the customer. This includes information on the
customer's decision-making process a4-1 (how the customer makes
purchase decisions), information on the customer's motivations a4-3
(what motivates that customer to close on a transaction),
information on how the customer prefers to have information
presented to them a4-5, an indication of when in the product
lifecycle the customer is most likely to make a purchase a4-2, the
marketing messages and images that are likely to appeal to the
customer a4-4, and the level of detail appropriate for the customer
a4-6.
[0064] The methodologies described herein are especially useful in
the context of e-commerce, and hence, frequent reference will be
made to the application of these methodologies within this context.
However, it will be appreciated that many of the methodologies
described herein are broadly applicable to the customization of web
content in any context to make it more compatible with the user's
behavioral portrait. Thus, unless otherwise indicated, the
methodologies and systems described herein should not be construed
as being specifically limited to their use in e-commerce
settings.
B. Hardware and Network Implementation
[0065] FIGS. B1 and B2 depict one particular, non-limiting
embodiment of a system which may be used to implement some of the
software and methodologies disclosed herein. This system
essentially consists of a front end and a back end. At the front
end of the system, the navigational attributes of a user are
captured, and those attributes are utilized to develop a behavioral
portrait for the user. At the back end of the system, the
behavioral portrait so generated is utilized to dynamically
reconfigure web content.
[0066] With reference to FIG. B1, the network b1-1 in this
particular embodiment has a server side b1-3 and a client side
b1-5. The server side b1-3 comprises a SAN/NAS (Storage Area
Network/Network Attached Storage) storage farm b1-7 comprising a
plurality of storage devices b1-9, a database server b1-11, a
variety of application servers b1-13, a dedicated server appliance
b1-15 which runs the software for implementing the methodology
described herein, a server farm b1-17 which includes a plurality of
web servers b1-19, and a firewall b1-21.
[0067] The web servers b1-19 communicate with a plurality of client
devices b1-23 and with a plurality of business partners b1-25
through the firewall b1-21 and over a suitable WAN (wide area
network) b1-27 such as the Internet. The business partners b1-23
will typically be businesses that wish to market goods or services
over the WAN b1-27. In some cases, these parties may supply
advertising content to the web servers b1-19.
[0068] FIG. B2 illustrates how the network b1-1 depicted in FIG. B1
may be utilized to generate user portraits and to dynamically
reconfigure web content based on the user portraits. As seen
therein, each web server b1-19 receives behavioral information from
a user of the WAN b1-27. That behavioral information is typically
in the form of context sensitive mouse clicks, keyboard entries,
searches, menu selections, verbal commands (in the case, for
example, of devices utilizing voice recognition software), and
other types of user input.
[0069] The dedicated server appliance b1-15 is equipped with an
application programming interface (API) b1-41 which enables it to
receive and analyze user input from the server b1-19. The API b1-41
further includes a portrait database b1-43 which stores existing
user portraits, and a portrait generator b1-45 which generates new
user portraits based on the user input. In the event that the user
already has a portrait stored in the portrait database b1-43, the
portrait generator b1-45 recognizes this fact and modifies the
existing user portrait as new information about the user's behavior
becomes available. The API b1-41 then forwards the resulting user
portrait to an application server b1-13. The application server
b1-13 acts upon the user portrait by generating re-mashed web
content which is personalized to the user's behavioral portrait,
and then passes the re-mashed web content to the web server b1-19
for transmission to the appropriate client device b1-23.
[0070] In a preferred mode of operation, the dedicated server
appliance b1-15 continuously updates the user portrait database
b1-43 in real time as new input from a user becomes available, and
promptly passes an updated portrait (or the updated portion
thereof) for the user to the application server b1-13. The
application server b1-13, in turn, serves up re-mashed content to
the user. Hence, the dedicated server appliance b1-15 provides
dynamic reconfiguration of web page content, and personalizes
subsequent web pages for that user.
[0071] FIG. B3 depicts a second embodiment of a system made in
accordance with the teachings herein. The system b3-1 depicted
therein is similar in many respects to the system depicted in FIGS.
B1-B2. However, while the system depicted in FIGS. B1-B2 passes a
portrait between the server appliance b1-15 and the application
server b1-13 to generate re-mashed web content, in the system b3-1
depicted in FIG. 3, the server appliance b3-15 generates the
re-mashed web content directly. Thus, in this system b3-1, the
server appliance b3-15 receives and analyzes user input from the
server b3-19, builds a behavioral portrait for the user (or
modifies an existing behavioral portrait for the user), generates a
new web page (or Portrait Enhanced Page (PEP)), and provides the
PEP to the e-commerce web server b3-19 for display to the user
(assuming that the e-commerce site accepts the PEP).
[0072] It will thus be appreciated that the server appliance b3-15
in FIG. 3 may be implemented as a "black box" device which
intercepts user behavioral data and outputs re-mashed web pages
based on that data. Such a device is advantageous in some
applications in that it provides a means by which the provider of
the device can extract a revenue stream from the re-mashed web
content using various business models, some of which are described
in greater detail below.
[0073] The manner in which the server appliance b3-15 achieves the
foregoing functionalities may be appreciated from FIG. B3. As seen
therein, the server appliance b3-15 comprises an API b3-41 that
receives user input from the server b3-19 and outputs PEPs to the
server b3-19, and which communicates with the application server
b3-13 as necessary to accomplish these tasks. The server appliance
b3-15 further includes a customer portrait engine b3-45 which
assembles and modifies customer portraits based on behavioral data
captured from the server b3-19, a page interpreter b3-47 which
interprets the various objects present on a web page, a PEP
generator b3-49 which utilizes the customer portrait to generate
PEPs, and a PEP database b3-51 where the PEPs so generated are
stored. Though not explicitly shown, the server appliance b3-15 may
further include a portrait database for storing customer portraits
generated by the customer portrait engine b3-45.
[0074] The server appliance b3-15 in the system b3-01 of FIG. B3 is
further adapted to communicate customer portraits, or information
contained in or relating to customer portraits, to various third
parties b3-55. Such third parties b3-55 may include, without
limitation, sales people, marketing teams, and call centers, and
may utilize this data to more effectively communicate with the
customer, either directly or via the Internet or another suitable
network.
[0075] The particular, non-limiting embodiments depicted in FIGS.
B1-B3 implement some of the methodologies described herein by
incorporation of a dedicated server appliance into the server side
of the network. Such an appliance solution is useful in some
applications in that it provides the ability to architect the
solution "out-of-band" with the current architecture, and also
provides a platform for the owners of a website to develop specific
content for a user's portrait in the future (this may include, for
example, specific marketing offers on an e-commerce website).
However, it will be appreciated that various other means are also
possible for implementing some of the methodologies described
herein.
[0076] For example, some of these methodologies may be implemented
as a software solution adapted to co-exist on a given web-server or
back-end server. In such embodiments, the dedicated server
appliance may be a hosted system. Instead of residing behind the
client's firewall and web servers, the appliance may reside in a
central location (for example, at an ISP (internet service
provider)). The software code used to observe customer web sessions
and to develop behavioral portraits may be injected into the client
web site using a JavaScript snippet placed in the client's web page
templates. These JavaScript snippets may be activated in real-time
to insert the most current JavaScript code from the host system
into the web page on demand. The JavaScript may then send the
customer's click stream or other captured information back to the
portrait engine on the hosted system. Using this captured
information, the portrait engine generates a behavioral portrait
for the customer, which is stored in a portrait database on the
hosted system.
[0077] In a similar manner, a portrait enhanced page (PEP) may be
generated using JavaScript code that is injected into the client
web page (or a template from which the web page is derived) using
one or more JavaScript snippets which are inserted by the client
into their web page structure. Following the same approach as
described above, a PEP generator may utilize the customer portrait
to generate PEPs. The difference in this case is that the software
code for the PEP generator resides on a hosted system instead of a
dedicated server appliance behind the client firewall.
[0078] FIG. B4 depicts a particular, non-limiting embodiment of the
foregoing type of implementation. In the system b4-0 depicted
therein, a client b4-1 is in communication with an e-commerce site
b4-2 and a service provider b4-3 over the Internet or another WAN
b4-21. A dedicated server appliance b4-4 is present as a hosted
system at the location of the service provider b4-3.
[0079] In operation, the client b4-1 requests a webpage from the
e-commerce site b4-2. The e-commerce site b4-2 provides the
requested web page, which includes JavaScript of the type
previously described. The JavaScript monitors the web activity of
the client b4-1, and sends information about that activity to the
web servers b4-5 of the service provider b4-3. The web servers b4-5
store the information in one or more database servers b4-6, where
it is used by the dedicated server appliance b4-4 to build user
behavioral portraits. When the client b4-1 requests the next web
page from the e-commerce site b4-2, the web servers b4-5 intercept
the requested web page and modify it based on the user's behavioral
portrait. The web servers b4-5 then provide the modified web page
to the client b4-1.
[0080] The hosted server appliance b4-4 in the particular system
b4-0 depicted in FIG. B4 consists of a portrait database b4-11, a
portrait engine b4-12 (which may be based on a neural network), a
tool set b4-13, an analyzer b4-14, an instrumentor b4-15, and an
augmentor b4-16. The analyzer b4-14 analyzes information concerning
the client's web activity as captured by the JavaScript inserted
into the client's web page templates, and works in conjunction with
the portrait engine b4-12 to develop a user portrait which is then
stored in the portrait database b4-11. A variety of callable
programs, functions, routines and the like may be used in this
analysis which are stored in the toolset b4-13. The augmentor b4-16
then utilizes portraits contained in the portrait database b4-11 to
provide modified web content. The instrumentor b4-15 assembles the
modified web content into a modified web page which is forwarded to
the client b4-1. In order to instrument a site, the major set of
web-page templates is identified. The sections of the web page, and
the existing portrait bias of each, are then categorized.
Modifications to the sections are dependent on the type of portrait
bias of that section.
[0081] Other variations and embodiments of the foregoing systems
are also possible. For example, while the user portraits are
preferably stored in a portrait database (see, e.g., profile
database b1-43 of FIG. B1), in some embodiments of the systems and
methodologies described herein, these portraits may be stored
instead on a cookie defined in a client device, assuming this is
permitted by the user's privacy software. In such embodiments, the
dedicated server appliance may be adapted to collect the user
information from the client device as necessary via a server. It
will be appreciated that hybrids of this embodiment are also
possible, where some user portraits (or portions thereof) are
stored in the portrait database, and other user portraits (or
portions thereof) are stored on a cookie defined in a client
device. In still other embodiments, the software for implementing
the methodologies described herein may be installed on a web
server, which may query the client device at appropriate intervals
for updated user input information.
C. Generation of a User Behavioral Portrait
[0082] In a preferred embodiment, the software described herein
applies a (possibly complex) algorithm to determine an individual's
psychological portrait score by interpreting the individual's
navigation through a website hosted by a server. The scoring
mechanism utilizes a derivative of a technique known as
"meta-linguistic programming". In particular, the algorithm weights
user actions that can take place on a given website by category,
and also by specific lexical analysis. Predicate and historical
analysis is also input into the weighting. The algorithm is
preferably adapted to reset to a generic weighting under
appropriate circumstances to ensure that model inaccuracies or
exceptional behavior can be accommodated.
[0083] Preferably, the algorithm is characterized by a tipping
point so that, once a sufficient portrait weighting is achieved,
the software portrait triggers a number of potential actions on the
website. These actions may include, without limitation, re-mashing
of the current website content for emphasis of particular data,
re-routing procedures to suit a user's behavior in the current
context, re-wording website content in accordance with a user's
presentation preferences, and re-presentation of, for example,
marketing offers to focus on the user's current context and
portrait.
[0084] The methodologies described herein, and the systems and
devices which implement them, offer a number of potential
advantages. In particular, these methodologies may be used to
dynamically reconfigure web pages to make them commensurate with a
user's current behavioral portrait. As a result, the user will feel
more comfortable on the website, will be less likely to browse away
from the website, and will be more likely to return to the website
in the future. This, in turn, will increase the likelihood of
closure on the sale of products or services advertised on the
website, will allow marketing offers to be customized for that
user's portrait at that particular time, and will provide the user
with customized procedures on the website.
[0085] The customer portraits which are generated may also be
provided to sales teams, marketing teams, call-centers, or other
such entities. These entities can use the information contained
therein to personalize conversations and advertising campaigns for
the respective users.
[0086] Some of the methodologies described herein also provide an
effective means for context sensitive marketing. These
methodologies provide the means to analyze the first few
interactions (e.g., mouse clicks) with a website and apply that
information to establish a behavior portrait (frequently with
respect to buying patterns) for the user for subsequent
interactions. These methodologies also provide the means to analyze
the phraseology and syntax of a search, and apply that information
to the user's portrait for improved click-through. All subsequent
user interactions in the same session are then customized for the
user's preferred information sources, preferred marketing messages,
preferred sales approach, and the like.
[0087] In a preferred embodiment of the methodologies described
herein, a meta-linguistic process is utilized which comprehends
fifteen different psychological attributes for a given user. These
attributes are described briefly in TABLE C1 below. Of course, it
will be appreciated that various other psychological attributes (of
greater or lesser number) could be utilized in the systems,
methodologies and software described herein. It will also be
appreciated that various combinations or sub-combinations of these
or other psychological attributes may be utilized in these systems,
methodologies and software, or in a given application or task.
Moreover, the number of attributes utilized may vary from one
application to another, and from one context to another.
TABLE-US-00001 TABLE C1 PSYCHOLOGICAL ATTRIBUTES Psychological
Attribute Description 1 Decision Making Determines the way in which
the customer makes decisions, via information or reference 2
Motivation Determines the rational for the website visit and
completing a transaction - should the product offer an opportunity
or prevent a problem 3 Information Presentation Indicates how a
customer prefers to receive information - process-based preferences
versus behavior that demands alternatives 4 Adoption Stage
Indicates when, in the product life-cycle, a customer is
comfortable making a purchase - early adopter versus conventional
adoption tendency 5 Relationships Describes the level of personal
interaction desired by a customer and amount of personal
relationship for transaction/product 6 Specificity Indicates the
level of detail a customer prefers 7 Frequency Indicates the
frequency/repetition of message required to close 8 Teamwork
Defines level of independence versus gradation of interaction
(decision making and product use) 9 Rules Determines agreed method
of transaction - rules of the transaction 10 Speed Determines rate
of navigations/speed of clicks - indicates surety of other
weightings - can indicate level of decisiveness 11 Empathy
Indicates the consideration/impact of decisions on other people 12
Intensity Indicates level of intensity of transaction/dialog 13
Temperature Determines comfort of color preference in context of
transaction/dialog 14 Shade Determines visual stimuli and
environment preference 15 Pattern Second level visual stimuli and
environmental preferences
[0088] Each psychological attribute is suitably weighted by the
Customer Portrait Engine. Preferably, this weighting occurs on a
numerical scale which is given an almost infinite number of
context-sensitive and real-time permutations for any given user at
any given time. For example, if the Customer Portrait Engine
utilizes the 15 psychological attributes noted above, each of these
attributes may be tracked using a sliding scale, with 0 as the
neutral score. A weighting significantly on either side of neutral
would indicate a tendency of behavior toward that setting.
[0089] The manner in which user behavior may be correlated to
psychological attributes may be better understood by considering
some specific examples. For example, a user navigating directly to
product specifications would demonstrate behavior typical of one
type of decision making tendency, while a user browsing through
customer recommendations would demonstrate behavior typical of
another type of decision making tendency. A customer clicking on a
toothpaste advertisement described as "bright smile" would indicate
a behavioral tendency towards one type of motivation, versus a
customer clicking on an offer to "prevent tooth decay", which would
demonstrate behavior typical of another motivational tendency. A
customer navigating through categories in a stepwise fashion (e.g.,
electronics, TV and video, DVD player) would indicate a behavioral
tendency towards one type of information gathering, versus a user
selecting different categories which would indicate a behavioral
tendency towards another type of information gathering.
EXAMPLE C1
[0090] The following example illustrates the application of some of
the methodologies taught herein to an e-commerce transaction.
[0091] User A decides to purchase a DVD player from a
multi-category website (that is, a website which sells electronics,
books, clothing, and various other goods and services). The user's
pass through the website involves the steps of: [0092] (a)
Searching for a specific DVD player model; [0093] (b) Checking the
detailed specifications of the model; [0094] (c) Comparing the
model with other top sellers; [0095] (d) Adding the selected model
to the shopping cart; [0096] (e) Choosing expedited shipping; and
[0097] (f) Checkout.
[0098] Each of these individual actions during the web session can
be mapped to a set of rules or a knowledge base in the Portrait
Engine. An example of such a mapping is shown in FIGS. C1-C2. In
particular, FIG. C1 shows a standard e-commerce web page with a
selection of electronics products, specifically DVD Players. FIG.
C2 shows the same web page with a link overlay showing the mapping
from each individual link to the associated rule set within the
knowledge base.
[0099] As User A performs the search in Step (a), a rule in the
knowledge base is triggered to recognize a search for a specific
item with multiple terms. This type of search indicates an
independent decision making behavior. Therefore, the score on the
Decision Making attribute (see TABLE C1 above) is increased by 10
points. Similarly, Steps (b) and (c) trigger rules for information
gathering, again indicating independent decision making behavior
and increasing the score on that attribute to +30. Given the
collection of Steps (a) through (d) identifying a specific product,
gathering specification data, comparing to similar models, and then
selecting the individual DVD Player--the knowledge base rules
recognize procedural behavior. Therefore, the score on the
Information Presentation attribute is increased by 20 points. In
Step (e), User A displays goal-oriented behavior by expediting the
shipping for this product and the knowledge base decreases the
score on the Motivation attribute by 10 points. Therefore, in this
example, User A's scores for the first three attributes in Table C1
are:
TABLE-US-00002 TABLE C2 User A score Psychological Attribute Score
Behavior 1 Decision Making +30 Independent decision making 2
Motivation -10 Goal-oriented motivation 3 Information Presentation
+20 Procedural information presentation
[0100] By comparison, User B also goes to the same website to buy a
DVD player, and this user's e-commerce dialog is as follows: [0101]
(a) Select electronics category; [0102] (b) Select DVD Players;
[0103] (c) Choose "Top 10 Sellers"; [0104] (d) Select number 1
seller; [0105] (e) Check what other people said about this model;
[0106] (f) Check editorials and reviews; [0107] (g) Select another
player referenced in the reviews; [0108] (h) Sort and read reviews
from most negative to least negative; and [0109] (i) Checkout and
purchase the referenced DVD player.
[0110] Again, with the mapping from the links and actions on the
web site to the rules in the knowledge base of the Portrait Engine,
each individual action by User B during this web session drives a
score in the behavioral portrait. In the case of User B, the focus
on the recommendations and reviews of other customers, shown in
Steps (c) through (h), demonstrate a collaborative decision making
behavior. The rules triggered by these steps collectively decrease
the score on the Decision Making attribute by 30 points. The shift
in User B's product direction, demonstrated by Step (g), triggers
rules which decrease the score on the Information Presentation
attribute by 10 points, indicating a choice-oriented preference. In
Step (h), User B exhibits a desire to avoid problems, and the rules
increase the score on the Motivation scale by 20 points. In this
example, User B's scores for the first three attributes in Table C1
are as follows:
TABLE-US-00003 TABLE C3 User B score Psychological Attribute Score
Behavior 1 Decision Making -30 Collaborative decision making 2
Motivation +20 Problem-avoidance motivation 3 Information
Presentation -10 Choice-oriented preference
[0111] Both users A and B have displayed key behavioral patterns in
each of the transactions which can be used to dynamically
reconfigure the web pages displayed on the website. Referring again
to FIGS. B1-B2, clicks and entries are passed from the clients' web
browsers b1-23 through the Internet b1-27 to the enterprise web
server b1-19. At this point, the web server b1-19 passes the entry
information through to the dedicated server appliance b1-15 (see
FIGS. 1-2) via the API b1-41 software component (in embodiments in
which the methodology is implemented with software installed on an
application server, the web server b1-19 would pass the entry
information to the appropriate application server b1-13
instead).
[0112] The API b1-19 passes the entry and user identification
information through to the portrait generator b1-45, which
interfaces with the portrait database b1-43 for historic user
patterns and portrait information. At this point, a current
portrait is generated for that user at that time. If the current
portrait is determined to be strong enough for representation of
the website data, the portrait and trigger is passed to the
enterprise application server b1-13 responsible for presenting the
website information to the web server b1-19 for potential
re-mashing of content and offers. The result of the processing in
an e-commerce application is a user-personalized web-page in the
context of the current user behavior and buying pattern.
[0113] In some embodiments, an initial matrix of behaviors may be
utilized to establish an initial portrait for a user. Such a matrix
may reflect the fact that consumers have different personalities,
and exhibit different behaviors, based on time and topic. Such
behaviors may be hard-wired, and may directly impact how a consumer
feels when they are presented with information concerning a product
or service.
EXAMPLE C2
[0114] This example illustrates how user behavior may be tracked,
weighted and utilized to develop a behavioral portrait for the
user.
[0115] A user's behavior on a website is recorded and analyzed.
That behavior may include such factors as: [0116] (a) navigation
selections (which navigation choices are made on the site); [0117]
(b) search text parsing and analysis (search used to navigate to a
particular area on the site in conjunction with term analysis);
[0118] (c) icon selection (where the user clicks); [0119] (d)
marketing offer selection (the type of marketing offer a user
selects); [0120] (e) order of behavior (e.g., whether the user
engages in seemingly random "browsing" or more focused selections);
[0121] (f) speed of navigation (timed clicks, e.g., speed of
checkout or skipping less relevant pages); and [0122] (g) omitted
selections (what the user does not do or select).
[0123] After each action, the user's portrait is recalculated. This
calculation is based upon a dynamic (real-time) weighting for each
of the 15 tracked psychological attributes shown in TABLE C1, with
each attribute having a neutral setting of 0. The calculation also
considers historical knowledge of that user's previous behavior in
known dialogs, and the previous weightings in the current dialog.
Each of the 15 behaviors in TABLE C1 is tracked using a sliding
scale with 0 as the default weighting for each attribute. A
weighting significantly on either side of 0 indicates a tendency of
behavior toward that setting.
[0124] As a specific example, a user may display the portrait
depicted in TABLE C4 below:
TABLE-US-00004 TABLE C4 Example Psychological Attribute Weighting
Psychological Attribute Weight A Decision Making 49 B Motivation 62
C Information Presentation -53 D Adoption Stage -77 E Relationships
0 F Specificity 0 G Frequency 28 H Teamwork 0 I Rules 65 J Speed 0
K Empathy 0 L Intensity 0 M Temperature 0 N Shade 61 O Pattern
-33
Such a user is demonstrating behaviors in A, B, C, D, G, I, N and O
that are significant enough to trigger a website change.
[0125] The Portrait Engine or Portrait Generator is preferably used
to dynamically recalculate the customer's scores on each of the
attributes during the web session. The Portrait Engine also
preferably considers historical knowledge of that user's previous
behavior in known dialogs, and the previous weightings in the
current dialog. The engine may be based on a transition table, a
rule-based knowledge base, other forms of artificial intelligence
tools, or various combinations or sub-combinations of any of those
tools.
[0126] While individual significant weightings (that is, weightings
which might impact the website display, format or navigation route)
may be sufficient to change web page content or layout in some of
the embodiments described herein, preferably, combinations of
attributes, the user's behavioral history, and the context of the
dialog will be used to set the "trigger" flag and to intercept and
analyze the web page being displayed to the user. Typically, the
page to be displayed will be modified (that is, a Portrait Enhanced
Page (PEP) will be generated). As previously stated, triggers and
fail-safes may be implemented throughout the system to ensure that
PEPs are not displayed until the behavior is known with a degree of
certainty, or, for example, if the user demonstrates behavioral
swings while in a transaction.
D. Modification of Web Site Content Based on User Behavioral
Portrait
[0127] It will be appreciated that Portrait Enhanced Pages (PEPs)
may be produced in the methodologies taught herein by amending web
pages in a number of ways. Such changes include, but are not
limited to: [0128] (a) content reorganization/re-prioritization;
[0129] (b) content addition; [0130] (c) content deletion; [0131]
(d) style alterations of content (i.e. highlighting, changing
font/size/color, borders, padding, etc); [0132] (e) content folding
(placing content in collapsible/expandable containers); [0133] (f)
marketing offer personalization; [0134] (g) icon personalization;
[0135] (h) language personalization; [0136] (i) expedited
navigation paths through the website; [0137] (j) elongated (added
pages) navigation paths through the website.
[0138] As previously noted, in some embodiments, the methodologies
described herein may be utilized to modify or augment web page or
website content or presentation as a function of user behavioral
portrait. This process may be appreciated with respect to FIG. D2.
As seen therein, the behavior of a user on a website is captured
d2-1, and is analyzed to build a behavioral portrait d2-2 for that
user. It is then determined whether content modification is
appropriate in light of the user's behavioral portrait d2-3. If so,
the content on the website is modified, augmented or enhanced d2-4
in light of the user's behavioral portrait. Typically, as seen with
reference to the sample web pages d2-5 (shown in greater detail in
FIG. D3), this will result in a portrait enhanced web page which is
substantially different from the standard, unmodified web page.
[0139] FIG. D1 provides an overview of some possible types of
webpage modification that may be effected with some of the
methodologies described herein. As seen therein, these
modifications preferably occur dynamically and in real-time, and
may involve varying levels of complexity and interaction with the
user.
[0140] In the particular embodiment depicted in FIG. D1, the first
level d1-2 of web page augmentation involves emphasizing or
de-emphasizing certain content. This may result in content being
highlighted or being presented with an increased or decreased font
size. It may also result in the collapse or expansion of certain
menus, and in the repositioning of content within a given web
page.
[0141] Again referring to the particular embodiment depicted in
FIG. D1, the second level d1-3 of webpage augmentation involves
personalizing offers appearing on the web page. This may involve,
for example, presenting banner ads, pop-ups, or hot links whose
content is targeted to the user based on their behavioral portrait.
This may also involve repositioning of offers on the web page in
light of the user's behavioral portrait.
[0142] Still referring to the particular embodiment depicted in
FIG. D1, the third level d1-4 of webpage augmentation involves
personalizing the content appearing on the web page. This may
involve modifying the format in which content appears (e.g., tab
reviews), modifying data appearing on the web page, mash-up of
local content (e.g., reviews on the home page), and mash-up of
remote content.
[0143] Still referring to the particular embodiment depicted in
FIG. D1, the fourth level D1-5 of webpage augmentation involves the
re-routing, expedition or delay of certain webpage content. For
example, the check-out process required to purchase an item
advertised on the web page may be modified in light of the user's
behavioral portrait to ensure a higher level of satisfaction and
success. Up-sell processes appearing on the web page (that is, the
practice of suggesting higher priced products or services to a
customer who is considering a purchase, such as an offer for a
better version of the same product or service the consumer is
considering purchasing) may also be modified.
[0144] The augmentor (see FIG. B4) uses portrait attributes and/or
type for deciding what applies to a given web page and a specific
portrait or portrait type. In a preferred embodiment, the augmentor
uses JavaScript injection, which requires the client to include a
JavaScript snippet on each page that should be tailored to the
current website user. Multiple insertion points may be required,
depending on the type of desired modifications and/or page
complexity.
[0145] Modification of the webpages is preferably accomplished via
JavaScript that gets executed in the customer.quadrature.s browser.
The JavaScript manipulating the page is preferably structured as
rules along with a core library (jQuery+extensions). Only the rules
specific for the combination of the client's web-page and the
customer.quadrature.s portrait type or attributes will be
downloaded into the browser.
[0146] The jQuery JavaScript library is preferably utilized for
creating rules to identify page sections (portrait-based content)
and to manipulate content. Extensions to the jQuery library may be
created to simplify rule creation, data selection, and content
manipulation. In order to reduce bloat, it is preferred to use two
types of extensions: those common across clients, and those
specific to a client. In order to further minimize load and
processing time, the jQuery library may be reduced to the minimum
code set required for the rules. The resulting library may then be
compressed.
[0147] In order to instrument a site, the major set of web-page
templates is identified. The sections of the web page, and the
existing portrait bias of each, are then categorized. Modifications
to the sections are dependent on the type of portrait bias of that
section. The major types of typical content are listed in TABLE D1
below.
TABLE-US-00005 TABLE D1 Content Types Decision Motivation
Presentation Best Sellers Other Opportune Featured Items Other
Choice Popular Other Search Self Process Recommendations Other User
Reviews Other Expert Reviews Self/Other Promotions Opportune
Advertisements Other Product Details Self Related Items Other
Choice Accessories Support Other Safety Informational Self Crumbs
Process Footnotes Process Terms/Policies Safety
The foregoing types of content may be represented in the following
formats: [0148] Headings [0149] Lists/Tables [0150] Links [0151]
Text [0152] Images [0153] Form Items [0154] Groupings of the above
items.
D1. Level 1 Modifications
[0155] Level 1 modifications may include bolding, highlighting,
changing the size of text, collapsing sections/lists, repositioning
sections, expanding sections/lists, and deleting sections/content
using rules based on portrait attributes. Modifications of the
identified sections on given web pages depend on the current user's
portrait data and on the existing content type in the given
sections. For example, reviews and recommended product lists can be
collapsed for a `self` user. Likewise, reviews and recommended
product lists might be positioned more prominently on the page for
an `other` user. Examples of some possible modifications which may
be implemented for various users are set forth in TABLES D2-D4
below.
TABLE-US-00006 TABLE D2 Modifications of Sections Based on User
Behavioral Portrait Type: Self/Other Self Other Best Sellers
Collapse Move to higher position Move to lower positioning Increase
font size and Reduce font size and bolding of header bolding of
heading and text Featured Items Collapse Move to higher position
Move to lower positioning Increase font size and Reduce font size
and bolding of header bolding of heading and text Popular Collapse
Move to higher position Move to lower positioning Increase font
size and Reduce font size and bolding of header bolding of heading
and text Search Move to higher position No manipulation necessary
Recommendations Collapse Move to higher position Move to lower
positioning Increase font size and Reduce font size and bolding
bolding of heading of header and text User Reviews Collapse Move to
higher position Move to lower positioning Increase font size and
Reduce font size and bolding of header bolding of heading and text
Expert Reviews No manipulation necessary No manipulation necessary
Advertisements Remove No manipulation necessary Product Details
Move to higher position Collapse Increase font size and Move to
lower positioning bolding of heading Reduce font size and bolding
of header and text Related Items Collapse Move to higher position
Move to lower positioning Increase font size and Reduce font size
and bolding bolding of heading of header and text Accessories No
manipulation necessary Move to higher position Increase font size
and bolding of heading Support Collapse Move to higher position
Move to lower positioning Increase font size and Reduce font size
and bolding bolding of heading of header and text Informational
Move to higher position Collapse Increase font size and Move to
lower positioning bolding of heading Reduce font size and bolding
of header and text
TABLE-US-00007 TABLE D3 Modifications of Sections Based on User
Behavioral Portrait Type: Opportune/Safety Opportune Safety Best
Sellers Move to higher position No manipulation necessary Increase
font size and bolding of heading Promotions Move to higher position
Collapse Increase font size and bolding Move to lower positioning
of heading Reduce font size and bolding of header and text Support
No manipulation necessary Move to higher position Increase font
size and bolding of heading Terms/ No manipulation necessary Move
to higher position Policies Increase font size and bolding of
heading
TABLE-US-00008 TABLE D4 Modifications of Sections Based on User
Behavioral Portrait Type: Choice/Process Opportune Safety Featured
Move to higher position No manipulation necessary Items Increase
font size and bolding of heading Search No manipulation necessary
No manipulation necessary Related Move to higher position No
manipulation necessary Items Increase font size and bolding of
heading Crumbs No manipulation necessary Move to higher position
Increase font size and bolding of heading Footnotes Collapse No
manipulation necessary Move to lower positioning Reduce font size
and bolding of header and text
D2. Level 2 Modifications
[0156] Level 2 modifications preferably target specific portrait
types by altering images and/or text. There are at least two
options for modifying text or images on a web page. One is to
create an explicit augmentor rule, and the other is to define
images for a page and section in the portal.
[0157] The portal will preferably include web pages where the
client can specify images and text that will be dynamically
displayed, based on the portrait type of the user. Prior setup work
will typically include defining the page templates (URL regex
matching) and page sections (jQuery selectors) using friendly
names. For example, the product page might have a friendly name of
`Product Page` and the advertising section might have a friendly
name of `Top Right Advertisement`. The user would then be able to
specify alternate images/text based on the portrait type. Images
may be specified using only a URL. Text content will typically be
specified in HTML format.
D3. Level 3 Modifications
[0158] Level 3 modifications preferably add or replace content from
the client, and add mash-up content from third party web sites. The
portal will include web pages where the client can specify an RSS
feed or URL from which to retrieve the mash-up content, along with
a list of the desired fields from the resulting content. The
mash-ups may be handled by the client.quadrature.s development team
to provide for optimal performance and client control.
[0159] Client content may be retrieved through a number of methods.
These may include, without limitation: [0160] an AJAX request from
the web browser to the client.quadrature.s systems, which is then
displayed; [0161] instrumentation of links with a portrait type
indicator to allow the client to display appropriate data on
subsequent pages; [0162] client addition of multiple content
options (one for each portrait type), which the JavaScript will
appropriately display based on portrait type; and [0163] client
data retrieval through the use of a client provided API (this
approach is not preferred, as it may require very tight integration
and substantial custom code).
D4. Level 4 and 5 Modifications
[0164] Level 4 modifications preferably provide re-routing,
expediting, and delaying website navigation.
[0165] Level 5 modifications preferably involve product description
personalization.
[0166] In the foregoing process, it is preferred that the clients
identify web page sections by ids and classes, as this may result
in higher performance and reduced rule volatility. It is also
preferred that the client assume as much responsibility for dynamic
content as possible, as this may provide for higher performance,
greater stability and consistency.
EXAMPLE D-1
[0167] This example illustrates the modification of content (on the
website of an on-line computer retailer) for two different types of
users in accordance with a particular, non-limiting embodiment of
the methodologies described herein.
[0168] With respect to FIG. D4 and FIG. D5, some of the behavioral
traits of two different hypothetical users are summarized. With
respect to FIG. D4, the first user ("Trish") is a "thinker" type
user. Trish is a type of user who is in control of her decisions
d4-1, and will often say "I have a gut feel" d4-2. Trish typically
makes her decisions based on a review of detailed specifications
and trusted sources d4-3, and is motivated by how an offered
product will help her to achieve her goals d4-4. She wants to see a
positive result from her purchase d4-5, is heavily dependent on
process and procedure d4-6, and is not likely to move to the next
step in a purchase process before the current step is completed
d4-7.
[0169] By contrast, the second hypothetical user ("Karl"--see FIG.
D5) is a "browser" type user. Karl likes to gather opinions on his
potential purchase d5-1. It is important to Karl that the product
is positively reviewed, has won awards, and is a top 10 choice
d5-2. Karl is motivated more by avoiding problems then by missing
opportunities d5-3. Karl will often ask "What do you think?" d5-4.
He feels constricted by process, and wants to be able to choose
from many options d5-5. As a consumer, Karl requires multiple paths
to the same result d5-6.
[0170] The behavioral portraits of Trish and Karl are summarized in
FIGS. D6 and D7, respectively. As seen therein, their behavioral
portraits in this particular embodiment consist of a scaled
weighting of 15 different behavioral characteristics, including
decision, motivation, information, adoption, relationships,
specificity, frequency, teamwork, rules, speed, empathy, intensity,
temperature, shade and pattern.
[0171] Referring now to FIGS. D8-D13, the manner in which
particular web pages on a web site are modified in light of Trish's
behavioral portrait is illustrated.
[0172] With reference to FIG. D8, on the homepage d8-4 of the web
site, the advertisement appearing on the top of the home page is
personalized d8-1 so that the language in the advertisement is more
aligned with Trish's decision-making attribute. Moreover, the
search bar is emphasized d8-2. Given Trish's behavioral portrait,
it is likely that Trish knows what she's here for, and she prefers
web sites that allow her to get to a result quickly. This
behavioral trait is accommodated by emphasizing the search bar,
which allows her to navigate through the site more quickly.
[0173] With reference to FIG. D9, on the notebook category page
d9-7 of the web site, the fact has been recognized that the
standard notebook category page does not give someone of Trish's
behavioral portrait sufficient detailed information to make an
informed choice. Consequently, the page has been augmented by
mashing up additional specification information d9-3 from other
areas on the website. In addition, non-relevant content has been
collapsed and de-emphasized d9-5, thus resulting in a web page that
more accurately reflects Trish's preferences. As with the home page
d8-4 (see FIG. D8), advertising has been personalized d9-1 and
offers have been reformatted d9-4 in accordance with Trish's
behavioral portrait. Also, the search bar has once again been
emphasized d9-2 to allow Trish to get to a result quickly.
[0174] With reference to FIG. D10, the notebook model page d10-6 of
the web site has been personalized with the addition of
specification detail d10-2 and re-formatted offers d10-3.
Non-relevant content has been collapsed d10-7, and search
tools/bread crumbs have been emphasized d10-1.
[0175] With reference to FIG. D11, the "1520" page d11-6 of the web
site has been augmented so that the detailed technical
specifications of the product under consideration are opened by
default d11-4. The advertising appearing on the web page, and the
layout of the web page itself, continues to be customized d1-1 in
accordance with Trish's information presentation preferences. The
reviews which would ordinarily appear on this web page have been
collapsed into a reviews tab d11-3 so that Trish may choose that
content if she decides to, but is otherwise not presented with it.
Once again, search tools and bread crumbs have been emphasized
d11-2.
[0176] Referring now to FIG. D12, the featured systems page d12-5
of the web site has been customized in light of Trish's preferences
with respect to advertising and page layout d12-1. A reviews tab
has been added d12-3 to the page so that the information it
contains is de-emphasized, but is available to Trish if she chooses
it. Search tools and bread crumbs have again been emphasized
d12-2.
[0177] With reference to FIG. D13, the configuration page d13-4 of
the web site has been reconfigured so that Trish is redirected past
the services page d13-1. This reflects her suspicion of content
which is introduced into the close process without her consent. All
other close pages which are appropriate for Trish's buying patterns
are available. Non-relevant content as been collapsed d13-2.
[0178] Referring now to FIGS. D14-D19, the manner in which
particular web pages on the same web site are modified in light of
Karl's behavioral portrait is illustrated.
[0179] With reference to FIG. D14, the homepage d14-4 of the web
site has been modified to reflect Karl's preference for gathering
product information from reviews, testimonials and customer success
stories. To that end, review content d14-3 has been added to the
page, and a community link d14-2 has also been provided. Moreover,
since Karl is motivated by choices, the advertising content
appearing on the page has been personalized D14-1 to stress the
choices that the product offers.
[0180] With reference to FIG. D15, which shows the notebook
selection page d15-6 of the web site, Karl's online buying patterns
have indicated a need for choices and recommendations. He is less
concerned with detailed specifications, but prefers peer reviews
and popular choices. Therefore, some of the product specification
information on this page has been replaced with a listing of all
notebooks available in all product lines d15-2, and the user
ratings of each of these products is specified. In addition,
recommended links have been expanded d15-3, and the availability of
help and support has been emphasized d15-4. Advertising has also
been personalized in light of Karl's behavioral portrait d15-1.
[0181] Referring now to FIG. D16, which shows the featured system
page d16-6, this page of the web site has been modified to reflect
the fact that Karl's primary information source is peer reviews.
Consequently, the specification content has been collapsed d16-3
(though it is still accessible), and a reviews tab has been added
d16-2 and expanded d16-4. The advertising appearing on the web page
has again been personalized d16-1 to reflect Karl's behavioral
portrait.
[0182] Karl's check-out page d17-3 is depicted in FIG. D17. Karl is
opened to recommended configuration options for his purchase,
including service options. Therefore, this page is not skipped as
it was for Trish. Again, the advertising appearing on this web page
has been personalized d17-1 to reflect Karl's behavioral
portrait.
[0183] FIG. D18 depicts Karl's configuration page d18-5. Karl
displays buying behaviors that indicate a suspicion of process and
of being "led down a path". He prefers to be able to somewhat
randomly access sections of the check-out process, and he always
desires choices. Consequently, the configuration page d18-5 has
been modified to ensure that Karl is comfortable with the check-out
process. In particular, new tab formats d18-1, new navigation
buttons d18-2, and new instructions d18-3 are provided to allow him
to randomly access sections of the check-out process.
[0184] FIG. 19 depicts Karl's check-out page d19-3. Since Karl is
open to purchasing additional products if they are recommended by
experts or peers, the "essential add-ons" tab has been moved d19-1
to the primary position in the configuration section.
E. Web Site Analysis Based on User Behavioral Portrait
[0185] As previously noted, some of the methodologies described
herein may be utilized to provide an analysis of browsing behavior
on a web site as a function of behavioral portrait type. Such an
analysis may help the owner of a website to improve e-commerce
closure rates, to improve the rate of return visits to the website,
to understand why customers (or potential customers) leave the
website, to understand how to improve the effectiveness of web
marketing programs and offline programs, to better align offline
marketing with website content, and to understand how to best
communicate with different customer segments.
EXAMPLE E1
[0186] This example illustrates the types of analytics (see FIG.
A1) which may be generated on an e-commerce web site using some of
the methodologies described herein.
[0187] FIGS. E5-E12 represent the results of an analysis report of
the type described herein which was prepared on an e-commerce web
site. Such an analysis provides trends and indicators which are
derived from portrait-based analytics for the website over a
predetermined period of time.
[0188] FIGS. E5-E6 depict the number of visits to the web site over
a predetermined period of time, broken down in percentages by
consumer behavioral portrait type (FIG. E5) and by the total number
of visits by each behavioral portrait type (FIG. E6). These results
demonstrate that, with respect to the particular web site under
consideration, consumers having portrait types F and H showed a
visit rate to the web site which was much higher than the average
visit rate, while consumers having portrait types B, D, and G
showed a much lower than average visit rate to the website. These
results suggest that the language used in external marketing
campaigns (both online and offline) is attracting consumers of
portrait types F and H to the website significantly more than
consumers having other portrait types. Similarly, these results
suggest that the language used in the external marketing campaigns
is attracting consumers with B, D and G type behavioral portraits
significantly less than consumers with other behavioral portrait
types. This result may be intentional, or it may be an indicator
that traffic generation activities are linguistically skewed to F
and H behavioral portrait types, and away from B, D, and G
behavioral portrait types.
[0189] FIGS. E7-E8 depict the number of return visits to the
website over a predetermined period of time, broken down in
percentages by user behavioral portrait type (FIG. E7) and by the
total number of visits by each behavioral portrait type (FIG. E8).
As these results illustrate, consumers having behavioral portrait
types F and H are showing much greater than average return rates as
compared to other behavioral portrait types. The combination of
above average total visit numbers described above, and the very
high return rates shown here, indicates that there is good
alignment between traffic generation activities and website
behavior.
[0190] As these results also illustrate, behavioral portrait types
B, D, and G show a much lower than average return rate as compared
to other behavioral portrait types. The combination of below
average total visit numbers for these behavioral portrait types as
indicated above, combined with the very low return rates indicated
here, suggest an issue with website behavior not matching
expectations for consumers of this behavioral portrait type.
[0191] FIGS. E9-E10 summarize the close rates as a function of
behavioral portrait type. These data suggest that consumers of
behavioral portrait type H are showing below average close rates
when compared other behavioral portrait types. This behavior
indicates a potential issue with the check-out process for
consumers of this behavioral portrait type. The high visitor rates
indicate good traffic generation. High return rates imply that the
website informational structure is sound. However, the low closure
rate is a concern. This may be due to a misalignment of the closure
process language and behavior with the rest of the web site. Thus,
it would be prudent to check the top ranking abandoned pages for
consumers of this behavioral portrait type to determine the key
pages to be examined and tuned.
[0192] Consumers of behavioral portrait types C, E, and G are
showing very low close rates on the website when compared to other
behavioral portrait types. The very poor visit rates exhibited by
consumers of this behavioral portrait type as compared to consumers
of other behavioral portrait types strongly suggests that traffic
generation for consumers of these behavioral portrait types is not
working. When it does work, these visitors close less than those
with other portrait types. This result may be intentional. However,
if it is not, these results suggest that the abandoned pages should
be checked, and the language or presentation on these pages should
possibly be modified, to better align them with consumers of these
behavioral portrait types.
[0193] Consumers of behavioral portrait types A and F are closing
and abandoning at approximately the same rates. These indicators
demonstrate very good alignment between marketing campaigns and
website behavior. Nonetheless, the web site manager may wish to
check the abandoned pages and program/marketing campaign statistics
to determine whether further tuning can occur.
[0194] Consumers of behavioral portrait types B and D are showing
very high close rates when compared to consumers of other
behavioral portrait types. This result suggests a serious
misalignment of traffic generation activities (online and offline
marketing campaigns, pay-per-click, etc.) and the behavior and
language used on the website. Consumers having these portrait types
are closing at a high rate, but traffic generation programs in the
outbound marketing activities in use appear to be driving these
customers to the site in lower numbers as compared to consumers
having other behavioral portrait types. Alignment of the language
used in those marketing campaigns may drive higher levels of
qualified, closing traffic to the site. The programs and
advertising statistics may also be checked for further details on
what is working and what is not working.
[0195] FIG. E11 summarizes the average time spent on the web site
as a function of consumer behavioral portrait type. The results
shown therein indicate that consumers of behavioral portrait types
G and H are spending an average amount of time on the website.
These consumers are leaving the website early in the website page
hierarchy, but are spending an average time on the pages they are
viewing. This may indicate that pages on the website are lengthy
and informational, but are not targeted to consumers of these
behavioral portrait types. Another possibility is that the website
behavior does not offer a clear path to the next step or a
procedure for closing on the product. This would suggest that the
abandoned pages and portrait definitions should be checked for
further information.
[0196] FIG. E12 summarizes the link analysis for the website as a
function of consumer behavioral portrait type. The results shown
therein suggest that consumers of behavioral portrait types C and E
are clicking through an average number of pages on the website, and
yet are abandoning the website more often than not. This may be an
indication that the language used in marketing and outbound
programs is misaligned with website behavior. In this case, it is
possible that the website demonstrates a similar flow throughout
and that consumers of this behavioral portrait type abandoned the
website at the first page that did not give them the information
that they needed. These results suggest that the abandoned pages
should be checked for more details on where these consumers are
leaving. It is also possible that consumers are satisfied with the
first few interactions with the web site, and then hit an area of
misalignment. Again, the abandoned page details may be consulted to
provide more information on this issue.
[0197] These results also show that consumers of behavioral
portrait types B and D are clicking through an average number of
pages on the website. Consumers having these behavioral portrait
types are closing within a few clicks. This suggests that the
website is organized appropriately for consumers of these portrait
types, and that such consumers are finding the information that
they need.
[0198] The results also demonstrate that consumers of behavioral
portrait types G and H are clicking through very few pages on the
website and are abandoning the website very quickly (on average,
within the first few clicks). It is possible that the first few
pages presented to these customers are the problem. For example,
specifically ending pages from outbound programs or common entry
points from pay-per-click activities may be demonstrating the
suspect behavior. These results suggest checking the abandoned page
details, and focusing on tuning these pages first. Another possible
approach would be to test specific marketing programs with new
landing pages more specifically focused on consumers of these
portrait types.
[0199] In a preferred embodiment of the web site analytical
software and methodologies provided herein, a knowledge base is
provided which conducts real-time analysis of the behavioral data
collected from web sessions. Using a set of rules or other
analytical tools, the exhaustive set of data may be mined for
patterns, trends, alignments, disconnects, and other observations.
This analysis recognizes and identifies the complex conditions
involving various combinations of the data and statistics collected
by the Portrait Engine (the software module that generates user
behavioral portraits; see FIG. B4). The key results can be
highlighted, and a manageable set of recommendations can be made
based on those results. In this way, a marketing team utilizing the
report receives more actionable information, instead of an overload
of data with no ability or means of identifying the critical
content.
[0200] The generated report describes the web analytics and
behavioral data presented in the report's graphs by pointing out
the important observations marketing or e-commerce teams should
notice. In the example shown above, the rules engine identifies
that customers with Portrait type F have a much higher visit rate
than average. However, the closure rate for Portrait type F is
considerably lower than that of Portrait type B. In this particular
example, the results could indicate that the outbound marketing
campaign is more oriented to those customers who display the
behaviors of Portrait F, while the site itself is more comfortable
to customers with behaviors of Portrait type B.
F. Strategic Information for Sales Groups based on User Behavioral
Portrait
[0201] The user behavioral portraits obtainable with the systems
and methodologies described herein may be utilized by various
groups to enable them to communicate more efficiently with the user
to whom the behavioral portrait corresponds. Such groups include,
without limitation, telemarketers, call centers, sales
representatives, help centers, and sales teams making face-to-face
sales pitches. The information contained in the user behavioral
portrait may be utilized, by itself or in combination with general
analyses or recommendations for users of the general behavioral
portrait type, to understand how to communicate with the user more
effectively, to understand how to handle common objections the user
may have, and to identify the best manner in which to close a sale
with the user.
[0202] In the case of call centers, sales forces, or other similar
teams, the portrait of the customer (or potential customer) may be
used to generate a customer passport. A review of the customer
passport, a non-limiting example of which is depicted in FIG. F1,
may provide the employee with information about the customer's
preferred approach for making decisions, their basic motivation for
completing a transaction, their preferred means of receiving
information, and their behavior in terms of each of the remaining
attributes. Recommended language may be provided to the employee to
create the highest level of comfort for the customer. Similarly,
language which will create a dissonance for the customer may be
identified. Furthermore, specific language recommendations may be
provided to advise the employee on preferred or optimal methods for
closing or re-engaging the customer, or responding to common
objections the customer may have. Finally, a sample engagement
script may be provided for the employee's specific company and
situation.
[0203] Again, all of this information and these suggestions may be
based on the customer's portrait which is dynamically generated
during their web session. It will thus be appreciated that the
passport allows a person using it to understand the proper language
to use to describe products and services to the customer, to
understand the proper way to personalize and position marketing
messages for the customer, and to understand the proper way to
close each customer.
[0204] The foregoing methodology is depicted schematically in FIG.
F2. As seen therein, after a user's behavior on a web site has been
captured f2-1, a comprehensive customer portrait is built f2-2
based upon that behavior. This portrait is then used to generate a
customer passport f2-3 which may be utilized by any of the groups
described above to facilitate interaction with the customer
f2-4.
G. Other Concepts and Applications
[0205] G1. General Internet Applications
[0206] While much of the foregoing discussion has dealt with
web-based marketing, it will also be appreciated that the systems,
methodologies and software disclosed herein are not particularly
limited to that application. In particular, the systems,
methodologies and software disclosed herein may be applied to a
wide variety of applications where customized web pages and/or
content are desirable, including, for example, gaming, nonprofit
websites, and general Internet use.
[0207] As a particular, non-limiting example, methodologies of the
type described herein may be utilized to provide customized web
pages or web content in noncommercial applications such as
government web sites. For instance, these methodologies may be
utilized to provide customization of an IRS web site. Since many
taxpayers consult such a site when they have questions relating to
their taxes, and since some of the methodologies described herein
may be utilized to ensure that information is being presented to
these users in a way that is most helpful to them, the use of these
methodologies may ensure that users are more likely to stay on the
website longer and to find the information they are looking for.
This, in turn, may reduce the burden on the IRS call center, and
may also reduce the number of mistakes in subsequent tax
returns.
[0208] In other embodiments, behavioral portraits for users may be
derived from web sites which are unrelated to a business. Those
behavioral portraits may then be utilized in the conduct of the
business with respect to the individual or to the public and
general.
[0209] For example, the behavior of a particular individual on a
noncommercial website may be monitored, and a behavioral portrait
of the individual may be derived. That behavioral portrait may then
be applied to customize content on a commercial website, or may be
provided to call centers or other marketing groups to help those
groups understand how to interact with the individual. Such
embodiments make it conducive for a business to underwrite a
website which provides free content of interest to the general
public, since the business may then apply the information it has
learned on the nonprofit site to provide a commercial benefit
(albeit indirectly) to its commercial business.
[0210] G2. Business Models
[0211] The methodologies disclosed herein may also give rise to a
number of unique business models. In the current business climate,
businesses are increasingly looking for methods to ensure that the
products and services they purchase are not burdened with onerous
up-front costs. Rather, the current trend is for products and
services which demonstrate business value before payment.
Consequently, a number of "pay-per-use" and "pay-per-performance"
products are available today.
[0212] In the present case, some of the systems and methodologies
described herein lend themselves well to a cost model that allows
the customer to decide whether the product has impact as the
transaction occurs. The unit of cost may be taken as the Portrait
Enhanced Page (i.e., the modified web page for a particular
customer during a particular web session). As the psychological
behavior of the customer is tracked and weighted, the software
described herein may be used to generate new web pages for that
dialog. These Portrait Enhanced Pages (PEPs) may be offered to the
website provider, who may either display the PEPs or opt out of
doing so. If the PEP is displayed, the website provider may be
charged a small fee, thus eliminating heavy up-front licensing
costs and allowing the customer to determine the limit of the
investment based upon business value. This model also lends itself
to subscription services, where an Internet service provider may
package availability of PEPs with their higher end service
packages.
[0213] In other business models possible in accordance with the
teachings herein, the user portraits described above may be
provided to various third party entities, such as call centers,
marketing teams, sales forces, and the like. Preferably, the fee
for such portraits is based only on the number of portraits
provided to, and accepted by, the third party entity, although
various other payment options may be utilized instead. For example,
the party providing the portraits may earn a commission which is
calculated as a percentage of, or is otherwise based on, the value
of one or more successful sales which are subsequently made by the
third party entity to the user whose portrait is provided to that
entity.
[0214] In some embodiments of the methodologies described herein,
the provision of user portraits may be coupled with the provision
of other leads relating to the users whose portraits are provided.
The party providing the user portraits may be the same as, or
different from, the party providing the leads. For example, the
party providing the user portraits may be a search engine, and the
party providing the leads may be the sponsor of one or more
websites that the user has visited, or a business entity (such as,
for example, a travel agency, retailer, bank or credit card
company) that the user has done business with in the past. In such
embodiments, the third party entity may decide whether or not to
accept the portrait with the lead, and fees may be charged only
when portraits are exchanged.
[0215] The resulting fees may be apportioned among the search
engine and lead generation company in accordance with a contractual
agreement between the organizations. If the party providing the
user portraits is the same as the party providing the leads, a
premium may be charged for the provision of a lead in conjunction
with a portrait, as compared to the fee charged for providing only
a portrait.
[0216] The above description of the present invention is
illustrative, and is not intended to be limiting. It will thus be
appreciated that various additions, substitutions and modifications
may be made to the above described embodiments without departing
from the scope of the present invention. Accordingly, the scope of
the present invention should be construed in reference to the
appended claims.
* * * * *