U.S. patent application number 13/895975 was filed with the patent office on 2013-09-26 for system and method for reporting website activity based on inferred attribution methodology.
This patent application is currently assigned to GOOGLE INC.. The applicant listed for this patent is Andrew Joel Erlichson, Jonathan Marc Heller, James Christopher Kim, Benjamin Chien-wen Lee, Dwight Allen Merriman. Invention is credited to Andrew Joel Erlichson, Jonathan Marc Heller, James Christopher Kim, Benjamin Chien-wen Lee, Dwight Allen Merriman.
Application Number | 20130254389 13/895975 |
Document ID | / |
Family ID | 39199299 |
Filed Date | 2013-09-26 |
United States Patent
Application |
20130254389 |
Kind Code |
A1 |
Heller; Jonathan Marc ; et
al. |
September 26, 2013 |
System and Method for Reporting Website Activity Based on Inferred
Attribution Methodology
Abstract
A method and system for reporting website activity. According to
an example embodiment, the system receives event-level data
representing visitor activity on a client website, infers
attribution of one or more metrics to at least one navigation
entity based on the visitor activity, and provides reports based on
the inferred attribution.
Inventors: |
Heller; Jonathan Marc; (New
York, NY) ; Kim; James Christopher; (New York,
NY) ; Merriman; Dwight Allen; (New York, NY) ;
Erlichson; Andrew Joel; (Metuchen, NJ) ; Lee;
Benjamin Chien-wen; (Bayside, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Heller; Jonathan Marc
Kim; James Christopher
Merriman; Dwight Allen
Erlichson; Andrew Joel
Lee; Benjamin Chien-wen |
New York
New York
New York
Metuchen
Bayside |
NY
NY
NY
NJ
NY |
US
US
US
US
US |
|
|
Assignee: |
GOOGLE INC.
Mountain View
CA
|
Family ID: |
39199299 |
Appl. No.: |
13/895975 |
Filed: |
May 16, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12073503 |
Mar 6, 2008 |
8452865 |
|
|
13895975 |
|
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|
|
10245579 |
Sep 18, 2002 |
7349827 |
|
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12073503 |
|
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Current U.S.
Class: |
709/224 |
Current CPC
Class: |
G06F 11/3495 20130101;
H04L 43/0876 20130101; G06F 2201/875 20130101; G06F 11/3438
20130101; G06F 2201/86 20130101 |
Class at
Publication: |
709/224 |
International
Class: |
H04L 12/26 20060101
H04L012/26 |
Claims
1. A method comprising: receiving event-level data representing
visitor activity through navigation entities on a website; parsing,
by one or more computers, contents of the visitor activity; and
inferring, based on the parsing and by the one or more computers,
attribution of one or more metrics to at least one navigation
entity, with inferring being independent of explicit attribution
information in the event-level data.
2. The method of claim 1, further comprising: attributing the one
or more metrics to the at least one navigation entity.
3. The method of claim 1, wherein at least one of the navigation
entities precedes another of the navigation entities in a
navigation path of a visitor associated with the visitor
activity.
4. The method of claim 1, wherein the navigation entities include
web pages provided by the website.
5. The method of claim 1, wherein at least one of the one or more
metrics includes a metric indicative of whether a particular type
of hyperlink is provided in at least one of the navigation
entities.
6. The method of claim 5, wherein the particular type of hyperlink
includes a traffic-driving hyperlink.
7. A system comprising: one or more processing devices; and one or
more machine-readable media storing instructions that are
executable by the one or more processing devices to perform
operations comprising: receiving event-level data representing
visitor activity through navigation entities on a website; parsing,
by one or more computers, contents of the visitor activity; and
inferring, based on the parsing and by the one or more computers,
attribution of one or more metrics to at least one navigation
entity, with inferring being independent of explicit attribution
information in the event-level data.
8. The system of claim 7, wherein the operations further comprise:
attributing the one or more metrics to the at least one navigation
entity.
9. The system of claim 7, wherein at least one of the navigation
entities precedes another of the navigation entities in a
navigation path of a visitor associated with the visitor
activity.
10. The system of claim 7, wherein the navigation entities include
web pages provided by the website.
11. The system of claim 7, wherein at least one of the one or more
metrics includes a metric indicative of whether a particular type
of hyperlink is provided in at least one of the navigation
entities.
12. The system of claim 11, wherein the particular type of
hyperlink includes a traffic-driving hyperlink.
13. One or more machine-readable media storing instructions that
are executable by the one or more processing devices to perform
operations comprising: receiving event-level data representing
visitor activity through navigation entities on a website; parsing,
by one or more computers, contents of the visitor activity; and
inferring, based on the parsing and by the one or more computers,
attribution of one or more metrics to at least one navigation
entity, with inferring being independent of explicit attribution
information in the event-level data.
14. The one or more machine-readable media of claim 13, wherein the
operations further comprise: attributing the one or more metrics to
the at least one navigation entity.
15. The one or more machine-readable media of claim 13, wherein at
least one of the navigation entities precedes another of the
navigation entities in a navigation path of a visitor associated
with the visitor activity.
16. The one or more machine-readable media of claim 13, wherein the
navigation entities include web pages provided by the website.
17. The one or more machine-readable media of claim 13, wherein at
least one of the one or more metrics includes a metric indicative
of whether a particular type of hyperlink is provided in at least
one of the navigation entities.
18. The one or more machine-readable media of claim 17, wherein the
particular type of hyperlink includes a traffic-driving hyperlink.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. application Ser.
No. 10/245,579, filed Sep. 18, 2002, which is related to U.S.
patent application Ser. No. 10/245,580, now U.S. Pat. No.
7,085,682, the disclosure of each of which is hereby incorporated
by reference in its entirety.
COPYRIGHT NOTICE
[0002] A portion of the disclosure of this patent document contains
material that is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document or patent disclosure as it appears in the
Patent and Trademark Office patent file or records, but otherwise
reserves all copyright rights whatsoever.
BACKGROUND OF THE INVENTION
[0003] The increase in electronic commerce over the Internet has
resulted in a growing demand for websites to track their online
customers' behavior and activity while at their sites. Tracking
this activity enables the website to better understand their
customers, which provides insight into ways in which the websites'
service and/or offerings can be improved. Websites can track their
information on their own, but larger sites enlist the aid of third
party application software or a third party application service
provider ("ASP") to do the work for them.
[0004] Client websites place great value on the ability of an ASP,
for instance, to report on overall website metrics, such as revenue
or cartings (i.e., the action of a visitor placing a product in a
cart), and on what is driving the metrics on the client's website.
Current ASP systems attribute overall website metrics to specific
navigation entities, like hyperlinks, pages and sections (i.e.,
collections of pages pertaining to a specific grouping of products,
like "sports equipment" or "laptop accessories"), which enables
clients to gain an understanding of which navigation entities have
significant impact on those metrics. However, in order for ASPs to
break down the overall metrics according to navigation entity, the
ASP requires knowledge of the mapping of the metrics to the
navigation entities prior to attribution taking place.
[0005] To implement this attribution methodology, an ASP system may
require, as an initial setup matter, a client web site map that
details which navigation entity will receive attribution of which
metric prior to the system being run. For example, if a client web
site showcases a specific digital video disc ("DVD") on its home
page, there may be an entry in the corresponding web site map
specifying that revenue resulting from the purchase of that DVD be
attributed to the home page, thereby crediting the home page for
directing the visitor to the DVD purchase. When the ASP sees that a
visitor bought the DVD, the ASP system consults the map and
attributes the corresponding revenue to the home page.
[0006] A major drawback to this mapping process is that there is an
up front effort required to initially map all relevant metrics to
their corresponding navigation entities. Additionally, if the
client web site makes any changes to a mapped navigation entity on
its site, the map held in the ASP system must be updated to reflect
those changes in order for the metrics to be properly attributed to
the changed navigation entities. This increases the client's
ongoing effort to maintain the ASP system.
[0007] Some ASPs may eliminate the need for the web site map by
requiring attribution information to be provided on each relevant
web page, and to be carried forward (e.g., via session variables or
query strings in the uniform resource locator ("URL")) through a
visitor's navigation path during a session. For example, if a
client web site showcases a specific DVD on its home page,
attribution information on the home page (such as "apply revenue to
home page") may follow the visitor through to check out. Thus, if a
visitor ends up buying the DVD, the ASP system will see the carried
through attribution information when the visitor pays for the DVD,
and know to attribute that revenue to the home page.
[0008] However, every time a change is made to a navigation entity
under this approach, client effort is still required to change the
attribution information that is held on that navigation entity and
to be carried forward during a session.
[0009] Accordingly, there is a need in the art for a
low-maintenance system and method for enabling attribution without
additional client effort when a navigation entity is added, removed
or changed.
SUMMARY OF THE INVENTION
[0010] The present invention is directed to a system and method for
analyzing online customer activity at a website in a cost-effective
and efficient manner. Efficient data collection, processing,
attribution and report presentation processes enable client
websites to quickly access and understand the interaction between
site traffic and transactions, and those factors that drive each
transaction.
[0011] According to an example embodiment, the system receives
event-level data representing visitor activity on a client website,
infers attribution of one or more metrics to at least one
navigation entity based on the visitor activity, and provides
reports based on the inferred attribution.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 is a block diagram that depicts a process for
providing a report on website activity based on an inferred
attribution methodology in accordance with an embodiment of the
present invention.
[0013] FIG. 2 is a block diagram that depicts a user computing
device in accordance with an embodiment of the present
invention.
[0014] FIG. 3 is a block diagram that depicts a network
architecture for an analysis system in accordance with an
embodiment of the present invention.
[0015] FIG. 4 is a block diagram that depicts a traffic-driving
hyperlink in accordance with an embodiment of the present
invention.
[0016] FIG. 5 is a block diagram that depicts a product hyperlink
in accordance with an embodiment of the present invention.
[0017] FIG. 6 is a block diagram that depicts generic navigation
entities in accordance with an embodiment of the present
invention.
[0018] FIG. 7 is a screen shot of a Visitor Section Navigation
Paths analysis page in accordance with an embodiment of the present
invention.
[0019] FIG. 8 is a screen shot of an Shelf Space Analysis page in
accordance with an embodiment of the present invention.
[0020] FIG. 9 is a screen shot of a Product Placement Analysis page
in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION
Overview
[0021] FIG. 1 provides an overview of a process and system
according to an embodiment of the present invention. The system
receives event-level data, representing specific events that
describe a customer's presence and/or activity through navigation
entities at a client website, such as clicking on a specific web
page or buying a specific product. Upon receipt of the event-level
data, the system sorts the event-level data by visitor and time
received (step 100), in order to group the activity by visitor
sessions. The system next performs an inferred attribution analysis
(step 110) on the event-level data, which attributes metrics, such
as revenue or cartings, to at least one navigation entity based on
the visitor activity and not explicit attribution information from
the event-level data. The system then generates page-pairing data
(step 120) to be used in providing a report to the client (step
130).
Architecture
[0022] FIG. 2 is a block diagram depicting the internal structure
of user computing device 200 in accordance with an embodiment of
the present invention. User computing device 200 may be a personal
computer, handheld personal digital assistant ("PDA"), or any other
type of microprocessor-based device. User computing device 200 may
include a processor 210, input device 220, output device 230,
storage device 240, web browser 250, and communication device
260.
[0023] Input device 220 may include a keyboard, mouse, pen-operated
touch screen, voice-recognition device, or any other device that
provides input from a user. Output device 230 may include a
monitor, printer, disk drive, speakers, or any other device that
provides tangible output to user.
[0024] Storage device 240 may include volatile and nonvolatile data
storage. Volatile data storage includes random-access memory
("RAM"), a cache, or any storage medium that temporarily holds data
while being processed; nonvolatile data storage includes a hard
drive, compact disc read-only memory ("CD-ROM") drive, tape drive,
removable storage disk, or any other non-temporary storage medium.
Communication device 260 may include a modem, network interface
card, or any other device capable of transmitting and receiving
signals over a network.
[0025] Web browser 250, which may be stored in storage device 240
and executed by processor 210, may include INTERNET EXPLORER.RTM.
web browser by Microsoft Corp. or the COMMUNICATOR.RTM. web browser
by Netscape Communications Corp., or any other software program
that displays data from a web server to a user via output device
230. One skilled in the art would appreciate that the components of
user computing device 200 may also be connected wirelessly,
possibly through an infrared connection.
[0026] FIG. 3 is a block diagram depicting a network architecture
for an analysis system in accordance with an embodiment of the
present invention. According to one particular embodiment, when
customer 300 visits the website of client 320, user computing
device 200 sends and receives via web browser 250 HTTP ("Hypertext
Transport Protocol") requests (or any similar protocol requests) to
and from web server 330 via network link 315a, computer network
310, and network link 315b. As customer 300 proceeds through client
320's website, web server 330 sends information about customer
300's online activity to application server 350 of analytics system
340 (via network link 315b, computer network 310 and network line
315c). After receiving this information (e.g., the event-level
data), application server 350 employs application software 360 to
perform the inferred attribution analysis and provide reports based
on that analysis. Throughout this process, transition tables
holding resultant data used for providing the reports are generated
and stored in database 370. Client 320 may view and interact with
the generated report through client 320's web browser (not
shown).
[0027] Network link 315 may include telephone lines, digital
subscriber line ("DSL"), cable networks, T1 or T3 lines, wireless
network connections, or any other arrangement that provides a
medium for the transmission and reception of computer network
signals. Computer network 310 may include a wide-area network
("WAN"), such as the Internet, and a local-area network ("LAN"),
such as an intranet or extranet. It should be noted that,
technically, user computing device 200, network link 315, web
server 330, application server 350 and any intermediate network
components, such as Internet service providers and routers (not
shown), are also part of computer network 310 because of their
connectivity.
[0028] Computer network 310 may implement any number of
communications protocols, including TCP/IP ("Transmission Control
Protocol/Internet Protocol"). The communication between user
computing device ("UCD") 200, web server 330 and application server
350 may be secured by any Internet security protocol, such as SSL
("Secured Sockets Layer").
[0029] Web server 330 and application server 350 each include a
processor and memory for executing program instructions, as well as
a network interface (not shown), and may include a collection of
servers working in tandem to distribute the network functionality
and load. In one particular embodiment, application server 320 may
include a combination of enterprise servers such as a web
application server, a web user interface server and a database
server, all of which could be manufactured by Sun Microsystems,
Inc. The web server (of analytics system 340 as well as web server
330) could run an HTTP server program in one embodiment, such as
Apache.RTM., as a process under an operating system such as
UNIX.RTM. (or any variant thereof). Database 370 may be part of a
relational database program, such as MySQL.RTM. or Oracle.RTM.,
that may be run as a process by a database server within the
UNIX.RTM. operating system, for example.
[0030] Application software 330 may take the form of custom-written
programs and libraries that run, either interpreted or compiled, in
part as a result of HTTP requests received by application server
320. These programs may be written in any programming language,
such as C, C++, or PERL ("Practical Extraction and Reporting
Language"), and they may generate an HTML ("Hypertext Markup
Language") client interface of analytics system 340. Application
software 360 may be built on a web-based enterprise application
platform, such as J2EE.RTM. ("Java 2 Platform, Enterprise
Edition").
Tagging
[0031] In one example embodiment of the present invention, Web
server 330 tracks and sends customer 300's online activity to
application server 350 through the use of IMG tags ("event tags")
placed on certain pages of client 320's website. The IMG tag is an
HTML image request for a 1.times.1 pixel GIF from application
server 350, and includes key-value pairs that are used to pass the
event-level data to application server 350.
[0032] For example, each event tag may include key-value pairs to
capture data about such events as identification of the client site
hosting the visitor, the web pages that the visitors (e.g.,
customer 300) view, the web pages where the visitors place products
in their shopping carts, and where the visitors came from before
they viewed a tagged web page. The following is an example such an
event tag (with key-value pairs highlighted in bold):
TABLE-US-00001 <img
src=`http://client.rpts.net/activity;src=12;ord=12121212?;pg
nm=Home+Page;sect=Home+Page;pgurl=http://www.client.com/Default.a
sp?;ref=http://search.yahoo.com/bin/search?p=client.com`>
[0033] (Note that, for readability purposes, the above example code
has left out URL encoding that may be applied to non-alphanumeric
characters in a working embodiment.) In the above tag, "src" is the
key for the client site ID (with value "12"), "ord" is the key for
a random number used to defeat inadvertent duplicate page loads
(with value "12121212"), "pgnm" is the key for the name of the
current web page, provided by client 320 (with value "Home+Page"),
"sect" is the key for the name of the website section to which the
current web page belongs, also provided by client 320 (with value
"Home+Page"), "pgurl" is the key for the URL of the current web
page (having value "http://www.client.com/Default.asp?"), and "ref"
is the key for the referring URL of the current web page (with
value "http://search.yahoo.com/bin/search?p=client.com").
[0034] Of course, additional data may be supplied using additional
keys. Other key-value pairs may be utilized to provide information
about a product clicked on by a visitor (via a product identifier
value), a product placed into a shopping cart, a product converted
(i.e., purchased after being placed in a shopping cart), visitor
segment membership and custom information. Client 320 may upload a
product information file (e.g., including product identifier, name
and category) to application server 350 so that application
software 360 can match a product identifier in the IMG tag with the
actual product information for reporting purposes.
Inferred Attribution Methodology
[0035] The event information automatically sent to application
server 350 from web server 330 through the event tag functionality
(i.e., the event-level data) may be collected in a log file by
application server 350. When the time arrives to analyze the
event-level data (e.g., once a day), application software 360 first
sorts the events from the log file of the event-level data by
visitor and time received by analytics system 340 (step 100). This
sort causes all events associated with each visitor during each
visitor's session to be listed in chronological order, grouped by
visitor. A visitor's session may be defined as any sequence of
events that occur within a certain time (e.g., 30 minutes) of one
another, and ending after a completed purchase. Further,
application software 360 may rely on its own "cookie" information,
passed to application server 350 from each visitor's web browser
250 during an event tag request, in order to determine which events
have originated from the same visitor (assuming, of course, that
the visitor has not opted out of client 320's analytics system 340
cookie, is not behind a proxy server which automatically blocks
cookies, or has not disabled receiving cookies via the browser's
settings).
[0036] According to an example embodiment of the present invention,
any one or more of the following five metrics may be attributed by
analytics system 340: [0037] 1. Clicks--the number of times a
hyperlink is clicked on a given page [0038] 2. Page Views--the
number of page loads of any individual page (product detail page or
section page) or a given section [0039] 3. Product Cartings--the
number of units of a given product that is added to a visitor
shopping cart irrespective of the eventual purchase of that product
[0040] 4. Product Purchases--the number of units of a given product
that is purchased [0041] 5. Revenue--the total revenue for the
product purchases
[0042] The following table specifies which of the above-referenced
example metrics are useful to one or more example navigation
entities:
TABLE-US-00002 Metric Product Page Product pur- Rev- Navigation
Entity Clicks Views cartings chases enue Product Detail Pages No
Yes Yes Yes Yes Section Pages No Yes Yes Yes Yes Sections No Yes
Yes Yes Yes Traffic-driving hyperlinks Yes No Yes Yes Yes Product
hyperlinks Yes No Yes Yes Yes
[0043] The following defines three navigation entities according to
an inferred attribution methodology (step 120): [0044]
Pages--standard content delivered into one individual web browser
window. The two types of pages may be: [0045] Product Detail
Pages--pages that have detailed product information, and may be
identified by the presence of a "prodinfo=######" key-value pair in
an event tag. Since product detail pages have no pre-defined
section membership, analytics system 340 will attribute activity
from the product detail page to the section of the last section
page seen by the visitor before the product detail page. [0046]
Section Pages--pages that do not have detailed product information
for one product alone, and may be all pages that do not have a
"prodinfo" key-value pair in the event tag (including a shopping
cart page). [0047] Sections--a collection of any combination of
pages (either section pages or product detail pages). Again,
product detail pages have no pre-defined section membership, but
analytics system 340 will attribute activity from the product
detail page to the section of the last section page seen by the
visitor before the product detail page. [0048] Hyperlinks--are
standard, clickable text or images (including pictures and buttons)
that bring the website visitor to another distinct page within the
website. [0049] Traffic-driving hyperlinks--hyperlinks that take
the visitor from any page to a section page. These can be thought
of as signs in a physical retail store that tell customers where to
go to find other similar or different types of products or other
information. [0050] Example: As shown in FIG. 4, presuming that a
visitor starts on Page P (which can be a section page or product
detail page), analytics system 340 assumes that a traffic-driving
hyperlink exists on Page P which is the last page seen before a
section page in a sequence of pages for a given cookie within a
given session. [0051] Product hyperlinks--hyperlinks that take the
visitor from any page to a product detail page. These can be
thought of as the action of taking a product off the shelf (section
page) and looking at it. [0052] Example: As shown in FIG. 5,
presuming that a visitor starts on Page P (which can be a section
page or product detail page), analytics system 340 assumes that a
product hyperlink exists on Page P which is the last page seen
before a product detail page in sequence of pages for a given
cookie within a given session.
[0053] Since an inferred attribution methodology is a complex
method of attributing metrics to the navigation entities, the
following tables show the attribution with a simple and defined
example which can be generalized to any website. As shown in FIG.
6, consider the following website for which: [0054] There are only
6 uniquely distinct pages [0055] There are only 2 products on sale
(Product X and Product Y) [0056] There are only 2 sections on the
site (Section A and Commerce Section) [0057] Product X costs $1 and
Product Y costs $5 [0058] The arrows indicate the only navigation
flow possible [0059] The visitor can leave the website off of any
of the 6 pages
[0060] Note that any event is attributable to at least one
navigation entity and the sum of all navigation entities may be
more than the total number of the occurrences of that event on the
website.
[0061] According to the following visitor's navigation path:
TABLE-US-00003 Page Visitor Action Taken on the Page 600 Click on
"Product X hyperlink" 610 Click on "Add Product X to Cart
hyperlink" 640 Visitor confirm purchase of Product X and submits
all relevant billing and shipping information 650 No action
taken
the following attribution may be inferred:
TABLE-US-00004 Metric Product Page Product pur- Rev- Navigation
Entity Clicks Views cartings chases enue Product Detail Pages No
Product X Detail Page 1 0 0 $0 Product Y Detail Page 0 0 0 $0
Section Pages No Section A: Page 1 1 1 Prod 1 Prod $1 X X Section
A: Page 2 0 0 0 $0 Commerce: Shopping Cart 1 0 0 $0 page Commerce:
"Thank You" 1 0 0 $0 page Sections No Section A 2 1 Prod 1 Prod $1
X X Commerce Section 2 0 0 $0 Traffic-driving hyperlinks No 600:
Page 2 of Section A 0 0 0 $0 610: Add Product X to Cart 0 0 0 $0
620: Add Product Y to Cart 0 0 0 $0 630: Add Product X to Cart 0 0
0 $0 630: Add Product Y to Cart 0 0 0 $0 Product hyperlinks No 600:
Product X 1 1 1 $1 600: Product Y 0 0 0 $0 630: Product X
(implicit) 0 0 0 $0 630: Product Y (implicit) 0 0 0 $0
[0062] According to the following visitor's navigation path:
TABLE-US-00005 Page Visitor Action Taken on the Page 600 Click on
"Product Y hyperlink" 620 Click on "Add Product Y to Cart
hyperlink" 640 Visitor confirm purchase of Product Y and submits
all relevant billing and shipping information 650 No action
taken
the following attribution may be inferred:
TABLE-US-00006 Metric Product Page Product pur- Rev- Navigation
Entity Clicks Views cartings chases enue Product Detail Pages No
Product X Detail Page 0 0 0 $0 Product Y Detail Page 1 0 0 $0
Section Pages No Section A: Page 1 1 1 Prod 1 Prod $5 Y Y Section
A: Page 2 0 0 0 $0 Commerce: Shopping Cart 1 0 0 $0 page Commerce:
"Thank You" 1 0 0 $0 page Sections No Section A 2 1 Prod 1 Prod $5
Y Y Commerce Section 2 0 0 $0 Traffic-driving hyperlinks No 600:
Page 2 of Section A 0 0 0 $0 610: Add Product X to Cart 0 0 0 $0
620: Add Product Y to Cart 0 0 0 $0 630: Add Product X to Cart 0 0
0 $0 630: Add Product Y to Cart 0 0 0 $0 Product hyperlinks No 600:
Product X 0 0 0 $0 600: Product Y 1 1 1 $5 630: Product X
(implicit) 0 0 0 $0 630: Product Y (implicit) 0 0 0 $0
[0063] According to the following visitor's navigation path:
TABLE-US-00007 Page Visitor Action Taken on the Page 600 Click on
"Page 2 of Section A" hyperlink 630 Click on "Add Product X to Cart
hyperlink" 640 Visitor confirm purchase of Product X and submits
all relevant billing and shipping information 650 No action
taken
the following attribution may be inferred:
TABLE-US-00008 Metric Product Page Product pur- Rev- Navigation
Entity Clicks Views cartings chases enue Product Detail Pages No
Product X Detail Page 0 0 0 $0 Product Y Detail Page 0 0 0 $0
Section Pages No Section A: Page 1 1 0 0 $0 Section A: Page 2 1 1
Prod 1 Prod $1 X X Commerce: Shopping Cart 1 0 0 $0 page Commerce:
"Thank You" 1 0 0 $0 page Sections No Section A 2 1 Prod 1 Prod $1
X X Commerce Section 2 0 0 $0 Traffic-driving hyperlinks No 600:
Page 2 of Section A 1 1 Prod 1 Prod $1 X X 610: Add Product X to
Cart 0 0 0 $0 620: Add Product Y to Cart 0 0 0 $0 630: Add Product
X to Cart 0 0 0 $0 630: Add Product Y to Cart $0 Product hyperlinks
No 600: Product X 0 0 0 $0 600: Product Y 0 0 0 $0 630: Product X
(implicit) 0 1 1 $1 630: Product Y (implicit) 0 0 0 $0
[0064] According to the following visitor's navigation path:
TABLE-US-00009 Page Visitor Action Taken on the Page 600 Click on
"Page 2 of Section A" hyperlink 630 Click on "Add Product Y to Cart
hyperlink" 640 Visitor confirm purchase of Product Y and submits
all relevant billing and shipping information 650 No action
taken
the following attribution may be inferred:
TABLE-US-00010 Metric Product Page Product pur- Rev- Navigation
Entity Clicks Views cartings chases enue Product Detail Pages No
Product X Detail Page 0 0 0 $0 Product Y Detail Page 0 0 0 $0
Section Pages No Section A: Page 1 1 0 0 $0 Section A: Page 2 1 1
Prod 1 Prod $5 Y Y Commerce: Shopping Cart 1 0 0 $0 page Commerce:
"Thank You" 1 0 0 $0 page Sections No Section A 2 1 Prod 1 Prod $5
Y Y Commerce Section 2 0 0 $0 Traffic-driving hyperlinks No 600:
Page 2 of Section A 1 1 Prod 1 Prod $5 Y Y 610: Add Product X to
Cart 0 0 0 $0 620: Add Product Y to Cart 0 0 0 $0 630: Add Product
X to Cart 0 0 0 $0 630: Add Product Y to Cart 0 0 0 $0 Product
hyperlinks No 600: Product X 0 0 0 $0 600: Product Y 0 0 0 $0 630:
Product X (implicit) 0 0 0 $0 630: Product Y (implicit) 0 1 1
$5
Page Pairing Implementation
[0065] Once attribution is inferred based on the visitor activity
determined from the event level data, analytics system 340
generates resultant data for providing client reports via a page
pairing implementation (step 120), according to an example
embodiment of the present invention.
[0066] For each visitor session in the event level data, every
page-by-page navigation is broken down. For example, assume the
following path for one visit by a visitor on the client's website,
which only has one product for sale for $1 each [0067] Page
A>Page B>Page C>Page D>Page B>Page C>Page
D>Page A>Purchased all products in Shopping Cart
[0068] Also, for simplicity, assume that all of these pages are
section pages (which, as mentioned above, may include a shopping
cart page), since a product detail page would cause the carting
attribution to go back further than one page from an "add-to-cart"
event.
[0069] In order to create a processed transition table to be used
for providing client reports, a raw transition table is first
created that holds the 5 transitions (i.e., pairings of
sequentially viewed pages):
TABLE-US-00011 Raw transition table Transition # "From" page "To"
Page 1 Page A Page B 2 Page B Page C 3 Page C Page D 4 Page D Page
B 5 Page B Page C 6 Page C Page D 7 Page D Page A
[0070] Any of these transitions can have an event associated with
it that is captured on the "to" page. These events can be
"add-to-cart" events or a purchase event indicating that a product
of a given quantity and total price is associated.
TABLE-US-00012 Raw transition table with event association and
Result Transition "From" "To" Product # page Page Event on sale
Result 1 Page A Page B Add 1 product Yes Purchased to cart product
2 Page B Page C Nothing No None 3 Page C Page D Nothing No None 4
Page D Page B Add 1 product Yes Purchased to cart product 5 Page B
Page C Nothing No None 6 Page C Page D Add 1 product Yes Purchased
to cart product 7 Page D Page A Nothing No None
[0071] The implication of each "add-to-cart" event based on the
inferred attribution methodology is to say that the "from" page in
an "add-to-cart" transition page is the page where the product is
on sale. Note that the inferred attribution process may take place
during or before the page pairing implementation.
[0072] Extra processing may then be performed on this table to
include all of the attribution and extra data associated with the
smallest overall number of transitions:
TABLE-US-00013 Processed transition table # non-unique # unique
"From" "To" session session Product page Page transitions
transitions on Sale Result Page A Page B 1 1 Yes Product purchased
for $1 Page B Page C 2 1 No None Page C Page D 2 1 Yes Product
purchased for $1 Page D Page B 1 1 Yes Product purchased for $1
Page D Page A 1 1 No None
[0073] The processed transition table may be stored in database
370, and the following reports may use information from this table
to provide client reports (step 130).
[0074] In order to generate a report that displays the visitor's
navigation path through a specified page of the client's website,
the following relevant data from the processed transition table is
used:
TABLE-US-00014 # unique session "From" page "To" Page transitions
Page A Page B 1 Page B Page C 1 Page C Page D 1 Page D Page B 1
Page D Page A 1
[0075] Note since two transitions (Page B to Page C and from page C
to Page D) happen twice in the same session, it is only counted
once for this particular report.
[0076] To create this the left hand side of this report, the "to"
page is filtered for Page B only and the following is extracted
from the "from" pages. The results are as follows:
TABLE-US-00015 # unique session "From" page "To" Page transitions
Page A Page B 1 Page D Page B 1
[0077] To create the right hand side of this report, the "from"
page is filtered by Page B only and the following is extracted from
the "to" pages. The results are as follows:
TABLE-US-00016 # unique session "From" page "To" Page transitions
Page B Page C 1
[0078] The output for the visitor's navigation path report is as
follows:
TABLE-US-00017 "From" Page # sessions "To" page # sessions Page A 1
Page B Page C 1 Page D 1
[0079] FIG. 7 shows a navigation path report using data
representing more than one visitor and session.
[0080] In order to generate a report that displays the visitor's
shopping activity generated by links clicked at least once on a
specified web page (i.e., page link performance), the following
relevant data from the processed transition table is used:
TABLE-US-00018 # non-unique "From" "To" session Product page Page
transitions on Sale Result Page A Page B 1 Yes Product purchased
for $1 Page B Page C 2 No None Page C Page D 2 Yes Product
purchased for $1 Page D Page B 1 Yes Product purchased for $1 Page
D Page A 1 No None
[0081] To generate this report, the "from" page is held constant to
determine what has happened from this page. Filtering for "from"
pages for Page D and all of the locations where the hyperlinks take
the individual, the resulting data is extracted
TABLE-US-00019 # non-unique "From" "To" session Product page Page
transitions on Sale Result Page D Page B 1 Yes Product purchased
for $1 Page D Page A 1 No None Page A Page B 1 Yes Product
purchased for $1 Page B Page C 2 No None
[0082] "From" Page A has revenue associated with it, therefore the
"traffic-driving" link to Page A has 1 carted item, 1 purchased
item and Revenue. Since the "from" Page B has no attributable
metrics to it, there is no change in the metrics.
[0083] The output for the page link performance report is as
follows:
TABLE-US-00020 Carted Purchased Link Type Item Clicks Items Items
Revenue Product Product 0 1 1 $1 Traffic-driving Page A 1 1 1
$1
[0084] Note that if: [0085] there were any product detail pages for
the product that had a hyperlink to that page from Page D, those
product detail page loads would be counted as "Clicks" in this
table [0086] there were no purchases made from Page A, the number
of Cart Items, Purchased Items and Revenue would all be zero in
value
[0087] FIG. 8 shows a page link performance report using data
representing more than one visitor and session.
[0088] In order to generate a report that displays all of the web
pages where a specified product is displayed (i.e., product
placement), the following relevant data from the processed
transition table is used:
TABLE-US-00021 # non-unique "From" page session transitions Result
Page A 1 Product purchased for $1 Page B 2 None Page C 2 Product
purchased for $1 Page D 1 Product purchased for $1 Page D 1
None
[0089] To generate this report, filter all "from" pages by whether
a product was purchased from that page and sum across all "From"
pages:
TABLE-US-00022 # non-unique "From" page session transitions Result
Page A 1 Product purchased for $1 Page C 2 Product purchased for $1
Page D 1 Product purchased for $1
[0090] The output for the product placement report is as
follows:
TABLE-US-00023 Page Page Views Purchase Items Revenue Page A 1 1 $1
Page C 2 1 $1 Page D 1 1 $1
[0091] Note that there are no "click" counts in this table--just
the number of times the "from" page has been loaded.
[0092] FIG. 9 shows a product placement report using data
representing more than one visitor and session.
[0093] Several embodiments of the invention are specifically
illustrated and/or described herein. However, it will be
appreciated that modifications and variations of the invention are
covered by the above teachings and within the purview of the
appended claims without departing from the spirit and intended
scope of the invention.
[0094] For example, embodiments of the invention can be applied to
non-merchandising websites by capturing the metrics mapped to the
navigation entities. Publishers interested in determining which ad
space is valuable can use such metrics as ad exposures, advertiser
hyperlink clicks, and website registration. Non-publishers
interested in determining what applications and documents are
accessed can use such metrics as application and documentation
downloads.
[0095] Also, this invention can be applied to multiple client
websites with distinct URLs by collating their respective data
under one client as recognized by the system. By defining either
individual sections for each distinct URL as a separate section or
by defining the entire website entities as separate sections, the
inferred attribution of metrics would apply in a similar fashion as
applied by the single client website embodiment described
herein.
[0096] Additionally, with a number of clients with similar
application of the system (e.g., selling furniture online,
newspaper publishing website, etc.), reports can be provided to
compare one client's metrics against an anonymous pool of other
clients to determine its relative standing in the industry on
several metrics.
* * * * *
References