WO2008070744A2

WO2008070744A2 - Centralized web-based software solution for search engine optimization

Info

Publication number: WO2008070744A2
Application number: PCT/US2007/086552
Authority: WO
Inventors: Ray Grieselhuber; Brian Bartell; Dema Zlotin; Russ Mann
Original assignee: Covario, Inc.
Priority date: 2006-12-05
Filing date: 2007-12-05
Publication date: 2008-06-12
Also published as: US20110320461A1; US8473495B2; WO2008070744A3; US20080071766A1; US7877392B2

Abstract

A system and method for modifying a parameter of a website in order to optimize an organic listing of the website at one or more search engines is described. Several embodiments include methods and systems for generating scored representations based upon different portions of data associated with a website, and then combining the scored representations to achieve a result. The result indicates a feature of the website that may be modified in order to optimize the organic ranking of the website at one or more search engines.

Description

CENTRALIZED WEB-BASED SOFTWARE SOLUTION FOR SEARCH ENGINE

OPTIMIZATION

FIELD OF THE INVENTION

[0001] The invention relates to, among other things, methods and systems for optimization of websites ("sites") with respect to organic search results generated by search engines in response to user queries. In particular, but not by way of limitation, aspects of the invention pertain to one or more centralized web-based software solutions that evaluate sites and identify features of those sites that may be optimized.

BACKGROUND OF THE INVENTION

[0002] With the growth of search engines, business entities (e.g., companies) are dedicating greater portions of their marketing budgets to search engine optimization (SEO) initiatives. Typically, SEO initiatives are driven by "organic" search results. In this regard, the organic listing of a website ("site") pertains to the relative ranking of that site in the algorithmic results generated by a particular search engine on the basis of particular keywords. This contrasts with sponsored or paid search results which are often listed proximate such organic search results and which list sites that have compensated the operator of the search engine for such listing. For various strategic reasons, a business entity may drive content of a site it owns or operates so that the site appears in organic search results created by one or more search engines. With respect to measuring the effectiveness of an organic SEO initiative, previously-known technology does not enable an enterprise-scale business entity (e.g., an enterprise-scale business entity) to measure the effectiveness of organic search results associated with various search engines. Furthermore, previously-known technology does not effectively allow a business entity to audit its site(s) in an automated fashion using SEO principles across many sites and across many search engines in a way that reflects enterprise-scale hierarchies of the business entity.

SUMMARY OF THE INVENTION

[0003] Exemplary embodiments of the invention that are shown in the drawings are summarized below. These and other embodiments are more fully described in the Detailed Description section. It is to be understood, however, that there is no intention to limit the invention to the forms described in this Summary of the Invention or in the Detailed Description. One skilled in the art can recognize that there are numerous modifications, equivalents and alternative constructions that fall within the spirit and scope of the invention as expressed in the claims.

[0004] In one aspect, the invention provides a system and method for modifying one or more features of a website in order to optimize the website in accordance with an organic listing of the website at one or more search engines. The inventive systems and methods include using scored representations to represent different portions of data associated with a website. Such data may include, for example, data related to the construction of the website and/or data related to the traffic of one or more visitors to the website. The scored representations may be combined with each other (e.g., by way of mathematical operations, such as addition, subtraction, multiplication, division, weighting and averaging) to achieve a result that indicates a feature of the website that may be modified to optimize a ranking of the website with respect to the organic listing of the website at one or more search engines.

[0005] In one embodiment, for example, the scored representations may be combined by generating a respective weight for each of the scored representations, and then applying the respective weights to their respective scored representations. Upon applying the respective weights, the weighted scored representations may be summed to achieve an intermediate result, which is then divided by a sum of the respective weights to achieve the result that may be used to optimize a ranking of the website with respect to the organic listing of the website at one or more search engines.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Various objects and advantages and a more complete understanding of the invention are apparent and more readily appreciated by reference to the following Detailed Description and to the appended claims when taken in conjunction with the accompanying Drawings wherein:

FIG. 1 shows a block diagram depicting a typical network system for analyzing search engine optimization effectiveness of a website;

FIG. 2 illustrates one implementation of a search engine optimization analysis system;

FIG. 3 depicts a process flow diagram illustrating steps taken by a software solution in accordance with one embodiment of the invention; FIG. 4 illustrates a first user interface that may be presented to a user when representing client-pertinent metrics developed during linear and/or non-linear combinations in accordance with certain aspects of the invention;

FIG. 5 illustrates a second user interface that may be presented to a user when representing client-pertinent metrics developed during linear and/or non-linear combinations in accordance with certain aspects of the invention;

FIG. 6 illustrates a third user interface that may be presented to a user when representing client-pertinent metrics developed during linear and/or non-linear combinations in accordance with certain aspects of the invention; and

FIG. 7 shows a block diagram depicting an alternative system for analyzing search engine optimization effectiveness of a website.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

[0007] The invention relates to, among other things, methods and systems for optimization of websites ("sites") to enhance organic search results generated by search engines in response to user queries. Several embodiments of the invention pertain to one or more centralized web- based software solutions that evaluate the effectiveness of search engine optimization (SEO) with respect to sites of a business entity. More specifically, embodiments of the software solutions may evaluate adherence to SEO best practices, track organic rankings of a site with respect to one or more search engines, determine one or more particular improvements for enhancing the organic rankings of the site, implement the one or more particular improvements, and/or develop one or more reports for display on a user interface.

[0008] Aspects of the invention are designed to operate on computer systems, servers, and/or other like devices. While the details of embodiments of the invention may vary and still be within the scope of the claimed invention, FIG. 1 shows a block diagram depicting a typical network system 100 for analyzing SEO effectiveness of a site in accordance with the invention. The network system 100 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the network system 100 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary network system 100.

[0009] Aspects of the invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer or server. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

[0010] As is shown, the network system 100 includes a communications network 110, such as the Internet or a private network, capable of providing communication between devices at search engine(s) 120, advertiser/client(s) 130, an SEO analysis system 140, and third party user(s) 150 described hereinafter. The devices of FIG. 1 communicate with each other via any number of methods known in the art, including wired and wireless communication pathways.

[0011] As shown in FIG. 1, a search engine 120 is accessible by a third party user 150, a client 130, and by the analysis system 140. The third party user 150 may utilize any number of computing devices that are configured to retrieve information from the World Wide Web ("WWW"), such as a computer, a personal digital assistant (PDA), a cell phone, a television (TV), and other network communications-enabled devices. The client 130 is typically a business entity with one or more websites that are to be indexed by a search engine 120 or a social network. The analysis system 140 operates one or more servers 141 capable of Internet-based communication with the search engine 120 and the client 130. As is discussed below, the modeling system 140 enables the client 130 to model the effectiveness of an SEO initiative with respect to other SEO initiatives of the client 130 or entities other than the clients 130. It is a feature of embodiments of the invention that these models enable the client 130 to quickly identify marketing inefficiencies and/or opportunities.

[0012] As those skilled in the art will appreciate, various intermediary network routing and other elements between the communication network 110 and the devices depicted in FIG. 1 have been omitted for the sake of simplicity. Such intermediary elements may include, for example, the public-switched telephone network (PSTN), gateways or other server devices, and other network infrastructure provided by Internet service providers (ISPs).

[0013] Attention is now drawn to Fig. 2, which depicts one implementation of the analysis system 140. As is shown, the analysis system 140 may include, but not by way of limitation, a processor 241 coupled to ROM 242, the database 143, a network connection 244, and memory 245 (e.g., random access memory (RAM)). [0014] The database 143 is described herein in several implementations as hard disk drive for convenience, but this is certainly not required, and one of ordinary skill in the art will recognize that other storage media may be utilized without departing from the scope of the invention. In addition, one of ordinary skill in the art will recognize that the database 143, which is depicted for convenience as a single storage device, may be realized by multiple (e.g., distributed) storage devices.

[0015] As shown, a software solution 290 includes a statistic generator module 291, a report generator module 292, and a user interface ("UI") module 293, all of which are implemented in software and are executed from the memory 244 by the processor 241. The solution 290 can be configured to operate on personal computers (e.g., handheld, notebook or desktop), servers or any device capable of processing instructions embodied in executable code. Moreover, one of ordinary skill in the art will recognize that alternative embodiments, which implement one or more components of the invention in hardware, are well within the scope of the invention. Each module 291-293 is associated with one or more functions of the invention describe herein.

[0016] Basic Operation of the Software Solution

[0017] In general terms, the solution 290 analyzes the construction of a website ("site") for any possible aspect of that site's construction that would affect the site's organic ranking with respect to one or more search engines. The solution 290 may make recommendations regarding improvements with respect to the site's construction. For example, the solution 290 may make recommendations based on the size of one or more webpages ("pages") belonging to a site. Alternative recommendations may pertain to whether keywords are embedded in a page's title, meta content and/or headers. The solution 290 may also make recommendations based on traffic referrals from search engines or traffic-related data from directories and media outlets with respect to the organic ranking of a site. Media outlets may include data feeds, results from an API call and imports of files received as reports offline (i.e., not over the Internet) that pertain to Internet traffic patterns and the like. One of skill in the art will appreciate alternative recommendations .

[0018] The modules 291-293 operate in concert with each other to perform certain functions of the solution 290. By way of example, Fig. 3 depicts a process flow diagram 300 illustrating steps taken by the solution 290 in accordance with one embodiment of the invention. As shown in step 310, the UI module 293 may receive filtering and configuration parameters from a user (e.g., a system administrator, the client 130, etc.). The UI module 293, in step 320, may export those parameters to the data retrieval module 291 and/or the reports module 292. The parameters may pertain to system administration parameters that apply to general implementations of the solution 290 (e.g., pre-confϊgured data collection) or to user parameters that apply to specific implementations of the solution 290 (e.g., real-time data collection). The data retrieval module 291, in step 330, uses the parameters to gather specific data defined at least in part by the parameters. The data retrieval module 291 may gather data from one or more search engine files, one or more content source files (e.g., video, image, document and various other non-html files), one or more web files associated with the client(s) 130, and/or one or more web analytics system files. Upon gathering data, the data retrieval module 291, in step 340, stores the data in the database 143. The reports module 292, in step 350, accesses the database 143 to retrieve data associated with the parameters, and then, in step 360, produces one or more types of reports. In step 370, the generated reports are exported to the UI module 293, which displays one or more visual representations of the reports to the user.

[0019] Data Retrieval Module

[0020] The data retrieval module 291 gathers data for use by the reports module 292 in generating one or more reports that are visually represented via the UI module 293. The data may be gathered from any number of sources, including by way of example, one or more search engines (e.g., the search engines 120), one or more content sources (e.g., one or more videos, images and/or documents such as .pdf, .doc, and .xls files, among others)), one or more sites associated with the client(s) 130, and/or one or more web analytics systems.

[0021] For example, the data collected by the data retrieval module 291 may include traffic levels from one or more search engines to one or more pages of one or more sites. Collected data may also include a number of pages for one or more sites that are indexed by one or more search engines or social networks, and whether particular keywords exist in the indexing. The data retrieval module 291 may also collect data associated with an indexed page's category, title, description, and URL with respect to the one or more search engines or social networks. Alternatively or additionally, data pertaining to whether one or more keywords are found in a page's title, meta content and/or headers may be collected. Collected data may also include a total number of pages for one or more sites, and whether a sitemap link exists on the home page(s) of one or more sites.

[0022] The data retrieval module 291 may alternatively or additionally collect page-level data, including URL character length, page size, keyword density, existence of flash navigation, existence of JavaScript navigation, existence of page errors, and existence of header tags, among others. One of skill in the art will recognize that the data retrieval module 291 may collect data specific to any type of page, including preferred landing pages.

[0023] Additional data collected by the data retrieval module 291 may include rankings or a number of ranked positions of one or more pages or sites with respect to one or more organic search engine results that are based on one or more search terms (e.g., one or more keywords) during one or more time periods.

[0024] One of skill in the art will appreciate alternative forms of data within both the scope and spirit of the invention that the data retrieval module 291 may gather, including additional web analytics data and/or data accessible via application programming interfaces (APIs) associated with search engines.

[0025] Report Generator Module

[0026] Attention is drawn to the reports module 292 of Fig. 2, which functions to receive parameters from the UI module 293, retrieve data from the database 143, generate one or more reports based on the parameters and the retrieved data, and then send the generated reports to the UI module 293. The generation of reports may be automated (e.g., the generation of reports occurs at specified time intervals). When generating the reports, the reports module 292 may use one or more linear and/or non-linear combinations involving one or more scored representations to achieve quantifiable metrics pertinent to the client 130.

[0027] A combination may include, by way of example, a mathematical operation such as addition, subtraction, multiplication, division, weighting, and averaging, among others.

[0028] A scored representation may include, but not by way of limitation, an alphanumeric representation of data collected by the data retrieval module 291 (e.g., 0, 1, 2, ..., n and a, b, c, ...z) and/or an alphanumeric representation of a resultant value derived from one or more linear/non- linear combinations.

[0029] A quantifiable metric may be, for example, indicative of a parameter or feature of a site that may be modified to optimize the site with respect to an organic ranking of the site at a search engine. By way of example, in one embodiment a feature may reflect an inefficient or an unrealized use of a keyword with respect of the site's paid or organic ranking in search engine results. In another embodiment, a feature may reflect an undesired visitor traffic pattern on the site following a selection of the site by the visitor from a listing of search results at a search engine. In yet another embodiment, a feature may reflect the existence of any number of aspects relating to a site, including accessibility-related aspects, site construction-related aspects, and/or search engine-related aspects. For example, accessibility-related aspects may reflect whether a sitemap exists on the site's homepage and/or whether the site exists in a Yahoo! and/or DMOZ (i.e., the Open Directory Project) directory. Site construction-related aspects may reflect exceeded page sizes, exceeded URL character lengths, lack of flash navigation, lack of header tags, lack of a keyword in header tags, lack of a keyword is a page title, and/or lack of a keyword in page meta content. Search engine -related aspects may reflect a ranking of a site or pages of the site in organic and/or paid search results of one or more search engines. One of skill in the art will appreciate various other features that may be indicated using configurable metrics, including any of the 'Collected Data' described below with respect to Table 1.

[0030] As stated above, the reports module 292 may employ computations that are configurable in terms of scored representations and combinations. For example, a first scored representation may be weighted, a second scored representation may be weighted, the resultant weighted scored representations may be summed to achieve a summed result, and the summed result may be divided by a sum of the weights. In such a case, the reports module 292 employs four combinations: 1) the weighting of the first scored representation, 2) the weighting of the second scored representation, 3) the summing of the two weighted scored representations, and 4) the dividing of the summed weighted scored representations by the sum of the weights. One of skill in the art will appreciate that any number of combinations of any number of scored representations may be used to quantify metrics pertinent to the client 130. By way of example, Table 1 displays a listing of data, scored representations of such data, and weights applied to the scored representations.

Table 1

[0031] As shown in Table 1, scored representation Vi represents whether a sitemap link exists on the home page of a site. If a sitemap exists, a scored representation of "1" is used to represent Vi. Otherwise, if a sitemap does not exist, a scored representation of "0" is used to represent Vi. In either case, a weight wi (e.g., "5") may be applied to the scored representation.

[0032] The scored representations may be represented by any type of strength or grading indicator (e.g., alphanumeric representations, color-coding). Each scored representation, as well as combinations of scored representations may be weighted with adjustable weights (e.g., rational numbers) configurable via the UI module 293.

[0033] By way of example, Table 2 presents a listing of combinations.

Table 2

Combinations

Ci r(Vi)*(wi) + (V₄)*(w₄) + (V₅)*(w₅)l / Kw₁) + (w₄) + (w₅)!

C₂ (V₁₄)*(w₁₄) + (V₁₅)*(w₁₅)+ (V₁₆)*(w₁₆)l / Kw₁₄) + (W₁₅) + (W₁₆)I

Average of C₂ for selected keywords

[(V₆)*(w₆) + (V₇)*(w₇) + (V₈)*(w₈) + (V₉)*(w₉) + (Vio)*(wio)+ (V₁₁Hw₁₁) 4 (V₁₂)*(w₁₂) + (V₁₃)*(w₁₃) (C₃)*(w_c3)l / [(W₆) + (W₇) + (W₈) + (W₉) + (W₁₀) + (W₁₁) + (W₁₂) + (W₁₃) + (W₀₃)I

C_5- Average of C₃ for selected pages

C_6- ^•(Ci)*(w_ci) + (CS)-(WC₅)I / [(Wd) + (W₀₅)I

CL- Average of V₁₇ for selected search engines C₈ Average of C₇ for selected keywords

[0034] As shown in Table 2, combination Ci is formed by dividing the sum of weighted scored representations wiVi, W4V4 and W5V5 by the sum of the scored representations weights wi, W4 and Wj. One of skill in the art will appreciated that combinations may be used as scored representations in other combinations. For example, combinations Q and Cj are used as scored representations in combination Ce, and combination C3 is used as a scored representation in combination C/. One of skill in the art will also appreciate that combinations used as scored representations in other combinations may be weighted. For example, weight w_C3 may be any negative or positive rational number (e.g., 5), weight w_ci may be any negative or positive rational number (e.g., 3), and weight w_cs may be any negative or positive rational number (e.g., 5). [0035] One of skill in the art will recognize alternative combinations than those shown in Table 2. Additionally, one of skill in the art will appreciate that combinations may be configurable, via the UI module 293, in terms of scored representations, weights and mathematical operations.

[0036] User Interface ("UI") Module

[0037] The UI module 293 receives filtering and customization parameters from a user, sends at least a portion of those parameters to the data retrieval module 291 and/or the reports module 292, receives one or more reports from the reports module 292, and displays one or more visual representations of the report(s) received from the reports module 292. The visual representations may be formed of alphanumerical, color-coded, graphical, image-based, or any other type of representation.

[0038] At least a portion of the filtering parameters received by the UI module 293 define the scope of data collection by the data retrieval module 291 and/or data retrieval by the reports generator 292. For example, the parameters may define the scope of data collection and/or data retrieval in terms of one or more instances or periods of time (e.g., date ranges, triggered events). Alternatively or additionally, the parameters may define the scope of data collection and/or data retrieval in terms of the types of data previously described with respect to the data retrieval module 291.

[0039] At least a portion of the customization parameters define the report(s) generated by the reports module 292. The customization parameters allow a user to configure the visual representation of the generated reports. Customization parameters may include parameters similar to those described above with respect to the filtering parameters. Additionally, the customization parameters may include drill-down, online analytical processing (OLAP), research and sorting parameters (e.g., ascending or descending organization), as well as display parameters (e.g., numeric, color-coded, or video/image representation display parameters).

[0040] Attention is now drawn to Figs. 4-6, which represent different user interfaces that the UI module 293 may present to a user when representing client-pertinent metrics developed during the linear and/or non-linear combinations described above with respect to the reports module 292. Fig. 4 includes a table 400 that displays client-pertinent metrics with respect to multiple sites. Fig. 5 displays a table 500 that lists multiple sites and their rank with respect to multiple search engines. Fig. 6 comprises multiple charts 600 that illustrate client-pertinent metrics with respect to a single site (e.g., a site selected from the multiple sites listed in Fig. 4 or Fig. 5). [0041] One of skill in the art will appreciate alternative embodiments wherein all or a portion of the reports generated by the reports module 292 are accessible by one or more computer systems/visual displays external to the analysis system 140 (e.g., via triggered or automatic emailing or other methods within both the scope and spirit of the invention). One of skill in the art will also appreciate alternative embodiments in which the reports module 292 develops one or more reports when triggering events occur (i.e., under preconfϊgured circumstances).

[0042] Client Architecture

[0043] Attention is now drawn to FIG. 7, which depicts an exemplary implementation of the client 130. As is shown, the client 130 includes a server 131 connected to a database 133, both of which may communicate either directly or indirectly with the communication network 110. FIG. 7 also includes a computing device/system 739 configured in accordance with one implementation of the invention. The computing device 739 may include, but not by way of limitation, a personal computer (PC), a personal digital assistant (PDA), a cell phone, a television (TV), etc., or any other device configured to send/receive data to/from the communication network 110, such as consumer electronic devices and hand-held devices.

[0044] The implementation depicted in FIG. 7 includes a processor 739a coupled to ROM 739b, input/output devices 739c (e.g., a keyboard, mouse, etc.), a media drive 739d (e.g., a disk drive, USB port, etc.), a network connection 739e, a display 739f, memory 739g (e.g., random access memory (RAM)), and a file storage device 739h.

[0045] The storage device 739h is described herein in several implementations as a hard disk drive for convenience, but this is certainly not required, and one of ordinary skill in the art will recognize that other storage media may be utilized without departing from the scope of the invention. In addition, one of ordinary skill in the art will recognize that the storage device 739h, which is depicted for convenience as a single storage device, may be realized by multiple (e.g., distributed) storage devices.

[0046] As shown, a software solution 741 includes a data retrieval module 741a, a reports generator module 741b, a user interface module 741c, all of which are implemented in software and are executed from the memory 739g by the processor 739a. The software 741 can be configured to operate on personal computers (e.g., handheld, notebook or desktop), servers or any device capable of processing instructions embodied in executable code. Moreover, one of ordinary skill in the art will recognize that alternative embodiments implementing one or more components in hardware are within the scope of the invention. Each module 741a-c function similarly to modules 291, 292 and 293, respectively, of Fig. 2.

[0047] The exemplary systems and methods of the invention have been described above with respect to the analysis system 140 and/or the client 130. One of skill in the art will appreciate alternative embodiments wherein the functions of the analysis system 140 are performed on other devices in the networked system 100.

[0048] Those skilled in the art can readily recognize that numerous variations and substitutions may be made in the invention, its use and its configuration to achieve substantially the same results as achieved by the embodiments described herein. Accordingly, there is no intention to limit the invention to the disclosed exemplary forms. Many variations, modifications and alternative constructions fall within the scope and spirit of the disclosed invention as expressed in the claims.

Claims

What is claimed is:

1. A method for optimizing a website in accordance with search engine results, comprising: acquiring data associated with the website; generating a plurality of scored representations based upon the data; and combining the plurality of scored representations to achieve a result; recommending, based on the result, a modification to a parameter of the website in order to improve an organic ranking of the website with respect to one or more search engines.

2. The method of claim 1 , wherein the combining comprises: generating a respective weight for each of the plurality of scored representations; weighting each of the plurality of scored representations with their respective weights to achieve a plurality of weighted scored representations; summing the plurality of weighted scored representations to achieve an intermediate result; and dividing the intermediate result by a sum of the respective weights to achieve the result.

3. The method of claim 1 , wherein the combining comprises: combining a first plurality of scored representations to achieve a first result; combining a second plurality of scored representations to achieve a second result, wherein the first plurality of scored representations and the second plurality of scored representations are included in the plurality of scored representations; and averaging at least the first result and the second result to achieve the result.

4. The method of claim 1 , wherein the data includes data related to content of the website for indexing by the one or more search engines.

5. The method of claim 1 , wherein the data includes data related to traffic of one or more visitors to the website.

6. The method of claim 2, wherein the data associated with the website includes data derived from one or more of a search engine file, a web analytics file, and a website file.

7. The method of claim 2, wherein the data associated with the website includes data selected from the group consisting of whether a sitemap link exists on the website's homepage, whether the website is listed in an Open Directory Project (DMOZ) directory, and whether the website is listed in a Yahoo! directory.

8. The method of claim 2, wherein the data associated with the website includes data selected from the group consisting of whether a character length of a URL of the website exceeds a maximum URL character length, whether a webpage size of the website exceeds a maximum webpage size, whether flash navigation is used on the website, whether javascript navigation is used on the website, whether session identifiers are used in the URL, and whether dynamic parameters are used in the URL.

9. The method of claim 2, wherein the data associated with the website includes data selected from the group consisting of whether a keyword exists in a title tag of the website, whether the keyword exists in meta content of the website, whether the keyword exists in a header tag of the website, whether the keyword exists in a body tag of the website, and whether the keyword exists in a URL of the website.

10. The method of claim 2, wherein the data associated with the website includes data selected from the group consisting of whether a webpage associated with the website has inbound links from .edu domains, whether the webpage has inbound links from .gov domains, whether the webpage has inbound links from social networks, whether the webpage has inbound links from blogs, whether the webpage has inbound links from wikis, and whether the website has internal links to the webpage.

11. The method of claim 2, wherein the data associated with the website includes data representative of whether keywords are included in the website in accordance with a density threshold.

12. The method of claim 1 , wherein the recommending a modification to a parameter of the website includes recommending an increase of a number of inbound links of the website, recommending a decrease of a length of a URL of the website, recommending a decrease of a webpage size, recommending a removal of dynamic parameters from the URL, recommending a removal of flash navigation from the website, or recommending a removal of JavaScript navigation from the website.

13. The method of claim 1 , wherein the recommending a modification to a parameter of the website includes recommending a use of a keyword in a title tag of the website, recommending a use of the keyword in meta content of the website, recommending a use of the keyword in a header tag of the website, recommending a use of the keyword in a body tag of the website, or recommending a use of the keyword in a URL of the website.

14. The method of claim 1 , wherein the recommending a modification to a parameter of the website includes recommending a use of a keyword in the website that exceeds a density threshold.

15. The method of claim 3, wherein the first result pertains to a first search engine and the second result pertains to a second search engine.

16. The method of claim 3, wherein the first result pertains to a first keyword and the second result pertains to a second keyword.

17. The method of claim 3, wherein the first result pertains to a first webpage of the website and the second result pertains to a second webpage of the website.

18. The method of claim 3, wherein the data is further associated with a second website, and wherein the first search result pertains to the website and the second search result pertains to the second website.

19. A system for optimizing a website in accordance with search engine results, comprising: at least one processor; a network interface for receiving data from at least data source; a memory, operatively coupled to the processor for storing logical instructions wherein execution of the logical instructions by the processor results in the performing of at least the following operations: acquiring data associated with the website; generating a plurality of scored representations based upon the data; and combining the plurality of scored representations to achieve a result; recommending, based on the result, a modification to a parameter of the website in order to improve an organic ranking of the website with respect to one or more search engines.

20. The system of claim 19, wherein the combining comprises: generating a respective weight for each of the plurality of scored representations; weighting each of the plurality of scored representations with their respective weights to achieve a plurality of weighted scored representations; summing the plurality of weighted scored representations to achieve an intermediate result; and dividing the intermediate result by a sum of the respective weights to achieve the result.

21. The system of claim 19, wherein the combining comprises : combining a first plurality of scored representations to achieve a first result; combining a second plurality of scored representations to achieve a second result, wherein the first plurality of scored representations and the second plurality of scored representations are included in the plurality of scored representations; and averaging at least the first result and the second result to achieve the result.

22. The system of claim 19, wherein the data includes data related to content of the website for indexing by the one or more search engines.

23. The system of claim 19, wherein the data includes data related to traffic of one or more visitors to the website.

24. The system of claim 20, wherein the data associated with the website includes data derived from one or more of a search engine file, a web analytics file, and a website file.

25. The system of claim 20, wherein the data associated with the website includes data selected from the group consisting of whether a sitemap link exists on the website's homepage, whether the website is listed in an Open Directory Project (DMOZ) directory, whether the website is listed in a Yahoo! directory, whether a character length of a URL of the website exceeds a maximum URL character length, whether a webpage size of the website exceeds a maximum webpage size, whether flash navigation is used on the website, whether javascript navigation is used on the website, whether session identifiers are used in the URL, whether dynamic parameters are used in the URL, whether a keyword exists in a title tag of the website, whether the keyword exists in meta content of the website, whether the keyword exists in a header tag of the website, whether the keyword exists in a body tag of the website, whether the keyword exists in a URL of the website, whether a webpage associated with the website has inbound links from .edu domains, whether the webpage has inbound links from .gov domains, whether the webpage has inbound links from social networks, whether the webpage has inbound links from blogs, whether the webpage has inbound links from wikis, whether the website has internal links to the webpage, and whether keywords are included in the website in accordance with a density threshold.

26. The system of claim 19, wherein the recommending a modification to a parameter of the website includes recommending an increase of a number of inbound links of the website, recommending a decrease of a length of a URL of the website, recommending a decrease of a webpage size, recommending a removal of dynamic parameters from the URL, recommending a removal of flash navigation from the website, recommending a removal of JavaScript navigation from the website, recommending a use of a keyword in a title tag of the website, recommending a use of the keyword in meta content of the website, recommending a use of the keyword in a header tag of the website, recommending a use of the keyword in a body tag of the website, recommending a use of the keyword in a URL of the website, or recommending a use of a keyword in the website that exceeds a density threshold.

27 The system of claim 21 , wherein the first result pertains to a first search engine and the second result pertains to a second search engine, or the first result pertains to a first keyword and the second result pertains to a second keyword, or the first result pertains to a first webpage of the website and the second result pertains to a second webpage of the website.

28. The system of claim 21, wherein the data is further associated with a second website, and wherein the first search result pertains to the website and the second search result pertains to the second website.