Abstract:
A system and method for the identification, analysis, attribution, and graphical display pertaining to the effectiveness of public relations is described. The methodology is based on a massively quantitative approach suitable for numerical processing. This method provides a computer-based means of consolidating both internal and external data and producing a graphical representation of the quantitative results to attribute individual contributions of separate data sources.

Description:
BACKGROUND 
     The invention pertains to the attribution to and effectiveness of single known data points and their effect on other single, but unknown data points, in a massive secondary, tertiary or higher linked system. One example of this is the attribution to and effectiveness of public relations or other marketing events on revenue. It is commonly accepted that public reputation and consumer awareness are key drivers of corporate revenue and brand value. However, the effects of particular public relation or marketing events on the reputation or public awareness of a firm or product is difficult to attribute quantitatively. 
     Traditionally, public relations and marketing professionals analyze paper or electronic sources to determine what effect, if any, their efforts to drive company reputation and consumer awareness through the media can have any measurable effect. And any subsequent measured effects deduced have been limited to a narrow set of metrics such as share-of-voice, number of impressions, etc. No further conclusions have been made to quantitatively link these already limited set of metrics and its effect on sales or revenue—the ultimate measure of corporate health. 
     Whereas earlier ages were hampered by the lack of paper sources, current analysts may be overwhelmed by the amount of data that are electronically available through search engines or third-party aggregated press databases. This information overload has made it harder, rather than easier to determine the cause and effect of public relations and marketing efforts and the effect of reputation and consumer awareness campaigns have in driving corporate revenue. Further, current methods of measuring PR and marketing performance have been limited to efficiencies on a per-impression acquisition or per-campaign basis. For example, cost per impression or cost per click through various media channels. A company with a far larger market share (or indeed PR and advertising budget) will naturally have wider media exposure than a smaller competitor, yet this in itself does not quantitatively indicate how effectively the available resources to impart consumer awareness in the media are being utilized as contributor to corporate revenue. There is a need, fulfilled by this invention, to resolve this massive data dump into coherent, graphical results. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to provide a method to use statistical modeling to establish discovery, trending and predictive relationships between any data sets. 
     It is an object of the present invention to provide a method of performance measurement for public relations and marketing events. 
     It is an object of the present invention to provide a method for consolidating information regarding events for evaluation of corporate key performance relevance from public relations and marketing events. 
     It is an object of the present invention to provide a method to integrate and compare on an equivalent basis the effectiveness of a variety of public relations events and marketing levers. 
     It is a further object of the present invention to provide a method of evaluating multiple external events to determine their public relations and marketing efforts with revenue significance. 
     It is a further object of the present invention to provide a relationship between data sets working from a finish-to-start methodology (Finish Line Approach) to determine the relationship. 
     It is a further object of the present invention to provide a scalable, Software-as-a-Service (SaaS) solution that uses statistical modeling to establish relationships between any data. The system has the ability to digest any data, establish a mathematic relationship between the data, and identify the actions that the data was measured against to show which has the most quantitative impact on business, and finally, use this information to build a predictive model. 
     It is a further object of the present invention to provide this information dynamically in real-time. 
     Thus according to the principles of the invention, there is provided a method of doing business and a system for gathering a plurality of external promotional events having significance to a customer, indexing the external events for an electronic database and abstracting predefined portions thereof for inclusion in the database, evaluating the influence of each of the external references to the defined customer and generating at least one report summarizing the influence or return that each of the external events has on revenue. 
     The following describes an exemplary data set from the automotive industry that may be utilized in the present invention. The data set may include, without limitation: (i) media car loans, (ii) story requests, (iii) press/marketing events, (iv) published/advertising content, (v) PR/marketing spend, (vi) key performance indicators such as car sales, and (vii) a custom field. These options may then be combined and packaged to provide a graphical result. 
     The exemplary data set headers are defined as:
         Media Car Loans: Car manufacturers provide automobiles to the media for test drives and pre-release evaluation.   Story Requests: Media queries to car manufactures regarding interest to publish stories.   Press/Marketing Events: Media or consumer invited to events sponsored by car manufacturers for new product introduction and tests.   Published/Advertising Content: Media coverage or advertising on TV, in magazines, on Internet, or in any other emerging media such as social networks.   PR/Marketing Spend: The cost associated with PR and marketing levers.   Key Performance Indicators (KPI): Important metrics describing the ultimate company goals determined and selected by the software user. Examples: car sales, unit sales, etc.   Custom: Placeholder for one or more Key Performance Indicators (KPI) such as share-of-voice, conquests buys, brand awareness or reputation measures common to PR and       

     Further features and advantages of the invention as well as the structure and operation of the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. In the drawings, like numbers indicate identical or functionally similar elements. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Preferred embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings in which: 
         FIG. 1  is an exemplary block diagram that depicts the structure of the system embodying the present invention. 
         FIG. 2  is a graphical representation showing the detail of the flow of information in the Modeling Engine operation. 
         FIG. 3  is an exemplary graphical representation of the flow of information and decision making of the present invention. 
         FIG. 4  is an exemplary graphical representation of the iterative process in determining the Dynamic Sphere of Influence. 
         FIG. 5  is a graphical representation showing the Software flow of information from a User standpoint. 
         FIG. 6  is an exemplary graphical representation that depicts the results of the present invention&#39;s analysis of the relationship between several external variables and revenue. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     With respect to  FIG. 1 , system  100  of the present invention is utilized as follows: a query is sent via a User Interface  102  using the Graphical User Interface (GUI) module  104  for input. Relevant information from one or more of a plurality of databases  106   a ,  106   b ,  106   c  and  106   d , containing one or more data strings,  107   a ,  107   b ,  107   c ,  107   d , will be assessed and collected through a Data Ingestion Module  108 . This information, along with information from the existing database  110  is sorted in the Indexing Engine  112  and forwarded to the Data Query Engine  114 . The data is then sent to the Quantitative Analysis Method Module  120 , specifically to the Data Input/Choose Analysis  122 . Based on the analysis chosen, information is fed to the Discovery Modeling Engine  124  the Trending Modeling Engine  126  and Predictive Modeling Engine  128 . The Discovery Modeling Engine  124 , Trending Modeling Engine  126  and Predictive Modeling Engine  128  can request more data (new or updated) from the Data Input/Choose Analysis  122 . Alternatively, data from the Discovery Modeling Engine  124  can be sent to Trending Modeling Engine  126 , and to Predictive Modeling Engine  128  if complete analysis utilizing all Modeling Engines is desired. Data from the Modeling Engines  124 ,  126  and  128  may then go directly to Test Significance  130 . If the result of Test Significance  130  is No, Data Input/Choose Analysis  122  is repeated. If the result of Test Significance  130  is Yes, information is fed into the Decision Engine  132 , and results are may be forwarded to the Dashboard  134  for display, and/or to Actions Items  136  for additional analysis or to obtain additional required information. The information may then be graphically represented through the Visualization module  138 , and fed through the GUI Output  140  back to the User Interface  102 . 
     With respect to  FIG. 2 , the Modeling Engine System  200 , is shown in detail. The Modeling Engine system comprises a Data Input/Choose Analysis module  122 . Based on user selection, Data Input/Choose Analysis  122  transfers data to Discovery Math Modeling  124 , Trending Math Modeling  126 , or Predictive Math Modeling  128 . 
     When Data is fed into Discovery Math Modeling  124 , Pattern Statistical Engine  222  performs pattern recognition calculations. Analysis executed here may include existing pattern recognition math techniques, but not limited to descriptive statistics, correlation, time series, etc. If any there are no desired patterns within, or between the data at the Patterns of Interest module  224 , the data is examined again, looping back to Data Input/Choose Analysis  122  for additional or updated data. If Pattern of Interest  224  is detected, information is forwarded to Trending Math Modeling  126  for next step of analysis, or can be sent to Test Significance  130 . 
     When Data is fed into Trending Math Modeling  126 , Trending Statistical Engine  242  performs comparative calculations. Analysis executed here may include existing comparative math techniques, but not limited to correlation, principle component, cluster, boot-strapping, etc. If any there are no desired relationships within, or between the data at the Data Relationship module  244 , the data is examined again, looping back to Data Input/Choose Analysis  122  for additional or updated data. If Data Relationship  224  is detected, information is forwarded to Predictive Math Modeling  128  for next step of analysis, or can be sent to Test Significance  130 . 
     When Data is fed into Predictive Math Modeling  128 , Forecasting Statistical Engine  262  performs future projection calculations. Analysis executed here may include existing prediction math techniques, but not limited to regression, discriminate functions, etc. If any there are no predictive models established within, or between the data at the Data Forecasting module  264 , the data is examined again, looping back to Data Input/Choose Analysis  122  for additional or updated data. If Data Forecasting  264  is established, information is forwarded to Test Significance  130 . 
     With respect to  FIG. 3 , a graphical representation of the “Finish-Line Approach” system  300  of the present invention is exemplified. Taken Data Input from outside of system  300 , and/or the Customer Defined KPI (Key Performance Indicator) (End Goal)  302  is selected. The KPI is used as a Benchmark/Filter for Data Analysis  304 . The data set or sets are then entered into the Quantitative Analysis module  120  along with Upstream Measured Data A  306   a , Upstream Measured Data B  306   b , Upstream Measured Data C  306   c , Upstream Measured Data N  306   d . The output of the Quantitative Analysis Method  120  is then analyzed to determine if a Statistical Relationship  308  exists. If No, then More Data and Analysis Needed  310  is flagged and additional Upstream Measured Data  360   a ,  306   b ,  306   c ,  306   d  is required. If Yes, then Identified Data Influence on KPI is flagged and the Updated Data Test  314  is considered. Data Input from outside of system  300  can also be fed into Updated Data Test  314  for consideration. If the result of Updated Data Test  314  is Yes, More Data and Analysis  310  is flagged. If Updated Data Test is No, Data Identified to Drive KPI  318  is confirmed, and the output may be iteratively applied back through the Customer Defined KPI (End Goal) module  302  for continuous improvement, and/or sent as Data Output to outside of system  300 . 
     With respect to  FIG. 4 , a graphical representation of the Sphere of Influence (SOI) Index system  400  is depicted. From Input Data outside of SOI Index System  300 , and/or the user initially identifies Sphere Of Index (SOI) Relevant to a Business Function  402 , that data is entered into Customer Defined KPI (End Goal)  302  within “Finish-Line Approach” system  300 . If Data Identified to Drive KPI  318  is detected, the result is used to Define the Number of Influence Data in the SOI Index  404 . The data is then analyzed to determine the Weighting of Individual Data in SOI Index  406 . A check is made to determine if there Real-Time Update SOI  408  is needed. If Yes, the data is checked for Updated Data Test  314  in the “Finish-Line Approach” system  300 . If No, SOI Index Snapshot  410  is created, and can be forwarded to optimize, modify and/or refine SOI Relevant to Business Function  402 , and/or sent as Output Data to outside SOI Index System  300 . 
     With respect to  FIG. 5 , Software User Execution system includes a User Login  502  and an Authentication Module  504 . Upon authentication, the user may Select Data for Analysis  506 . The system may query if the selected data is New Data  508 . If the data is new, the user may choose a Data File/Type/Location  510 , Upload/Parse Data for Database  514 . 
     If the data is already resident in the database, either one, multiple or combinations of, Quantitative Analysis Method, Finish Line Method or Dynamic Sphere of Index (SOI), may be pursued. In the first, the Quantitative Analysis Method  120  performs Discovery, Trending and Predictive functions on the data. The results may then be sent to Graphical Output  526  for the user to view, and may be saved, transmitted, printed or deleted. The user may also exit the system through the User Logout  528 . 
     Alternatively, separately, in concert or combination with, to the above data manipulation, may be sent to the Finish Line Method  300 , where the user can define Key Performance Indicators (KPIs)/Benchmarks, test all relevant data, and identify the data are key drivers of the said KPIs/benchmarks. The results may then be sent to Graphical Output  526  for the user to view, and may be saved, transmitted, printed or deleted. The user may also exit the system through the User Logout  528 . 
     Alternatively, separately, in concert or combination with, to the above data manipulation, may be sent to the Dynamic Sphere of Influence (SOI) Index, where the user can detect, select and customize a Sphere of Influence (SOI) Index that is relevant and complements a specific business goal(s). The results may then be sent to Graphical Output  526  for the user to view, and may be saved, transmitted, printed or deleted. The user may also exit the system through the User Logout  528 . 
     If the Authentication  504  fails, the user may be prompted to a Sign Up/Password Retrieval module  530 . If the user has an account does not wish to create a new account, they will be sent a Password Recovery Email  534  and returned to the User Login  502 . 
     If the user wishes to sign up, they may do so and a Sign Up Confirmation Email  538  will be generated and sent to the user, who is then returned to the User Login  502 . 
     With respect to  FIG. 6 , the Output GUI  140  of  FIG. 1  may be represented in the manner of the Software Graphical User Interface system screen  600 . Function Bar  610  contains multiple functions that may be selected through an input device, for example, a mouse, a pen, a touch screen or by voice command. One embodiment of the present invention includes a New/Open Analytics tab, a Custom Data tab, Standard Editing Functions, Benchmark Data Configuration, Standard/Favorite Analytic Scenarios, Share/Publish Report and Help Functions. Additional or alternative functions may be configured as necessary or appropriate for different embodiments. A Filter or Search by Keyword Box  612  allows a user to search for particular data, results or other elements of the database by text, keyword or other search strings. Both the Search and Filter function may be contextual, literal or employ Boolean arguments to generate results. Display Tabs  630  allow the user to select the type of analysis that he or she wishes to have displayed. 
     In one embodiment of the present invention a Trending Analysis tab shows the relationship between several plotted variables in a graphical output display. There are additional Discovery, Predictive and Dashboard tabs also shown. These tabs are exemplary and are not to be considered limiting. 
     Key Performance Indicators (KPIs)/Benchmarks variables selectable by user, shown as Select KPI Data  642 . Relationship variables are shown as Select Test Data  644 . In one embodiment of the present invention, Sales is chosen as KPI data, Media Car Loan, Consumer Confidence Index (CCI) and Web Traffic are chosen as Test Data. As with the display tabs above, these categories are exemplary and not to be considered limiting. Analysis Date Range  640  allows the user to identify the start and end date for the analysis to be performed. 
     In the graphical visualization  650 , plots are displayed based on user selection of KPI and Test data. A Time Slider tab  652  is available to scroll chronologically through a display showing the effect of different test data over time against KPI data. A custom View Date range  654  option is also available to display results between a specific start and end date. The types charting presented, line plots, scatter, bar, etc., are exemplary and are not to be considered limiting. 
     Tables are available to show Discovery Results  660 , Trending Results  662  and Predictive Results  664  corresponding to the types of analysis chosen based on Display Tab  630 . Action Items  670  presents the recommended actions to be taken also based aon the corresponding analysis chosen with Display Tab  630 . 
     While various embodiments of the disclosed system, software, and method have been described above, it should be understood that they have been presented by way of example only, and should not limit the claimed invention. Likewise, the various diagrams may depict an example architectural or other configuration for the disclosed system, software, and method. This is done to aid in understanding the features and functionality that can be included in the disclosed system, software, and method. The claimed invention is not restricted to the illustrated example architectures or configurations, rather the desired features can be implemented using a variety of alternative architectures and configurations. Indeed, it will be apparent to one of skill in the art how alternative functional, logical or physical partitioning and configurations can be implemented to implement the desired features of the disclosed system, software, and method. Also, a multitude of different constituent module names other than those depicted herein can be applied to the various partitions. Additionally, with regard to flow diagrams, operational descriptions and system or method claims, the order in which the steps are presented herein shall not mandate that various embodiments be implemented to perform the recited functionality in the same order unless the context dictates otherwise. 
     Although the disclosed system, software, and method is described above in terms of various exemplary embodiments and implementations, it should be understood that the various features, aspects and functionality described in one or more of the individual embodiments are not limited in their applicability to the particular embodiment with which they are described. Thus, the breadth and scope of the claimed invention should not be limited by any of the above-described exemplary embodiments. 
     Terms and phrases used in this document, and variations thereof, unless otherwise expressly stated, should be construed as open ended as opposed to limiting. As examples of the foregoing: the term “including” should be read as meaning “including, without limitation” or the like; the term “example” is used to provide exemplary instances of the item in discussion, not an exhaustive or limiting list thereof; the terms “a” or “an” should be read as meaning “at least one,” “one or more” or the like; and adjectives such as “conventional,” “traditional,” “normal,” “standard,” “known” and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass conventional, traditional, normal, or standard technologies that may be available or known now or at any time in the future. Likewise, where this document refers to technologies that would be apparent or known to one of ordinary skill in the art, such technologies encompass those apparent or known to the skilled artisan now or at any time in the future. 
     A group of items linked with the conjunction “and” should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as “and/or” unless expressly stated otherwise. Similarly, a group of items linked with the conjunction “or” should not be read as requiring mutual exclusivity among that group, but rather should also be read as “and/or” unless expressly stated otherwise. Furthermore, although items, elements or components of the disclosed method and apparatus may be described or claimed in the singular, the plural is contemplated to be within the scope thereof unless limitation to the singular is explicitly stated. 
     The presence of broadening words and phrases such as “one or more,” “at least,” “but not limited to” or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent. The use of the term “module” does not imply that the components or functionality described or claimed as part of the module are all configured in a common package. Indeed, any or all of the various components of a module, whether control logic or other components, can be combined in a single package or separately maintained and can further be distributed in multiple groupings or packages or across multiple locations. 
     Additionally, the various embodiments set forth herein are described in terms of exemplary block diagrams, flow charts and other illustrations. As will become apparent to one of ordinary skill in the art after reading this document, the illustrated embodiments and their various alternatives can be implemented without confinement to the illustrated examples. For example, block diagrams and their accompanying description should not be construed as mandating a particular architecture or configuration.