Abstract:
Elements of a document that can affect viewership are identified, monitored and recorded as change events. The numbers of computers, individuals or others viewing a publication (viewers) and what times they viewed it are collected from the appropriate source for the medium. One or more groups of change events for a given period of time are compared to the corresponding viewers via statistical regression to determine how much a given set of change events affects the changes in total viewers.

Description:
TECHNICAL FIELD 
       [0001]    This invention relates to the collection, classification and correlation of document changes, more specifically as it relates to individual interaction with those documents, the frequency thereof as well as the affects this might have on related document indexing systems. 
       BACKGROUND OF THE INVENTION 
       [0002]    The internet has allowed commerce a new channel to gain revenue. The expansion of the World Wide Web has businesses relying more and more upon revenues generated through their web pages. These revenues are dependent upon people visiting their websites and purchasing products there. If a business can increase the number of people visiting their website it often follows that more products are then sold on that website. This has created an industry devoted to increasing the number of visitors to websites sometimes referred to as Inbound Marketing. 
         [0003]    To increase the number of visitors Inbound Marketers have relied on trial and error to achieve success. In most cases there are no real measurements that tell them if a specific action is successful other than a general increase in traffic. If a correlation method is used, they are reminded that correlation is not causation and they are left with general measurements to decide if their tactics are working. The need then becomes: how to create meaningful cause and effect measurements that show if the changes made actually affect the number of visitors to a website? 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0004]      FIG. 1  Is a flow chart showing general methods &amp; systems. 
           [0005]      FIG. 2  Is a flow chart covering the acquisition of change events. 
           [0006]      FIG. 3  Is a flow chart covering the acquisition of visitor data. 
           [0007]      FIG. 4  Is a flow chart showing the correlation of change events vs. visitor data. 
           [0008]      FIG. 5  Is a flow chart showing the differences added by Claim  2 . 
       
    
    
     DETAILED DESCRIPTION 
       [0009]    This invention is meant to solve the lack of meaningful measurements for document changes and how they affect the number of visitors to that document. In order to build a system to achieve the object of this invention it adopts the means of a server device consisting of a processor, storage device and input/output connections (computer). The systems and methods described hereafter are assumed to be executed by one of these devices (computers). The end product is a useful, automated system that will reduce the number of man hours required to analyze and understand the effectiveness of inbound marketing. 
         [0010]    There are three major components of the automated process being identifying change events, gathering visitor traffic data and correlating the findings. 
         [0011]    Step one involves defining what elements will be tracked for a given document. This can change from document to document, but can include any element in the document that might change at some future date. Some examples include title, description, headings, paragraphs, images, references and other document elements that can be defined. The elements to be tracked are extracted from the document, sorted by type of element and stored in a database. The document is then monitored for changes to any of the tracked elements. When a change is detected the element, the date and time of the detected change are stored as a change event. 
         [0012]    Step two gathers data on the number of visitors that view the document over a period of time that includes the change events. This data can be obtained from many different sources and it does not matter which source is chosen, only that the all the sources parameters for defining visitors remain the same for the full time period the change events cover. The traffic is split into smaller, regular time segments showing the number of visitors per period. For example visitors per month, visitors per day, visitors per hour, etc. This information can be stored temporarily or permanently as the situation requires. 
         [0013]    Step Three uses the data from steps one and two. The change events are converted to number of change events per time period to match the time period from the visitor data. So if the visitor data is number of visitors per day the corresponding data would be number of change events per day. This data, for a specific range of time, is then correlated using statistical regression to determine how much the change events affect the number of visitors. The correlation coefficient, intercept, range of time, time period, change event type and the confidence interval are recorded in a database. 
         [0014]    The information created by this process can be displayed, sent to another system or printed out for use. 
         [0015]    The process can be modified by adding in the means by which a visitor became aware of the document. 
         [0016]    This would be a visitor traffic source. If a visitor traffic source has means to become aware of changes to the document and this awareness is detectable then it is possible to get a more accurate result from step three. This is achieved by recording the change event as outlined above, detecting when a source becomes aware of the change event, recording the source and date it became aware of the change. Use this information in executing step three by replacing the change event date with the source awareness date before converting the change events into number of change events per time period. The visitor traffic would also need to be limited to visitors that were referred by the source for this to work. 
         [0017]    Change events can use elements that are not part of the document. These elements need to be related to the document in some way and can be reliably tracked. These can then replace standard change events and yield similar correlation results. 
         [0018]    The process can also be modified by monitoring external factors that can affect the number of visitors to a document. These external factors can include the number of sources that can make visitors aware of a document, what those sources say about the document (bad or good) or any other external factor. If these external factors can be identified, defined and their changes can be tracked then it is possible to use them in the primary method above. External factors can be defined as external change events and they can be substituted for the change events in step one, producing similarly useful results. 
       BEST MODE FOR CARRYING OUT THE INVENTION 
     Embodiment 1 
       [0019]      FIG. 1  shows a general description of the overall process for the systems and methods for determining if change events affect viewership. Once created this system will require input about what document elements will be monitored determined in  102 . These identified elements will then be indexed and monitored for changes in  103  which are then recorded. Information about viewers of the document is obtained from an outside source in  104  and indexed. The data from  102  &amp;  103  is then correlated using regression analysis to determine the effects of the change events on the visitors for a specific document. 
         [0020]    In  FIG. 2  the parameters of  102  determine what elements are selected in  202 .  203  first involves recording the existing data that will be monitored. Monitoring of the element involves checking the current element to the one recorded in the index. At each check of the document in  204 , if there is no change then monitoring continues. When a change is detected in  204  it is recorded as a change event in  205  as part of the index and the same change event will be used as the new base for comparison for the subsequent  204  check. 
         [0021]    In  FIG. 3  information about viewers of a document are recorded by an outside entity using whatever methods they have developed outside this system. In  302  the date range is then set for the viewer data to be retrieved. The data is then retrieved via  304  and formatted in  303  to the time delimiter set by the user in  302 . This could be viewers per day, viewers per year, etc. This data is then stored, either temporarily or permanently, in  305 . 
         [0022]      FIG. 4  then takes the element and date range set by the user in  402  to get the data in  403  from the change event index/ 403 A and the visitor data/ 403 B. This data is then formatted in  404  to be processed in a regression analysis in  405 . 
       Embodiment 2 
       [0023]      FIG. 5  shows a similar process to Embodiment 1 where  FIG. 1  &amp;  FIG. 2  are the same.  FIG. 5  has a process by which a source (a source is the means by which the viewer followed to view a document) to be monitored is selected  502  and then steps  503 ,  504 , and  505  are run just like steps  203 ,  204 , and  205  from  FIG. 2 . After  505  viewers of the document are then monitored in  506  to see when the source becomes aware of the change in  507 . If the source is detected the date and time the source became aware of the change event is recorded in  508  and stored in  514 . The regression analysis in  509 - 516  is similar to steps  402 - 406  in  FIG. 4  except the steps  513  and  514  are inserted between  404  and  405 . After the data is formatted in  404  the date the source became aware of the change event is substituted for the date of the change event for each change event in  513 . This new data set would then be correlated using regression analysis in  514 . 
       Embodiment 3 
       [0024]    Similar to Embodiment 1 this includes all the components from  FIG. 1 ,  FIG. 2 ,  FIG. 3  and  FIG. 4 . The change is made when dealing with  202  and  203  where the elements to be monitored are not a component, format or attribute of the document. The element chosen in  202  should be related to the document being correlated against in some way (a reference, referral, fame, arbitrary measurement, etc.) and can be obtained through an external method or system. When a change is detected in  203  for the external element it is recorded as a change event. Otherwise, all other methods will work the same.