SYSTEM AND METHOD FOR MEASURING LONGITUDINAL VIDEO ASSET VIEWING AT A SECOND-BY-SECOND LEVEL TO UNDERSTAND BEHAVIOR OF VIEWERS AS THEY INTERACT WITH VIDEO ASSET VIEWING DEVICES THAT ACCESS A COMPUTER SYSTEM THROUGH A NETWORK

• US 20140351835A1
• Filed: 01/28/2012
• Published: 11/27/2014
• Est. Priority Date: 12/29/2010
• Status: Active Grant

• Current Assignee: Comcast Cable Communications LLC (Comcast Corporation)
• Original Assignee: Comcast Cable Communications LLC (Comcast Corporation)
• Inventors: Orlowski, Robert
• Granted Patent: US 10,089,592 B2
• Time in Patent Office: Days
• US Class Current: 725/9
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: H04N 21/23424 involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/4383 Accessing a communication channel
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4821 using a grid, e.g. sorted out by channel and broadcast time
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

A computer-implemented method of using channel tuning data from a video asset viewing device connected to a network to measure video asset viewing at a second-by-second level during one or more user defined lead-in periods, and then correlating that with video asset viewing during a user defined target period, for the purpose of analyzing how viewing activity during the lead-in period(s) correlates with viewing activity during the target period, thus producing longitudinal viewing metrics; all while maintaining viewer anonymity. Additionally, viewing metrics can be categorized based on user defined demographic, geographic, and histogram groupings representing the percentage of video asset viewing with the result that the analyst is able to gain detailed insight into customer viewing behavior. The lead-in video asset may be any video asset or assets. The target may be any subsequent video asset. The metrics produced are useful to service providers, advertisers, and content producers.

Claims

1. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of defining a user activity data structure and loading said user activity data structure with data for the purpose of determining longitudinal viewing patterns resulting from a plurality of viewer interactions by a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprising the steps of:

a. providing on said data analysis computer system a data analysis program,

b. creating a user activity data structure in said data analysis program run on said data analysis computer system containing identifying fields where said identifying fields include at least one member selected from the group consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

c. creating in said user activity data structure buckets representing individual seconds of time during a window of time of interest for analysis wherein said buckets are correlated with said identifying fields,

d. receiving in computer readable format video asset viewing device usage data resulting from said viewer interaction and making said video asset viewing device usage data available to said data analysis program run on said data analysis computer system,

e. using said video asset viewing device usage data to load to said identifying fields in said user activity data structure identifying information for at least one member selected from the group of identifying fields consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

f. using said video asset viewing device usage data to determine the beginning date and time and the ending date and time and the channel tuned for each said viewer interaction with said video asset viewing device,

g. using said beginning date and time and said ending date and time and said channel tuned to load channel identifiers that identify at a second-by-second level the channel being viewed to selected buckets in said user activity data structure, where said buckets loaded are correlated with said identifying fields in said user activity data structure, and where each said bucket represents a second of time during which said video asset viewing device was tuned to said channel tuned thus allowing said data analysis program to track said channel being viewed against said identifying fields,

h. analyzing said channel being viewed which was loaded to said one second buckets in said user activity data structure to determine longitudinal viewing patterns,

i. outputting information regarding said longitudinal viewing patterns in a useful format,

whereby said longitudinal viewing patterns;

(i) provide insight into the viewing patterns of said viewer as they interact with said video asset viewing device interacting with said computer system accessed through said network,

(ii) provide insight into the video asset viewing device usage pattern of said viewer, and

(iii) provide insight into the behavior of said viewer.

2. The computer-implemented method of claim 1 wherein said video asset viewing device usage includes both real time video asset viewing device usage and video asset viewing device usage that occurs as a result of a previous viewer action.

23. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where the channel being viewed in the at least one previous second is the same as the channel being viewed in the current second where said current second is represented by one of the buckets in said user activity data structure and said at least one previous second is represented by a set of buckets having an earlier point in time in said user activity data structure.

24. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where the channel being viewed in the at least one subsequent second is the same as the channel being viewed in the current second where said current second is represented by one of the buckets in said user activity data structure and said at least one subsequent second is represented by a set of buckets having a later point in time in said user activity data structure.

25. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a specific channel identifier is present in the at least one said bucket prior to a specified point in time.

26. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a specific channel identifier is present in the at least one said bucket subsequent to a specified point in time.

27. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a set of consecutive buckets is analyzed to determine how many of the set of consecutive buckets contain a specific channel identifier.

28. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a set of consecutive buckets is analyzed to determine how many of the set of consecutive buckets contain a specific channel identifier prior to a particular point in time.

29. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a set of consecutive buckets is analyzed to determine how many of the set of consecutive buckets contain a specific channel identifier subsequent to a particular point in time.

30. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a set of consecutive buckets is analyzed to determine how many of the set of consecutive buckets contain a specific channel identifier during a window of time.

31. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a set of consecutive buckets is analyzed to determine whether the set of consecutive buckets contain a specific channel identifier.

32. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where a set of consecutive buckets is analyzed to determine whether the set of consecutive buckets contain a specific channel identifier during a window of time.

33. The computer-implemented method of claim 1 wherein said longitudinal viewing patterns include viewing patterns where the at least one said bucket is searched to determine whether said bucket contains a particular channel identifier.

34. The computer-implemented method of claim 1 wherein said useful format in which said longitudinal viewing patterns are output includes at least one member selected from the group consisting of:

a data file that can be read by a computer program, a data file that can be read by a human being, a data base table, an electronic message, and a spreadsheet.

3. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of defining a lead-in viewing analysis data structure and loading said lead-in viewing analysis data structure with data in preparation for analyzing a plurality of viewer interactions by a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprising the steps of:

a. providing on said data analysis computer system a data analysis program,

b. creating in said data analysis program running on said data analysis computer system a lead-in viewing analysis data structure containing fields for recording lead-in video asset data where said fields include at least one member selected from the group consisting of;

(i) channel on which the lead-in video asset was aired,

(ii) lead-in amount of time to qualify as an exposure,

(iii) lead-in video asset play begin date and time,

(iv) lead-in video asset play end date and time,

(v) lead-in video asset duration,

(vi) lead-in video asset identifier,

(vii) geographic area in which the lead-in video asset was aired,

(viii) network system computer equipment identifier,

c. receiving in computer readable format lead-in video asset data regarding lead-in video assets for which viewing activity is to be analyzed and making said lead-in video asset data available to said data analysis program running on said data analysis computer system,

d. loading said lead-in video asset data to said fields for recording lead-in video asset data in said lead-in viewing analysis data structure,

all in preparation for calculating lead-in viewing metrics related to lead-in video asset viewing at the level of the lead-in video asset, all resulting from a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network.

4. The computer-implemented method of claim 3, further including:

a. creating in said lead-in viewing analysis data structure fields for tracking lead-in video asset viewing by various groupings where said fields for tracking lead-in video asset viewing by various groupings are correlated with said fields for recording lead-in video asset data and where said fields for tracking lead-in video asset viewing by various groupings include at least one member selected from the group consisting of;

(i) a plurality of demographic codes by which to classify said viewers of said lead-in video asset,

(ii) a plurality of geographic codes by which to classify said viewers of said lead-in video asset,

(iii) a plurality of histogram buckets by which to classify said viewers of said lead-in video asset,

b. receiving in computer readable format a combination of demographic codes, geographic codes, and histogram bucket definitions by which to categorize lead-in video asset viewing,

c. loading said combination of demographic codes, geographic codes, and histogram bucket definitions to said fields for tracking lead-in video asset viewing by various groupings,

all in preparation for calculating lead-in viewing metrics related to lead-in video asset viewing at the level of demographic code, geographic code, and histogram bucket definition, all resulting from a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network.

5. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of defining a target viewing analysis data structure and loading said target viewing analysis data structure with data in preparation for analyzing a plurality of viewer interactions by a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprising the steps of:

a. providing on said data analysis computer system a data analysis program,

b. creating in said data analysis program running on said data analysis computer system a target viewing analysis data structure containing fields for recording target video asset data where said fields include at least one member selected from the group consisting of;

(i) channel on which the target video asset was aired,

(ii) target amount of time to qualify as an exposure,

(iii) target video asset play begin date and time,

(iv) target video asset play end date and time,

(v) target video asset duration,

(vi) target video asset identifier,

(vii) geographic area in which the target video asset was aired,

(viii) network system computer equipment identifier,

c. receiving in computer readable format target video asset data regarding target video assets for which viewing activity is to be analyzed and making said target video asset data available to said data analysis program running on said data analysis computer system,

d. loading said target video asset data to said fields for recording target video asset data in said target viewing analysis data structure,

all in preparation for calculating target viewing metrics related to target video asset viewing;

(i) at the level of target video asset,

all resulting from a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network.

6. The computer-implemented method of claim 5, further including:

a. creating in said target viewing analysis data structure fields for tracking user defined target video asset segments for said target video asset where said target video asset segment is defined by various target video asset segment fields where said target video asset segment fields include at least one member selected from the group consisting of;

(i) target video asset segment play begin date and time,

(ii) target video asset segment play end date and time,

(iii) target video asset segment duration,

b. creating in said target viewing analysis data structure fields for tracking target video asset segment viewing by demographic geographic histogram groupings where said fields for tracking target video asset segment viewing by demographic geographic histogram groupings include at least one member selected from the group consisting of;

(i) a plurality of demographic codes by which to classify said viewers of said target video asset segment,

(ii) a plurality of geographic codes by which to classify said viewers of said target video asset segment,

(iii) a plurality of histogram buckets by which to classify said viewers of said target video asset segment,

c. creating in said target viewing analysis data structure fields for tracking target video asset segment viewing by channel within said demographic geographic histogram groupings,

d. receiving in computer readable format target video asset segment data regarding target video asset segments for which viewing activity is to be analyzed and making said target video asset segment data available to said data analysis program running on said data analysis computer system,

e. loading said target video asset segment data to said target video asset segment fields for said target video asset in said target viewing analysis data structure,

f. receiving in computer readable format a combination of demographic codes, geographic codes, and histogram bucket definitions by which to categorize target video asset segment viewing,

g. loading said combination of demographic codes, geographic codes, and histogram bucket definitions to said demographic geographic histogram groupings for said target video asset segment for said target video asset in said target viewing analysis data structure,

h. receiving in computer readable format channel definitions by which to categorize target video asset viewing,

i. loading said channel definitions to said fields for tracking target video asset viewing by channel for said demographic geographic histogram groupings for said tracking target video asset segment for said target video asset in said target viewing analysis data structure,

all in preparation for calculating target viewing metrics related to target video asset viewing;

(i) at the level of channel within demographic codes, geographic codes, and histogram bucket definitions within user defined segments of said target video asset, and

(ii) at the level of demographic codes, geographic codes, and histogram bucket definitions within user defined segments of said target video asset, and

(iii) at the level of user defined segments of said target video asset, all resulting from a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network.

7. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of analyzing a plurality of viewer interactions by a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprising the steps of:

a. providing on said data analysis computer system a data analysis program,

b. creating a user activity data structure in said data analysis program run on said data analysis computer system containing identifying fields where said identifying fields include at least one member selected from the group consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

c. creating in said user activity data structure buckets representing individual seconds of time during a window of time of interest for analysis wherein said buckets are correlated with said identifying fields,

d. receiving in computer readable format video asset viewing device usage data resulting from said viewer interaction and making said video asset viewing device usage data available to said data analysis program run on said data analysis computer system,

e. using said video asset viewing device usage data to load to said identifying fields in said user activity data structure identifying information for at least one member selected from the group of identifying fields consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

f. using said video asset viewing device usage data to determine the beginning date and time and the ending date and time and the channel tuned for each said viewer interaction with said video asset viewing device,

g. using said beginning date and time and said ending date and time and said channel tuned to load values that identify second-by-second video asset viewing activity to selected buckets in said user activity data structure, where said buckets loaded are correlated with said identifying fields in said user activity data structure, and where each said bucket represents a second of time during which said video asset viewing device was tuned to said channel thus allowing said data analysis program to track said video asset viewing activity against at least one said identifying field,

h. creating in said data analysis program running on said data analysis computer system a lead-in viewing analysis data structure containing fields for recording lead-in video asset data where said fields include at least one member selected from the group consisting of;

(i) channel on which the lead-in video asset was aired

(ii) lead-in amount of time to qualify as an exposure

(iii) lead-in video asset play begin date and time

(iv) lead-in video asset play end date and time

(v) lead-in video asset duration

(vi) lead-in video asset identifier

(vii) geographic area in which the lead-in video asset was aired

(viii) network system computer equipment identifier

i. receiving in computer readable format lead-in video asset data regarding lead-in video assets for which viewing activity is to be analyzed and making said lead-in video asset data available to said data analysis program running on said data analysis computer system,

j. loading said lead-in video asset data to said fields for recording lead-in video asset data in said lead-in viewing analysis data structure,

k. executing algorithms in said data analysis program running on said data analysis computer system to create lead-in video asset viewership metrics for said lead-in video asset represented in said lead-in viewing analysis data structure by analyzing said second-by-second video asset viewing activity represented in said user activity data structure against said lead-in video asset data in said lead-in viewing analysis data structure,

l. outputting said viewership metrics in a useful format,

whereby said metrics

(i) provide insight into the viewership of said lead-in video asset by said viewers as they interact with said video asset viewing device interacting with said computer system accessed through said network,

(ii) provide insight into the video asset viewing device usage pattern of said viewer, and

(iii) provide insight into the behavior of said viewer.

8. The computer-implemented method of claim 7 wherein said viewer interaction includes both real time viewer interactions with said video asset viewing device and interactions with said video asset viewing device that occur as a result of a previous viewer action.

9. The computer-implemented method of claim 7 wherein said useful format in which said analytics are output includes at least one member selected from the group consisting of:

a data file that can be read by a computer program, a data base table, an electronic message, and a spreadsheet.

10. The computer-implemented method of claim 7 wherein said lead-in video asset viewership metrics include at least one member selected from the group consisting of:

For the entire analysis study across all lead-in video assets

AGGREGATE-VIEWING-ALL-STB,

AGGREGATE-EXPOSURES-ALL-STB,

For each lead-in video asset

LEAD-IN-STB-COUNT,

LEAD-IN-AGGR-VIEW-SECS,

LEAD-IN-EXPOSURES,

LEAD-IN-STB-COUNT-AT-BEG,

LEAD-IN-STB-COUNT-AT-END,

LEAD-IN-STB-COUNT-VIEW-ALL.

11. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of analyzing a plurality of viewer interactions by a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprising the steps of:

a. providing on said data analysis computer system a data analysis program,

b. creating a user activity data structure in said data analysis program run on said data analysis computer system containing identifying fields where said identifying fields include at least one member selected from the group consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

c. creating in said user activity data structure buckets representing individual seconds of time during a window of time of interest for analysis wherein said buckets are correlated with said identifying fields,

d. receiving in computer readable format video asset viewing device usage data resulting from said viewer interaction and making said video asset viewing device usage data available to said data analysis program run on said data analysis computer system,

e. using said video asset viewing device usage data to load to said identifying fields in said user activity data structure identifying information for at least one member selected from the group of identifying fields consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

f. using said video asset viewing device usage data to determine the beginning date and time and the ending date and time and the channel tuned for each said viewer interaction with said video asset viewing device,

g. using said beginning date and time and said ending date and time and said channel tuned to load values that identify second-by-second video asset viewing activity to selected buckets in said user activity data structure, where said buckets loaded are correlated with said identifying fields in said user activity data structure, and where each said bucket represents a second of time during which said video asset viewing device was tuned to said channel thus allowing said data analysis program to track said video asset viewing activity against at least one said identifying field,

h. creating in said data analysis program running on said data analysis computer system a lead-in viewing analysis data structure containing fields for recording lead-in video asset data where said fields include at least one member selected from the group consisting of;

(i) channel on which the lead-in video asset was aired,

(ii) lead-in amount of time to qualify as an exposure,

(iii) lead-in video asset play begin date and time,

(iv) lead-in video asset play end date and time,

(v) lead-in video asset duration,

(vi) lead-in video asset identifier,

(vii) geographic area in which the lead-in video asset was aired,

(viii) network system computer equipment identifier,

i. creating in said lead-in viewing analysis data structure fields for tracking lead-in video asset viewing by various groupings where said fields for tracking lead-in video asset viewing by various groupings are correlated with said fields for recording lead-in video asset data and where said fields for tracking lead-in video asset viewing by various groupings include at least one member selected from the group consisting of;

(i) a plurality of demographic codes by which to classify said viewers of said lead-in video asset,

(ii) a plurality of geographic codes by which to classify said viewers of said lead-in video asset,

(iii) a plurality of histogram buckets by which to classify said viewers of said lead-in video asset,

j. receiving in computer readable format lead-in video asset data regarding lead-in video assets for which viewing activity is to be analyzed and making said lead-in video asset data available to said data analysis program running on said data analysis computer system,

k. loading said lead-in video asset data to said fields for recording lead-in video asset data in said lead-in viewing analysis data structure,

l. receiving in computer readable format a combination of demographic codes, geographic codes, and histogram bucket definitions by which to categorize lead-in video asset viewing,

m. loading said combination of demographic codes, geographic codes, and histogram bucket definitions to said fields for tracking lead-in video asset viewing by various groupings,

n. executing algorithms in said data analysis program running on said data analysis computer system to create lead-in video asset viewership metrics for said lead-in video asset represented in said lead-in viewing analysis data structure by analyzing said second-by-second video asset viewing activity represented in said user activity data structure against said lead-in video asset data in said lead-in viewing analysis data structure,

o. outputting said viewership metrics in a useful format,

whereby said metrics

(i) provide insight into the viewership of said lead-in video asset by said viewers as they interact with said video asset viewing device interacting with said computer system accessed through said network,

(ii) provide insight into the video asset viewing device usage pattern of said viewer, and

(iii) provide insight into the behavior of said viewer.

12. The The computer-implemented method of claim 11 wherein said lead-in video asset viewership metrics include at least one member selected from the group consisting of:

For the entire analysis study across all lead-in video assets

AGGREGATE-VIEWING-ALL-STB,

AGGREGATE-EXPOSURES-ALL-STB,

For each lead-in video asset

LEAD-IN-STB-COUNT,

LEAD-IN-AGGR-VIEW-SECS,

LEAD-IN-EXPOSURES,

LEAD-IN-STB-COUNT-AT-BEG,

LEAD-IN-STB-COUNT-AT-END,

LEAD-IN-STB-COUNT-VIEW-ALL,

For each Lead-In Demographic Geographic Histogram combination

LEAD-RESLT-STB-COUNT,

LEAD-RESLT-AGGR-VIEW-SECS,

LEAD-RESLT-EXPOSURES,

LEAD-RES-STB-COUNT-AT-BEG,

LEAD-RES-STB-COUNT-AT-END,

LEAD-RES-STB-COUNT-VIEW-ALL.

13. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of analyzing a plurality of viewer interactions by a plurality of viewers interacting with a plurality of video asset viewing devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprising the steps of:

a. providing on said data analysis computer system a data analysis program,

b. creating a user activity data structure in said data analysis program run on said data analysis computer system containing identifying fields where said identifying fields include at least one member selected from the group consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

c. creating in said user activity data structure buckets representing individual seconds of time during a window of time of interest for analysis wherein said buckets are correlated with said identifying fields,

d. receiving in computer readable format video asset viewing device usage data resulting from said viewer interaction and making said video asset viewing device usage data available to said data analysis program run on said data analysis computer system,

e. using said video asset viewing device usage data to load to said identifying fields in said user activity data structure identifying information for at least one member selected from the group of identifying fields consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device,

f. using said video asset viewing device usage data to determine the beginning date and time and the ending date and time and the channel tuned for each said viewer interaction with said video asset viewing device,

g. using said beginning date and time and said ending date and time and said channel tuned to load values that identify second-by-second video asset viewing activity to selected buckets in said user activity data structure, where said buckets loaded are correlated with said identifying fields in said user activity data structure, and where each said bucket represents a second of time during which said video asset viewing device was tuned to said channel thus allowing said data analysis program to track said video asset viewing activity against at least one said identifying field,

h. creating in said data analysis program running on said data analysis computer system a lead-in viewing analysis data structure containing fields for recording lead-in video asset data where said fields include at least one member selected from the group consisting of;

(i) channel on which the lead-in video asset was aired,

(ii) lead-in amount of time to qualify as an exposure,

(iii) lead-in video asset play begin date and time,

(iv) lead-in video asset play end date and time,

(v) lead-in video asset duration,

(vi) lead-in video asset identifier,

(vii) geographic area in which the lead-in video asset was aired,

(viii) network system computer equipment identifier,

i. receiving in computer readable format lead-in video asset data regarding lead-in video assets for which viewing activity is to be analyzed and making said lead-in video asset data available to said data analysis program running on said data analysis computer system,

j. loading said lead-in video asset data to said fields for recording lead-in video asset data in said lead-in viewing analysis data structure,

k. creating in said data analysis program running on said data analysis computer system a target viewing analysis data structure containing fields for recording target video asset data where said fields include at least one member selected from the group consisting of;

(i) channel on which the target video asset was aired,

(ii) target amount of time to qualify as an exposure,

(iii) target video asset play begin date and time,

(iv) target video asset play end date and time,

(v) target video asset duration,

(vi) target video asset identifier,

(vii) geographic area in which the target video asset was aired,

(viii) network system computer equipment identifier,

l. receiving in computer readable format target video asset data regarding target video assets for which viewing activity is to be analyzed and making said target video asset data available to said data analysis program running on said data analysis computer system,

m. loading said target video asset data to said fields for recording target video asset data in said target viewing analysis data structure,

n. executing algorithms in said data analysis program running on said data analysis computer system to create lead-in video asset viewership metrics for said lead-in video asset represented in said lead-in viewing analysis data structure by analyzing said second-by-second video asset viewing activity represented in said user activity data structure against said lead-in video asset data in said lead-in viewing analysis data structure,

o. executing algorithms in said data analysis program running on said data analysis computer system to create target video asset viewership metrics for said target video asset represented in said target viewing analysis data structure by analyzing said second-by-second video asset viewing activity represented in said user activity data structure against said target video asset data in said target viewing analysis data structure,

p. outputting said viewership metrics in a useful format,

whereby said metrics

(i) provide insight into the viewership of said lead-in video asset and said target video asset by said viewer as they interact with said video asset viewing device interacting with said computer system accessed through said network,

(ii) provide insight into the video asset viewing device usage pattern of said viewer, and

(iii) provide insight into the behavior of said viewer.

14. The computer-implemented method of claim 13 wherein said lead-in viewing analysis data structure contains breakouts for combinations of demographic and geographic and histogram groupings by which lead-in viewing can be measured to produce viewership metrics by said demographic and geographic and histogram groupings including at least one member selected from the group consisting of:

(i) STB-COUNT,

(ii) AGGR-VIEW-SECS,

(iii) EXPOSURES,

(iv) STB-TUNED-AT-BEG-OF-LEAD-IN-CNT,

(v) STB-TUNED-AT-END-OF-LEAD-IN-CNT,

(vi) STB-TUNE-FOR-ALL-OF-LEAD-IN-CNT.

15. The computer-implemented method of claim 13 wherein said target viewing analysis data structure contains breakouts for user defined target video asset segments by which target video asset segment viewing can be measured to produce viewership metrics by said target video asset segment including at least one member selected from the group consisting of:

(i) AP-DEF-TOT-CHAN-VIEW-SEC,

(ii) AP-DEF-TOT-CHAN-EXPOSURE,

(iii) AP-DEF-TOT-STB-ANY-VIEW.

16. The computer-implemented method of claim 13 wherein said target viewing analysis data structure contains breakouts for combinations of demographic and geographic and histogram groupings by which target viewing can be measured to produce viewership metrics for said demographic and geographic and histogram groupings including at least one member selected from the group consisting of:

(i) RESULT-TOT-CHAN-VIEW-SEC,

(ii) RESULT-TOT-CHAN-EXPOSURE,

(iii) RESLT-TOT-STB-ANY-VIEW.

17. The computer-implemented method of claim 13 wherein said target viewing analysis data structure contains breakouts for channels by which target viewing can be measured to produce viewership metrics for said channels including at least one member selected from the group consisting of:

(i) RESULT-AGGR-CHAN-VIEW-SECS,

(ii) RESULT-CHAN-EXPOSURES,

(iii) RESULT-STB-ANY-VIEWING,

(iv) CHAN-PCT-SHARE-OF-VIEWING.

18. The computer-implemented method of claim 13 wherein said lead-in video asset viewership metrics include at least one member selected from the group consisting of:

AGGREGATE-VIEWING-ALL-STB,

AGGREGATE-EXPOSURES-ALL-STB,

LEAD-IN-STB-COUNT,

LEAD-IN-AGGR-VIEW-SECS,

LEAD-IN-EXPOSURES,

LEAD-IN-STB-COUNT-AT-BEG,

LEAD-IN-STB-COUNT-AT-END,

LEAD-IN-STB-COUNT-VIEW-ALL,

LEAD-RESLT-STB-COUNT,

LEAD-RESLT-AGGR-VIEW-SECS,

LEAD-RESLT-EXPOSU RES,

LEAD-RES-STB-COUNT-AT-BEG,

LEAD-RES-STB-COUNT-AT-END,

LEAD-RES-STB-COUNT-VIEW-ALL.

19. The computer-implemented method of claim 13 wherein said target video asset viewership metrics include at least one member selected from the group consisting of:

AGGR-ACTUAL-VIEW-SEC,

AGGR-ACTUL-EXPOSURES,

AP-DEF-TOT-CHAN-VIEW-SEC,

AP-DEF-TOT-CHAN-EXPOSURE,

AP-DEF-TOT-STB-ANY-VIEW,

RESULT-TOT-CHAN-VIEW-SEC,

RESULT-TOT-CHAN-EXPOSURE,

RESLT-TOT-STB-ANY-VIEW,

RESULT-AGGR-CHAN-VIEW-SECS,

RESULT-CHAN-EXPOSURES,

RESULT-STB-ANY-VIEWING,

CHAN-PCT-SHARE-OF-VIEWING.

20. The computer-implemented method of claim 13 wherein said target viewing activity is correlated with said lead-in viewing activity using at least one member selected from the group consisting of:

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device.

21. The computer-implemented method of claim 13 wherein said target viewing activity for each said video asset viewing device is correlated with said lead-in viewing activity for same said video asset viewing device and said target viewing activity for each said video asset viewing device is grouped with other said video asset viewing devices that had a similar amount of aggregate viewing of the lead-in video assets.

22. The computer-implemented method of claim 13 wherein:

a. said target viewing activity is correlated with said lead-in viewing activity using at least one member selected from the group consisting of;

(i) the identifier of said video asset viewing device,

(ii) the identifier of said computer system accessed through said network,

(iii) demographic information about said viewer operating said video asset viewing device,

(iv) geographic information about the location of said video asset viewing device,

(v) the identifier of the operator of said video asset viewing device,

(vi) the identifier of the household associated with said video asset viewing device, and

b. said target viewing activity for said video asset is aggregated into groups based upon a similar amount of aggregate viewing of the lead-in video assets.