Methods and apparatus to characterize households with media meter data

• US 9,185,435 B2
• Filed: 06/24/2014
• Issued: 11/10/2015
• Est. Priority Date: 06/25/2013
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Shankar, Balachander, Poppie, Molly, Kurzynski, David J, Garcia, Jarrett, Chmura, Lukasz, You, Huaxin, Doe, Peter, Bourquin, Christine, Dolson, Timothy, Cui, Xiaoqi, Lee, Choongkoo, Spoentgen, Remy
• Primary Examiner(s): Alata, Yassin
• Application Number:
  US14/313,390
• Publication Number:
  US 20140380348A1
• Time in Patent Office: 504 Days
• Field of Search: 725 9- 21
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/66 for using the result on distributors' side
• CPC Class Codes: H04N 21/233 Processing of audio elementary streams monitoring, identification or recognition of audio in broadcast systems H04H60/58
• CPC Class Codes: H04N 21/239 Interfacing the upstream path of the transmission network, e.g. prioritizing client content requests hybrid fiber coaxial [HFC] networks for upstream channel allocation for video distribution H04L12/2801; flow control in data networks H04L47/10; real-time communication protocols in data switching networks H04L65/00; scheduling or organising the servicing of application requests H04L67/60
• CPC Class Codes: H04N 21/2407 Monitoring of transmitted content, e.g. distribution time, number of downloads arrangements for monitoring programmes for broadcast or for distribution combined with broadcast H04H20/14
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• 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 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to characterize households with media meter data. An example method includes identifying, with a processor, a target set of household categories associated with a target research geography, when a quantity of households within the target research geography representing the target set of household categories does not satisfy a threshold value, generating a first subset of categories and a second subset of categories from the target set of household categories, identifying a first set of households representing the first subset of categories from the target set of household categories and identifying an associated total number of household tuning minutes and a total number of household exposure minutes associated therewith, for each category in the second subset of categories from the target set of household categories, calculating a household tuning proportion and an exposure proportion, the household tuning proportion and exposure proportion based on the total number of household tuning minutes and exposure minutes, respectively, and calculating the panelist behavior probability based on the exposure proportion and the household tuning proportion.

Claims

1. A method to calculate a panelist behavior probability, comprising:

identifying, with a processor, a target set of household categories associated with a target research geography;

when a quantity of panelist households within the target research geography representing the target set of household categories does not satisfy a threshold value required to support statistical significance,

increasing an available sample size of households by generating a first subset of categories and a second subset of categories from the target set of household categories;

identifying a first set of households representing the first subset of categories from the target set of household categories and identifying an associated total number of household tuning minutes and a total number of household exposure minutes associated with the first set of households;

for each category in the second subset of categories from the target set of household categories, calculating a household tuning proportion and an exposure proportion, the household tuning proportion for each category in the second subset being based on a ratio of respective category tuning minutes and the total number of household tuning minutes associated with the first set of households, and the exposure proportion for each category in the second subset based on a ratio of respective category exposure minutes and the total number of exposure minutes associated with the first set of households; and

calculating the panelist behavior probability for the combined first and second subset of categories based on the respective exposure proportions and the household tuning proportions.

2. A method as defined in claim 1, further including applying a temporal weight to the total number of household tuning minutes, the temporal weight having a greater bias associated with a first portion of the total number of household tuning minutes acquired more recently than a second portion of the total number of tuning minutes.

3. A method as defined in claim 2, further including applying a proportionally lower weight to the second portion of the total number of tuning minutes having a relatively older acquisition timestamp.

4. A method as defined in claim 1, further including applying a temporal weight to the total number of household exposure minutes, the temporal weight having a greater bias associated with a first portion of the total number of household exposure minutes acquired more recently than a second portion of the total number of exposure minutes.

5. A method as defined in claim 4, further including applying a proportionally lower weight to the second portion of the total number of exposure minutes having a relatively older acquisition timestamp.

6. A method as defined in claim 1, further including:

multiplying the household tuning proportions for each category together and multiplying by the total number of household tuning minutes to calculate expected household tuning minutes associated with the second subset of categories;

multiplying the exposure proportions for each category together to form a combined exposure proportion; and

multiplying the combined exposure proportion by the total number of exposure minutes to calculate expected exposure minutes associated with the second subset of categories.

7. A method as defined in claim 6, wherein a ratio of the expected exposure minutes and the expected household tuning minutes results in the panelist behavior probability for the target research geography.

8. A method as defined in claim 1, wherein the second subset of categories includes at least one of households tuned to a particular station, households associated with a particular education level, households with a particular number of television sets, households tuned to a station during a particular daypart, or households having a particular life stage.

9. An apparatus to calculate panelist behavior probability, comprising:

a categorizer to identify a target set of household categories associated with a target research geography;

a category manager to, when a quantity of panelist households within the target research geography representing the target set of household categories does not satisfy a threshold value sufficient to support statistical significance, increase an available sample size of households by generating a first subset of categories and a second subset of categories from the target set of household categories;

a proportion manager to;

identify a first set of households representing the first subset of categories from the target set of household categories;

identify a total number of household tuning minutes and a total number of household exposure minutes associated associated with the first set of households;

calculate a household tuning proportion and an exposure proportion for each category in the second subset of categories from the target set of household categories, the household tuning proportion based on a ratio of category tuning minutes and the total number of household tuning minutes associated with the first set of households, and the exposure proportion based on a ratio of category exposure minutes and the total number of exposure minutes associated with the first set of households; and

a distribution engine to calculate the panelist behavior probability for the combined first and second subset of categories based on the respective exposure proportions and the household tuning proportions.

10. An apparatus as defined in claim 9, further including a weighting engine to apply a temporal weight to the total number of household tuning minutes, the temporal weight having a greater bias associated with a first portion of the total number of household tuning minutes acquired more recently than a second portion of the total number of tuning minutes.

11. An apparatus as defined in claim 10, wherein the weighting engine is to apply a proportionally lower weight to the second portion of the total number of tuning minutes having a relatively older acquisition timestamp.

12. An apparatus as defined in claim 9, further including a weighting engine to apply a temporal weight to the total number of household exposure minutes, the temporal weight having a greater bias associated with a first portion of the total number of household exposure minutes acquired more recently than a second portion of the total number of exposure minutes.

13. An apparatus as defined in claim 12, wherein the weighting engine is to apply a proportionally lower weight to the second portion of the total number of exposure minutes having a relatively older acquisition timestamp.

14. An apparatus as defined in claim 9, wherein the distribution engine is to:

multiply the household tuning proportions for each category together and multiply by the total number of household tuning minutes to calculate expected household tuning minutes associated with the second subset of categories;

multiply the exposure proportions for each category together to form a combined exposure proportion; and

multiply the combined exposure proportion by the total number of exposure minutes to calculate expected exposure minutes associated with the second subset of categories.

15. An apparatus as defined in claim 14, wherein the distribution engine is to calculate a ratio of the expected exposure minutes and the expected household tuning minutes to identify the panelist behavior probability for the target research geography.

16. An apparatus as defined in claim 9, wherein the second subset of categories includes at least one of households tuned to a particular station, households associated with a particular education level, households with a particular number of television sets, households tuned to a station during a particular daypart, or households having a particular life stage.

17. A tangible machine readable storage medium comprising instructions that, when executed, cause a machine to at least:

identify a target set of household categories associated with a target research geography;

when a quantity of panelist households within the target research geography representing the target set of household categories does not satisfy a threshold value sufficient to support statistical significance;

increase an available sample size of households by generating a first subset of categories and a second subset of categories from the target set of household categories;

identify a first set of households representing the first subset of categories from the target set of household categories and identify an associated total number of household tuning minutes and a total number of household exposure minutes associated with the first set of households;

for each category in the second subset of categories from the target set of household categories, calculate a household tuning proportion and an exposure proportion, the household tuning proportion for each category in the second subset based on a ratio of respective category tuning minutes and the total number of household tuning minutes associated with the first set of households, and the exposure proportion for each category in the second subset being based on a ratio of respective category exposure minutes and the total number of household exposure minutes associated with the first set of households; and

calculate a panelist behavior probability for the combined first and second subset of categories based on the respective exposure proportions and the household tuning proportions.

18. A storage medium as defined in claim 17, wherein the instructions, when executed, further cause the machine to apply a temporal weight to the total number of household tuning minutes, the temporal weight having a greater bias associated with a first portion of the total number of household tuning minutes acquired more recently than a second portion of the total number of tuning minutes.

19. A storage medium as defined in claim 18, wherein the instructions, when executed, further cause the machine to apply a proportionally lower weight to the second portion of the total number of tuning minutes having a relatively older acquisition timestamp.

20. A storage medium as defined in claim 17, wherein the instructions, when executed, further cause the machine to apply a temporal weight to the total number of household exposure minutes, the temporal weight having a greater bias associated with a first portion of the total number of household exposure minutes acquired more recently than a second portion of the total number of exposure minutes.

21. A storage medium as defined in claim 20, wherein the instructions, when executed, further cause the machine to apply a proportionally lower weight to the second portion of the total number of exposure minutes having a relatively older acquisition timestamp.

22. A storage medium as defined in claim 17, wherein the instructions, when executed, further cause the machine to:

multiply the household tuning proportions for each category together and multiply by the total number of household tuning minutes to calculate expected household tuning minutes associated with the second subset of categories;

multiply the exposure proportions for each category together to form a combined exposure proportion; and

multiply the combined exposure proportion by the total number of exposure minutes to calculate expected exposure minutes associated with the second subset of categories.

23. A storage medium as defined in claim 22, wherein the instructions, when executed, further cause the machine to identify the panelist behavior probability for the target research geography based on a ratio of the expected exposure minutes and the expected household tuning minutes.