Methods, apparatus, and systems to collect audience measurement data
Methods, apparatus, and systems to collect audience measurement data are disclosed. Disclosed example systems include a first processor to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location. Disclosed example systems also include a second processor to (i) develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location and (ii) identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.
1. An audience measurement system comprising:
a first processor to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location; and
a second processor to:
develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period; and
identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.
2. The audience measurement system of claim 1 , further including a people meter to collect the people meter data during the first time period.
3. The audience measurement system of claim 2 , wherein the people meter is to prompt people to self-identify.
4. The audience measurement system of claim 1 , wherein the first processor is to collect network usage data corresponding to the second time period and associated with the monitored location, and the second processor is to identify an audience member associated with network usage during the second time period at the monitored location based on the behavior data and without access to any people meter data corresponding to the second time period.
5. The audience measurement system of claim 4 , wherein the network usage data is second network usage data, the first processor is to collect first network usage data corresponding to the first time period and associated with the monitored location, and the second processor to develop the behavior data based on first network usage data.
6. The audience measurement system of claim 1 , wherein the second processor is to identify the audience member associated with the second media identification data based on the behavior data by:
matching characteristics of the second media identification data to a corresponding portion of the behavior data corresponding to the audience member, the characteristics including at least one of a day, a time, a program title, a program genre, or a broadcast channel;
calculating a probability based on the characteristics, the probability corresponding to a likelihood that the audience member is associated with the second media identification data; and
determining whether the probability satisfies a threshold.
7. A method to collect audience measurement data comprising:
collecting first media identification data corresponding to a first time period;
collecting second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location; an
developing, by executing an instruction with a processor, behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period; and
identifying, by executing an instruction with the processor, an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.
8. The method of claim 7 , further including collecting the people meter data during the first time period with a people meter.
9. The method of claim 8 , wherein the people meter is to prompt people to self-identify.
10. The method of claim 7 , further including:
collecting network usage data corresponding to the second time period and associated with the monitored location; and
identifying an audience member associated with network usage during the second time period at the monitored location based on the behavior data and without access to any people meter data corresponding to the second time period.
11. The method of claim 10 , wherein the network usage data is second network usage data, and further including:
collecting first network usage data corresponding to the first time period and associated with the monitored location; and
developing the behavior data based on first network usage data.
12. The method of claim 7 , wherein the identifying of the audience member associated with the second media identification data based on the behavior data includes:
matching characteristics of the second media identification data to a corresponding portion of the behavior data corresponding to the audience member, the characteristics including at least one of a day, a time, a program title, a program genre, or a broadcast channel;
calculating a probability based on the characteristics, the probability corresponding to a likelihood that the audience member is associated with the second media identification data; and
determining whether the probability satisfies a threshold.
13. An article of manufacture comprising computer readable instructions that, when executed, cause one or more processors to at least:
collect first media identification data corresponding to a first time period;
collect second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location; and
develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period; and
identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.
14. The article of manufacture of claim 13 , wherein the people meter data is collected during the first time period with a people meter.
15. The article of manufacture of claim 13 , wherein the instructions, when executed, cause the one or more processors to:
collect network usage data corresponding to the second time period and associated with the monitored location; and
identify an audience member associated with network usage during the second time period at the monitored location based on the behavior data and without access to any people meter data corresponding to the second time period.
16. The article of manufacture of claim 15 , wherein the network usage data is second network usage data, and the instructions, when executed, further cause the one or more processors to:
collect first network usage data corresponding to the first time period and associated with the monitored location; and
develop the behavior data based on first network usage data.
17. The article of manufacture of claim 13 , wherein the instructions, when executed, cause the one or more processors to:
match characteristics of the second media identification data to a corresponding portion of the behavior data corresponding to the audience member, the characteristics including at least one of a day, a time, a program title, a program genre, or a broadcast channel;
calculate a probability based on the characteristics, the probability corresponding to a likelihood that the audience member is associated with the second media identification data; and
determine whether the probability satisfies a threshold.