Methods and apparatus to determine when a smart device is out-of-tab
Methods, apparatus, systems and articles of manufacture to determine whether a smart device is in-tab are disclosed. An example apparatus includes a labelled data generator to identify a first smart television that corresponds to a first panelist by comparing first data from the first smart television to second data from the first panelist; and generate training data by labelling the first smart television as in-tab or out-of-tab based on the second data from the first panelist; and a model trainer to train a model using the training data, the model trainer to train the model to determine a tab status for a second smart television.
1. An apparatus comprising:
a labelled data generator to:
identify a first smart television that corresponds to a first panelist by comparing first data from the first smart television to second data from the first panelist; and
generate training data by labelling the first smart television as in-tab or out-of-tab based on the second data from the first panelist; and
a model trainer to train a model using the training data, the model trainer to train the model to determine a tab status for a second smart television.
2. The apparatus of claim 1 , wherein the first data includes at least one of a media identifier, a smart television identifier, a timestamp, tuning data, or disconnect data.
3. The apparatus of claim 1 , wherein the second data is meter data indicative of media exposure of the first panelist.
4. The apparatus of claim 1 , wherein the labelled data generator is to identify that the first smart television corresponds to the first panelist when a threshold amount of tuning data of the first data is consistent with the second data.
5. The apparatus of claim 1 , wherein the labelled data generator is to label the first smart television as out-of-tab when tuning data of the first data is inconsistent with meter data of the second data.
6. The apparatus of claim 1 , further including a model implementor to implement the model to determine the tab status for the second smart television based on third data from the first smart television.
7. The apparatus of claim 6 , wherein the tab status is at least one of in-tab or out-of-tab.
8. The apparatus of claim 6 , wherein the third data is part of fourth data from a population of smart televisions, the second smart television included in the population.
9. The apparatus of claim 8 , further including a report generator to:
in response to the model determining that the second smart television is out-of-tab, remove the third data from the fourth data; and
credit media based on the fourth data.
10. A non-transitory computer readable storage medium comprising instructions which, when executed cause one or more processors to at least:
identify a first smart television that corresponds to a first panelist by comparing first data from the first smart television to second data from the first panelist;
generate training data by labelling the first smart television as in-tab or out-of-tab based on the second data from the first panelist; and
train a model using the training data to determine a tab status for a second smart television.
11. The computer readable storage medium of claim 10 , wherein the first data includes at least one of a media identifier, a smart television identifier, a timestamp, tuning data, or disconnect data.
12. The computer readable storage medium of claim 10 , wherein the second data is meter data indicative of media exposure of the first panelist.
13. The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to identify that the first smart television corresponds to the first panelist when a threshold amount of tuning data of the first data is consistent with the second data.
14. The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to label the first smart television as out-of-tab when tuning data of the first data is inconsistent with meter data of the second data.
15. The computer readable storage medium of claim 10 , wherein the instructions cause the one or more processors to implement the model to determine the tab status for the second smart television based on third data from the first smart television.
16. The computer readable storage medium of claim 15 , wherein the tab status is at least one of in-tab or out-of-tab.
17. The computer readable storage medium of claim 15 , wherein the third data is part of fourth data from a population of smart televisions, the second smart television included in the population.
18. The computer readable storage medium of claim 17 , wherein the instructions cause the one or more processors to:
in response to the model determining that the second smart television is out-of-tab, remove the third data from the fourth data; and
credit media based on the fourth data.
19. A method to determine a status of a smart television, the method comprising:
identifying, by executing an instruction with a processor, a first smart television that corresponds to a first panelist by comparing first data from the first smart television to second data from the first panelist;
generating, by executing an instruction with the processor, training data by labelling the first smart television as in-tab or out-of-tab based on the second data from the first panelist; and
training, by executing an instruction with the processor, a model using the training data to determine a tab status for a second smart television.
20. The method of claim 19 , wherein the first data includes at least one of a media identifier, a smart television identifier, a timestamp, tuning data, or disconnect data.