Methods, apparatus, and articles of manufacture to determine penetration and churn of streaming services
Methods, apparatus, systems, and articles of manufacture are disclosed that determine a penetration and a churn of a streaming service. Example apparatus disclosed herein includes processor circuitry to instantiate at least interval determination circuitry to determine an interval between the first tuning event and the second tuning event, the first and second tuning events corresponding to streaming of media in a first household using the streaming service via a media device, characterization circuitry to characterize a status of the first household as subscribed when the interval satisfies a threshold, aggregation circuitry to aggregate statuses of households over a time period, the households including the first household, and penetration determination circuitry to generate first analytical data based on a number of subscribed statuses per a total number of households at a timestamp, the timestamp within the time period.
1. A computing system for determining a penetration of a streaming service and a churn of the streaming service, the computing system comprising:
a network interface configured to obtain a first tuning event and a second tuning event; and
one or more processors configured to perform operations comprising:
determining an interval between the first tuning event and the second tuning event, the first and second tuning events corresponding to streaming of media in a first household using the streaming service via a media device;
characterizing a status of the first household as subscribed when the interval satisfies a threshold;
aggregating statuses of households over a time period, the households including the first household;
generating first analytical data based on a number of subscribed statuses per a total number of the households at a timestamp, the timestamp within the time period; and
generating second analytical data based on a number of canceled subscriptions per a time frame, the time frame within the time period, the number of canceled subscriptions corresponding to a number of households with at least one subscribed status and at least one unsubscribed status during the time frame.
2. The computing system of claim 1 , wherein the first analytical data corresponds to the penetration of the streaming service, and the second analytical data corresponds to the churn of the streaming service.
3. The computing system of claim 1 , the operations further comprising:
determining penetration data points; and
combining the penetration data points to represent the penetration of the streaming service.
4. The computing system of claim 3 , wherein determining the penetration data points comprises determining the penetration points based on a ratio of the number of subscribed statuses at a time indicated by the timestamp to the total number of households.
5. The computing system of claim 1 , the operations further comprising:
determining churn data points; and
combining the churn data points to represent the churn of the streaming service.
6. The computing system of claim 5 , wherein determining the churn data points comprises determining the churn data points based on a ratio of the number of canceled subscriptions to the time frame.
7. The computing system of claim 1 , the operations further comprising:
generating the first tuning event and the second tuning event based on tuning data collected from a streaming meter in the first household, wherein the streaming meter is configured to measure network traffic corresponding to the media device and the streaming service.
8. The computing system of claim 1 , wherein the first tuning event includes a first start timestamp, a first end timestamp, a uniform resource locator of the streaming service, and a media device identifier, and wherein the second tuning event includes a second start timestamp, a second end timestamp, the uniform resource locator of the streaming service, and the media device identifier.
9. The computing system of claim 8 , wherein the interval is a length of time between the first end timestamp and the second start timestamp.
10. A non-transitory machine readable storage medium comprising instructions that, when executed, cause a processor to perform operations comprising:
computing an interval between a first tuning event and a second tuning event, the first and second tuning events corresponding to streaming of media in a first household using a streaming service via a media device;
indicating a status of the first household as subscribed when the interval satisfies a threshold;
gathering statuses of households over a time period, the households including the first household;
calculating first analytical data based on a number of subscribed statuses per a total number of households at a timestamp, the timestamp within the time period; and
calculating second analytical data based on a number of canceled subscriptions per a time frame, the time frame within the time period, the number of canceled subscriptions corresponding to a number of households with at least one subscribed status and at least one unsubscribed status during the time frame.
11. The non-transitory machine readable storage medium of claim 10 , wherein the first analytical data corresponds to a penetration of the streaming service, and the second analytical data corresponds to a churn of the streaming service.
12. The non-transitory machine readable storage medium of claim 11 , the operations further comprising:
generating penetration data points; and
combining the penetration data points to represent the penetration of the streaming service.
13. The non-transitory machine readable storage medium of claim 12 , wherein generating the penetration data points comprises generating the penetration data based on a ratio of the number of subscribed statuses at a time indicated by the timestamp to the total number of households.
14. The non-transitory machine readable storage medium of claim 11 , the operations further comprising:
generating churn data points; and
combining the churn data points to represent the churn of the streaming service.
15. The non-transitory machine readable storage medium of claim 14 , wherein generating the churn data points comprises generating the churn data points based on a ratio of the number of canceled subscriptions to the time frame.
16. A method comprising:
determining an interval between a first tuning event and a second tuning event, the first and second tuning events corresponding to streaming of media in a first household using a streaming service via a media device;
characterizing a status of the first household as subscribed when the interval satisfies a threshold;
aggregating statuses of households over a time period, the households including the first household;
generating first analytical data based on a number of subscribed statuses per a total number of households at a timestamp, the timestamp within the time period; and
generating second analytical data based on a number of canceled subscriptions per a time frame, the time frame within the time period, the number of canceled subscriptions corresponding to a number of households with at least one subscribed status and at least one unsubscribed status during the time frame.
17. The method of claim 16 , wherein the first analytical data corresponds to a penetration of the streaming service, and the second analytical data corresponds to a churn of the streaming service.
18. The method of claim 16 , further comprising:
determining penetration data points; and
combining the penetration data points to represent the penetration of the streaming service.
19. The method of claim 18 , wherein determining the penetration data points comprises determining the penetration points based on a ratio of the number of subscribed statuses at a time indicated by the timestamp to the total number of households.
20. The method of claim 16 , further comprising:
determining churn data points; and
combining the churn data points to represent the churn of the streaming service.
21. The method of claim 20 , wherein determining the churn data points comprises determining the churn data points based on a ratio of the number of canceled subscriptions to the time frame.
22. The method of claim 16 , further comprising:
generating the first tuning event and the second tuning event based on tuning data collected from a streaming meter in the first household, wherein the streaming meter is configured to measure network traffic corresponding to the media device and the streaming service.
23. The method of claim 16 , wherein the first tuning event includes a first start timestamp, a first end timestamp, a uniform resource locator of the streaming service, and a media device identifier, and wherein the second tuning event includes a second start timestamp, a second end timestamp, the uniform resource locator of the streaming service, and the media device identifier.
24. The method of claim 23 , wherein the interval is a length of time between the first end timestamp and the second start timestamp.