System and method for tracking advertiser return on investment using set top box data

1. A method comprising:

associating a plurality of electronic devices with a plurality of viewers;

enriching electronic content device data for the plurality of electronic devices with consumer enrichment data, the electronic content device data identifying a plurality of purchasers;

setting a time window associated with a portion of each electronic content instance among a plurality of electronic content instances on which a message was presented based on a predetermined time window size, the time window size being greater than a duration of the message;

determining whether an individual viewer of the plurality of viewers viewed the electronic content instance for at least a threshold percentage of the time window;

upon determining that the individual viewer viewed the electronic content instance for at least the threshold percentage of the time window, identifying a single message exposure event for the individual viewer, wherein the message exposure event comprises a truncated time stamp for a time period that the message was presented and viewed by the individual viewer;

calculating, for each viewer of the plurality of viewers, message weight as a function of a number of exposures of the message as applied to the viewer based on the message exposure event;

calculating, for each viewer, a score representing a degree-of-targetedness between a target for the message and the viewer;

generating a multi-dimensional model of message effectiveness that models combined effects of the message weight and a degree of targetedness based on a combination of message weights and degrees of targetedness for the plurality of viewers; and

applying the multi-dimensional model to the electronic content device data and viewer activity data to track viewer activity attributable to the message.

2. The method of claim 1 , wherein associating the plurality of electronic content devices with the plurality of viewers comprises:

associating each electronic content device of the plurality of electronic content devices with a household of a plurality of households based on billing or subscription records of a person who is paying for a television service provided by one of the plurality of electronic content devices; and

associating each household of the plurality of households with additional persons in the household based on third party data about the additional persons residing within the household.

3. The method of claim 1 , wherein the degree of targetedness is further calculated by measuring a probability that an individual exposed to the message perform an activity, and wherein determining the degree of targetedness as applied to an individual viewer comprises:

identifying programs viewed by the individual viewer;

determining a first quantity of individual viewers that viewed the programs;

determining a second quantity of the individual viewers that also; and

dividing the second quantity by the first quantity.

4. The method of claim 1 , further comprising:

managing messaging based on the message weight and the degree-of-targetedness.

5. The method of claim 1 , wherein the score representing the degree-of-targetedness is a plurality of values including a direct targeting metric and a demographic targeting metric, and the demographic targeting metric is based on a probability of viewer activity from a media instance on which the message was presented.

6. A tangible, non-transitory computer readable storage medium storing instructions that, when executed by a computing system, causes the computing system to perform a method including:

associating a plurality of electronic devices with a plurality of viewers;

enriching electronic content device data for the plurality of electronic devices with consumer enrichment data;

setting a time window associated with a portion of each electronic content instance among a plurality of electronic content instances on which a message was presented based on a predetermined time window size, the time window size being greater than a duration of the message;

determining whether an individual viewer of the plurality of viewers viewed the electronic content instance for at least a threshold percentage of the time window;

upon determining that the individual viewer viewed the electronic content instance for at least the threshold percentage of the time window, identifying a single message exposure event for the individual viewer, wherein the message exposure event comprises a truncated time stamp for a time period that the message was presented and viewed by the individual viewer;

calculating, for each viewer of the plurality of viewers, message weight as a function of a number of exposures of the message as applied to the viewer based on the message exposure event;

calculating, for each viewer, a score representing a degree-of-targetedness between a target for the message and the viewer;

generating a multi-dimensional model of message effectiveness that models combined effects of the message weight and a degree of targetedness based on a combination of message weights and degrees of targetedness for the plurality of viewers; and

applying the multi-dimensional model to the electronic content device data and viewer activity data to track viewer activity attributable to the message.

7. The tangible, non-transitory computer readable storage medium of claim 6 , wherein associating the plurality of electronic content devices with the plurality of viewers comprises:

associating each electronic content device of the plurality of electronic content devices with a household of a plurality of households based on billing or subscription records of a person who is paying for a television service provided by one of the plurality of electronic content devices; and

and associating each household of the plurality of households with additional persons in the household based on third party data about the additional persons residing within the household.

8. The tangible, non-transitory computer readable storage medium of claim 6 , the operations further comprising:

using the multi-dimensional model to predict a number of viewer actions for a particular message weight applied to a program having a particular degree of targetedness.

9. The tangible, non-transitory computer readable storage medium of claim 6 , wherein determining a message weight as applied to an individual viewer comprises:

receiving programming guide data;

analyzing the electronic content device data and combining with the programming guide data to determine programs watched by the individual viewer over a time period;

calculating the message exposure events for the individual viewer by further combining messaging data to the combined viewing data and programming data; and

aggregating the message exposure events for the individual viewer.

10. The tangible, non-transitory computer readable storage medium of claim 6 , wherein the degree of targetedness is further calculated at least in part by measuring a probability that an individual exposed to the message perform an activity, and wherein determining the degree of targetedness as applied to an individual viewer comprises:

identifying programs viewed by the individual viewer;

determining a first quantity of individual viewers that viewed the programs;

determining a second quantity of the individual viewers that performed the activity; and

dividing the second quantity by the first quantity.

11. The tangible, non-transitory computer readable storage medium of claim 6 , the operations further comprising:

managing messaging based on the message weight and the degree-of- targetedness.

12. A system comprising:

a data storage device storing instructions in an electronic storage medium; and

a processor configured to execute the instructions to perform a method including:

associating a plurality of electronic devices with a plurality of viewers;

enriching electronic content device data for the plurality of electronic devices with consumer enrichment data;

setting a time window associated with a portion of each electronic content instance among a plurality of electronic content instances on which a message was presented based on a predetermined time window size, the time window size being greater than a duration of the message;

determining whether an individual viewer of the plurality of viewers viewed the electronic content instance for at least a threshold percentage of the time window;

upon determining that the individual viewer viewed the electronic content instance for at least the threshold percentage of the time window, identifying a single message exposure event for the individual viewer, wherein the message exposure event comprises a truncated time stamp for a time period that the message was presented and viewed by the individual viewer;

calculating, for each viewer of the plurality of viewers, message weight as a function of a number of exposures of the message as applied to the viewer based on the message exposure event;

calculating, for each viewer, a score representing a degree-of-targetedness between a target for the message and the viewer;

generating a multi-dimensional model of message effectiveness that models combined effects of the message weight and a degree of targetedness based on a combination of message weights and degrees of targetedness for the plurality of viewers; and applying the multi-dimensional model to the electronic content device data and viewer activity data to track viewer activity attributable to the message.

13. The system of claim 12 , wherein the system is further configured to:

manage messaging based on the message weight and the degree-of-targetedness.

14. The system of claim 12 , wherein the score representing the degree-of-targetedness is a plurality of values including a direct targeting metric and a demographic targeting metric, and the demographic targeting metric is based on a probability of viewer activity from a media instance on which the message was presented.