Method and apparatus for managing advertising

• US 8,782,683 B2
• Filed: 10/12/2012
• Issued: 07/15/2014
• Est. Priority Date: 10/12/2012
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Balakrishnan, Suhrid, Applegate, David Lee, Chopra, Sumit, Urbanek, Simon
• Primary Examiner(s): MENGESHA, MULUGETA A
• Application Number:
  US13/650,470
• Publication Number:
  US 20140109123A1
• Time in Patent Office: 641 Days
• Field of Search: 725/14, 725 34- 36, 725/100, 725/131, 725/139, 725/151
• US Class Current: 725/14
• CPC Class Codes: G06Q 30/0244 Optimization
• 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 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25435 involving characteristics of content or additional data, e.g. video resolution or the amount of advertising
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/433 Content storage operation, e.g. storage operation in response to a pause request, caching operations
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

A system that incorporates teachings of the subject disclosure may include, for example, determining identified impressions that are detected from consumption data collected from a group of media processors where the identified impressions represent viewing of selected content and where the consumption data indicates channel tuning events at the group of media processors including changing of channels, applying a ridge regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot, and generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot. Other embodiments are disclosed.

Claims

1. A device, comprising:

a memory to store computer instructions; and

a processor coupled to the memory, wherein the processor, responsive to executing the computer instructions, performs operations comprising;

obtaining consumption data from a group of set top boxes, wherein the consumption data indicates channel tuning events at one or more set top boxes of the group of set top boxes including changing of channels presenting broadcast content;

detecting identified impressions from the consumption data, wherein the identified impressions represent display of selected content;

determining campaign criteria associated with an advertising campaign;

responsive to the campaign criteria including a cost-per-impression criteria, applying a regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot; and

generating a first media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot.

2. The device of claim 1, wherein the regression analysis includes a ridge regression analysis, wherein the identified impressions are detected from the consumption data by determining whether a set top box of the group of set top boxes was tuned to a channel for more than a first threshold time period and less than a second threshold time period.

3. The device of claim 1, wherein the processor, responsive to executing the computer instructions, performs operations comprising:

responsive to the campaign criteria including a reach criteria, determining predicted user-based slot impressions based on the identified impressions; and

generating a second media plan based on the advertisement slot cost and the predicted user-based slot impressions.

4. The device of claim 3, wherein the generating of the second media plan is based on applying a greedy heuristic to the identified impressions.

5. The device of claim 1, wherein the regression analysis comprises a weighted ridge regression analysis using weighting factors applied to the identified impressions detected from the consumption data, wherein the weighting factors are determined based on a regularization parameter and an identity matrix.

6. The device of claim 1, wherein the first media plan is generated for a plurality of time periods, and wherein the plurality of time periods utilizes different prediction models that are based on applying of the regression analysis to the identified impressions.

7. The device of claim 6, wherein each of the different prediction models utilizes a common matrix based on the identified impressions detected from the consumption data.

8. The device of claim 1, wherein the consumption data includes timestamp data for each of the channel tuning events, and wherein the consumption data is collected by at least a subset of the group of set top boxes.

9. The device of claim 1, wherein the channel tuning events include changing of volume by at least a subset of the group of set top boxes.

10. A method, comprising:

obtaining, by a system including a processor, consumption data from a group of media processors, wherein the consumption data indicates channel tuning events at one or more media processors of the group of media processors including changing of channels;

detecting, by the system, identified impressions from the consumption data, wherein the identified impressions represent display of selected content;

determining, by the system, campaign criteria associated with an advertising campaign;

responsive to the campaign criteria including a reach criteria, determining, by the system, predicted user-based slot impressions based on the identified impressions; and

generating, by the system, a first media plan based on an advertisement slot cost and the predicted user-based slot impressions, wherein the generating of the first media plan includes applying a greedy heuristic to the identified impressions.

11. The method of claim 10, comprising:

applying a regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot; and

generating a second media plan based on a ratio of the advertisement slot cost to the predicted number of target impressions per advertisement slot.

12. The method of claim 11, wherein the regression analysis comprises a weighted ridge regression analysis using weighting factors applied to the identified impressions detected from the consumption data, wherein the weighting factors are determined based on a regularization parameter and an identity matrix.

13. The method of claim 11, wherein the second media plan is generated for a plurality of time periods, and wherein the plurality of time periods utilize different prediction models that are based on the applying of the regression analysis to the identified impressions.

14. The method of claim 10, wherein the identified impressions are detected from the consumption data by determining whether a media processor of the group of media processors was tuned to a channel for more than a first threshold time period and less than a second threshold time period.

15. The method of claim 10, wherein the consumption data includes timestamp data for each of the channel tuning events, and wherein the consumption data is collected by at least a subset of the group of media processors.

16. A non-transitory computer-readable storage medium, comprising executable instructions which, responsive to being executed by a processor, cause the processor to perform operations comprising:

determining identified impressions that are detected from consumption data collected from a group of media processors, wherein the identified impressions represent display of selected content, and wherein the consumption data indicates channel tuning events at the group of media processors including changing of channels;

applying a regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot; and

generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot.

17. The non-transitory computer-readable storage medium of claim 16, wherein the operations further comprise:

determining predicted user-based slot impressions based on the identified impressions; and

adjusting the media plan to generate an adjusted media plan based on the advertisement slot cost and the predicted user-based slot impressions, wherein the adjusting of the media plan includes applying a greedy heuristic to the identified impressions.

18. The non-transitory computer-readable storage medium of claim 16, wherein the consumption data includes timestamp data for each of the channel tuning events.

19. The non-transitory computer-readable storage medium of claim 16, wherein the determining of the identified impressions includes determining whether a media processor of the group of media processors was tuned to a channel for more than a first threshold time period and less than a second threshold time period.

20. The non-transitory computer-readable storage medium of claim 16, wherein the regression analysis comprises a weighted ridge regression analysis using weighting factors applied to the identified impressions detected from the consumption data, wherein the weighting factors are determined based on a regularization parameter and an identity matrix, wherein the media plan is generated for a plurality of time periods, and wherein the plurality of time periods utilize different prediction models that include the applying of the ridge regression analysis to the identified impressions.