SYSTEM AND METHOD FOR DELIVERING AND OPTIMIZING MEDIA PROGRAMMING IN PUBLIC SPACES

• US 20110016483A1
• Filed: 09/23/2010
• Published: 01/20/2011
• Est. Priority Date: 08/06/2003
• Status: Abandoned Application

• Current Assignee: DS-IQ Incorporated
• Original Assignee: DS-IQ Incorporated
• Inventors: Opdycke, Thomas C.
• Application Number:
  US12/888,834
• Publication Number:
  US 20110016483A1
• Time in Patent Office: Days
• US Class Current: 725/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: H04H 60/06 Arrangements for scheduling broadcast services or broadcast-related services
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/26258 for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list retrieval of multimedia data based on playlists G06F16/40
• CPC Class Codes: H04N 21/41415 involving a public display, viewable by several users in a public space outside their home, e.g. movie theatre, information kiosk
• CPC Class Codes: H04N 21/42201 biosensors, e.g. heat sensor for presence detection, EEG sensors or any limb activity sensors worn by the user input arrangements for interaction with the human body based on nervous system activity detection G06F3/015
• CPC Class Codes: H04N 21/4223 Cameras H04N23/00 takes precedence
• CPC Class Codes: H04N 21/441 Acquiring end-user identification , e.g. using personal code sent by the remote control or by inserting a card
• CPC Class Codes: H04N 21/4415 using biometric characteristics of the user, e.g. by voice recognition or fingerprint scanning cryptography using biological data H04L9/3231; authentication in networks using biometric H04L63/0861
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

A system and corresponding methods for automating the execution, measurement, and optimization of in-store promotional digital media campaigns are provided. In one embodiment, a method in a computing system for deploying content to digital signage networks includes receiving from a user a marketing campaign goal and at least one optimization constraint suitable for generating a playlist. The method also includes generating a playlist designed to maximize a learning opportunity to achieve the marketing campaign goal. The method further includes provisioning the playlist to a point of presence on the digital signage network.

Claims

1. A computer-readable medium whose contents cause a computing system to deploy content to digital signage networks by:

receiving from a user a marketing object, the marketing object comprised of a goal and at least one independent variable suitable for generating a testing plan;

generating two or more playlists and an association between the two or more playlists and one or more displays in a digital signage network, wherein the two or more playlists and the association between the two or more playlists and the one or more displays comprises the testing plan, the testing plan designed to assess the effect of the at least one independent variable on reaching the goal; and

provisioning the two or more playlist˜

to the digital signage network.

2. The computer-readable medium of claim 1, wherein the at least one independent variable comprises one or more content parts and a template specification, such that the template specification is used for rendering multiple variations of a plurality of play ready clips generated from the content parts.

3. A method in a computing system for generating playlists of marketing content to present on media devices in physical stores and achieve a marketing campaign goal, the computer-implemented method comprising:

receiving a marketing campaign goal, the marketing campaign goal associated with one or more products and pieces of marketing content related to the one or more products;

receiving an indication of one or more independent variables selected from the group consisting of temporal characteristics, locale characteristics, and demographic characteristics;

generating a testing matrix comprising;

a plurality of playlists, each of the playlists specifying one or more pieces of marketing content related to the one or more products to be presented on a media device, the plurality of playlists differing from one another by one or more variations of value of the indicated independent variables, wherein the testing matrix enables a measurement of an independent variable'"'"'s effect on reaching the marketing campaign goal; and

an association between a subset of the plurality of playlists and one or more media devices in a digital signage network; and

provisioning the plurality of playlists in accordance with the testing matrix to the digital signage network, each playlist causing the marketing content associated with that playlist to be presented to an audience on one or more media devices;

wherein one or more computer processors execute the steps above to implement the method.

4. The computer-implemented method of claim 3, further comprising:

receiving data characterizing behavior of the audience in locations where the playlists have been executed;

analyzing the received audience behavior data to identify those values of the independent variables that were beneficial to reaching the marketing goal; and

utilizing the identified playlists values of the independent variables that were beneficial to reaching the marketing goal to modify the testing matrix and create one or more improved playlists to achieve the marketing goal when presented to an audience.

5. The computer-implemented method of claim 4, wherein stochastic optimization algorithms are utilized to create the one or more improved playlists.

6. The computer-implemented method of claim 4, wherein the data characterizing audience behavior is selected from the group consisting of sales data, inventory tracking data, foot traffic data, and data characterizing interaction with a device.

7. The computer-implemented method of claim 4, further comprising:

receiving data characterizing the marketing content that was presented on the one or more media devices; and

using the received data characterizing the presented marketing content in conjunction with the analysis of the received audience behavior data to identify those independent variables that were beneficial to reaching the marketing goal.

8. The computer-implemented method of claim 3, wherein the marketing goal comprises revenue, volume, units associated with the one or more products or audience traffic.

9. The computer-implemented method of claim 3, wherein a temporal characteristic is selected from the group consisting of date, daypart, time, and repeat play characteristics.

10. The computer-implemented method of claim 3, wherein a locale characteristic is selected from the group consisting of store site, group of store sites, channel, retailer, network nodes, and network.

11. The computer-implemented method of claim 3, wherein a demographic characteristic is selected from the group consisting of income level, education level, and cluster of audience grouped based on similar behavior pattern and mapped to geography or location.

12. The computer-implemented method of claim 3, further comprising determining an initial number of trials that a playlist should be repeated on a corresponding media device.

13. The computer-implemented method of claim 3, wherein the media devices are displays.

14. The computer-implemented method of claim 3, wherein a conditional rule is specified which alters the playlists when the conditional rule is satisfied.

15. The computer-implemented method of claim 3, wherein the marketing campaign goal comprises a scope of a product or a service, and a metric to measure.

16. The computer-implemented method of claim 15, wherein the scope is selected from the group consisting of a category, a brand, a line, and a stock keeping unit.

17. The computer-implemented method of claim 15, wherein the metric is selected from the group consisting of revenue, volume, and units.

18. The computer-implemented method of claim 3, wherein the marketing campaign goal comprises a scope of an audience member action, and a metric to measure.

19. The computer-implemented method of claim 18, wherein the metric is selected from the group consisting of a location of an audience member, an audience member interaction with a display device, and an audience member interaction with a data gathering system.

20. The computer-implemented method of claim 3, further comprising receiving a conditional rule, the conditional rule causing a modification to the testing matrix when the conditional rule is satisfied.

21. The computer-implemented method of claim 20, wherein the conditional rule is linked to an event that is exogenous to the digital signage network.

22. The computer-implemented method of claim 20, wherein the modification to the testing matrix is selected from the group consisting of:

a selection of a display that is to receive content, a selection of content that is to be delivered to a display, and a selection of a playlist for provisioning to a display.

23. The computer-implemented method of claim 21, wherein the event is selected from the group consisting of:

a content of a shopping cart, an identification of an individual, an identification of an audience, an inventory level, and a weather condition.

24. The computer-implemented method of claim 3, further comprising:

receiving a constraint applicable to the testing matrix; and

modifying the testing matrix in accordance with the received constraint.

25. The computer-implemented method of claim 24, wherein the received constraint is selected from the group consisting of a temporal constraint, a locale constraint, and a demographic constraint.

26. The computer-implemented method of claim 24, wherein the received constraint is a repetition constraint on content.

27. The computer-implemented method of claim 24, wherein the received constraint pertains to an independent variable.

28. The computer-implemented method of claim 24, wherein the received constraint is based on an analysis of behavioral response data from a prior testing matrix associated with a different marketing campaign goal.

29. The computer-implemented method of claim 24, wherein the received constraint is based on an analysis of behavioral response data from playlists associated with a different marketing campaign goal.

30. The computer-implemented method of claim 3, further comprising generating an interface to allow a user to review an aspect of the testing matrix prior to provisioning the plurality of playlists in accordance with the testing matrix.

31. The computer-implemented method of claim 3, wherein a playlist comprises one or more pointers to content.