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

• US 20110016011A1
• 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:
  US12/888,768
• Publication Number:
  US 20110016011A1
• Time in Patent Office: Days
• US Class Current: 705/26.1
• 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 method in a computing system for deploying content to digital signage networks comprising:

receiving from a user a marketing campaign goal;

receiving from a user at least one independent variable which is to be tested to determine the effect of varying the value of the at least one independent variable on reaching the marketing campaign goal;

generating with one more computer processors a plurality of playlists in a testing matrix designed to maximize a learning opportunity to achieve the marketing campaign goal, wherein the testing matrix systematically varies the value of the at least one independent variable in the plurality of playlists in order to assess the effect of the at least one independent variable on reaching the marketing campaign goal; and

provisioning the plurality of playlists to a digital signage network.

2. The method of claim 1, wherein the independent variable comprises a single piece of content.

3. The method of claim 2, wherein varying the value of the single piece of content comprises presenting the single piece of content in a playlist or not presenting the single piece of content in a playlist.

4. The method of claim 1, wherein the independent variable comprises two or more pieces of content.

5. The method of claim 4, wherein varying the value of the two or more pieces of content comprises presenting a first selection of the two or more pieces of content in a first playlist and a second selection of the two or more pieces of content in a second playlist, wherein the first selection is different from the second selection.

6. The method of claim 4, wherein the two or more pieces of content are generated from one or more content parts and a template specifying how the content parts should be assembled into the two or more pieces of content.

7. The method of claim 1, wherein the independent variable is a temporal variable.

8. The method of claim 1, wherein the independent variable is a location variable.

9. The method of claim 1, wherein the independent variable is a demographic variable.

10. The method of claim 1, wherein the marketing campaign goal and indication of at least one independent variable are received from the user as part of a marketing object.

11. The method of claim 1 wherein a playlist comprises one or more pointers to content.

12. The method of claim 1, further comprising:

receiving a constraint applicable to the testing matrix; and

modifying the testing matrix in accordance with the received constraint.

13. The method of claim 12, wherein the received constraint is selected from the group consisting of a temporal constraint, a locale constraint, and a demographic constraint.

14. The method of claim 12, wherein the received constraint is a repetition constraint on content.

15. The method of claim 12, wherein the received constraint pertains to an independent variable.

16. The method of claim 12, 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.

17. The method of claim 12, wherein the received constraint is based on an analysis of behavioral response data from playlists associated with a different marketing campaign goal.

18. The method of claim 1, further comprising generating an interface to allow a user to review an aspect of the testing matrix prior to provisioning the plurality of playlists to the digital signage network.

19. The method of claim 1, wherein the marketing campaign goal comprises:

a scope of a product or a service; and

a metric to measure.

20. The method of claim 19, wherein the scope is selected from the group consisting of a category, a brand, a line, and a stock keeping unit.

21. The method of claim 19, wherein the metric is selected from the group consisting of revenue, volume, and units.

22. The method of claim 1, wherein the marketing campaign goal comprises:

a scope of an audience member action; and

a metric to measure.

23. The method of claim 22, 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.

24. The method of claim 1, further comprising receiving a conditional rule from the user, the conditional rule causing a modification to the testing matrix when the conditional rule is satisfied.

25. The method of claim 24, wherein the conditional rule is linked to an event that is exogenous to the digital signage network.

26. The method of claim 25, 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.

27. The method of claim 24, 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.

28. A method in a computing system of creating playlists of marketing content to present on display devices and maximize sales of one or more items, the computer-implemented method comprising:

receiving a specification of one or more items and one or more pieces of marketing content associated with the one or more items;

generating a plurality of playlists, each of the plurality of playlists being comprised of a plurality of content pieces and information to cause the plurality of content pieces to be presented on a display device, the plurality of playlists containing one or more pieces of marketing content associated with the one or more items and differing from one another in a manner that allows shopper response to be measured to the marketing content and an analysis to be performed to generate improved playlists to maximize sales of the one or more items;

generating a testing plan which specifies the distribution of the plurality of playlists within a digital signage network comprised of a plurality of display devices that are located in physical stores; and

provisioning the plurality of playlists in accordance with the testing plan to the digital signage network, each playlist causing the corresponding plurality of content pieces associated with that playlist to be presented to shoppers on the corresponding display device;

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

29. The computer-implemented method of claim 28, further comprising:

receiving data characterizing shopper behavior in locations where the playlists are provisioned;

analyzing the received shopper behavior data to identify those of the plurality of playlists that resulted in greater sales of the one or more items; and

utilizing elements of the identified playlists that were more successful in generating sales to create an improved playlist for the testing plan that is more likely to generate sales of items when presented to shoppers.

30. The computer-implemented method of claim 29, wherein stochastic optimization algorithms are utilized to create the improved playlist.

31. The computer-implemented method of claim 29, wherein the data characterizing shopper behavior is selected from the group consisting of sales data, inventory tracking data, foot traffic data, and data characterizing interaction with the device.

32. The computer-implemented method of claim 29, further comprising:

receiving data characterizing the content pieces that were presented on the plurality of display devices; and

using the received data characterizing the presented content pieces in conjunction with the analysis of the received audience behavior data to identify those of the plurality of playlists that were more effective in maximizing sales of one or more items.

33. The computer-implemented method of claim 29, further comprising:

generating an additional playlist for the testing plan, wherein the additional playlist incorporates different content pieces to be presented on a display device instead of one or more pieces of marketing content associated with the one or more items;

provisioning the additional playlist to the digital signage network, the additional playlist causing the different content pieces to be presented to an audience on one or more display devices;

receiving additional data characterizing audience behavior as a result of the presentation of different content pieces associated with the additional playlist; and

utilizing the received additional audience behavior data in the analysis to identify those of the plurality of playlists that resulted in greater sales of the one or more items.

34. The computer-implemented method of claim 28, wherein the items are products.

35. The computer-implemented method of claim 28, wherein the items are services.

36. The computer-implemented method of claim 28, wherein sales are measured by revenue.

37. The computer-implemented method of claim 28, wherein sales are measured by units.

38. The computer-implemented method of claim 28, wherein the playlists differ from each other with respect to a temporal characteristic.

39. The computer-implemented method of claim 38, wherein the temporal characteristic is selected from the group consisting of date, daypart, time, and repeat play characteristics.

40. The computer-implemented method of claim 28, wherein the playlists differ from each other with respect to a locale in which an associated plurality of content pieces are presented.

41. The computer-implemented method of claim 40, wherein the locale is selected from the group consisting of store site, group of store sites, channel, retailer, network nodes, and network.

42. The computer-implemented method of claim 28, wherein the playlists differ from each other with respect to a demographic of an audience to which an associated plurality of content pieces are presented.

43. The computer-implemented method of claim 42, wherein the demographic 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.

44. The computer-implemented method of claim 28, further comprising determining an initial number of trials that a playlist should be repeated on a corresponding display device.

45. The computer-implemented method of claim 28, wherein the testing plan is a testing matrix that correlates playlists to locations containing display devices.

46. The computer-implemented method of claim 28, further comprising:

receiving a level of statistical significance; and

adjusting the plurality of playlists in accordance with the received level of statistical significance.

47. The method of claim 28, wherein a playlist comprises a plurality of pointers to content.

48. The method of claim 28, further comprising generating an interface to allow a user to review an aspect of the testing plan prior to provisioning the plurality of playlists in accordance with the testing plan.