System for Improving Shape-Based Targeting By Using Interest Level Data

• US 20140310098A1
• Filed: 06/25/2014
• Published: 10/16/2014
• Est. Priority Date: 05/28/2010
• Status: Abandoned Application

• Current Assignee: MaxPoint Interactive (MX Holdings One LLC)
• Original Assignee: MaxPoint Interactive (MX Holdings One LLC)
• Inventors: Carlson, Kurt M., Lowe, Mark, Gonzalez, Robert, Epperson, Joseph W., Farmer, Christopher R.
• Application Number:
  US14/314,659
• Publication Number:
  US 20140310098A1
• Time in Patent Office: Days
• US Class Current: 705/14.53
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0275 Auctions

Abstract

A system for improving shape-based targeting by using interest level data is disclosed. According to one embodiment, a computer-implemented method comprises creating one or more trade zones, wherein creating a trade zone comprises grouping a set of parameters to deliver custom shapes, clustering the custom shapes according to offline data and geographic distribution of IP addresses, and mapping clusters of the custom shapes to IP addresses. Data indicating consumption of a content source is received, the content source having a plurality of tokens. The plurality of tokens is organized according to the one or more trade zones, wherein the one or more trade zones consume the tokens at a calculated rate and the calculated rate is analyzed to determine an interest associated with each trade zone. Targeting is based on a selected trade zone, wherein the selected trade zone is selected based upon a desired interest representative of a desired audience. A targeting request is transmitted including instructions or information associated with the target action, and the target action is performed.

Claims

1. A computer-implemented method, comprising:

creating one or more trade zones, wherein creating a trade zone comprises

grouping a set of parameters to deliver custom shapes;

clustering the custom shapes according to offline data, online data, and geographic distribution of IP addresses; and

mapping clusters of the custom shapes to IP addresses;

receiving data indicating consumption of a content source by one or more trade zones at a calculated rate;

analyzing the calculated rate to determine an interest associated with each trade zone;

selecting the one or more trade zones based upon a desired interest representative of a desired audience;

transmitting a targeting request to the one or more selected trade zones including display instructions and information associated with a target action; and

performing the target action.

2. The computer-implemented method of claim 1, wherein online data and offline data associated with the one or more trade zones are independent of cookies, and wherein the custom shapes are independent of geographic location.

4. The computer-implemented method of claim 1, wherein offline data comprises demographic data, point of sale data, and business information data, and online data comprises pane content, user clicks, advertisement impressions, and pages visited.

6. The computer-implemented method of claim 1, wherein offline data uses location information in the offline data to map to the custom shapes, and online data uses IP addresses to map to the custom shapes.

8. The computer-implemented method of claim 1, wherein transmitting a targeting request to the one or more selected trade zones results in properly targeted actions, wherein properly targeted actions reach only the desired audience.

9. The computer-implemented method of claim 1, wherein a trend of an interest for a trade zone is calculated, wherein the trend is one of upward or downward, and further comprising at least one of mapping the trend to the one or more trade zones to use as a targeting configuration for campaigns and discovering new search engine marketing keywords using the trend.

10. The computer-implemented method of claim 1, wherein the content source is one of a webpage, a purchased server log, a mobile application, a television programming transcript, or an on-demand video selection.

11. The computer-implemented method of claim 1, wherein consumption of the content source comprises:

organizing one or more keywords according to the one or more trade zones, wherein the one or more trade zones consume the one or more keywords at a calculated rate determined by multiplying a first number of times that a content source was viewed by a second number of occurrences of a chosen keyword in the content source.

13. The computer-implemented method of claim 1, wherein consumption of the content source at a calculated rate comprises:

using a data mining technique to produce an output, wherein the output is mapped into one or more phrases having one or more words; and

organizing the one or more phrases according to the one or more trade zones,

wherein the calculated rate is determined by multiplying a first number of times a content source was viewed by a second number of occurrences of a chosen phrase in the content source, and

wherein data mining techniques comprise linear and nonlinear classification methods, supervised and unsupervised learning algorithms and neural networks.

16. The computer-implemented method of claim 1, wherein creating a trade zone further comprises combining custom shapes that are too small to represent an IP range mapping based on consideration of one or more sets of parameters comprising geographic areas that best conform to the probable distribution of the IP address range, demographic homogeneity, natural geographic boundaries and types of IP address ranges, wherein types of IP address ranges comprise business, residential and public locations.

19. The computer-implemented method of claim 1, wherein selecting one or more trade zones is further based on using probability to find a range of interests that related to a desired interest of a desired audience.

20. The computer-implemented method of claim 1, wherein transmitting a targeting request to the one or more selected trade zones further comprises transmitting simultaneously to similar trade zones based on one or more sets of parameters.

21. The computer-implemented method of claim 1, transmitting a targeting request to the one or more selected trade zones further comprises using a selection process to select the best target action, wherein the selection process comprises weighted, random and sequential processes.

23. The computer-implemented method of claim 1, further comprising optimizing the targeting request based upon performance metrics collected when performing the target action, wherein performance metrics comprise click-through rates, coupon downloads, impressions delivered, price per impression, lead submissions, landing page arrivals, product queries and product purchases.

25. The computer-implemented method of claim 1, wherein information associated with a target action comprises a weighted score dynamically assigned to each content source based upon one or more desired interests.

26. The computer-implemented method of claim 1, wherein creating a trade zone further comprises assigning one or more secondary custom shapes based on census tracts that are in close proximity to the clusters of custom shapes.

27. The computer-implemented method of claim 1, wherein performing the target action occurs independent of keywords and a a prior online presence of the desired audience.

28. The computer-implemented method of claim 1, further comprising:

determining whether multiple target items match the one or more selected trade zones; and

determining a best fit target item of the multiple target items.

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32. A system, comprising:

a server, hosting a first webpage, in communication with a network, wherein a client system accesses the first webpage via the network; and

a targeting system in communication with the network,

wherein the targeting system creates one or more trade zones by

grouping a set of parameters to deliver custom shapes;

clustering the custom shapes according to offline data, online data, and geographic distribution of IP addresses; and

mapping clusters of the custom shapes to IP addresses;

receives data indicating consumption of a content source by one or more trade zones at a calculated rate;

analyzes the calculated rate to determine an interest associated with each trade zone;

selects the one or more trade zones based upon a desired interest representative of a desired audience;

transmits a targeting request to the one or more selected trade zones including display instructions and information associated with a target action; and

performs the target action.

58. The system of claim 32, further comprising a real-time bidding system and a plurality of real-time bidders in communication with the network, wherein the targeting system acquires impressions from the real-time bidders for serving advertisements, and a publisher site in communication with the network, wherein the publisher site uses the targeting system to perform target actions for the publisher site'"'"'s audiences.

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