Method and system for targeted advertising based on topical memes

• US 8,429,011 B2
• Filed: 01/20/2009
• Issued: 04/23/2013
• Est. Priority Date: 01/24/2008
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Newton, Christopher Daniel, Lebrun, Marcel Albert, Ramsey, Christopher Bennett
• Primary Examiner(s): Hamilton, Matthew L
• Application Number:
  US12/356,429
• Publication Number:
  US 20090192896A1
• Time in Patent Office: 1,554 Days
• Field of Search: 705/14
• US Class Current: 705/14.4
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0224 based on user history

Abstract

A targeted advertising system and method based on memes contained in content sources are disclosed. Content matching keywords-defining topics are identified from content sources and are further processed to extract the memes. Ad networks servicing the content are also identified and their reach for each meme determined. The system and method extract also viral dynamics of the content associated to a meme and use the aggregation of the viral dynamics as a measure of engagement level for the meme. The system and method allow a Marketer to select a meme based on the engagement level and to run an ad campaign against the meme. The advertisements are delivered through an Ad network and inserted at the meme page level when the content hosting the meme is accessed, the Ad network being selected based on its reach.

Claims

1. A computer implemented method for targeting advertisement, comprising steps of:

analyzing, by a computer-based system, web content against a defined topic;

obtaining, by the computer-based system and based on the analyzing, topic-matching content related to the defined topic;

processing, by the computer-based system, the topic-matching content;

extracting, by the computer-based system and based on the processing, topical memes associated with the topic-matching content, wherein each of the extracted topical memes is a point of discussion within the defined topic;

measuring, by the computer-based system, an engagement level for each piece of the topic-matching content;

obtaining, by the computer-based system and based on the measuring, a respective viral dynamics metric for each said piece of the topic-matching content, wherein a viral dynamics metric indicates popularity of the piece of the topic-matching content;

accumulating, by the computer-based system and for each of the extracted topical memes, the viral dynamics metrics of the pieces of the topic-matching content;

obtaining, by the computer-based system and based on the accumulating, a respective aggregate viral dynamics value for each of the extracted topical memes;

determining, by the computer-based system, a most active meme from the extracted topical memes, based on the aggregate viral dynamics value for each of the extracted topical memes;

identifying, by the computer-based system, web pages containing the most active meme;

selecting, by the computer-based system, an advertising network servicing a number of content sources hosting the identified web pages;

selecting, by the computer-based system, an advertisement assigned to the most active meme; and

delivering, by the computer-based system, said selected advertisement to said number of said content sources through said advertising network.

2. The method of claim 1, further comprising storing the aggregate viral dynamics value for each of the extracted topical memes along with their associated extracted topical meme in a database.

3. The method of claim 1, wherein the step of processing the topic-matching content comprises applying a feature extraction algorithm to said topic-matching content.

4. The method as described in claim 1, wherein the step of selecting the advertising network comprises:

extracting a list of advertising networks servicing the content sources; and

selecting from said list the advertising network, having a widest reach.

5. The method of claim 1, wherein the step of selecting the advertisement assigned to the most active meme comprises:

setting an advertisement deployment threshold for said advertisement;

comparing the deployment threshold with the aggregate viral dynamics value associated with the most active meme; and

assigning the advertisement to the most active meme provided the deployment threshold matches the aggregate viral dynamics value associated with the most active meme.

6. The method of claim 5, further comprising:

maintaining the assigned advertisement on said web pages provided that the aggregate viral dynamics value associated with the most active meme is above the deployment threshold; and

removing the assigned advertisement from said web pages provided that the aggregate viral dynamics value associated with the most active meme is below the deployment threshold.

7. A system for performing meme-based targeted advertising, comprising:

a computer, having a processor and a computer readable storage medium storing computer readable instructions for execution by the processor, wherein the instructions, when executed by the processor, cause the computer to perform a method comprising;

analyzing web content against a defined topic;

obtaining topic-matching content provided by one or more content sources, the topic-matching content being related to the defined topic;

processing the topic-matching content;

extracting topical memes associated with the topic-matching content, wherein each of the extracted topical memes is a point of discussion within the defined topic;

measuring an engagement level for each piece of the topic-matching content;

obtaining, based on the measuring, a respective viral dynamics metric for each said piece of the topic-matching content, wherein a viral dynamics metric indicates popularity of the piece of the topic-matching content;

accumulating, for each of the extracted topical memes, the viral dynamics metrics of the pieces of the topic-matching content;

obtaining, based on the accumulating, a respective aggregate viral dynamics value for each of the extracted topical memes;

determining a most active meme from the extracted topical memes, based on the aggregate viral dynamics value for each of the extracted topical memes;

associating the most active meme to subset content containing the most active meme, wherein the subset content is a subset of said topic-matching content;

selecting advertising networks servicing said content sources; and

delivering advertisements through the selected advertising networks to web pages containing the most active meme and included in the subset content.

8. The system as described in claim 7, further comprising a database stored in a computer readable storage medium for storing the aggregate viral dynamics values.

9. The system as described in claim 7, wherein the defined topic is defined by a set of keywords.

10. The system as described in claim 7, wherein the instructions, when executed by the processor, cause the computer to select one or more advertisements matching the most active meme, and cause the computer to compare a deployment threshold associated with said one or more advertisements with the aggregate viral dynamics value associated to said most active meme.

11. A non-transitory computer readable medium, comprising computer code instructions stored thereon, which, when executed by a computer, perform a method comprising:

analyzing web content against a defined topic;

obtaining, based on the analyzing, topic-matching content related to the defined topic;

processing the topic-matching content;

extracting, based on the processing, topical memes associated with the topic-matching content, wherein each of the extracted topical memes is a point of discussion within the defined topic;

measuring an engagement level for each piece of the topic-matching content

obtaining, based on the measuring, a respective viral dynamics metric for each said piece of the topic-matching content, wherein a viral dynamics metric indicates popularity of the piece of the topic-matching content;

accumulating, for each of the extracted topical memes, the viral dynamics metrics of the pieces of the topic-matching content;

obtaining, based on the accumulating, a respective aggregate viral dynamics value for each of the extracted topical memes;

determining a most active meme from the extracted topical memes, based on the aggregate viral dynamics value for each of the extracted topical memes;

identifying web pages containing the most active meme;

selecting an advertising network servicing a number of content sources hosting the identified web pages;

selecting an advertisement assigned to the most active meme; and

delivering said selected advertisement to said number of said content sources through said advertising network.