Methods for cross-market brand advertising, content metric analysis, and placement recommendations

Patent metadata

Field	Value
Publication number	US-9898753-B2
Application number	US-86318707-A
Country	US
Kind code	B2
Filing date	Sep 27, 2007
Priority date	Sep 27, 2007
Publication date	Feb 20, 2018
Grant date	Feb 20, 2018

Abstract

In another embodiment, a computer-implemented method for processing and optimizing selection of placement, of advertising content related to a brand, in websites of a network is provided. The computer-implemented method is processed by a server in response to communication from a user that is connected to the server over the Internet. The method includes receiving from the user, attributes of an advertisement to be placed on a brand-centric website that relates to the brand, and also receiving selections for types of websites to place the advertisement. The types of websites do not have to be brand-centric websites, but should include content related to the brand. Then, the method includes obtaining metrics from selected websites and historical performance for similar advertisements when placed on the selected websites. The method includes processing the obtained metrics and historical performance to preliminarily define an advertising model. The advertising model defines a score correlated to effectiveness of the advertisement. Optimizing the advertising model is then performed to define a recommended advertising model, where the recommended advertising model defines optimal selections of websites for placement of the advertisement. The optimizing uses multivariable optimization to correlate the obtained metrics to a desired optimization criteria.

First claim

What is claimed is:

1. A method comprising:
receiving attributes of an advertisement to be placed on one of a plurality of brand-centric websites, the brand-centric websites focused on a brand;
presenting the brand-centric websites to an advertiser based on the attributes;
receiving a selection of the one of the brand-centric websites from the advertiser;
analyzing the one of the brand-centric websites to search a computer network to generate suggestions of content-related websites in which the advertisement is to be placed, wherein the content-related websites in which the advertisement is to be placed has less information regarding the brand than that in the one of the brand-centric websites in which the advertisement is to be placed, wherein the content-related websites include content related to the brand and other brands, wherein the one of the brand-centric websites has a plurality of content modules, wherein each of the content modules has some of the content regarding the brand, wherein the content is transferred via the computer network from the content-related websites to the one of the brand-centric websites, wherein data for generating the content is obtained by a harvester, wherein a data type processor is configured to classify the data into video, audio, and text to generate classified data, wherein the classified data is stored in a content repository, wherein the content-related websites request the classified data via corresponding application programming interfaces from the content repository;
obtaining metrics from selected websites and historical performance for similar advertisements when placed on the selected websites, the selected websites including the one of the brand-centric websites and one or more of the content-related websites;
processing the obtained metrics and the historical performance to preliminarily define an advertising model, the advertising model defining a score correlated to effectiveness of the advertisement; and
optimizing the advertising model to define a recommended advertising model, the recommended advertising model defining optimal suggestions of websites including the one of the brand-centric websites that has the classified data obtained from the content repository and in which the advertisement is to be placed,
wherein the optimizing of the advertising model includes multivariable optimization to enable emphasis or non-emphasis of selection criteria, and the multivariable optimization being adjustable to achieve optimization for popularity, or monetization, or product introduction, or a combination thereof.

2. A method as recited in claim 1 , wherein the attributes of the advertisement relate to at least one of product or service type, target customer, marketing budget, type of ad, and level of promotion.

3. A method as recited in claim 1 , wherein the one of the brand-centric websites is defined by the content modules, each of the content modules contains one or more of text, or images, or video clips, or graphics, or a combination thereof, and each of the content modules includes the content focused on the brand.

4. A method as recited in claim 1 , further comprising identifying the brand-centric websites that are part of a network of websites that include the content related to the brand, wherein the method is executed by a processor.

5. A method as recited in claim 4 , wherein the brand-centric websites include a linkage to the brand.

6. A method as recited in claim 1 , wherein the obtained metrics include data related to activity of the content in terms of popularity and the historical performance includes monetization realized from the similar advertisements when placed on the selected websites.

7. A method as recited in claim 1 , wherein the processing includes generation of performance and cost data for the advertising model, the performance including anticipated monetization for the advertisement and anticipated cost for placement of the advertisement.

8. A method as recited in claim 1 , wherein the recommended advertising model includes selections to achieve the optimization.

9. A computer-implemented method being processed by a server in response to communication from a remote computer that is connected to the server over the Internet, comprising:
receiving from an advertiser, attributes of an advertisement to be placed on one of brand-centric websites that focuses on a brand;
presenting the brand-centric websites to the advertiser based on the attributes;
receiving a selection of the one of the brand-centric websites from the advertiser;
analyzing the one of the brand-centric websites to search a computer network to generate suggestions of content-related websites in which the advertisement is to be placed, wherein the content-related websites in which the advertisement is to be placed has less information regarding the brand than that in the one of the brand-centric websites in which the advertisement is to be placed, wherein the content-related websites include content related to the brand, wherein the one of the brand-centric websites has a plurality of content modules, wherein each of the content modules has some of the content regarding the brand, wherein the content is transferred via the computer network from the content-related websites to the one of the brand-centric websites, wherein data for generating the content is obtained by a harvester, wherein a data type processor is configured to classify the data into video, audio, and text to generate classified data, wherein the classified data is stored in a content repository, wherein the content-related websites request the classified data via corresponding application programming interfaces from the content repository;
obtaining metrics from selected websites and historical performance for similar advertisements when placed on the selected websites, the selected websites including the one of the brand-centric websites and one or more of the content-related websites;
processing the obtained metrics and the historical performance to preliminarily define an advertising model, the advertising model defining a score correlated to effectiveness of the advertisement; and
optimizing the advertising model to define a recommended advertising model, the recommended advertising model defining optimal suggestions of websites including the one of the brand-centric websites that has the classified data obtained from the content repository and in which the advertisement is to be placed, the optimizing using multivariable optimization to correlate the obtained metrics to a desired optimization criterion,
wherein the optimizing of the advertising model includes multivariable optimization to enable emphasis or non-emphasis of selection criteria, and the multivariable optimization being adjustable to achieve optimization for popularity, or product introduction, or monetization, or cost of placement, or demographics, or a combination thereof.

10. A computer-implemented method as recited in claim 9 , wherein the attributes of the advertisement relate to at least one of product or service type, target customer, marketing budget, type of ad, and level of promotion.

11. A computer-implemented method as recited in claim 9 , wherein the one of the brand-centric websites is defined by the content modules, each of the content modules contains text, or images, or video clips, or graphics, or a combination thereof, and each of the content modules includes the content focused on the brand.

12. A computer-implemented method as recited in claim 9 , further comprising identifying the brand-centric websites that are part of a network of websites that include the content related to the br

Assignees

• Lara Ankarino
• Bedard Scott
• Chen Anthony D
• Goli Shravan K
• Stoliartchouk Alexei
• Excalibur Ip Llc

Inventors

• Lara Ankarino
• Bedard Scott
• Chen Anthony D
• Goli Shravan K
• Stoliartchouk Alexei

Classifications

• G06Q30/0244 Primary

  Optimization · CPC title

• G06Q30/02

  Marketing; Price estimation or determination; Fundraising · CPC title