METHOD OF NETWORK MERCHANDISING INCORPORATING CONTEXTUAL AND PERSONALIZED ADVERTISING

• US 20080097843A1
• Filed: 03/14/2007
• Published: 04/24/2008
• Est. Priority Date: 10/19/2006
• Status: Abandoned Application

• Current Assignee: Tumri Incorporated
• Original Assignee: Tumri Incorporated
• Inventors: Rice, James, Loeser, Tony, Billington, Richard, Handley, Simon, Doshi, Bhavin, Menon, Hari, Javangula, Pradeep
• Application Number:
  US11/686,230
• Publication Number:
  US 20080097843A1
• Time in Patent Office: Days
• US Class Current: 705/14.1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0252 based on events or environment, e.g. weather or festivals
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

A method of network merchandising incorporates contextual and personalized advertising with human input to deliver relevant retail offers to interested online consumers efficiently and intelligently. Catalog content from retailers is downloaded, semantically analyzed and merged. The merged content is filtered using machine- and human-generated specifications to produce a merchandisable universe of products (MUP). Marketers and publishers create and modify corner store ad units and specify product offers from the MUP to display in those units. Publishers deploy the ad units on their web pages. Compensation of publishers by retailers can use a pay-for-performance model. Users visit the publisher'"'"'s pages, viewing the product offers in the rendered ad unit. Performance data related to context, history, network and geo-location, product attributes and product combinations is derived from server log files is used to dynamically optimize and refine product placement in real time, providing a highly personalized experience for the user.

Claims

1. A computer-implemented method of matching interested online users with relevant retail offers intelligently and efficiently comprising the steps of:

creating an aggregate catalog of semantically-analyzed and organized catalog content from a plurality of retailers;

identifying a merchandisable universe of products (MUP) from said aggregate catalog based at least on human-authored parameters and past performance based on any of products, categories and retailers;

deploying a publisher-specified or merchandiser-specified, interactive advertising unit to advertising space on a publisher'"'"'s site, wherein product offers displayed in said advertising unit are selected from said MUP by parametric query targeted to an audience of said publisher; and

serving up from a web server an impression of said advertising unit to a site visitor for display in a client application, wherein said advertising unit optimized in real time based at least on context of said impression and a user profile;

wherein said impression displays at least one product offer highly personalized to said visitor.

2. The method of claim 1, wherein a fully structured representation of the items in the aggregate catalog includes, for each product offer:

category;

at least one category-specific attribute;

price;

brand and merchant information, if available;

discount and other special incentives to buy, if applicable; and

demographic information, including any of age and gender.

3. The method of claim 1, wherein the step of identifying a MUP comprises at least the steps of:

making at least one parametric query of the aggregate wherein said query is authored by a user by means of an interactive MUP creator application, said query specifying characteristics of said MUP;

constraining said query of the aggregate with data regarding past performance of products, categories and retailers; and

evaluating images and deal characteristics of product offers provided by retailers.

4. The method of claim 1, wherein said step of deploying a publisher-specified, interactive advertising unit to advertising space on a publisher'"'"'s site comprises the steps of:

providing a network-accessible publisher portal on a merchandising network, said publisher portal including an interactive store builder software application;

affiliating publishers with the merchandising network via a registration page on said publisher portal;

developing said parametric query of said MUP by one or more of the steps of;

selecting by said publisher a previously-authored targeting specification, where a targeting specification comprises a parametric query of said MUP;

customizing a previously-authored targeting specification by said publisher; and

authoring a new targeting specification by said publisher;

selecting and customizing a storefront for displaying product offers selected according to said targeting specification;

generating a client-side script, which when deployed to a page and executed by the client application causes a web server to generate an impression of said advertising unit for display on said client.

5. The method of claim 1, wherein said step of deploying merchandiser-specified, interactive advertising units to advertising space on a publisher'"'"'s site comprises at least the steps of:

providing a targeting console to merchandisers affiliated with the merchandising network;

developing said parametric query of said MUP to define a selection of products; and

providing a means to specify the ad impression contexts in which said parametric query will be used to determine the products to be displayed in said ad impression.

6. The method of claim 3, wherein a service controller controls interactions of said advertising unit with the client application, controlling implementation of high-level services and providing a testing infrastructure.

7. The method of claim 1, further comprising the steps of:

monitoring user activity, wherein records of said activities are written to at least one of a plurality of log files, said plurality of log files comprising;

an impression log that records context of each impression and which advertising unit was served, parameters that determine the configuration or behavior of the advertising unit, and which product offers were shown in the advertising unit;

a widget event log that records user interactions with the advertising unit

a click-through log that records click-throughs when a user is sent to a merchant and the attributes of the product clicked-on; and

purchase logs that record CPO (cost-per-order data downloaded from the retailers.

8. The method of claim 1, wherein targeting a parametric query to an audience of said publisher comprises the steps of:

selecting product offers for display based on a parametric search of said MUP;

said publisher constraining the search for product offers by adding the publisher'"'"'s own parameters to a system ad placement methodology, wherein said publisher'"'"'s own parameters comprise any of;

limiting the type of goods to be advertised on the site to one or more particular categories;

limiting the advertising so that it targets a particular demographic; and

using the parametric search to select a particular roster of product offers that the publisher would like advertised on his pages.

9. The method of claim 1, further comprising the steps of:

selecting an advertising unit by a publisher from a set of advertising units having different functional capabilities and interactive features;

adjusting advertising unit settings by said publisher dynamically, in real time; and

displaying sample product offerings to the publisher as the publisher experiments with settings, giving real-time feedback for the selection process.

10. The method of claim 1, wherein personalizing product offers to a site visitor comprises the steps of:

tracking the visitor; and

any of the steps of;

automatically personalizing said product offers; and

manually personalizing said product offers.

11. The method of claim 10, wherein the step of automatically personalizing said product offers comprises the steps of:

determining context of the present impression;

monitoring the visitor'"'"'s navigational choices;

obtaining location information based on the user'"'"'s IP address;

determining time of day and calendar driven context including holidays, special events and seasons;

monitoring past offers the visitor has been exposed to;

monitoring past behavior of the visitor and similar visitors; and

providing targeting input to a product selection engine.

12. The method of claim 10, wherein manually personalizing said product offers comprises the steps of:

eliciting profile information from the visitor; and

providing targeting input to a product selection engine.

13. The method of claim 10, wherein personalization parameters are centrally maintained so that they are dispersible, applying on any affiliated publisher site.

14. The method of claim 1, wherein optimizing said advertising unit in real time comprises any of the steps of:

optimizing based on context;

optimizing based on history;

optimizing based on product attributes; and

optimizing product combinations.

15. The method of claim 14, wherein optimizing based on context comprises the steps of:

determining visitor context, wherein visitor context includes any of;

where the visitor is coming from;

if the visitor has visited before;

the URL the visitor currently is placed on;

where the visitor arrives at the current page from;

the geographic location of the visitor;

the visitor'"'"'s browse patterns;

similarity between visitors; and

for users registered with a particular publisher that embeds said ad units, the user'"'"'s characteristics as provided by said publisher;

determining page context, wherein page context includes any of;

page content;

page intent; and

characteristics of the page audience; and

determining temporal context, wherein temporal context includes any of;

date;

time of day;

day of week; and

holidays and special occasions.

16. The method of claim 14, wherein optimizing based on history comprises the step of:

maintaining activity logs and providing feedback loops from said logs into product selection, wherein activities affected by said feedback loops include;

MUP construction; and

product selection for an advertising unit impression.

17. The method of claim 14, wherein optimizing product combinations comprises:

comparing effectiveness of product combination methods;

testing particular product combinations; and

identifying opportunities for cross-selling and up-selling.

18. The method of claim 14, wherein optimizing based on product attributes comprises:

optimizing product selection according to product wherein product attributes include;

price point;

value;

usability; and

prestige.

19. The method of claim 14, further comprising the steps of:

performing direct comparison between different product selection algorithms;

performing A-B comparisons in otherwise identical streams of impressions, controlling for whatever variables are necessary; and

logging experimental data and analyzing test results.

20. The method of claim 1, further comprising the step of:

combining brand advertising with specific product offers in a single advertising unit.

21. The method of claim 1, further comprising the step of:

automatically generating a merchandising campaign by means of a generation, selection and feedback-oriented control system loop, wherein a merchandiser can quickly look at a set of merchandising campaigns that the merchandiser can preview and launch with minimal knowledge of system internals.