Keyword set and target audience profile generalization techniques

Abstract

A variety of techniques are described by which keyword sets and target audience profiles may be generalized in a systematic and effective way with reference to relationships between keywords, profiles, and the data of an underlying user population.

Description

RELATED APPLICATION DATA[0001]The present application is a continuation of, and claims priority under 35 U.S.C. 120 to, U.S. patent application Ser. No. 11 / 444,996 for KEYWORD SET AND TARGET AUDIENCE PROFILE GENERALIZATION TECHNIQUES filed on May 31, 2006 (Attorney Docket No. YAH1P016 / Y01507US01), the entire disclosure of which is incorporated herein by reference for all purposes.BACKGROUND OF THE INVENTION[0002]The present invention relates to techniques for generalizing keyword sets and target audience profiles and, more specifically, using such generalized keyword sets and profiles to enhance online advertising campaigns.[0003]A key success metric for an online advertisement campaign is the number of conversion events (e.g., sale of goods or services, registration, or lead generation) that are attributed to the campaign. Online advertisers are therefore interested in maximizing conversion events given a specific budget. Branded ads and sponsored search ads are considered the two ...

AI Technical Summary

Benefits of technology

These techniques enable more precise and effective advertising campaigns by expanding keyword sets and audience profiles, incorporating less obvious but highly relevant keywords and user segments, thereby improving ad relevance and reducing costs.

Problems solved by technology

Unfortunately, such conventionally derived profiles and keyword sets have shortcomings which are becoming more problematic as the online advertising market matures.

For example, given that there are relatively few “obvious” keywords relating to a given product or service as compared with the number of potential advertisers, the cost of bidding on the common keywords is becoming prohibitive for even the larger advertisers.

This forces advertisers to use undesirably small sets of keywords, and / or to attempt to identify related but less desirable keywords to include in their keyword set, thus potentially reducing the efficacy of their campaigns.

However, because such techniques are typically based on lexical or content analysis, they are not particularly instructive to an advertiser in terms of how effective the additional keywords might be with regard to the intended target audience.

In addition, such techniques are not useful in identifying additional keywords having an unexpected or unobvious relationship with the original keyword(s).

Similarly, the target audience profiles typically used by advertisers in branded advertising campaigns may be too narrowly or inappropriately focused due to the fact that there are unappreciated correlations in the underlying user population.

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