Automatic adjustment of advertiser bids to equalize cost-per-conversion among publishers for an advertisement
US8175914B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2007 |
| Grant date | May 8, 2012 |
| Priority date | — |
| Expiry date | Nov 21, 2030 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q40/08
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
A learning model is built on a combination of advertiser, publisher and user data. The learning model can be applied to all advertisers in an advertising system. The learning model provides predicted conversion rates for a given advertisement (“ad”) appearing on different publisher networks. A predicted conversion rate represents the probability that a click on a given ad appearing on a given publisher will lead to a conversion. The predicted conversion rates are used to generate a multiplier. The multiplier is used to automatically adjust the advertiser's bid (e.g., maximum cost-per-click (CPC)) for the given ad prior to an auction for the ad. Adjusting the advertiser's bid equalizes a cost-per-conversion among the publishers for the ad.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.