The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising

TL;DR

This paper introduces the pollution effect in search engine advertising, showing that favoring relevant ads can improve revenue and utility by reducing the long-term negative impact of irrelevant ads.

Contribution

It extends existing auction models to include the pollution effect, demonstrating how prioritizing relevance can optimize revenue and utility in search advertising.

Findings

A modest pollution effect significantly alters optimal ad ranking.

Prioritizing relevance can increase revenue and utility.

Making relevant ads cheaper and irrelevant ads more costly benefits search engines.

Abstract

Most search engines sell slots to place advertisements on the search results page through keyword auctions. Advertisers offer bids for how much they are willing to pay when someone enters a search query, sees the search results, and then clicks on one of their ads. Search engines typically order the advertisements for a query by a combination of the bids and expected clickthrough rates for each advertisement. In this paper, we extend a model of Yahoo's and Google's advertising auctions to include an effect where repeatedly showing less relevant ads has a persistent impact on all advertising on the search engine, an impact we designate as the pollution effect. In Monte-Carlo simulations using distributions fitted to Yahoo data, we show that a modest pollution effect is sufficient to dramatically change the advertising rank order that yields the optimal advertising revenue for a search engine. In addition, if a pollution effect exists, it is possible to maximize revenue while also increasing advertiser, and publisher utility. Our results suggest that search engines could benefit from making relevant advertisements less expensive and irrelevant advertisements more costly for advertisers than is the current practice.