Ranking for the conversion funnel

Bagherjeiran, Abraham; Hatch, Andrew; Ratnaparkhi, Adwait

doi:10.1145/1835449.1835476

Cited by 15 publications

(11 citation statements)

References 9 publications

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“…Our paper is complementary to this work because we identify what other factors could be playing a role in the user's conversion behavior. In [3], the authors propose a new model that optimizes the conversion funnel even for CPC campaigns. Since the authors evaluate their model for contextual advertising their analysis of conversion prediction is heavily focused on keyword matches.…”

Section: Related Workmentioning

confidence: 99%

Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

Rosales

Cheng

Manavoglu

2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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In on-line search and display advertising, the click-trough rate (CTR) has been traditionally a key measure of ad/ campaign effectiveness. More recently, the market has gained interest in more direct measures of profitability, one early alternative is the conversion rate (CVR). CVRs measure the proportion of certain users who take a predefined, desirable action, such as a purchase, registration, download, etc.; as compared to simply page browsing. We provide a detailed analysis of conversion rates in the context of non-guaranteed delivery targeted advertising. In particular we focus on the post-click conversion (PCC) problem or the analysis of conversions after a user click on a referring ad. The key elements we study are the probability of a conversion given a click in a user/page context, P(conversion | click, context). We provide various fundamental properties of this process based on contextual information, formalize the problem of predicting PCC, and propose an approach for measuring attribute relevance when the underlying attribute distribution is non-stationary. We provide experimental analyses based on logged events from a large-scale advertising platform.

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Section: Related Workmentioning

confidence: 99%

Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

Rosales

Cheng

Manavoglu

2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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“…These transactions are commonly called conversions and can be used to optimize the user segment generation specifically for a given advertiser [6,3,4,9,14,2]. Using conversions enables the ad network to optimize for users that are actually inclined to perform a transaction as opposed to a casual (and in some cases random) visit to the web site through a click.…”

Section: Www 2012 Companionmentioning

confidence: 99%

“…We compared our approach with existing behavioral targeting methods (such as [7,16]) which optimize for click-through rates and showed how optimizing directly for conversions can lead to improved performance. Compared to previous work on conversion optimization [6,3,4,9,14], our work makes several new contributions: we look into understanding the effect of different user activities on prediction, give insights about the temporal aspect of user behavior (recency vs. long-term trends) and explore different variants (user representation and target label) through large offline and online experiments.…”

Section: Related Workmentioning

confidence: 99%

Web-scale user modeling for targeting

Aly

Hatch

Josifovski

et al. 2012

Proceedings of the 21st International Conference on World Wide Web

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We present the experiences from building a web-scale user modeling platform for optimizing display advertising targeting at Yahoo!. The platform described in this paper allows for per-campaign maximization of conversions representing purchase activities or transactions. Conversions directly translate to advertiser's revenue, and thus provide the most relevant metrics of return on advertising investment. We focus on two major challenges: how to efficiently process histories of billions of users on a daily basis, and how to build per-campaign conversion models given the extremely low conversion rates (compared to click rates in a traditional setting). We first present mechanisms for building web-scale user profiles in a daily incremental fashion. Second, we show how to reduce the latency through in-memory processing of billions of user records. Finally, we discuss a technique for scaling the number of handled campaigns/models by introducing an efficient labeling technique that allows for sharing negative training examples across multiple campaigns.

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“…To this end, we also experimented with augmenting the seed set of users who converted with an additional set (U click ) of pseudo-positive users, who clicked on the ad but did not convert (a similar approach was used in [1]). The assumption behind this approach is that the users who clicked on the ad found it relevant at least to some degree, and might still convert later:…”

Section: Vector Space Models For Audience Selectionmentioning

confidence: 99%

Retrieval models for audience selection in display advertising

Tyler

Pandey

Gabrilovich

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Web applications often rely on user profiles of observed user actions, such as queries issued, page views, etc. In audience selection for display advertising, the audience that is likely to be responsive to a given ad campaign is identified via such profiles. We formalize the audience selection problem as a ranked retrieval task over an index of known users. We focus on the common case of audience selection where a small seed set of users who have previously responded positively to the campaign is used to identify a broader target audience. The actions of the users in the seed set are aggregated to construct a query, the query is then executed against an index of other user profiles to retrieve the highest scoring profiles. validate our approach on a real-world dataset, demonstrating the trade-offs of different user and query models and find that our approach is particularly robust for small campaigns. The proposed user modeling framework is applicable to many other applications requiring user profiles such as content suggestion and personalization.

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Ranking for the conversion funnel

Cited by 15 publications

References 9 publications

Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

Web-scale user modeling for targeting

Retrieval models for audience selection in display advertising

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