Multi-Touch Attribution Based Budget Allocation in Online Advertising

TL;DR

This paper explores how multi-touch attribution models can be used to optimize budget allocation in online advertising, aiming to maximize campaign ROI by accurately attributing user actions to multiple ads.

Contribution

It introduces algorithms for multi-touch attribution and demonstrates their application in large-scale online advertising campaigns to improve budget allocation strategies.

Findings

Multi-touch attribution improves ROI over last-touch methods.

Parallel algorithms enable scalable attribution analysis.

Empirical results show better budget efficiency with multi-touch models.

Abstract

Budget allocation in online advertising deals with distributing the campaign (insertion order) level budgets to different sub-campaigns which employ different targeting criteria and may perform differently in terms of return-on-investment (ROI). In this paper, we present the efforts at Turn on how to best allocate campaign budget so that the advertiser or campaign-level ROI is maximized. To do this, it is crucial to be able to correctly determine the performance of sub-campaigns. This determination is highly related to the action-attribution problem, i.e. to be able to find out the set of ads, and hence the sub-campaigns that provided them to a user, that an action should be attributed to. For this purpose, we employ both last-touch (last ad gets all credit) and multi-touch (many ads share the credit) attribution methodologies. We present the algorithms deployed at Turn for the attribution problem, as well as their parallel implementation on the large advertiser performance datasets. We conclude the paper with our empirical comparison of last-touch and multi-touch attribution-based budget allocation in a real online advertising setting.