Beyond the Last Touch: Attribution in Online Advertising
Author(s) -
Ron Berman
Publication year - 2018
Publication title -
marketing science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.938
H-Index - 127
eISSN - 1526-548X
pISSN - 0732-2399
DOI - 10.1287/mksc.2018.1104
Subject(s) - attribution , bidding , advertising , externality , competition (biology) , economics , microeconomics , network effect , ranking (information retrieval) , two sided market , online advertising , business , the internet , computer science , psychology , social psychology , ecology , world wide web , biology , machine learning
Online advertisers often utilize multiple publishers to deliver ads to multi-homing consumers. These ads often generate externalities and their exposure is uncertain, which impacts advertising effectiveness across publishers. We analytically analyze the inefficiencies created by externalities and uncertainty when information is symmetric between advertisers and publishers, in contrast to most previous research that assumes information asymmetry. Although these inefficiencies cannot be resolved through publisher side actions, attribution methods that measure the campaign uncertainty can serve as an alternative solution to help advertisers adjust their strategies. Attribution creates a virtual competition between publishers, resulting in a team compensation problem. The equilibrium may potentially increase the aggressiveness of advertiser bidding leading to increased advertiser profits. The popular last-touch method is shown to over-incentivize ad exposures, often resulting in lowering advertiser profits. The Shapley value achieves an increase in profits compared to last-touch. Popular publishers and those that appear early in the conversion funnel benefit the most from advertisers using last-touch attribution. The increase in advertiser profits comes at the expense of total publisher profits and often results in decreased ad allocation efficiency. We also find that the prices paid in the market will decrease when more sophisticated attribution methods are adopted.
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