Multi-Touch Attribution Based Budget Allocation in Online Advertising
  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.
