About the Author
Catherine Tucker is the Mark Hyman Jr. Career Development Professor and Associate
Professor (with tenure) of Marketing at MIT Sloan. Her research interests lie in how technology allows rms to use digital data to improve their operations a
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media planning or as part of a more generalized and rigorous scheme of ad testing which I
have not explored. Notwithstanding these limitations, I believe this paper to be a useful rst
step in understanding how the ability to accurately measure advertising
Aggregate Reporting (http:/www.w3.org/TR/tracking-compliance). Such uses could
conceivably allow attribution technologies such as the ones studied in this paper to persist.
By contrast, the EU Working Party 29 takes the view that for this proposed Do Not
tential consequences of inhibiting the diusion and use of these attribution and measurement
technologies. The most obvious of these are that underlying these attribution technologies is
a great deal of anonymized data, commonly about an individual cookie.
Implications and Policy Discussion
There are two separate sets of policy implications that can be drawn from this analysis.
The rst set of policy implications are the consequences of these new attribution technologies for understanding how advertising m
advertising has the largest incremental eect rather than being simply associated with the
largest average eect (Lambrecht and Tucker, 2011; Goldfarb and Tucker, 2011c). The use
of such eld experiments to improve ad performance has been discussed as the ne
Figure 20: Changing length of Campaign for Display Ads by Campaign Success
attribution technology, then this implies that the platform was able to reduce costs. Figure
20 suggests that indeed this was the case. By the nal quarter, underperforming campaign
Figure 17: Cross-Channel: Length of targeting criteria for paid search ads over time
by Figure 19. The dierence in price decline between Figure 18 and Figure 19 makes sense,
given that advertisers could already use the internal search engine metrics to im
Figure 15: Cross-Channel: Change in conversion rate associated with display campaigns
Figure 16: Cross-Channel: Change in conversion rate associated with dierent campaigns at
dierent paid search providers over time
Figure 14: Cross-Channel: Change in conversion rate associated with campaign over time
appear to favor one advertising platform over another.
Figure 17 examines how the length simply in terms of number of characters of the search
terms used for targeting
Table 2: Summary statistics for Cross-Channel Attribution Technology data
Average # Days User Exposed to Ads for Campaign
Average # of Search Ads Seen
Average # of Display Ads Seen
Average # of Untargeted Display
Average # of B
Analysis of a cross-channel attribution technology
The second dataset was provided by a rm that allows cross-channel attribution of dierent
forms of online advertising campaigns. It allows me to study the evolution over three quarters of data of adver
Figure 11: Single Channel: Change in clickthrough rate of ads for an average campaign
translates into click rates. Generally, these statistics suggest that there was little change in
actual advertising and exposures from the adoption of this technology. T
However, a key condition for this positive outcome is that rms actually behave in the manner
predicted by the latter set of theories, and use this improved measurability to increase the
eciency of advertising allocation by seeking out sets of target consu
and Shapiro (1984) hypothesized as applying to advertising. In their model, captures the
fraction of the target population that is exposed to a message and captures advertising
technology which drives the total and marginal advertising costs. In their mod
online advertising platforms can oer performance-based pricing in ways impossible with the
traditional types of advertising discussed by Anderson and Coate (2005); Ferrando et al.
(2008); Reisinger (2012) or in the more general two-sided market (Armstrong
Figure 7: Changes to Advertiser Behavior from Attribution
Source: Econsultancy (2012)
dividual advertisers. It emphasizes that one of the major eects of such technologies is to
facilitate transition between dierent advertising channels such as search and
Figure 3: Dierent Advertising Events That Can Lead to Conversion
Source: C3 Metrics, Inc.
Osur (2012) suggests that half of all vendors oer such a capacity and 30% of reference
clients were using this capacity. Figure 6 summarizes the current use of such