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
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
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
Levin, J. and P. Milgrom (2010, May). Online advertising: Heterogeneity and conation in
market design. American Economic Review 100 (2), 60307.
Lewis, R. A., J. Rao, and D. Reiley (2011). Here, there, everywhere: Correlated online
behaviors can lead to ov
Chatterjee, P., D. L. Homan, and T. P. Novak (2003). Modeling the clickstream: Implications for web-based advertising eorts. Marketing Science 22 (4), 520541.
Chittilappilly, A. (2012, July 11). Using experiment design to build condence in your
Anderson, S. P. and S. Coate (2005). Market Provision of Broadcasting: A Welfare Analysis.
Review of Economic Studies 72 (4), 947972.
Armstrong, M. (2006, Autumn). Competition in two-sided markets. RAND Journal of
Economics 37 (3), 668691.
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
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
online videos, Twitter, Facebook and mobile advertising.
Another recent development has been the ability of these technologies to integrate oine
media including television; catalog; direct mail; point of sale; and call center into their attribution models
Collection of Data on Advertising Exposure
Attribution technology evolved in part due to the relative ease of collecting data for the paid
search and online display advertising channels.
One of the attractive properties of search advertising is that
Figure 1: Screenshot from typical cross-channel attribution technology
Figure 1 is a screenshot of a cross-channel attribution providers websites, showing that
these technologies typically provide easy-to-read dashboards that allow easy
making a TV infomercial that uses an identiable phone number. In the oine world,
many retailers do not observe exactly who purchases their products, let alone whether it is
the same person who was exposed to their ads. Last, even if rms can observe a clea
provide some initial empirical tests of this theory, by looking at the evolving behavior of
advertisers after adopting an attribution technology.
I use two dierent datasets that track advertiser behavior after the adoption of new
technologies which facili
The digital revolution has often been heralded for the transformation it has implied for
digital advertising. In particular, the ability to collect data about the individual, parse it
automatically and then serve ads on that basis has trans
The Implications of Improved Attribution and
Measurability for Online Advertising Markets
November 20, 2012
Digital data has transformed the ability of advertisers to assess the performance of
their online paid and display advert