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Do Display Ads Influence
Search? Attribution and
Dynamics in Online
Advertising
Pavel Kireyev
Koen Pauwels
Sunil Gupta

Working Paper
13-070
February 9, 2013

Copyright © 2013 by Pavel Kireyev, Koen Pauwels, and Sunil Gupta
Working papers are in draft form. This working paper is distributed for purposes of comment and
discussion only. It may not be reproduced without permission of the copyright holder. Copies of working
papers are available from the author.
 
 
 
 
Do Display Ads Influence Search? 
Attribution and Dynamics in Online Advertising 
 
Pavel Kireyev 
 Koen Pauwels 
Sunil Gupta1 
 
February 9, 2013 

 

 

                                                            
1

 Pavel Kireyev is a Ph.D. student and Sunil Gupta is the Edward Carter Professor of Business Administration at the 
Harvard Business School, and Koen Pauwels is Professor at Ozyegin University, Istanbul, Turkey. 
Do Display Ads Influence Search? 
Attribution and Dynamics in Online Advertising 
Abstract 
As firms increasingly rely on online media to acquire consumers, marketing managers 
feel comfortable justifying higher online marketing spend by referring to online metrics such as 
click‐through rate (CTR) and cost per acquisition (CPA). However, these standard online 
advertising metrics are plagued with attribution problems and do not account for dynamics. 
These issues can easily lead firms to overspend on some actions and thus waste money, and/or 
underspend in others, leaving money on the table.  
We develop a multivariate time series model to investigate the interaction between 
paid search and display ads, and calibrate the model using data from a large commercial bank 
that uses online ads to acquire new checking account customers. We find that display ads 
significantly increase search conversion. Both search and display ads also exhibit significant 
dynamics that improve their effectiveness and ROI over time. Finally, in addition to increasing 
search conversion, display ad exposure also increases search clicks, thereby increasing search 
advertising costs. After accounting for these three effects, we find that each $1 invested in 
display and search leads to a return of $1.24 for display and $1.75 for search ads, which 
contrasts sharply with the estimated returns based on standard metrics. We use these results 
to show how optimal budget allocation may shift dramatically after accounting for attribution 
and dynamics. Although display benefits from attribution, the strong dynamic effects of search 
call for an increase in search advertising budget share by up to 36% in our empirical context.  
 

 

 

1
Introduction 
Firms are motivated to spend more of their marketing budget online as consumers 
increasingly use online media to find information. Worldwide digital advertising spending in 
2012 was $103 billion, or about 20% of total money spent on advertising, and is expected to 
increase to $163 billion, or 25% of total advertising spend, by the end of 2016 (eMarketer 2013). 
In 2012, almost half of all digital ad dollars worldwide were spent on paid search, and 38% were 
used for display ads (ZenithOptimedia 2012). 
The introduction of online metrics such as click through rate (CTR) and cost per 
acquisition (CPA) by Google and other online advertisers has made it easy for marketing 
managers to justify their online ad spend in comparison to the budgets used for television and 
other media. However, these metrics suffer from the fundamental problem of attribution, since 
they give credit to the last click and ignore the impact of other ad formats that may have helped 
a consumer move down the conversion funnel. Consider, for example, a consumer searching 
online for a bank to open a new checking account. During this search, the consumer sees a paid 
search ad for a particular bank, clicks on it, and converts, recalling that she saw display ads of 
the same bank a few weeks earlier. How should search and display ads be credited for the 
conversion, and to what extent?  
Most managers recognize the attribution problem, and intuitively believe that display 
and search ads interact to influence consumers. Recently, analytical firms and ad agencies have 
started addressing this problem, but most of their solutions tend to be ad‐hoc. For example, 
some industry models give equal weight or credit to all ad exposures received by a consumer in, 
say, a two week period; others give more weight to recent ad exposures and exponentially 
lower weight to past ads (Havas Digital 2010).  
As firms spend more of their ad dollars on online search and display, managers and 
researchers alike recognize a need for more careful attribution adjustment that takes into 
account the journey consumers follow before conversion. Unfortunately, the effects different 
advertising media have on consumers at different stages of consideration are not yet well 

 

2
understood (Marketing Science Institute 2012). In this research we use time series models to 
infer the interaction between search and display ads. Specifically, we address the following 
questions: 


Do display ads influence paid search and vice versa? 



If so, how large are these effects and what dynamic patterns do they follow? 



What are the implications for online marketing metrics and optimal budget allocation? 
This research draws broadly on two streams of literature – online advertising 

effectiveness (specifically to our context, display and search) and the spillover effects of online 
advertising.   
In the context of display ads, researchers have studied the impact of ad exposure on 
click‐through behavior (Chatterjee et al. 2003), long‐term brand awareness (Drèze and Hussherr 
2003), and repurchase decisions (Manchanda et al. 2006). Research has also explored the 
potential of targeted display advertising (Sherman and Deighton 2001, Shamdasani et al. 2001, 
Moore et al. 2005) and the consequences of its intrusiveness (Edwards et al. 2002, Goldfarb and 
Tucker 2011a, 2011b). Lewis and Riley (2011) use a randomized experiment to measure the 
causal effect of online display advertising on offline retail sales.  
In the context of paid search, researchers have focused on understanding optimal 
advertising strategy in complex search engine environments. Ghose and Yang (2009) and Rutz 
et al (2012) adopt a keyword‐specific approach to understand the performance of individual 
keywords and guide optimal keyword investment decisions. Further work examined spillover 
within search. Yang and Ghose (2010) identify complementarities across organic and paid 
search listings, and Rutz and Bucklin (2011) find spillover effects from generic search to branded 
search. Wiesel et al (2011) model consumer progression through the purchase funnel, and 
explain how online advertising may drive sales in the offline channel. In contrast to the above 
study, which mainly examines advertising effectiveness within a particular online channel, we 
study ad effectiveness taking into account the interaction and feedback between both search 
and display channels. Furthermore, we focus on the role of search and display advertising in 

 

3
customer acquisition in the commercial banking industry, where consumer decision process 
tends to be longer and more involved, and the attribution problem is more severe. 
Several studies consider spillovers and synergies in online and offline consumer 
behavior. Naik and Peters (2009) propose a hierarchical model to capture synergies within the 
offline channel and across online and offline channels. Their model builds on earlier work (Naik 
and Raman 2003), and argues that investing in offline and online advertising simultaneously 
generates greater revenues than investing in each channel individually.  
A small number of studies examine the interaction between paid search and display. 
Papadimitrou et al (2012) conduct a field experiment to explore the impact of display exposure 
on search queries. They find that exposure to a display ad increases the number of relevant 
search queries submitted by 5%‐25%. Lewis and Nguyen (2011) conduct a field experiment to 
explore the impact of display advertising on advertiser‐ and competitor‐branded search queries. 
Using a very short time window (10 minutes) they find a 27%‐45% lift in searches attributable 
to the display advertising exposure. A number of studies conducted by industry researchers also 
explore the impact of display advertising on paid search. A survey conducted by iProspect 
(2009) finds that about 50% of all internet users react to a display ad by conducting a search 
related to the brand or product described in the ad. The study finds that 14% of users make a 
purchase after conducting the search. A study by comScore (Fulgoni and Mom, 2008) finds a 
38% lift in branded search activity for consumers exposed to a display ad. The study tracked 
individual consumers exposed to a display ad, and compared their behavior with a group of 
similar consumers not exposed to display advertising. An iCrossing study (Malm and Hamman 
2009) finds a 14% change in search visits after a company activated its display advertising 
campaign. In our application for the bank, the bank’s ad agency also conducted an experiment 
to find that display ads improve search ad conversion by 15‐20%. 
As these studies suggest, display ads appear to influence the effectiveness of search ads. 
However they lack two important elements that we consider in our study. First, almost all of 
these studies ignore the dynamic effects of advertising, whereby display ads may impact 
consumers’ search behavior over time. Studies that attempt to incorporate dynamics do so in 

 

4
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