Digital Advertising and Firm Size

Last registered on November 06, 2022
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Pre-Trial

Trial Information

General Information

Title
Digital Advertising and Firm Size
RCT ID
AEARCTR-0010260
Initial registration date
October 18, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 06, 2022, 6:55 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
Argentina
Region
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Australia
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Brazil
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Canada
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Germany
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Spain
Region
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France
Region
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United Kingdom of Great Britain and Northern Ireland
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Indonesia
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India
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Italy
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Mexico
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Poland
Region
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United States of America
Region
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Viet Nam
Region

Primary Investigator

Name
Asad Liaqat
Affiliation
Meta Platforms Inc.
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Jean-Pierre Dubé
PI Affiliation
The University of Chicago Booth School of Business
Contact Investigator
PI Name
Sarah Moshary
PI Affiliation
Haas School of Business, UC Berkeley
Contact Investigator
PI Name
Nils Wernerfelt
PI Affiliation
Meta Platforms, Inc.
Contact Investigator

Additional Trial Information

Status
In development
Start date
2022-10-17
End date
2023-06-30
Keywords
Firms & Productivity
Additional Keywords
Advertising, Business Strategy
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We test whether the return to digital advertising differs by firm size using data from a large-scale randomized control trial on Facebook. We conduct thousands of experiments that randomly assign users to test and control conditions, where treatment users are eligible to see ads from the advertiser in question but control users are not. We then conduct a meta-analysis using the cross-section of advertising treatment effects to test for a systematic firm-size difference. Of interest is whether advertising effects are systematically larger for smaller firms. Our findings will shed light on whether digital advertising lowers the marketing barriers to entry that have facilitated market concentration in 20th century consumer goods markets.
External Link(s)

Registration Citation

Citation
Dubé, Jean-Pierre et al. 2022. "Digital Advertising and Firm Size." AEA RCT Registry. November 06. https://doi.org/10.1257/rct.10260-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
For each advertiser in our sample, we will randomly allocate a small fraction of their ad budget across users on the Facebook platform, including holding some users out from seeing the ads.
Intervention Start Date
2022-10-19
Intervention End Date
2022-10-26

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes of interest are cost per incremental customer and return on advertising spending.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will select a number of advertisers at random from Facebook who either answered our survey or are similar on observables, where we will stratify by firm size where applicable. For each advertiser in our sample, we will randomly allocate a small fraction of their ad budget across users on the Facebook platform, including holding some users out from seeing the ads. Using this sample of randomly-allocated ad spend, we will estimate the following equation for each advertiser:

Conversion_{ic} = a_c +b_c*AdExposure_{ic}+e_{ic}

Where i denotes the consumer and c denotes that company. The LHS variable will be measured both as a purchase indicator and using spending in USD where applicable.

We can then plot the estimated ad effects (b_c) against our measure of firm size (scatterplot). To test whether ad effects differ systematically by firm size, we then regress the estimated ad effect coefficients (b_c) on our measures of firm size:

\hat{b}_c = \alpha + \sum_{b=1}^B \beta_b * size_{bc} + \epsilon_{c}.

It will be important to correct for the fact that our dependent variable is a generated variable. We are interested in testing whether we can reject the null hypothesis that the coefficients on the size indicators are zero.

To allow a more flexible analysis and incorporate shrinkage, we may also explore using empirical Bayes methods (e.g., Efron (2016)) as other recent digital advertising studies have.
Experimental Design Details
Randomization Method
Randomization done using Facebook internal experimentation infrastructure.
Randomization Unit
Advertiser
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
8,000 advertisers
Sample size (or number of clusters) by treatment arms
8,000 advertisers
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials