10.2 Econometric Approach
10.2.1 Product Placements
(Fossen and Schweidel 2019a) Measuring the Impact of Product Placement with Brand-Related Social Media Conversations and Website Traffic
Investigation of relationship between product placement in TV programs and social media activity/website traffic for featured brand
Study uses data on nearly 3,000 product placements for 99 brands from fall 2015 television season
Results show that prominent product placements, especially verbal placements, lead to increases in online conversations and web traffic
Decreasing returns observed at high levels of prominence
Most placement modalities not enhanced by TV advertising in close proximity to placement activities.
10.2.2 Deceptive Advertising
Regulatory reports of deceptive advertising have negative impact on stock market performance, but brand reputation can mitigate this negative effect (Wiles et al. 2010)
Fake news advertising are those that “mimics the format of its surrounding news content in both digital and print media, promotional segments aired on news programs without disclousre to the public.” (p. 534)
Research question: What happens when the fake news ad path is shutdown?
Data:
Browsing data: comScore
Purchase data: Ripoff Report, Complaints Board
Ad spend: Neilsen AdSpend data
keep only first visits.
Total site visits = organic visits + fake news ad referrals + regular ad referrals.
\[ \begin{aligned} Q_{merchant} &= Q_{org} + Q_{fn} + Q_{reg} \\ Q_{org} &= \alpha_{org} + \gamma_1^{fn} Ad_{fn} + \gamma_1^{reg} Ad_{reg} \\ Q_{fn} &= \alpha_{fn} Ad_{fn} + \gamma_2^{reg} Ad_{reg} I(Ad_{fn} > 0) \\ Q_{reg} &= \alpha_{reg} Ad_{reg} + \gamma_3^{fn} Ad_{fn} I(Ad_{reg} > 0) \end{aligned} \]
where
\(fn\) = fake news
\(reg\) = regular advertising
\(Q\) = demand
\(\alpha\) = direct effect
\(\gamma\) = spillover effect
Two effects of interest of fake news
Treatment Effect: directly to \(Q_{fn}\), spillover to organic searchers \(\gamma_1^{fn}\) or regular ad referrals \(\gamma_3^{fn}\)
Selection Effect: In the absence of fake news, use other channels (substitution effect), 2 segments
Those who will use other channels (e.g., regular)
Those who will stop
In the presence of fake news,
Results
Fake news ads and regular ads are substitutes (i.e., increase in regular ad referrals after FTC shutdown)
Probability of a merchant receiving a complaint declines by 8% after the FTC shutdown (somewhat mechanistically)
The selection effect is small (i.e., substitution to the regard ad channel is dominated by the decline in organic demand + fake news ad referrals)
Supply side: no change in spend, duration and impressions from the customers, or new fake news ad campaigns from merchants.
Robustness
Negative publicity (from the press) does not explain visits reduction.
Google might change algorithm because of the FTC shutdown. Hard to know. Hence, claim that the measured decline is the upper bound of the true impact of the FTC regulations.
Might consider using logistic regression in rare events data here (G. King and Zeng 2001) (but this is a borderline case since they have 95% 0, whereas rare events are usually 99% 0).
10.2.3 Advertising Effects
A. Mitra and Lynch, Jr. (1995)
Advertising affect price elasticity via:
the size of consideration set
the relative strength of preference
Anand and Shachar (2011)
- Horizontal differentiation: “advertisement (informational role) decreases the consumer’s probability of not choosing her best alternative by approximately 10%.”
(Gopinath, Chintagunta, and Venkataraman 2013) Blogs and Advertising on Movie Performance
Objective:
- Examine how pre- and postrelease blog activity and advertising affect the box office performance of 75 movies across 208 U.S. geographic markets.
Factors in Consideration:
Blog Volume: Quantity of blog discussions.
Blog Valence: Sentiment orientation of blogs.
Advertising: Promotional efforts by studios.
Demographics:
- Market differences are attributed to variations in demographic characteristics which influence access to, exposure to, and response to blogs.
Time Phases:
Prerelease Factors: Influence on opening day box office performance.
Postrelease Factors: Impact on box office performance one month after release.
Methodology:
- Use of instrumental variables to account for potential confounders in measurement.
Key Findings:
Heterogeneity in Effects: Differences are found across consumer and firm-generated media and across various markets. Variations are influenced by demographics like gender, income, race, and age.
Release Day Impact: Prerelease blog volume and advertising have the most pronounced effect on opening day performance.
Postrelease Impact: Performance a month post-release is influenced by postrelease blog sentiment and advertising.
Variation in Elasticities: Greater variance exists in advertising and postrelease blog valence than in prerelease blog volume across markets.
Most Responsive Markets: 20 markets are identified as having the highest sensitivities to advertising, prerelease blog volume, and postrelease blog valence.
Market Classification: Markets are classified based on their responsiveness to these factors, aiding studios in targeting for limited release strategies.
Overlap Analysis: Studios, at first release, only cater to 53% of the most responsive advertising markets and 44% of the most responsive prerelease blog volume markets, suggesting room for optimization in their limited release strategies.
Implications:
Movie studios can greatly benefit from understanding market sensitivities to blogs and advertising.
Given the clear impact of both pre- and postrelease blog activity, studios should consider integrating them into their promotional strategies.
A more nuanced approach to market targeting can help studios maximize their box office returns, especially in limited release scenarios.
Lewis and Reiley (2014)
(Gopinath, Thomas, and Krishnamurthi 2014) Impact of Online Word of Mouth vs. Advertising on Firm Performance
Objective:
- Analyze the comparative significance of consumer-generated online word of mouth (OWOM) and advertising on firm performance over time post-product launch.
OWOM Distinctions:
Volume: Total quantity of conversations.
Valence: Specific sentiment orientation.
Attribute-oriented: Focus on product features.
Emotion-oriented: Relates to feelings or experiences.
Recommendation-oriented: Direct suggestions or endorsements.
Advertising Content Classifications:
Attribute Advertising: Rational, feature-based messages.
Emotion Advertising: Affective, sentiment-focused messages.
Analytical Approach:
Utilized a Dynamic Hierarchical Linear Model (DHLM).
Benchmarked DHLM against dynamic linear model, vector autoregressive model, and a generalized Bass model.
Addressed potential endogeneity concerns in key metrics.
Key Insights:
OWOM’s Direct Impact: Only the valence of recommendation-based OWOM directly affects sales, emphasizing that not all OWOM types are equally influential.
Time Factor: The influence of recommendation OWOM increases with time, while the impact of both attribute and emotion advertising wanes.
Ad Message Wear-out: Rational messages (attribute-oriented) exhibit faster wear-out than emotion-oriented advertisements.
Volume vs. Content: The overall volume of OWOM doesn’t significantly impact sales. Instead, the sentiment or “what people say” is more crucial than sheer quantity or “how much people say”.
OWOM Valence Dynamics: Initially, recommendation OWOM is influenced by attribute valence. As the product ages, emotion valence becomes dominant.
Brands Classification:
Based on the research outcomes, brands can be categorized as:
Consumer Driven: Where OWOM is the dominant influencer.
Firm Driven: Where advertising efforts are more impactful.
Implication:
- Brands need to be aware of the evolving nature of OWOM and adjust their advertising strategies accordingly, emphasizing the kind of message being communicated over mere volume.
Draganska, Hartmann, and Stanglein (2013)
N. Sahni, Zou, and Chintagunta (2014)
Hoban and Bucklin (2015)
Stephens-Davidowitz, Varian, and Smith (2017)
W. R. Hartmann and Klapper (2018)
(Fossen and Schweidel 2017) Advertising Effect on WOM
Research focuses on the link between TV advertising and online word-of-mouth (WOM).
Concept introduced: “social TV” (joint TV program consumption and social media activity).
Goals:
Determine how TV ads affect online WOM about brands and programs.
Identify what boosts or hinders viewer engagement in social TV.
Data used:
TV advertising instances.
Minute-by-minute social media mentions.
Key findings:
TV ads influence online WOM for the brand and the program.
Most talked-about programs online aren’t always best for brand engagement.
Specific brand, ad, and program traits can promote or deter social TV engagement.
(Fossen and Schweidel 2019b) Social TV,Advertising, and Sales
Television viewers often engage in media-multitasking, especially using another screen.
“Social TV” is defined as simultaneous TV viewing and social media discussions about the program.
While social TV can increase audience engagement, it might also divert attention from ads.
Central research question: Are “social shows” (programs with high social TV activity) beneficial for advertisers?
Study examines:
Connection between TV advertising, social TV, online traffic, and online sales.
Impact of program-related online discussions on online shopping for advertisers during the show.
Key findings:
Ads in programs with higher social TV activity lead to greater online shopping engagement.
Ad responsiveness is influenced by the ad’s mood.
Affective ads, especially funny and emotional ones, drive the most online shopping activity.
(Lu et al. 2022) Frenemies: Corporate Advertising Under Common Ownership
Ownership structure impacts corporate advertising expenditures.
Mutual fund mergers serve as an exogenous shock to ownership structure.
Competing firms with common institutional blockholders experience reduced advertising spending.
Reduction is more likely in industries with higher competition and advertising intensity.
Greater common ownership and concentrated institutional ownership intensify the effect.
Firms located in the same state show a stronger impact on advertising strategy.
(Fossen et al. 2022) Political advertising effectiveness
Objective: Understand the connection between TV political ad content and ad effectiveness, focusing on message slant and alignment with primary campaign messaging.
Metrics Evaluated:
Online word-of-mouth (WOM)
Voter preference (via daily polls)
Dataset: 2016 presidential election.
Key Insights:
Centrist Messages: Ads with a more moderate tone correlate with increased online WOM and voter preference for the respective candidate.
Consistency: Ads mirroring the candidate's primary-election messages associate with gains in online WOM and voter preference.
Temporal Significance of Consistency: Ad messaging consistency is crucial in the early campaign phase (before October).
Post-Primary Messaging Strategy: While moderation post-primary might benefit candidates, abandoning primary messaging early in the general election can be detrimental.
Implication on Extreme Messaging:
- Despite its growing popularity, extremist political advertising may harm candidate WOM and preference.
(Jindal and Slotegraaf 2023) Impact of Marketing Investments on Bankruptcy Spillovers:
Objective:
- Examine how a firm’s marketing investments, specifically in advertising and R&D, influence the spillover effects when a competitor faces bankruptcy.
Context:
- While the relationship between marketing investments and a firm’s own bankruptcy is well-studied, little is known about how these investments affect spillover outcomes from a rival’s bankruptcy.
Key Findings:
Contrasting Effects: In the realm of bankruptcy spillovers, the typical positive impacts of advertising and R&D can manifest differently—both positively and negatively.
Advertising:
Industry Growth as a Moderator:
In low-growth industries, advertising tends to decrease a firm’s stock return when a competitor files for bankruptcy.
Conversely, in high-growth industries, advertising can increase a firm’s stock return under similar circumstances.
R&D:
Industry Concentration as a Moderator:
In low-concentration industries, R&D investments decrease a firm’s stock return when a rival goes bankrupt.
However, in high-concentration industries, R&D can enhance the firm’s stock return.
Advertising in High-Concentration Industries: Advertising exerts a more potent impact on stock returns in industries that have higher concentration.
10.2.4 Estimation of Advertising Effects
Shapiro (2016)
Blake and Coey (2014)
Lewis and Rao (2015)
Lewis, Rao, and Reiley (2011)
Guitart and Stremersch (2021)
10.2.5 Spillovers
Kitts et al. (2014)
Joo et al. (2014)
N. S. Sahni (2016)
S. Yang and Ghose (2010)
Ghose and Todri-Adamopoulos (2016)
Oliver J. Rutz and Bucklin (2011)
(Fossen, Mallapragada, and De 2021) Spillover Effects of Political Ads on TV
Context: Concerns about the influence of political TV ads on viewers’ responses to subsequent advertisements.
Objective: Examine how political TV ads impact:
Subsequent ad viewership.
Online conversations about subsequent ads.
Data Source:
- 849 national political TV ads from the 2016 election.
Methodology:
- Quasi-experimental design to assess the impact of a political ad on the following ad.
Core Findings:
Political ads lead to positive spillover effects.
Ads after a political ad see an 89% reduction in audience decline, reaching a larger audience.
A 3% increase in positive online chatter is observed for ads airing after political ads.
Contribution:
Fills the gap in advertising research regarding ad-to-ad spillover effects.
Offers insights into the influence of political messages on consumers.
(Ghosh Dastidar, Sunder, and Shah 2023) Societal Spillovers of TV Advertising
Research Question: Can TV advertising influence societal outcomes, especially beyond typical marketing results like sales and brand awareness?
Context: Analyzing the effect of TV advertising during the COVID-19 pandemic.
Data Source:
Daily advertising and mobility data.
2,194 counties across 204 designated market areas in the U.S.
Methodology:
- Used a border identification strategy to leverage discontinuities across TV markets.
Key Findings:
TV ads with COVID-19 narratives positively affect social distancing behaviors.
Effects are 11 times larger in counties without government interventions (e.g., mask mandates, shelter-in-place) compared to those with interventions.
Overall effect of government ads on social distancing is nonsignificant, but:
Effect is negative with policy interventions.
Effect is positive without policy interventions.
Robustness: Findings are consistent across different model specifications, variable definitions, and data considerations.
Implications:
TV ads, particularly from brands, can have spillover effects during significant public crises.
Brand-sponsored TV ads can compensate for the absence of local government policies.
Advertising can potentially enhance public health outcomes and promote societal well-being.
10.2.6 Attribution of Advertising Effects
Simonson et al. (2001)
Lambrecht and Tucker (2013)
H. (Alice). Li and Kannan (2014)
Blake, Nosko, and Tadelis (2015)
Zantedeschi, Feit, and Bradlow (2017)
(Fossen and Bleier 2021) Online Program Engagement and Audience Size During Television Ads
Research topic: Connection between television viewers’ online program engagement (OPE) and the audience size during ads of those programs.
Data source:
8417 ad instances.
OPE activity metrics (Twitter mentions about the program).
Audience size during the advertisements.
Core findings:
Increase in OPE volume and positive deviations from an episode’s average OPE before an ad are associated with a larger ad audience size.
OPE is indicative of viewers’ program involvement, which reduces their likelihood to change channels during ads.
Positive OPE deviations result in larger ad audiences, especially for the initial ads in a commercial break.
Implication: TV networks and advertisers can enhance ad audience size by strategically placing ads during “social episodes” (high OPE volume) and “social moments” (positive OPE deviations).