From Ads to Action: Evaluating How Direct-to-Consumer Drug Advertising Influences Awareness and Prescription Trends
Advisor: Moshofsky
Internship Location: University of California, Los Angeles
Onsite Mentor: Dr. Franklin Ow; Chemistry Professor; University of California, Los Angeles
Tired of restless nights? Ever feel like nothing you try actually works? What if you could find lasting relief without the side effects?” From catchy slogans to lengthy lists of medical disclaimers, direct-to-consumer (DTC) pharmaceutical advertisements appear constantly throughout everyday life. This research project examines the impact of DTC pharmaceutical advertising on consumer awareness of the drug or illness featured in these commercials, with a central focus on whether analytical comparison using online metrics supports or contradicts the results of previous findings with alternate methods. It will assess whether the funds spent on such advertising meaningfully contribute to producing lasting health effects through expanded medication consumption.
The study will employ a novel methodological approach that contrasts previous methodologies that solely rely on self-reported or hypothetical behavior with internet-based research and statistical analysis of open-access data. Awareness and public interest will be measured using online platforms such as Google Trends, records of advertisement airtimes from closed-caption tracking databases, and social media keyword frequency to track the spread and persistence of advertisement acknowledgement over time. By analyzing these publicly available datasets, the project will evaluate the correlations among advertisement airtime, online public interest, and prescription behavior. The final product will be an informed report that demonstrates the extent to which DTC advertising influences public awareness as determined by an analysis of open-access data.
Project Blog
Week 1: Cue the Commercials
Hello everyone! My name is Caitlin and welcome to the first week of my senior project blogs!
Since you’ve decided to start reading my blog, it probably means that you’re at least a little interested in what those Ozempic ads on your TV have to offer beyond videos of patients singing in unison in a park behind long lists of medical disclaimers. If so, you’re definitely in the right place!
Background
Before we start our deep dive into the rabbit hole of direct-to-consumer (DTC) pharmaceutical advertisements, I’ll start off with a brief introduction about myself. I’m currently a senior at BASIS Independent Silicon Valley, who has an intense passion for chemistry and healthcare. After seeing how global pandemics and various natural phenomena have affected my loved ones, I aim to dedicate myself to a future of combatting health concerns worldwide. Over the past few years, I’ve become increasingly more interested in healthcare and the realistic concerns surrounding its industry, especially in light of recent socioeconomic events.
As a high schooler and aspiring chemist, I’ve already had my fair share of hands-on lab experiments in and outside of school classes like AP Chemistry and Biology. From researching neurodegenerative diseases in a computational setting to conducting five hour long experiments concerning the efficacy of an anti-cancer drug in a lab, I aim to gain a better understanding of what medically plagues our lives and how we can attack these concerns firsthand.
Context
Returning back to my project, what comes to your mind when I say direct-to-consumer (DTC) advertisements? Right now, you’re probably thinking about the 1 minute ads that you see during ad breaks while watching your favorite shows. Similarly, that thought process is how this project came to be!
As an impassioned chemist, avid binge-watcher, and cinephile, I have chosen this project because while watching shows from Survivor to Brooklyn 99, I frequently encounter the typical DTC ad during commercial breaks. These ads particularly caught my attention, and I started to get interested in whether they have a real-world significant impact on who they are advertising the drug to or if the extensive list of disclaimers accompanied by sing-songy tunes are ineffective in their attempts to garner interest.
This research project will analyze the effect of DTC pharmaceutical advertising on consumer awareness of the drug or illness featured in these commercials. With a primary focus on whether analytical comparison using online metrics supports or contradicts results of past findings that apply alternate methods, the project will assess whether such advertising does meaningfully contribute to producing lasting health effects through increases in prescription uptake.
While there are several past scientific papers analyzing what I seek to research, my research path is novel in that past conclusions are often broad as they focus on self-reported awareness records tracked by surveyable and hypothetical behavior. In contrast, through my study, I intend to focus on behavioral digital evidence. So while prior research establishes that DTC advertising increases awareness and prescriptions in general, my project aims to examine that extent and the strength of these ads’ influence with digital attention metrics in relation to advertising exposure.
Inquiry
The principal questions that I hope to answer through my methodology include the following:
To what extent does direct-to-consumer (DTC) pharmaceutical advertising influence public awareness of the mentioned drug/illness? Does increased public awareness from DTC advertising cause an increase in prescription of the drug being advertised? Specifically, does an analysis of online data regarding DTC advertisement’s effectiveness support or contradict previous findings with alternate methodologies?
Answer to the Ads
I hypothesize that DTC ads will increase public awareness and consumer demand minimally, not necessarily displaying any long-term lasting outcomes. I also do believe that my methodology will yield similar results in comparisons to past methods.
The final product of my study will be an informed report that demonstrates the extent to which DTC advertising influences public awareness as determined by an analysis of open-access data, distinguishing whether my hypothesis is correct or not.
That’s it for now! I’m looking forward to updating you all with my progress across these next months, starting with rewinding the tape and taking a look at previous studies in my next blog!
Week 2: Rewinding the Tape
Welcome back, curious consumers! This week we’re taking a step back from the commercials themselves and taking a look at what researchers have already discovered in the past about their impact. More specifically in this blog, I will be referring to three important articles from my selected course texts in particular. With each article, I have prioritized focusing on three main important aspects related to my senior project, namely measurable indicators of public awareness, advertising exposure, and prescription uptake.
(1) Donohue et al. (2007): “A Decade of Direct-to-Consumer Advertising of Prescription Drugs”
An important source for understanding the history and scale of direct-to-consumer pharmaceutical advertising comes from Donohue’s “A Decade of Direct-to-Consumer Advertising of Prescription Drugs.” The central claim of this article is that DTC advertising rapidly increased in the United States during the late 1990s and early 2000s and gradually has become an important marketing strategy for pharmaceutical companies today.
Using national promotional spending data, Donohue found that overall pharmaceutical promotion spending increased from $11.4 billion in 1996 to $29.9 billion in 2005, while spending on DTC advertisements alone has increased by about 330%. These significant increases in spending highlight how heavily drug manufacturers began relying on advertising directed towards consumers. This particularly establishes a measurable indicator of advertising exposure through advertising spending. While my project measures exposure using advertising airtime instead of spending, both these approaches provide a way to quantify how much advertising consumers are exposed to.
(2) Mintzes et al. (2003): “How Does Direct-to-consumer Advertising (DTCA) Affect Prescribing? A Survey in Primary Care Environments With and Without Legal DTCA”
Mintzes’s study examines how DTC advertising affects prescribing decisions in primary care settings, directly aligning with what my study aims to decipher in a broader setting. This DTCA survey compares physicians and patients in the US, where DTC advertising is legal, with those in Canada, where advertising is heavily regulated.
Using physician surveys, researchers evaluated how often patient requests influenced various prescription decisions. The results showed that patients exposed to more pharmaceutical advertising were more likely to request specific medications, and these requests frequently led to more prescriptions. For example, patients in Sacramento requested medications more often than patients in Vancouver, where exposure to advertising is significantly lower. The results from these surveys specifically highlight the relationship between advertising exposure and prescription uptake. It demonstrates how patient requests can play a significant role in treatment decisions, suggesting that advertising may indirectly influence prescription rates by encouraging patients to initiate conversations with physicians.
(3) Robinson et. al (2004): “Direct-To-Consumer Pharmaceutical Advertising: Physician and Public Opinion and Potential Effects on the Physician-Patient Relationship”
An additional important perspective from Robinson’s article examines both physician and public attitudes toward pharmaceutical advertising.
Through surveying a number of households and physicians, the study found that opinions about DTC advertising are generally mixed. Physicians typically viewed these advertisements negatively and believed that they could complicate future interactions with patients. In contrast, some members of the public reported that advertisements helped them become more informed about medical issues. Specifically, about 29% of respondents believed DTC advertising was a positive trend, and 28.6% said advertisements made them better informed about medical conditions. These findings highlight how DTC advertising can influence public awareness and patient engagement, despite some medical professionals remaining skeptical about their benefits.
Bridging the Research Gap
While these studies all provide valuable insights into DTC pharmaceutical advertising, many researchers in these articles rely on surveys, physician reports, or other forms of self-reported behavior to measure its impact. Although these methods are useful, they may not always fully capture the extent to which consumers are aware of advertisements and their contents.
On the other hand, my project aims to address this gap by analyzing behavioral digital evidence, specifically search trends, social media key word frequencies, advertising airtime tracked by closed-caption databases, and prescription sales data. By comparing advertising exposure with various measurable indicators of interest and financial records, I hope to determine whether today’s modern digital metrics support or challenge the conclusions of previous studies.
Next week, we’ll move beyond past studies and begin outlining the methodology that will guide my own investigation into the impact of direct-to-consumer pharmaceutical advertising. Until next time!
Citations
Donohue, Julie M., et al. “A Decade of Direct-to-Consumer Advertising of Prescription Drugs.” The New England Journal of Medicine, vol. 357, no. 7, Aug. 2007, pp. 673–81. https://doi.org/10.1056/nejmsa070502.
Mintzes, Barbara, et al. “How Does Direct-to-consumer Advertising (DTCA) Affect Prescribing? A Survey in Primary Care Environments With and Without Legal DTCA.” PubMed, 2 Sept. 2003, pubmed.ncbi.nlm.nih.gov/12952801.
Robinson, Andrew R., et al. “Direct-To-Consumer Pharmaceutical Advertising: Physician and Public Opinion and Potential Effects on the Physician-Patient Relationship.” JAMA Internal Medicine, vol. 164, no. 4, 23 Feb. 2004, pp. 427–432, https://doi.org/10.1001/archinte.164.4.427.
Week 3: Breaking down the Broadcast
Hey hey binge watchers! In this week’s blog, I’ll be breaking down the methodology that has and will continue to serve as the foundation for the next several weeks of my research. I will also explain why I chose this method and highlight a few challenges I expect to encounter along the way.
Type of Method
For this project, I am focused on applying a quantitative, data-based correlational analysis method. Rather than relying on surveys or self-reported behavior like the previous studies I reviewed in my past post, my approach aims to analyze open-access digital data to identify measurable patterns between advertising exposure, public awareness, and prescription rates.
I chose this method because it particularly aligns with my research goals to determine whether DTC advertising influences public awareness and evaluate whether modern metrics support or contradict findings from traditional research methods. By using behavioral data, such as search trends and sales figures, I aim to capture how people actually respond to these advertisements in real time.
Stage 1: Compiling Data
The first stage of my methodology focuses on collecting various relevant data across a number of sources. I have begun by selecting 1–3 prescription drugs that have actively been advertised on television within the past few years.
From there, I will collect three other main elements. I will gather advertising airtime data using closed-caption tracking databases to measure how frequently each drug is advertised on each day of the years I have selected to study. I will collect public awareness data through Google Search trends and other social media platforms (namely Twitter (X), Reddit, and Facebook) using keyword and hashtag frequency. Lastly, I will record prescription or sales rate data by gathering records of the annual sales or market value figures from publicly available pharmaceutical company financial reports.
Stage 2: Statistical Analysis
Once all the data is collected, I will organize it into a singular dataset and begin analyzing trends over time. Advertising frequency will be compared directly with search trends and social media activity to determine whether increases in advertising directly correspond to spikes in public interest.
To address the second half of my question, I will also graph sales figures against advertising frequency to evaluate whether increased exposure is associated with increased drug usage over a period of years. Additionally, to further assess the strength of these relationships, I plan to conduct a statistical analysis, specifically a t-test, to compare public awareness during advertising periods versus periods with no advertising periods.
Through these analyses, awareness will be measured by percentage changes in search activity and sales, allowing for a clearer interpretation of how strongly advertising impacts consumer behavior.
Stage 3: Drawing Conclusions
In the final stage, I will interpret the results from my statistical analysis to determine whether significant relationships between advertising, awareness, and prescription uptake truly exist.
If increases in advertising rates consistently align with rises in search activity and sales, this would suggest that DTC advertising plays a significant role in influencing public awareness and consumer demand. I will also compare my findings to previous research to evaluate whether digital data supports or contradicts traditional survey-based methods.
Limitations
While my method offers a modern and data-driven approach, there are a few challenges I have anticipated and encountered.
One main limitation is the availability of data, particularly when it comes to drug-specific prescription rates. Some pharmaceutical companies do not publicly disclose detailed financial data, especially regarding prescription uptake, for individual drugs, which may slightly limit the precision of my analysis. To address this, I’ve planned on focusing on drugs with the most accessible data and using broader financial reports as approximations when necessary. Another challenge is the time-intensive nature of my data collection process. Since much of the data must be gathered manually from various platforms, the process may take longer than I originally expected. To manage this, I will continue to stay organized and prioritize collecting data early on in my research timeline.
With the framework now in place, the next step is putting my methodology into action. Stay tuned as we move on from planning to data collection next week!
Week 4: Analyzing Airtime
Greetings, airtime analysts! Now that our framework is set, we can begin exploring what public databases have to offer about the selected drugs of choice. Over the past weeks, I’ve identified three drugs that are advertised direct-to-consumer with relatively similar amounts of airtime data for consistent comparisons. After selecting these drugs, I manually collected data on how often a commercial for a drug appeared on television over a two-year period, specifically 2023 and 2024.
Selected Drugs
The specific drugs I have chosen to study across the course of my project are Ozempic®, SKYRIZI®, and DUPIXENT®.
Ozempic® (semaglutide) is a GLP-1 injection for adults with type 2 diabetes. It is proven to lower blood sugar and A1C; lower the risk of major cardiovascular events like stroke, heart attack, or death in adults who already have heart disease; and lower the risk of worsening kidney disease and other complications. Its recent widespread advertising and strong presence in today’s media and online discussions make it a key drug for analyzing public awareness trends.
SKYRIZI® (risankizumab‐rzaa) is a biologic used to treat moderate to severe plaque psoriasis, psoriatic arthritis, and Crohn’s disease. Its advertisements are widely recognized to this day for their consistent messages and emphasis on long-term symptom relief, making SKYRIZI® a strong candidate for tracking how repeated exposure influences consumer interest over time.
DUPIXENT® (dupilumab) is a monoclonal antibody prescription used to treat conditions such as eczema, asthma, and chronic sinusitis with nasal polyps. In its commercials, DUPIXENT® frequently highlights improvements in quality of life, making it especially useful for analyzing emotional impact’s correlation to public engagement.
Collecting Airtime Data
Beyond the fact that most of you have probably seen at least one of these ads while watching your favorite TV shows, these drugs also have substantial amounts of publicly available airtime data.
Using Internet Archive’s TV News Archive (archive.org/details/tv), I filtered each drug by name within the years 2023 and 2024 to track how often each drug was mentioned daily. After spending days distinguishing between mentions in news segments, other programs, and actual commercials, I compiled a spreadsheet of this data that reflects the true frequency of televised advertisements for each drug. The numbers on the chart under each date on each year represents the number of times a commercial was aired on that day. For example, for January 1, 2023 for the drug Ozempic®, the number 9 means that 9 Ozempic® commercials were aired that day.
If you’re interested in exploring the data yourself, feel free to check it out here: docs.google.com/spreadsheets/d/163-6EA6IB6Wc043hF1K0sJT0yqWN5yuMI4Wk4RopeS0/edit?usp=sharing
Now that the airtime data has been collected, the next step is to break it down into patterns by identifying when these ads are most prominent and how these periods align with other search trends. See ya in the next one!
Citations
“What Is Ozempic®? | Ozempic® (Semaglutide) Injection 0.5 Mg or 1 Mg.” Ozempic, Novo Nordisk, 2024, www.ozempic.com/why-ozempic/what-is-ozempic.html.
“Discover SKYRIZI® (Risankizumab-Rzaa).” Skyrizi, www.skyrizi.com/.
“Take Action with DUPIXENT® (Dupilumab).” Dupixent, www.dupixent.com/.
“Internet Archive TV NEWS : Search Captions. Borrow Broadcasts.” Internet Archive, archive.org/details/tv.
Week 5: Tracking the Trends
What’s up, trend trackers! Welcome back to Week 5 of my blog! Continuing on from last week, I’ve continued collecting key data that will support my full analysis later on. In recent weeks, I focused on identifying periods with no advertising and gathering the Google Search Trends for each of my selected drugs.
New Data
If you’re interested in exploring the data on non-advertising periods, feel free to check out the fourth tab on the same Google Sheet from last week!
docs.google.com/spreadsheets/d/163-6EA6IB6Wc043hF1K0sJT0yqWN5yuMI4Wk4RopeS0/edit?usp=sharing
Following my methodology, I used Google Trends to track how search interest fluctuates for each drug over time. To improve accuracy, I collected data for both the brand names (Ozempic®, SKYRIZI®, and DUPIXENT®) and their generic names (semaglutide, risankizumab, and dupilumab). I also limited my data to the United States to maintain consistency with my airtime data and to keep the data collection process focused.
Additionally, I utilized multiple Google Trends categories, namely web, image, news, Google Shopping, and YouTube searches, to better capture the different ways people may be engaging with the specific terms online.
Furthermore, it’s important to note that Google Trends does not track the exact amount of times a term was searched throughout each month. Instead, it reports data on a relative scale from 0 to 100, where 100 represents peak popularity within the selected time period, 50 indicates half the popularity, and 0 suggests insufficient data. This means that my data reflects proportional changes in interest rather than an absolute search number.
With this in mind, I plan to use these values as indicators of relative public awareness and align them with my other datasets. Additionally, since Google Trends reports data on a monthly scale, I will have to adjust all my airtime and sales data to match this timeframe for accurate comparisons.
If you’re interested in exploring the search trend data, feel free to check it out here:
https://docs.google.com/spreadsheets/d/1W0KJ-N1_Ch1uZXbPoitE0uAwdygc0SF7_c7ogmgqeCs/edit?usp=sharing
With search trends now recorded, the next step is to expand beyond and begin capturing how these drugs show up across social media. Catch you in the next one!
Citations
Google Trends. “Google Trends.” Google Trends, 2025, trends.google.com/trends/.
Week 6: Metrics & Method Tweaks
Hey there, data trackers! As we reach the halfway point, this week I’m taking a step back to review the progress I’ve made so far while also sharing a few adjustments to my original methodology, setting the stage for what’s to come.
Beyond getting my wisdom tooth extracted, I’ve spent the past week continuing my data collection for social media keyword frequency as an added factor of public awareness. As this process has progressed, I have encountered a few limitations that led me to refine parts of my original approach.
Google Trends
In my previous post, I mentioned that I would be using Google Trends as a proportional measure of search interest rather than an exact count, since it reports data on a relative scale. Since then, after meeting with my faculty advisor Dr. Mo, we discovered an external extension called Glimpse, which provides estimates of total search volume over time.
Using this tool, I was able to gather more precise data for web searches related to each drug. However, it does not provide accurate data for other search categories such as Images, News, Shopping, or YouTube. Due to this limitation, I will be refining my method to focus primarily on web search data, as it is the most consistent and reliable while still aligning with my original goals.
If you’re interested in exploring the precise web search trend data, feel free to check it out on the second tab of this sheet:
https://docs.google.com/spreadsheets/d/1W0KJ-N1_Ch1uZXbPoitE0uAwdygc0SF7_c7ogmgqeCs/edit?usp=sharing
Social Media Keyword Frequency
In addition to refining my search data, I’ve also adjusted my approach to social media analysis. While I initially planned to include platforms like X (Twitter) and Facebook, I found that these platforms do not provide accessible or reliable public data on keyword frequency due to data restrictions and the sheer volume of posts.
As a result, I’ve decided to focus primarily on Reddit, which offers more open and structured access to historical data through the website search.pullpush.io. This allows for more precise tracking of keyword frequency over time and aligns closely with my project’s main emphasis on using open-access, transparent data sources.
With these adjustments in place, my dataset is becoming more accurate and focused, setting me up to begin deeper analysis in the coming weeks. Till the next one!
Citations
“Glimpse - Discover Trends before They’re Trending.” Glimpse, meetglimpse.com/.
“PullPush Reddit Search Tool.” Pullpush.io, search.pullpush.io.
Week 7: From Posts to Prescriptions
Welcome back social media dwellers! I hope all of you had a great spring break and are ready to dive back into more data collection antics. This week, I’ll be covering social media keyword frequency data as well as the prescription and sales information that will help build the foundation for combining all of my datasets for analysis in the coming weeks.
Social Media Frequency
Continuing from last week’s adjustments, I focused on collecting keyword frequency data from Reddit as an added measure of public awareness. Using keyword searches for the name of each drug, I tracked how often posts with these terms appeared over my selected time period from January 1, 2023 to December 31, 2024.
Reddit proved to be especially useful because of its open-access structure and consistent archive system, allowing me to gather monthly data that aligns with my other datasets through the PullPush website I mentioned in my last post. Compared to other platforms which have more restricted data access, Reddit provides a clearer and more reliable picture of how often people discuss these drugs online.
The dataset I linked below will serve as an additional layer to my Google search data, helping capture the active engagement and discussion of these drugs. The numbers on the Social Media Frequent tabs represent the number of posts with that term on a certain day or month.
https://docs.google.com/spreadsheets/d/1W0KJ-N1_Ch1uZXbPoitE0uAwdygc0SF7_c7ogmgqeCs/edit?usp=sharing
Prescription and Sales Info
To complement my advertising and awareness data, I also gathered prescription-related insights through sales data for each drug. Since direct prescription counts are not always publicly available, sales figures serve as a strong source for prescription uptake and drug use.
Across 2023 and 2024, all three of my selected drugs showed significant growth in sales, reinforcing their prominence in the pharmaceutical market and consumer awareness. Ozempic experienced rapid growth, reaching around $17.5 billion in global sales in 2024 and displaying a 25% increase from 2023 sales. SKYRIZI showed some of the fastest growth among the three, reaching $11.7 billion in 2024 sales with a growth rate of over 50% from 2023. DUPIXENT also demonstrated strong growth, increasing from around $11.6 billion to over 14 billion, a rise of about 22% from 2023 to 2024.
All the above data was gathered from a secondary source, and thus there is the underlying concern about inaccuracies. I have crosschecked the data from the source used with various other sources, namely from the ProAct 2025 Drug Trend Report, Statista, and reports from the pharmaceutical companies of these drugs (Novo Nordisk, AbbVie, Sanofi), which display similar numbers and trends. Due to discrepancies in the wide array of different reports, I’ve chosen to focus on a singular source that provides this data for all the drugs I’ve selected to prevent further discrepancies.
Here is the data for the sales difference between 2023 and 2024:
With advertising data, search trends, social media frequency, and sales figures now all in place, the next step is bringing everything together to uncover whether spikes in attention translate into real impact.
Citations
Buntz, Brian. “2024’S Blockbusters: Top 25 Drugs by Sales.” Drug Discovery and Development, 4 Apr. 2025, www.drugdiscoverytrends.com/2024s-blockbusters-top-50-pharmaceuticals-by-sales/.
“PullPush Reddit Search Tool.” Pullpush.io, search.pullpush.io.
Sources Referenced
“AbbVie Reports Full-Year and Fourth-Quarter 2024 Financial Results.” AbbVie News Center, 2024, news.abbvie.com/2025-01-31-AbbVie-Reports-Full-Year-and-Fourth-Quarter-2024-Financial-Results.
Becker, Zoey. “AbbVie, Hitting Record Sales on Skyrizi Gains, Holds Its Own in Growing IBD Arena despite J&J Competition.” Fierce Pharma, 4 Feb. 2026, www.fiercepharma.com/pharma/abbvie-hitting-record-sales-high-skyrizi-gains-holds-its-own-growing-ibd-arena-despite-jj.
Fourreau, Valentine. “Infographic: Ozempic Now a Major Player on the Diabetes Drug Market.” Statista Daily Data, Statista, 26 Nov. 2025, www.statista.com/chart/35435/revenue-diabetes-medication/?srsltid=AfmBOor-XVW_8qc1yamifh4XFwePCnFSEGiKShnQeb8NXJsAMgvVmWeW.
“ProAct 2025 Drug Trend Report.” ProAct, https://7006190.fs1.hubspotusercontent-na1.net/hubfs/7006190/DTR/Drug%20Trend%20Report%202025.pdf
“U.S. Dupixent Market Size & Share | Industry Report, 2033.” Grand View Research, 2024, www.grandviewresearch.com/industry-analysis/us-dupixent-market-report.
Week 8: Plotting the Patterns
Greetings, graph gurus! After weeks of collecting data from multiple sources, this week is where everything begins to come together visually. With datasets ranging from advertising airtime to search trends, social media activity, and sales figures, I’ve begun transforming raw data onto graphs to better understand the patterns behind them.
Visualizing the Data
Over the past week, I focused on organizing and graphing each dataset individually to observe how trends change over time. For each of my selected drugs, I created visual representations of advertising frequency (ads per month), Google search trends (searches per month), social media post frequency (Reddit posts per month), and sales data (annual sales across 2023 and 2024).
By plotting these variables, I’m able to begin identifying whether spikes in advertising align with increases in public interest or drug usage before I start aligning each dataset together.
Below is a brief overview of the data trends I’ve observed so far visualized on line graphs:
While I have not yet conducted formal statistical analysis and relayed these trends together on the same graphs, observing them separately already highlights a few interesting trends.
At a glance, the rate at which advertisements are aired do not particularly seem to be entirely aligned with social media post frequency and search frequency. While there are brief moments where the trends do align, further work in the coming weeks will help to decipher how accurate these alignments are.
Ozempic consistently dominates across all datasets other than airtime. It shows the highest sales figures in both 2023 and 2024, as well as significantly higher search frequency and social media activity compared to SKYRIZI and DUPIXENT.
All three drugs additionally show growth from 2023 to 2024 in sales, search frequency, and social media post frequency. For search frequency, all drugs display a gradual upward trend over time rather than sharp changes. This suggests that public interest builds more steadily rather than reacting instantly to short-term changes in increases in advertisement airtime. Similarly, social media activity mirrors this gradual increase, mainly for DUPIXENT and SKYRIZI, which increases throughout 2024. Ozempic, while already quite high in post numbers, continues to fluctuate consistently, highlighting its strong online presence.
With the individual graphs now completed, my next steps involve integrating all datasets into combined visualizations, allowing me to directly compare advertising exposure with public awareness and sales in a single view. I will also begin conducting a statistical analysis to determine whether the patterns I’ve observe are statistically significant.
Week 9: Connecting the Dots
Hello, correlation connoisseurs! After weeks of collecting and organizing data, this week I've shifted my focus toward finally analyzing the correlations among graphs that directly compare advertising airtime with public awareness and prescription-related outcomes.
From Individual Graphs to Direct Comparisons
Until now, I’ve been looking at each dataset individually, and while this helped identify general patterns, it didn’t help me answer my central question of whether increases in advertising directly correspond to increases in public awareness and sales trends?
To address this and directly seek out the correlation, I created new graphs that plot airtime vs. search frequency, airtime vs. social media post frequency, and airtime vs. sales trends for all three drugs. By visualizing these relationships, I can begin to see whether increases in advertising aligns with measurable changes in public behavior.
Plotting these different datasets against one another will help me approximate a trendline and determine whether there is a negative or positive correlation depending on whether the slope of the line is positive or negative. Additionally, when determining the trendline, I will be able to decipher the R2 value. R2, otherwise known as the coefficient of determination, is a statistical measurement that determines what proportion of a dependent variable can be predicted by an independent variable in a regression model. This measurement ranges from 0 to 1 with 1 indicating a perfect fit of the model to the data. For example, a graph that plots airtime vs. search frequency and outputs an R2 value of 0.995 would indicate a high fit, meaning that airtime and search frequency heavily correlate to one another.
Airtime vs. Search Frequency
When comparing airtime with search frequency, there appears to be a very small positive correlation for Ozempic and negative correlations for SKYRIZI and DUPIXENT. For Ozempic, periods with higher advertising exposure tended to slightly align with increases in search activity, though not perfectly synchronized, hence why the R2 value is quite low despite the positive correlation.
As for SKYRIZI and DUPIXENT, both graphs displayed the slightest negative correlations with an evidently small R2 value. When comparing these two drugs to Ozempic, it’s clear that Ozempic’s prevalence on the news and popularity amongst celebrities might be a strong contributing factor to the positive correlation displayed. However, looking at these more generic, less mainstream drugs that haven’t been popularized across the internet, it’s much more evident that advertisements don’t inherently correlate with search frequency.
All three drugs had a relatively low R2 value. Ozempic had an R2 value of 0.189, SKYRIZI had one of 0.005, and DUPIXENT had a value of 0.049. These values indicate that advertising airtime explains only a small portion of the variations in search frequency, suggesting that while there may be a small correlation, it is not particularly strong. Ozempic’s higher R2 value implies a slightly more clear connection between advertising and search behavior, but still, less than 20% of the variation in search trends can be linked to changes in airtime. For SKYRIZI and DUPIXENT, the extremely low values suggest that there may be other external factors, such as word-of-mouth or other health trends, that play a much larger role in influencing search activity as opposed to advertising alone.
Airtime vs. Social Media Post Frequency
A similar pattern persists when comparing airtime to social media post frequency. Increased periods of advertising generally have minimal effects on levels of online discussion on Reddit. Similar to Google search trends, Ozempic continues to persist as the only drug with a positive correlation, while the other drugs have a more strong negative correlation.
However, these correlations appear to be more consistent than search trends, as the trendlines on these graphs appear more significantly negative or positive. This is likely due to the fact that social media activity is less varied and influenced by additional factors compared to Google searches as a whole.
Once again, all three drugs had relatively low R2 values. Ozempic had a slightly higher R2 value of 0.229, SKYRIZI had one of 0.038, and DUPIXENT had a value of 0.074. Similar to the search frequency analysis, these results also indicate that advertising airtime is not a strong correlator of social media post frequency. Although Ozempic shows a somewhat stronger relationship compared to the other two drugs again, its R2 value still suggests that the majority of variation in social media activity may be driven by other external factors rather than advertising being the main leading factor. The slightly higher values in this comparison indicates that social media discussion is a slightly more responsive to advertising than search behavior, but the overall weakness of these R2 values continues to indicate that online engagement is more likely affected by a wider range of variables.
Airtime vs. Sales
While overall sales increase from 2023 to 2024 for all three drugs, the connection to advertising appears to be more complex and gradual, as the number of ads per year do not indicate a positive correlation to the changes in sales figures.
Unlike search and social media trends, sales trends only reflect long-term growth measured once per year. Due to this form of tracking, I could not calculate an R2 value for airtime vs. sales data, as two points would resemble an R2 value of 1 since the trendline would be a straight line.
By visually observing the airtime vs. sales graphs, there appears to be the consistent trend of Ozempic having a positive correlation, while SKYRIZI and DUPIXENT still have a negative correlation. Despite increases in advertising, SKYRIZI and DUPIXENT’s trendlines do not depict an increase in sales aligned with more advertisement exposure. Meanwhile, Ozempic continues to display that increases in advertising correlated with increased sales. At the moment, these graphs indicate that Ozempic is the one drug that has enough advertising presence and outreach influenced by that presence to portray a significant increase in sales that may be attributed to advertising.
While these visual comparisons provide many valuable insights that have differed from what I originally predicted, I still need to conduct further statistical analysis to determine whether these relationships are truly statistically significant.
Citations
Fernando, Jason. “What Is R-Squared?” Investopedia, 13 Nov. 2024, www.investopedia.com/terms/r/r-squared.asp#toc-what-is-r-squared.
Week 10: Correlations to Conclusions
What’s up, stats specialists! Following last week, I’ve now taken the next step in my analysis to determine whether the correlations I observed are truly statistically significant, or whether they could be the result of chance through a t-test.
Setting Up Hypotheses
Before running my calculations, I needed to define what I was testing. For each of the six comparisons in my dataset, namely airtime vs. search frequency and airtime vs. social media post frequency across three different drugs, I set the following hypotheses:
Null Hypothesis: There is no linear relationship between advertising airtime per month and the public awareness metrics.
Alternative Hypothesis: There is a linear relationship between advertising airtime per month and the public awareness metrics.
The alternative hypothesis in my study simply states that a relationship exists, rather than specifying whether it is a positive or negative relationship. I’ve used a two-tailed test, since my earlier analysis and observations indicated that some drugs showed positive correlations while others showed negative ones. Since I can’t assume the direction of the relationship for all three drugs I’ve analyzed, I’ve chosen this two-tailed test to apply a more rigorous approach.
Pearson Correlation and T-Test
For my statistical analysis, I’ve used the Pearson correlation coefficient (r) combined with a t-test for significance. The Pearson r measures the strength and direction of the relationship between airtime and the various awareness metrics, and the t-test will determine whether that correlation is statistically significant.
The t-statistic is calculated using the formula:
In this formula, r represents the Pearson correlation coefficient which can be found by square rooting the R values determined by the graphs from last week. N represents the number of data points, being 24 months across 2023 and 2024, and with additional degrees of freedom, it would be df = 22.
Once the t-statistic is calculated, it will be compared against a critical value from the t-distribution table. For a two-tailed test at a significance level of a = 0.05 with df = 22, the critical value is 2.074. If the absolute value of the t-statistic calculated is over 2.074, the result is statistically significant, and I reject the null hypothesis.
Bonferroni Correction
Since I’m running six separate t-tests at once, there may be an increased risk of getting a false positive by chance. To control for this, I applied a Bonferroni correction, which divides the significance threshold (a = 0.05) by the number of tests being conducted (6) to get 0.0083.
The closest critical value on the t-distribution table for df = 22 at this new threshold is 2.819. Any result that may exceed this higher value can be considered significant even after acknowledging that multiple comparisons are being made.
Airtime vs. Search Frequency Results
Drug | t (Search Frequency) | Outcome
Ozempic | 2.2656 | Sig at a = 0.05
SKYRIZI | -0.3339 | Not Sig
DUPIXENT | -1.0630 | Not Sig
For Ozempic, the t-statistic of 2.266 exceeds the critical value of 2.074, meaning that I can reject the null hypothesis where a = 0.05. There is a statistically significant positive relationship between Ozempic’s advertising airtime and search frequency. However, this does not clear the corrected threshold of 2.819.
For SKYRIZI and DUPIXENT, the absolute t-values of 0.334 and 1.063 do not exceed the critical value, meaning that I fail to reject the null hypothesis for these drugs as there is insufficient statistical evidence of any type of relationship, positive or negative, between airtime and search frequency.
Airtime vs. Reddit Post Frequency Results
Drug | t (Reddit Post Frequency) | Outcome
Ozempic | 2.5587 | Sig at a = 0.05
Skyrizi | -0.9325 | Not Sig
Dupixent | -1.3258 | Not Sig
The social media post frequency analysis yields similar results. Ozempic again has the only significant result, with a t-statistic of 2.559 exceeding the threshold of 2.074, being the strongest result amongst all six comparisons without clearing the corrected threshold of 2.819. SKYRIZI and DUPIXENT also similarly show no statistically significant relationship.
In the table, the negative t-values for SKYRIZI and DUPIXENT reflect their negative Pearson correlation coefficient r values, meaning that in the data, higher airtime months correlated with lower Reddit post activity for these drugs. This may suggest that Reddit discussion and search frequency for these drugs is driven more by patient discussion and word-of-mouth rather than by advertising exposure.
Airtime vs. Sales Results
For airtime vs. sales results, I cannot conduct a statistical analysis as there are only two datapoints on the graphs across the three drugs, meaning that a Pearson r can not be calculated. Instead, the main analysis I can conduct for the following data is the simple observational conclusions I came to on my last post.
Conclusions on Analyses
Across all six comparisons for airtime vs. search frequency and social media post frequency, only Ozempic produced statistically significant results. This aligns pretty well with what I observed visually in my previous post, as I concluded that Ozempic’s positive correlations appear to display a relationship between advertising activity and measurable public engagement, while SKYRIZI and DUPIXENT’s trends are not strong enough to rule chance out as an explanation.
However, overall, statistical significance by itself does not prove that advertising inherently caused increases in awareness. There may be a range of other factors, such as Ozempic’s celebrity endorsements, widespread media coverage, and the other broader conversations about weight loss drugs, especially as the oral version has become more popularized amongst other injection drug forms, that are contributing to its positive relationships. The t-tests in my study confirm that the positive relationship between airtime and awareness factors for Ozempic is real and not simply chance, even if advertising is not the sole factor causing these correlations.
Next time, I’ll be drawing the final conclusions surrounding my study, diving deeper into whether my results were what I anticipated in comparison to past methodologies. See you in the next one!
Sources Referenced
Bobbitt, Zach. “How to Perform a T-Test for Correlation.” Statology, 14 July 2021, www.statology.org/t-test-for-correlation/.
“Bonferroni Correction Explained: Managing Multiple Testing in Statistics | Amplitude.” Amplitude, 2024, amplitude.com/explore/experiment/what-is-bonferroni-correction.
“Programming Col Sig (Correlations): Classical Student Test.” Askia Help Centre - Automating Insight, 2022, support.askia.com/hc/en-us/articles/360000410117-Programming-Col-Sig-Correlations-Classical-Student-Test.
Week 11: The Final Verdict & Reflections
Welcome back for the last time, advertisement analysts! After months of collecting data, building graphs, evaluating correlations, and running t-tests, it’s finally time to bring everything back together and answer the questions that began this project.
What the Data Displayed
Across all six comparisons of advertising airtime against search frequency and Reddit post frequency, only Ozempic produced statistically significant results. Its t-statistics of 2.266 and 2.559 both exceeded the critical value of 2.074, confirming that the relationship between advertising airtime and public engagement is significant and not by chance. In contrast, SKYRIZI and DUPIXENT did not clear the critical value across all comparisons, and their negative t-values suggest that higher airtime months aligned with lower online activity for those drugs.
The R² values displayed the same type of story. Even Ozempic’s strongest values of 0.189 and 0.229 indicated that advertising airtime accounts for less than 25% of the variation in its public engagement metrics. For SKYRIZI and DUPIXENT, the extremely low R² values suggest that online activity for these drugs is driven almost entirely by external factors, likely patient communities and other word-of-mouth discussions.
General Conclusions
Ozempic’s stronger results likely are attributed to its unique cultural presence. From its celebrity endorsements to expansive media coverage, the broader public conversation surrounding weight loss drugs gives Ozempic added visibility that amplifies the effect which advertising produces. SKYRIZI and DUPIXENT, as slightly more niche condition-specific drugs, did not have the same cultural presence, which the data reflects.
These findings also very broadly align with prior research. From my Week 2 post, Robinson et al. (2004) noted that DTC advertising can increase public awareness, but its effects are limited. Other findings noted that most DTC ads provide little information about disease cause or risk factors, which helps explain why less culturally prominent drugs can’t gain as much public engagement from solely advertising. Meanwhile, Mintzes et al.’s (2003) connection between advertising exposure and prescription requests is consistent with Ozempic being the only drug to show a positive airtime vs. sales correlation; however, clearly external market factors play a significant role.
To answer the question on whether online metrics offer a more effective method for analyzing DTC advertising’s effects compared to traditional survey-based methods, these results provide some support and contradictory perspectives. Tracking Google searches and Reddit post activity on a monthly basis allowed for a better more real-time view of how public engagement responds to advertising that other surveys can’t specifically replicate. Still, at the same time, online metrics capture digital engagement rather than true awareness, and they also can’t capture the effect of external events.
Final Product
If you are interested in viewing the final research paper with all the precise data I’ve collected all in one place, please feel free to read more about my findings through the link below:
https://drive.google.com/file/d/1wy8lDRCeH0CaaAIas_Z1EZzY1tYBK0yl/view?usp=sharing
Reflections
Looking back to my first blog post, it's clear that what started as a straightforward question about advertising and awareness quickly revealed itself to be far more complex than anticipated. My assumption going in was that more advertising would definitely mean more public engagement, a prediction that the data only partially supported for one of the three drugs in the end. Working through data collection, correlation analysis, and statistical tests not only produced unexpected findings but also displayed how much external factors like cultural visibility and media presence can overshadow the direct effects of advertising. This project reinforced that real-world situations rarely fit into a single explanatory conclusion, and that choosing to measure public awareness through digital behavioral data opened up a more nuanced view of how advertising actually functions in the first place.
Thank you for accompanying me on this journey through these weeks across the past months! I hope this enlightening project has shown you all that the relationship between advertising and public awareness is anything and everything but a straight line.