Context

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient . The data is a collection collection of 13 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor.

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Content

The dataset was created by The Criteo AI Lab .The dataset consists of 13M rows, each one representing a user with 12 features, a treatment indicator and 2 binary labels (visits and conversions). Positive labels mean the user visited/converted on the advertiser website during the test period (2 weeks). The global treatment ratio is 84.6%. It is usual that advertisers keep only a small control population as it costs them in potential revenue.

Following is a detailed description of the features:

• f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
• treatment: treatment group (1 = treated, 0 = control)
• conversion: whether a conversion occured for this user (binary, label)
• visit: whether a visit occured for this user (binary, label)
• exposure: treatment effect, whether the user has been effectively exposed (binary)

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Context

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient . The data is a collection collection of 13 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor.

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Content

The dataset was created by The Criteo AI Lab .The dataset consists of 13M rows, each one representing a user with 12 features, a treatment indicator and 2 binary labels (visits and conversions). Positive labels mean the user visited/converted on the advertiser website during the test period (2 weeks). The global treatment ratio is 84.6%. It is usual that advertisers keep only a small control population as it costs them in potential revenue.

Following is a detailed description of the features:

• f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
• treatment: treatment group (1 = treated, 0 = control)
• conversion: whether a conversion occured for this user (binary, label)
• visit: whether a visit occured for this user (binary, label)
• exposure: treatment effect, whether the user has been effectively exposed (binary)

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Starter Kernels

• HistGradientBoostingClassifier Base Model

Acknowledgement

The data provided for paper: "A Large Scale Benchmark for Uplift Modeling"

https://s3.us-east-2.amazonaws.com/criteo-uplift-dataset/large-scale-benchmark.pdf

• Eustache Diemert CAIL e.diemert@criteo.com
• Artem Betlei CAIL & Université Grenoble Alpes a.betlei@criteo.com
• Christophe Renaudin CAIL c.renaudin@criteo.com
• Massih-Reza Amini Université Grenoble Alpes massih-reza.amini@imag.fr

For privacy reasons the data has been sub-sampled non-uniformly so that the original incrementality level cannot be deduced from the dataset while preserving a realistic, challenging benchmark. Feature names have been anonymized and their values randomly projected so as to keep predictive power while making it practically impossible to recover the original features or user context.

Inspiration

We can foresee related usages such as but not limited to:

• Uplift modeling
• Interactions between features and treatment
• Heterogeneity of treatment

More Readings

• Supercharging customer touchpoints with uplift modeling
• CasualML
• PyLift

MORE DATASETs ...

Big Data Pharmaceutical Advertising Market Outlook

The global big data pharmaceutical advertising market size was valued at approximately USD 2.8 billion in 2023 and is projected to reach around USD 8.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.6% during the forecast period. This impressive growth trajectory is propelled by the increasing adoption of data-driven strategies in the pharmaceutical sector to more effectively reach and engage healthcare professionals and patients. The rising importance of personalized medicine and the growing volume of digital health data are driving pharmaceutical companies to leverage big data analytics to target their advertising efforts more precisely and efficiently.

One of the primary growth factors contributing to the expansion of the big data pharmaceutical advertising market is the shift towards personalized and targeted advertising strategies. Pharmaceutical companies are increasingly recognizing the value of big data in creating more personalized marketing campaigns. By analyzing vast amounts of data, such as patient demographics, prescribing behaviors, and treatment outcomes, companies can develop more targeted advertising strategies that resonate with specific audiences. This targeted approach not only enhances the effectiveness of advertising campaigns but also reduces costs by minimizing wastage and increasing return on investment. As the emphasis on personalized medicine continues to grow, so too does the demand for data-driven advertising solutions that can cater to individual patient needs.

The integration of advanced technologies, such as artificial intelligence (AI) and machine learning, into the pharmaceutical advertising landscape is another significant growth driver. These technologies enable advertisers to process and analyze complex datasets at unprecedented speeds, uncovering valuable insights into consumer behavior and preferences. By leveraging AI and machine learning algorithms, pharmaceutical companies can optimize their advertising strategies in real time, ensuring that their messages reach the right audience at the right time. This ability to swiftly adapt and refine marketing efforts is particularly crucial in an industry where regulatory constraints and market dynamics can change rapidly.

Furthermore, the increasing reliance on digital channels for healthcare information is fueling the demand for big data in pharmaceutical advertising. With more patients and healthcare professionals turning to online sources for medical information, pharmaceutical companies are shifting their advertising budgets from traditional media to digital platforms. This transition necessitates the use of big data analytics to track and measure the effectiveness of digital advertising campaigns. By analyzing online engagement metrics, such as click-through rates and conversion rates, companies can assess the impact of their digital marketing efforts and make data-driven decisions to optimize future campaigns.

Regionally, North America dominates the big data pharmaceutical advertising market, driven by the presence of major pharmaceutical companies and a highly developed healthcare infrastructure. The United States, in particular, is a key contributor to market growth, with pharmaceutical companies in the region increasingly investing in data-driven advertising strategies. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digitization of healthcare systems and the increasing adoption of big data analytics in countries such as China and India. Europe is also a significant market, with pharmaceutical companies leveraging big data to navigate the region's diverse regulatory landscape. Meanwhile, the Latin America and Middle East & Africa markets are gradually emerging as pharmaceutical companies look to expand their reach in these regions.

Component Analysis

The big data pharmaceutical advertising market is segmented into software and services components, each playing a pivotal role in the overall ecosystem. The software component, encompassing analytics platforms, data management solutions, and AI-driven tools, is at the forefront of driving market growth. These software solutions enable pharmaceutical companies to manage and analyze vast datasets, extracting actionable insights that can be used to refine advertising strategies. The increasing complexity of healthcare data and the need for advanced analytics capabilities are driving the demand for sophisticated software solutions in this space. Moreover, the continuous evolution of software technologies, including clo

A comprehensive dataset providing insights into the advertising industry for 2025, highlighting global advertising spending, digital and traditional marketing trends, the influence of social media advertising, mobile ad growth, advertising impact on consumer behavior, and the rise of programmatic advertising.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

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              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
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As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.