Impact of Generative AI on Pharma Marketing Analytics

Generative AI has taken the world by storm. The release of OpenAI’s ChatGPT and related platforms has triggered discussions around a new world of possibilities. While some organizations welcomed the winds of change with open arms, others still remain skeptical. However, with several IT giants recently jumping on the bandwagon and pledging their support to aid further augmentation of this technology, Generative AI is here to stay.

According to a recent Gartner report^[1], Generative AI could play a major role in a wide range of industries, including pharma, manufacturing, media, architecture, automotive, aerospace, etc. Notably, the report predicts a remarkable surge in its influence, forecasting that by 2025, Generative AI models will create approximately 30% of outbound marketing messages. This marks a substantial leap from the mere 2% recorded in 2022. This rapid adoption underscores the growing significance of Generative AI in shaping the future of business and marketing strategies.

In this article, we attempt to zoom in on how Generative AI could play a part in the life sciences industry, specifically with respect to marketing analytics and promotional impact measurement in commercial pharma.

#1 Improving data management:

Data management is an inherently complex and multifaceted process, encompassing the entire data pipeline from ingestion to integration and preparation. The importance of maintaining data quality cannot be overstated, as any inaccuracies or omissions can significantly impact the accuracy of models. In fact, reports suggest that data scientists spend a significant amount of time cleaning and preparing the data before training models^[2].

However, Generative AI has emerged as a potential game-changer. Although its integration into data management is still in its early stages, it holds immense potential in optimizing different facets of this critical process, including data warehousing, data observability, data cleaning, data imputation, anomaly detection, standardization, and much more.

For instance, Generative AI can address the challenge of lack of training data by producing synthetic data that closely mimics the attributes of the original dataset. It can also clean and rectify datasets by inferring missing values or correcting errors. For organizations grappling with data management challenges, introducing Generative AI will likely be a breath of fresh air. It can empower improved data modeling practices by ensuring the highest data quality, ultimately leading to more effective marketing strategies.

#2 Enhancing data analysis and exploration:

AI and Machine Learning (ML) excel in analyzing vast data sets, uncovering insights, and revealing hidden patterns and correlations. However, human intervention often introduces cognitive biases that hinder their effective utilization.

Generative AI transcends these limitations, empowering organizations to overcome human bias. This technology equips brand managers with unparalleled clarity in understanding promotional trends. Armed with these insights, they can make more informed decisions regarding campaign launches and ongoing initiatives, fostering a data-driven culture in pharma marketing.

Incorporating Generative AI enhances data analysis efficiency and fuels innovation in pharma marketing. This fusion of cutting-edge technology and data-driven decision-making holds the potential to revolutionize the industry.

#3 Streamlining promotion impact modeling and measurement:

Pharma companies have predominantly relied on traditional marketing mix modeling (MMx) methods to assess their marketing channel’s effectiveness. However, these conventional methods are gradually becoming obsolete with the pharma marketing landscape undergoing digital transformation and the proliferation of marketing channels.

By integrating Generative AI into their MMX strategies, companies can enhance and overhaul their measurement methods, ensuring greater precision and effectiveness. Generative AI models and applications can analyze voluminous amounts of customer and HCP (Health Care Professional) data (structured, semi-structured, and unstructured). This enables companies to extract valuable insights regarding channel effectiveness and customer behavior.

Moreover, thanks to Generative AI’s deep learning algorithms, companies can quickly identify sales correlation with promotional efforts, even when dealing with slightly compromised data quality. This implementation of Generative AI into promo impact modeling and measurement promises a substantial reduction in processing time and enhanced result accuracy.

#4 Generating advanced recommendations:

Marketing analytics goes beyond assessing individual marketing channel performance; it can unveil valuable insights such as physician-patient segment details and regional nuances. For pharmaceutical brand managers, this information is crucial in shaping their overarching marketing strategy.

Even though traditional AI and ML models can deliver these insights, Generative AI can take it a notch higher and further empower information drill-throughs effectively. And brand teams will have the data at their fingertips to make informed marketing decisions. This could help with:

1. Better targeting efforts by identifying key HCP and patient segments of interest
2. Highly personalized campaigns with tailored content apt for the target audience

#5 Optimizing promotional efforts:

The crux of marketing analytics lies in optimizing promotional efforts to meet both short-term and long-term business objectives. Recent industry data reveals that pharmaceutical companies worldwide spent approximately USD 8 billion on advertisements in 2022^[3]. Maximizing returns from this advertising expenditure is paramount for maintaining competitiveness in the market.

Empowered by robust predictive algorithms, Generative AI systems can assist executives by suggesting different marketing strategies. These systems can simulate various marketing scenarios, compare their outcomes, and predict results for each scenario. Consequently, business leaders can make well-informed decisions, enhancing promotional efficiency and reducing marketing costs.

Conclusion:

Generative AI is poised to democratize solutioning in pharma marketing analytics. Tomorrow’s data scientists may not require state-of-the-art systems or a master’s degree in data science to create marketing mix models. Generative AI is likely to speed up the “rapid scaling of analytics”, making it more accessible than ever before. It promises to usher in a new era in marketing analytics, brimming with potential for future marketers, albeit with a word of caution to use it responsibly.

Organizations must embrace this new outlook and be willing to evolve. While change management is not easy, it is imperative for competing. As Alvin Toffler brilliantly remarked decades ago, “To survive, to avert what we have termed as future shock, the individual must become infinitely more adaptable and capable than ever before.”

Bibliography:

1. “Generative AI: What Is It, Tools, Models, Applications and Use Cases.” Gartner. Accessed September 15, 2023. https://www.gartner.com/en/topics/generative-ai

2. Granville, Vincent. “How to Automate Data Cleaning, in a Nutshell.” Data Science Central, October 1, 2022. https://www.datasciencecentral.com/how-to-automate-data-cleaning-in-a-nutshell/.

3. Adams, Ben. “The Top 10 Pharma Drug Ad Spenders for 2022.” Fierce Pharma, May 1, 2023. https://www.fiercepharma.com/special-reports/top-10-pharma-drug-brand-ad-spenders-2022.