Forbes reported that global spending on AI in the pharmaceutical industry will surpass $3.6 billion by 2026. Almost half of global life science professionals are either using or interested in using AI in their research. Despite this interest and commitment of capital and resources to the development and use of AI, confusion remains on how to best utilize AI and how to implement those strategies in light of requirements for data integrity, compliance and government oversight. Risk mitigation often outweighs continuous innovation. Does your commercial analytics utilize AI?
Implementation
Limited data access, quality and standardization are significant obstacles to the development and use of AI. Getting existing data in order can mean a complete reorganization of the data foundation and digitization of offline records. Advances in cloud technology are giving companies easier access to all data types, structured, unstructured and semi-structured, as well as the ability to more easily integrate these disparate data sets. To be effective, access to this data must be allowed at all levels of the organization from management to lab technicians to sales professionals in the field.
AI driven analysis must be done by individuals with deep pharmaceutical knowledge and expertise. Effective AI research requires an understanding of the global regulatory environments, payer expectations, physician and patient behavior and therapeutic knowledge. They must do this research on a state-of-the-art artificial intelligence and machine learning platform. AI and ML models must be continuously monitored, updated and refined based on new data and insights, and the company culture must be structured to maximize the value of these analytical efforts.
Historical Analytics
Commercial analytics traditionally relied on past experience, descriptive analytics and predictions based on prior sales. In the era of blockbuster drugs this sales strategy proved to be adequate. In today’s market closely monitored drug prices, increased patient engagement and treatments targeted at smaller patient populations means commercial teams must create a more targeted approach.
How Commercial Analytics Utilize AI
In market research and insights AI can be used to do predictive analytics on market trends, demand fluctuations and potential market opportunities. Sentiment analysis can be done with natural language processing to gauge public and professional sentiment towards products and competitors form social media, forums and publications.
In sales and marketing ML algorithms can be applied to segment customers based on behavior, preferences and purchasing patterns to tailor marketing strategies. Predictive modeling can be used to identify potential high value customers and personalize marketing campaigns.
In supply chain and Inventory management ML models can be used to predict demand for drugs, ensuring optimal inventory levels and reducing shortages or overstock. Companies are optimizing logistics and distribution routes using AI to minimize costs and improve delivery times.
Pricing strategy can be AI-driven through adjusting prices based on demand, competition and market conditions in real-time. ML can analyze how different pricing strategies impact sales volume and revenue.
For customer relationship management (CRM) companies use AI-powered chatbots and virtual assistants to handle routine customer inquiries, provide timely support and free up human agents for complex issues. They use ML to identify signs of potential customer churn and take proactive measures to retain them.
Commercial analytics utilize AI to analyze sales force performance by identifying strengths, weaknesses, and areas for improvement. They utilize predictive models to forecast future sales and set realistic targets.
By integrating diverse data sources such as electronic health records, insurance claims, and patient registries using AI companies can generate comprehensive real-world evidence. They also use ML to analyze real-world data to understand the effectiveness and safety of products post-launch.
Commercial analytics professionals utilize AI to automate the generation of business reports and dashboards, providing real-time insights. They use advanced AI tools to create intuitive visualizations that make complex data easily understandable for stakeholders.
By strategically incorporating AI and ML into commercial analytics, pharmaceutical companies can gain a competitive edge, optimize operations, and better serve their customers and patients.
