As the life sciences industry becomes more data-driven, companies are harnessing advanced analytics to personalize interactions with healthcare professionals, patients, and stakeholders across multiple channels. In this scenario, identifying the best algorithm for omnichannel and the next best action (NBA) recommendations is crucial for optimizing customer engagement and achieving intended business outcomes.
Read this highly insightful paper that identifies algorithms commonly used for NBA implementation and outlines the criteria for selecting the best algorithm based on the business need, as there is no one-size-fits-all solution. The model objectives, performance, and operational requirements guide the criteria for algorithm selection.
The paper focuses on three machine-learning techniques:
- Tree-based models,
- Neural networks,
- Bandit algorithms.
To conclude, in the last decade the progression of omnichannel strategies and the integration of AI/ML technologies in the life sciences industry have underscored the vital significance of the complex relationship between advanced algorithms, data analytics, and industry expertise. A deep understanding of this intricacy is paramount when choosing the best algorithm for omnichannel engagement and the NBA recommendations within the life sciences industry.