AI Powered Marketing Recommendation System – NBE

Pradeep Kumar

Principal Consultant – Data Science

Hemanth Kumar Gandham

Associate Consultant

Client: BioPharma
Year: 2019

The Sales & Marketing teams of Healthcare & Life Sciences industry have minimal bandwidth to capture the attention of the prescribers and customers who use their products. They must make quick decisions about their marketing and sales efforts. But without any information regarding the impact those decisions have on prescriptions, they had to rely on instinct and personal experience to customize their approach. Healthcare companies that integrate AI & machine learning into their sales and marketing strategies can replace this instinct with evidence-based insights that predict complex patterns in prescriber behavior. These technologies make it possible to quantify physician potential and characteristics, optimize multichannel marketing (MCM), run brand diagnostics, and to profile, target and segment HCPs.

With extensive experience in data science and advanced technology like artificial intelligence and machine learning, and deep understanding of pharma commercial teams’ day-to-day needs and responsibilities, D Cube Analytics has developed a tool-based Marketing Recommendation Engine. This recommendation system analyzes both traditional pharma data sources and new data sets to discover customer trends and provide users with recommendations for their next best actions. Given the fact that AI can process enormous data in no time, you’d be running more efficient campaigns with a better ROI. Not the mention the time you’d save on A/B testing! The tool leverage hyper-personalization approach to improve the efficacy of the targeting tactics. The engine then develops a personalized sequential learning algorithm based on inputs such as Physician Specialty, Sales, Affiliations, Promotions, Channel Responsiveness, and Content Affinity to classify physician prescribing behavior. The physicians are provided respective propensity scores for different channels and campaigns based on historical trends. For each physician, the scores provide insight into their attitude towards that channel or campaign and are subject to further improvement depending upon the strategy.

The Impact Analysis for the Implementation of the Recommendation Engine is also effectively put in place. In the beginning, we roll out various campaigns to the target universe. The impact of the upstream strategies deployed on the field is then quantified through the campaign period: Pre-Promotion, During Promotion, and Post Promotion. The response and attribution of each physician towards each tactic is then captured and remodeled into the engine. The responsiveness of the physician towards different channels and campaigns is then studied along with their respective instigators/triggers towards behavior change. This helps us assess the impact of the recommendation engine and the learning of new behavior promotes continuous gain in accuracy towards the next set of execution.

Request a demo to find out how D Cube can help you in running smarter and more efficient marketing campaigns.


D Cube Analytics is an Integrated Data Sciences company focused on extracting transformational insights from syndicated, real world and digital data to increase revenue realization, avert revenue loss, enhance internal productivity and improve end user experience for global Pharmaceutical organizations.

D Cube is pioneering a Digital Transformation wave within BioPharma by leveraging new age tools and methodologies like Artificial Intelligence, Machine Learning and Robotic Process Automation to greatly improve the productivity of workforce and significantly enhancing speed to insight. Through this new age product-based approach to delivering analytics, we greatly reduce the cost and complexity of deployments and provide measurable value across multiple business functions.

Find out how D Cube can help you to elevate your market access intelligence and develop rigorous strategies that enable success in the market, throughout the product life cycle.


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