#FutureReadyHealthcare
Indegene accelerates lead conversion for a global pharma major by 30% with AI-powered content personalization
The Customer
A global pharma company with 40+ brands needed a way to continuously track healthcare professional (HCP) preferences, understand their needs, and deliver personalized content. While a system to track HCP preferences and prescription practices was currently in place, the company realized the need for a reliable methodology to associate its existing content to these preferences at scale. Indegene’s proprietary artificial intelligence (AI) solution, NEXT Commercial Content Intelligence, was introduced to help solve this challenge.
Challenges
Content in traditional channels: The customer’s current content was designed for traditional channels, including iDetails, representative triggered emails (RTEs), Headquarters (HQ) emails, banners, reference documents, webpages, etc., making it difficult to atomize, tag, and personalize the content effectively.
Constantly changing HCP preferences: HCP preferences and prescription practices were continuously evolving, necessitating personalization methodologies that the current system was unable to handle.
Scalability: The scale of the task was immense, with a single brand having over 2,000 pages of content and over 450 HCPs associated, and a total of 40+ brands requiring effective and efficient personalization.
The Solution
Segmentation Messaging Framework (SMF) Development: HCP preferences were used to define personas, journey stages, and corresponding key messages, resulting in an SMF that accurately represented HCP content preferences.
Taxonomy Design and Configuration: Indegene’s experts analyzed the customer’s asset library and formulated a suitable tagging taxonomy, which was then configured into NEXT Commercial Content Intelligence.
Content Fingerprinting: Utilizing NEXT Commercial Content Intelligence, all content locked in various traditional channel types underwent processing to extract and classify the content at different asset, unit, and component levels, using machine learning (ML) models.
Content SMF Mapping: By leveraging key messages identified at the unit and component levels, Indegene calculated content relevancy scores for each customer segment, effectively mapping the content to the most relevant segment.
Outcomes
Indegene developed an automated, scalable, and effective solution for the AI personalization of pharma content by associating and hyper-personalizing content to match continuously evolving HCP preferences. NEXT Commercial Content Intelligence addressed any type of content in any channel type, and the metadata produced was utilized to improve content discoverability, analytics and dashboarding within the ecosystem. With NEXT Commercial Content Intelligence, the customer was able to better deliver relevant and engaging content, ultimately leading to more informed decision-making and improved patient care.
100%
automated content personalization
30%
increase in lead conversion