Abstract: Recommender systems have become increasingly popular in recent years and are utilized in various areas, including movies, music, news, books, research articles, social tags, and products in general. Most of the existing e-commerce recommender systems have been designed to recommend the right products to users based on the history of previous users' transaction records. Thus, the recommendation system for marketing analytics is a subclass of information filtering system that seeks the similarities between users and items with different combinations.
This work focuses on content-based scoring, which aims to quantify the impact of content quality of promotional emails sent across to healthcare practitioners upon the immediate engagement.
An enhanced version of OCR is being used to extract the content from the digital media (i.e., scanned email images). The process has strived to detect the context and the localization of text information from the email and project it into a higher dimensional space with diffused information. At the same time, the pictorial features have been extracted via Morphological transformations and the HLS threshold algorithm. A semi-supervised K-Nearest Neighbor technique was implemented to extrapolate the distribution of the emails with known impact onto the ones with unknown engagement metrics due to the scarcity of performance data mapping being a significant challenge; this type of scoring system can help the business to provide more appropriate documents to suit a user's personal information need. The simulation results showed that our model could score relevant documents sent to users with higher precision than other non-hybrid information filtering models.
This approach can be extrapolated to various industries trying to optimize their marketing strategy for the digital end users. It will enable the process of getting instantaneous unbiased rating and will act as a guide to the content development team to create and publish relevant, impactful and decision-driven content. It can be used to personalize and aid precision digital marketing on the content while keeping the turn around time to minimum.
Bio: Ritesh is currently working as an Associate Director – Data Science, Analytics and Digital at Novartis Pharmaceuticals US. Ritesh is a data science leader with ~ 12 years of work experience in Advanced Analytics, AI, Digital Strategy, and Product development/management across domains such as Travel, Pharma, BFSI, Retail, Automobile & FMCG. He has spent most of his career acting as an Analytics solutions consultant, a bridge between the data science, technology, and business teams, Leveraging functional expertise, ensuring free flow of information and timely delivery. He has engaged with key stakeholders in effectively integrating and synthesizing the data to build a complete, cohesive picture. His extensive consulting experience in different geographies i.e., USA, UAE, and Canada, helps him perform in a challenging environment. An IIM B alumni, he has worked with Fortune 30 companies like Lowes in USA and built the analytics team/infrastructure for companies like CarDekho and Yatra online. Very active on the Indian Analytics scene, Ritesh has been one of the speakers at multiple analytics conferences and college events. A National Geographic Moments photograph awards winner, he likes to click photographs and write short stories in his free time.