A Predictive Analytics Framework for Optimizing Preventive Healthcare Sales and Engagement Outcomes

• Author(s): Ololade Shukrah Abass ; Oluwatosin Balogun ; Paul Uche Didi
• Paper ID: 1710068
• Page: 497-516
• Published Date: 31-05-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 11 May-2019

Abstract

The increasing emphasis on preventive healthcare has created a pressing need for data-driven strategies that improve sales effectiveness and engagement outcomes across health-focused industries. This study proposes a predictive analytics framework designed to optimize the sales and engagement performance of preventive healthcare products and services. Leveraging machine learning algorithms, behavioral segmentation, and real-time consumer interaction data, the framework forecasts purchasing intent, identifies high-value customer segments, and tailors engagement strategies to individual preferences and health behavior patterns. The proposed framework integrates structured and unstructured data sources, including electronic health records, wearable device metrics, demographic profiles, and social media interactions, to generate predictive insights and personalized outreach models. A key component of the framework is its ability to dynamically adjust marketing and outreach strategies based on engagement feedback, conversion rates, and evolving health needs, thereby increasing campaign relevance and sales efficiency. The framework was tested using a real-world dataset from a digital health firm offering wellness subscriptions, diagnostic tests, and telehealth consultations. Results demonstrated a 24% increase in lead conversion, a 30% improvement in customer retention, and a 40% boost in engagement rates compared to traditional segmentation and targeting methods. Furthermore, the framework’s adaptability supports continuous optimization of sales approaches across different regions, age groups, and health risk categories. The research highlights how predictive analytics can bridge the gap between health data and commercial outcomes, enabling healthcare companies to deliver value-driven preventive solutions while achieving measurable business impact. The findings contribute to the evolving literature on precision marketing in healthcare and offer a scalable, ethical model for data-informed decision-making in consumer health engagement. This study recommends broader adoption of predictive frameworks across public and private health promotion initiatives to increase the uptake of preventive services, improve health literacy, and support long-term population health goals. Future research should explore the integration of generative AI for content customization and the application of explainable AI techniques to foster transparency and trust in predictive healthcare marketing.

Keywords

Predictive Analytics, Preventive Healthcare, Consumer Engagement, Sales Optimization, Health Behavior, Personalized Marketing, Machine Learning, Health Tech, Data-Driven Strategy, Digital Health.

Citations

IRE Journals:
Ololade Shukrah Abass , Oluwatosin Balogun , Paul Uche Didi "A Predictive Analytics Framework for Optimizing Preventive Healthcare Sales and Engagement Outcomes" Iconic Research And Engineering Journals Volume 2 Issue 11 2019 Page 497-516

IEEE:
Ololade Shukrah Abass , Oluwatosin Balogun , Paul Uche Didi "A Predictive Analytics Framework for Optimizing Preventive Healthcare Sales and Engagement Outcomes" Iconic Research And Engineering Journals, 2(11)