Analytical Consulting and BI

Cross-Sell Model for a Healthcare Insurance Company

Client Proposition:

A healthcare insurance company sought to enhance revenue and customer satisfaction by effectively identifying and recommending the next best service to cross-sell to existing customers.

Service Delivered:

We gathered and integrated comprehensive customer data, incorporating various sources like demographics, insurance plans, claims history, and service logs. Through data cleaning and preprocessing, we created a unified dataset suitable for analysis. Employing advanced association rule mining techniques, we identified key patterns in service combinations, revealing valuable insights into which services tend to be purchased together.

Furthermore, we uncovered usage patterns among customers, shedding light on common service transitions over time. By leveraging machine learning models such as logistic regression and random forest, we developed predictive models to identify cross-selling and upselling opportunities. These models were seamlessly integrated into operational workflows, enabling the deployment of personalized recommendations. Continuous monitoring and refinement of these strategies were performed based on performance metrics and user feedback.

Outcome:

As a result, the client achieved improved cross-selling effectiveness and boosted revenue through strategically targeted upsell opportunities. This data-driven approach enhanced customer satisfaction by offering personalized service recommendations tailored to individual needs and usage patterns. Overall, our advanced analytics techniques optimized the client’s cross-selling and upselling strategies, driving significant revenue growth while elevating the customer experience.

Risk Management for a Healthcare Insurance Company

Client Proposition:

A healthcare insurance company aimed to proactively identify risky customers to mitigate financial risks and optimize resource allocation by analyzing demographics, firmographics, and historical financial data, and developing advanced machine learning models for risk prediction.

Service Delivered:

To address the need for comprehensive data to assess customer risk factors, we collected and integrated diverse data sources, including customer demographics, firmographics, and historical financial data, consolidating them into a unified dataset. Through feature engineering, we identified key risk factors such as customer age, health status, insurance coverage history, and financial metrics. We selected features with significant predictive power to build accurate risk assessment models.

We developed and trained machine learning models, including logistic regression, random forest, and gradient boosting, to predict customer risk levels effectively. By leveraging these model predictions, we implemented proactive risk mitigation strategies, such as targeted interventions and resource allocation adjustments. These predictive models were integrated into operational workflows for real-time risk assessment, with ongoing performance monitoring and refinement based on updated data and feedback.

Outcome:

As a result, the client successfully identified high-risk customers early, enabling proactive risk management and mitigation. This strategy improved resource allocation and optimized insurance pricing by leveraging accurate risk profiles, enhancing financial stability while minimizing losses. Overall, the client enhanced their risk prediction and mitigation capabilities, achieving greater operational efficiency and increased customer satisfaction through fair pricing and personalized solutions.

Digital Performance Marketing and Analytics for Healthcare Insurance

Client Proposition:

A healthcare insurance company aimed to enhance customer experience and optimize digital marketing strategies through a comprehensive analytics program.

Service Delivered:

Improved customer experience by introducing personalized health content, user-friendly portals, and proactive support, based on user research and feedback. To better understand digital interactions, we employed tools like Google Analytics, heatmaps, and session recordings, which helped optimize digital touchpoints.

A measurement framework using KPIs such as conversion rates and customer satisfaction scores to evaluate digital marketing success. Decision support tools and predictive analytics were implemented to guide customers toward optimal treatment options and providers. A customer segmentation strategy was used to ensure message relevance, and we fostered collaboration between marketing, IT, and healthcare teams for integrated digital projects. Additionally, preference management systems and customer data platforms (CDPs) were utilized to deliver personalized communications.

Outcome:

The strategic analysis resulted in increased customer engagement and satisfaction, more accurate measurement of digital campaign performance, and the launch of innovative initiatives in areas such as musculoskeletal (MSK) and behavioral health. This data-driven approach not only enhanced marketing effectiveness but also supported strategic growth and differentiation within the competitive healthcare insurance market.

Measurement Framework for Campaign Success

Objective:

To establish a robust measurement framework for evaluating the success of marketing campaigns, focusing on Application Scorecards and Lifetime Customer Value (LCV).

Application Scorecards:

To address the lack of standardized metrics for evaluating marketing campaigns’ effectiveness in acquiring new insurance applications, we developed Application Scorecards. These scorecards quantified and tracked key metrics related to campaign performance and application acquisition rates. We defined metrics such as application conversion rates, application quality scores, and acquisition costs, and created a scorecard system to assign scores based on predefined benchmarks.

By integrating data sources for real-time reporting, the healthcare insurance provider was able to accurately evaluate marketing campaign effectiveness, make data-driven decisions, and optimize resource allocation based on scorecard results.

Lifetime Customer Value (LCV):

To help clients measure and maximize the lifetime value (LCV) of customers acquired through marketing campaigns, we developed a comprehensive methodology for calculating and monitoring LCV. Leveraging predictive analytics models, we forecasted the future value and profitability of customer segments using historical data and behavioral patterns.

Our approach involved collecting and analyzing data on acquisition costs, revenue, retention rates, and cross-sell/up-sell opportunities. Advanced analytics techniques were applied to calculate LCV, incorporating critical factors such as customer churn and average revenue per customer. Additionally, we established a continuous monitoring framework to optimize marketing strategies and enhance customer lifetime profitability.

This solution enabled our clients to prioritize investments based on potential LCV, implement targeted retention programs, and make data-driven strategic decisions to maximize long-term profitability.

Outcome and Conclusion:

By implementing Application Scorecards and monitoring LCV, our client improved their ability to measure and optimize marketing campaign success. This data-driven approach enhanced campaign performance evaluation, drove long-term profitability, and strengthened customer relationship management, supporting strategic growth in the competitive healthcare insurance market.