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Enabled Healthcare Plan Enrolments Forecast Leveraging Analytics for Future Projections

A3logics developed a game-changing technology that helps businesses optimize resources and plan for long-term success.

OVERVIEW

Our client is a big player in the healthcare industry and was struggling to predict healthcare plan enrollment, leading to resource inefficiencies and difficulties setting competitive premiums. By using data analytics and strategic forecasting, A3Logics was able to improve accuracy leading to resource optimization, competitive pricing, and targeted marketing strategies.

 

CHALLENGES
  1. 1)Inaccurate predictions were leading to resource shortages or overallocation.
  2. 2) Customer retention rate was dropping due to non competitive premiums or underpricing plans.
  3. 3) Integrating historical enrollment data, employee demographics, and internal pricing adjustments into a single model can be complex and prone to human error.
  4.  4) It was difficult to tackle unforeseen events like pandemics or changes in healthcare legislation.
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Our Approach
  1. 1. Data was collected from insurance companies, government agencies, and academic research to better understand enrolment patterns and trends. 
  2. 2. Our team analyzed data to understand how factors like premium changes and economic indicators affect enrolment.
  3. 3. Advanced machine learning algorithms, such as Gradient Boosting Machines, were used to improve prediction accuracy by considering customer profiles and enrollment history. 
  4. 4. We evaluated the results to see if the predictions were accurate. Compared current trends with the model’s predictions and measured the performance of different algorithms. 
  5. 5. Used predictive models to optimize the enrollment process by improving premiums, encouraging past customers to re-enroll, and offering personalized deals based on customer data.

 

OUR SOLUTIONS

 1) Data Gathering: Gathered historical enrolment data spanning 510 years along with demographic and premium-related information.

2) Descriptive Analysis: Conducted a detailed analysis to comprehend historical enrolment patterns and trends.

3) Segmentation: Grouped customers based on age, income, family size, and past enrolment behaviors.

4) Correlation Analysis: Explored connections between enrolment figures and external factors.

5) Predictive Modeling: Utilized Regression Analysis, integrating variables such as premium changes and economic indicators.

6) Machine Learning Integration: Deployed advanced Machine Learning algorithms like Gradient Boosting Machines for precise predictions.

7) Resource Optimization & Strategic Decision-Making: Predictive insights enable healthcare providers to optimize resource allocation and inform strategic decision-making.

 

 

Stats That Defines
Our Results

93%

of healthcare companies were able to predict medical, dental, and vision plan enrollments for the upcoming year using A3logics’ data analytics and strategic forecasting methods.

 

89%

believed in the optimized resource process in the healthcare company to meet the demand for healthcare plans.

 

97%

of respondents said that A3logics solutions helped the company target specific demographics or regions for marketing based on predicted enrollments.

 

85%

of healthcare companies accurately predicted enrollments and set competitive premiums to maximize profitability.

CONCLUSION

This case demonstrates the power of data-driven healthcare plan enrollment forecasting. A3logics’ solution enhanced resource allocation, enabled competitive pricing strategies, and optimized marketing efforts, ultimately leading to improved customer retention and increased profitability for the client.

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