Hedgeness offers customizable AI models to extract actionable insights at a granular level from your historical and real-time data.
The insights are across departments with overall objective to efficiently increase and sustain your annuity assets in alignment with your strategic goals of annuity product mix.
ANALYTICS USE CASES
Predict with a high level of accuracy which customers are likely to surrender their annuity contract and proactively reach out to retain them. Recognize what variables trigger the surrender behavior and prioritize resources accordingly.
Leverage data from all sources to enhance marketing with precision in targeting, segmentation and persona creation.
Go beyond traditional app user analytics to dig deeper into data logs for demographic and behavioral insights.
AGENT OR ADVISOR SCORING
Use a financial professional's historical or current onboarding data to forecast their LTV to your annuity business.
Prioritize resources to optimize outreach to your agents or advisors.
HEDGENESS CUSTOMER ENGAGEMENT
Discuss and establish a clear understanding of your business objectives, the data available to you, and the type of problem you are trying to solve.
Data Collection and Preparation:
Collect data from various sources such as databases, files, and external data sources.
Clean and prepare the data by handling missing values, removing outliers, and transforming variables.
Feature Selection and Engineering:
Select the most important variables or create new variables that will help in making predictions.
Choose a suitable machine learning algorithm based on the problem at hand (classification, regression, clustering, etc.).
Split the data into training and testing sets.
Train the model on the training data.
Model Evaluation and Validation:
Evaluate the model's performance on the testing data.
Use appropriate metrics (accuracy, precision, recall, F1 score, RMSE, etc.) to assess the model's performance.
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