Executive Summary

Understanding your members is essential for effective superannuation fund management. This project demonstrates a practical, low-code approach to member segmentation using synthetic data, a transparent clustering model, and an interactive web app. The synthetic data is designed with embedded segments, allowing robust validation of the model’s ability to recover meaningful groupings—an essential step before applying such models to real-world data. Model training is tracked with a robust framework for reproducibility and auditability.

While the focus here is on superannuation sector, the approach is broadly applicable to segmentation challenges in banking, insurance, retail, healthcare, and beyond. The public demonstration uses a deliberately simple model for clarity, but more sophisticated, production-ready solutions are available on request.

Superannuation Member Segmentation: A Practical, Transparent Approach

Unlocking Value with Data-Driven Segmentation

Australian super funds serve a diverse membership base—different ages, balances, professions, engagement levels, and advice needs. Traditional segmentation (e.g. by age or balance alone) often misses the nuances that drive member behaviour and satisfaction. By leveraging both demographic and behavioural data, funds can:

• Identify disengaged members for targeted re-engagement
• Tailor product recommendations by life stage or risk profile
• Proactively support members approaching retirement
• Optimise marketing and service delivery for different cohorts

Project Overview

This demonstration provides a robust, ethical, and transparent framework for member segmentation:

• Interactive Streamlit App: Explore, visualise, and experiment with segmentation results in real time—no coding required.
• MLflow-Backed Model Training: All model experiments and parameters are tracked for reproducibility and robust deployment.
• Synthetic Data with Embedded Segments: The synthetic dataset is constructed with realistic, embedded clusters (segments), not just random noise. This allows us to rigorously test whether the segmentation models can recover meaningful groupings—if they can’t, they’re unlikely to work on real data.
• Business-Ready Insights: Segments are profiled for immediate business actionability, supporting member engagement, tailored communications, and compliance.

Why Use Synthetic Data with Embedded Segments?

Privacy is paramount in superannuation. To avoid any risk of exposing personally identifiable information (PII), all data in this project is synthetic. However, unlike purely random data, this dataset is deliberately designed with embedded clusters that reflect plausible member segments. This enables:

• Robust Model Validation: If the model cannot recover these known segments, it is unlikely to perform well on real-world data.
• Safe Experimentation: Stakeholders can explore segmentation techniques and outcomes without any privacy risk.

How It Works

1. Feature-Rich Synthetic Data

The dataset includes a mix of demographic, behavioural, and psychographic features typical of Australian super members:

2. Simple, Transparent Segmentation

• KMeans clustering groups members by feature similarity, with all numeric features standardised and categorical features one-hot encoded.
• The number of clusters is chosen based on business needs and model evaluation metrics (see Appendix).

3. Interactive Streamlit App

Please contact me if you would like to take the interactive app for a test drive. It allows you to:

• Visualise segments in 2D (via PCA/t-SNE), explore sizes, and compare average profiles.
• Profile members: Enter hypothetical or real member details and see predicted segment and suggested actions.

4. MLflow for Tracking and Reproducibility

• Every model run, parameter, and metric is logged.
• Supports robust, auditable, and production-ready workflows.

Applicability Beyond Superannuation

Although this demonstration is tailored for superannuation, the approach is equally applicable to any segmentation problem. For example:

• Retail & Banking: Customer segmentation for marketing, churn prediction, or product targeting.
• Insurance: Grouping policyholders for risk management and tailored offerings.
• Healthcare: Patient stratification for personalised care.
• Image Analysis: Segmenting images for medical diagnostics, autonomous vehicles, or satellite imagery.
• Manufacturing: Identifying product types or defects on assembly lines.

Any domain where entities can be grouped by shared characteristics or behaviours can benefit from this approach.

Ethical and Legal Considerations

• Australian Privacy Principles (APPs): The project is designed to comply with APPs, ensuring open management, data minimisation, and secure handling.
• Fairness: Segmentation avoids reinforcing stereotypes or unfairly excluding groups.
• Transparency: Users can review and understand how segments are formed and used.

When adapting for real data, ensure all privacy, consent, and ethical requirements are fully met. See the Technical Appendix for more detail.

Get Started or Go Further

• Try the Demo: Clone the repo and launch the Streamlit app to explore segmentation in action.
• Customise: Adjust features, rules, or clustering methods to suit your fund or business context.
• Scale Up: For advanced models (e.g., fuzzy clustering, dynamic segmentation, deep learning), or integration with production systems, get in touch.

This article introduces a simple, transparent segmentation approach. For more sophisticated or bespoke solutions—including advanced modelling, production integration, or custom analytics—contact me directly.

Technical Appendix

A. Model and Feature Engineering

• KMeans clustering minimises within-segment variance; each member is represented as a standardised feature vector.
• Categorical features are one-hot encoded to ensure fair distance calculations.
• The number of clusters ($K$) is chosen based on business needs and silhouette score.

B. Evaluation Metrics

C. MLflow Integration

• Models, parameters, and metrics are logged for every experiment.
• Model versions can be registered, compared, and deployed as REST endpoints.
• Streamlit interacts with the latest production model, ensuring up-to-date insights.

D. Streamlit User Interface

• Built for less-technical users: visualise, filter, and profile segments without writing code.
• Supports quick experimentation and stakeholder engagement.

E. Synthetic Data Design

• Synthetic data is generated with embedded cluster structure to mimic real-world segmentation.
• This allows for robust validation: if the model cannot recover these known clusters, it is unlikely to work on real data.

F. Broader Use Cases

• The pipeline is modular: swap in new features, segmentation methods, or visualisations as needed.
• Suitable for any segmentation challenge, not just superannuation.

G. Ethics and Privacy

• Strict adherence to Australian Privacy Principles (APPs) and best practice in data privacy and fairness.
• Only synthetic data is used for demonstration; real deployments require robust privacy and consent processes.

Conclusion

Effective segmentation is the key to personalising member journeys and driving engagement in superannuation—and beyond. This project demonstrates how low-code tools and transparent models can deliver actionable insights, fast. For more advanced analytics or tailored solutions, reach out for a discussion.

Want to see it in action or discuss a custom solution? Contact me via DataBooth.