Last Deployed: Thu Aug 28 19:26:34 2025 -0400

Welcome to my Flask BI project!
This is a personal, AI-assisted learning app.

Flask app Demo & Resources:

Use Case: Evaluating Wholesaler Effectiveness…

Key Business Questions

  1. Which wholesalers are generating the highest net retained assets under management (AUM)?
  2. How “sticky” is the AUM each wholesaler brings in—i.e., how long does it stay on the books?
  3. Are high-volume wholesalers bringing in assets at disproportionately low fee rates?
  4. How do management fee revenues vary across wholesalers when factoring in both volume and retention?
  5. What’s the effective return (fee-adjusted contribution) per wholesaler over time?

Context

Wholesalers are responsible for securing new investments into managed products (e.g., mutual funds, model portfolios). While raw inflow volume is often used as a performance metric, this doesn’t capture long-term business value. For example, short-lived or low-fee AUM may contribute little to revenue. By contrast, smaller but stickier flows into high-fee products may be more profitable.

Goals

  • Tie asset inflows to individual wholesalers
  • Track the retention of those assets over time
  • Associate each AUM flow with a management fee rate based on the product
  • Calculate revenue contribution by wholesaler
  • Compare wholesalers not just by volume, but by profitability and asset durability

Planned Outputs

  • Visualizations comparing gross vs. net AUM by wholesaler
  • Revenue contribution (AUM × fee rate × duration)
  • Churn or decay rates of AUM over time
  • Dashboard views for executive/sales leadership
  • Analytical models to surface underperforming or undervalued wholesalers