Aaron R.

Problem:

A top-tier wealth manager was allocating marketing spend and relationship manager capacity based on current AUM rather than future client value. This approach under-invested in high-growth segments while over-servicing mature, low-growth clients. The firm lacked a unified view of customer profitability across products.

Action:

I led a team that designed and deployed a Customer Lifetime Value (CLV) model incorporating investment behavior, fee generation, product holdings, and demographic trajectories. I built predictive segmentation logic to identify high-potential clients and created resource allocation frameworks for marketing and advisory teams. I established governance processes and trained business users on model interpretation.

Result:

Identified eight key customer segments based on their CLV trajectories. Opportunities that arose included identifying external assets of currently high AUM customers, prioritizing customers who demonstrated a propensity to significantly increase their asset holdings, and shifting the operating model of customers who were operating at a projected net loss. The net result was 10% reduction in costs for the negative CLV segment, and multiple subsequent projects to drive the CLV number higher.

Problem:

A major financial institution was running marketing campaigns with minimal analytical rigor—campaigns were designed based on intuition rather than customer behavior, leading to poor conversion rates and channel waste. There was no systematic approach to test, learn, and optimize campaign performance.

Action:

I built an optimization engine that integrated customer propensity modeling, channel attribution analysis, and A/B testing frameworks. I designed automated campaign selection algorithms that matched offers to customer segments based on predicted response and profitability. I created real-time performance dashboards and established a test-and-learn culture with marketing teams.

Result:

Generated $1.5M in incremental monthly campaign revenue through improved targeting and offer optimization. Reduced marketing waste by 40% while improving conversion rates by 25%. The optimization engine scaled across all retail product lines.

Problem:

Africa's largest bank needed to build wealth and insurance data science capability from fragmented analytics across business units. Leadership required assessment and roadmap to build models across customer lifespan: driving sales volumes, reducing churn, and moving customers up the value curve from basic banking to wealth/insurance products.

Action:

Assessed existing analytics capabilities across banking/wealth/insurance units; designed centralized data science org structure; built team from 0 to 15 data scientists; implemented behavioral modeling for wealth propensity, insurance campaign optimization, churn prediction for high-value clients, and CLV models; deployed Python-based technology stack with cloud infrastructure.

Result:

$20M+ annual profit through predictive analytics. Retained $200M in wealth assets (20% improvement) via churn modeling. Created $1.5M monthly revenue through optimized wealth/insurance campaigns. Increased insurance market share through bundling analytics. Introduced CLV/behavioral segmentation enabling identification of customers ready to move up product value curve.

Problem:

Leading wealth manager needed to scale analytics capabilities for digital transformation but faced high US costs and talent constraints with 8-person team overwhelmed by demand. Leadership required assessment of current team capabilities, technology platform evaluation, and recommendations for cost-effective scaling strategy to support growing analytics needs.

Action:

Conducted assessment of analytics team structure, skillsets, and processes; evaluated technology stack and identified gaps; designed hybrid delivery model with US strategic leadership and offshore execution; built business case for India delivery center; established recruiting and training programs; implemented modern Python analytics architecture; created governance for distributed collaboration.

Result:

Scaled team from 8 to 100+ FTEs while reducing unit costs, establishing $25M annual analytics capability. Modernized technology platform enabling graph-based anomaly detection and predictive modeling. Created replicable delivery model now serving 9 FSI clients. Team structure and technology choices enabled rapid deployment of CLV models, federated analytics, and AI/ML solutions.

Problem:

Fragmented customer data across 20+ systems prevented unified view for personalization and retention. Manual processes created inconsistent definitions and delayed insights.

Action:

Led discovery to identify and prioritize data sources for Customer360 based on business impact: transactional first, then behavioral/interaction, then external enrichment. Collaborated on Databricks medallion architecture. Bronze: raw customer data from core banking, CRM, digital channels, 3rd party data. Silver: master customer record with identity resolution and data quality rules. Gold: 360-degree customer view with elasticity, segmentation, and churn propensity models.

Result:

Unified customer data enabled $1M+ project revenue. Powered churn and elasticity models that improved retention and targeting capabilities.