Sales Strategy Analytics Portfolio Case Study

Alternative Investment Sales Strategy Analytics Platform

End-to-end analytics platform simulating how a Global Client Solutions / Wealth Solutions team uses data to optimize advisor coverage, relationship manager productivity, campaign ROI, and product demand across alternative investment products.

View GitHub Repo Read Executive Report View Methodology

$58.8B

Expected Pipeline

Modeled opportunity value across all stages.

$6.75B

Committed Capital

Closed commitment value in the synthetic dataset.

10.87%

Conversion Rate

Committed opportunities divided by total opportunities.

400

Advisors

Synthetic advisor universe across wealth and institutional channels.

Relationship Managers

Coverage team used for productivity benchmarking.

132

High-Priority Advisors

Top-tier advisor targets from the priority scoring model.

All figures are generated from deterministic synthetic data and are used to demonstrate the analytical framework.

Executive Summary Snapshot

A portfolio-quality analytics artifact for distribution strategy and sales enablement.

Business Context

Alternative investment distribution is long-cycle, capital-concentrated, and relationship-driven.

Analytics Scope

The platform analyzes funnel conversion, RM productivity, product demand, campaign ROI, and advisor prioritization.

Decision Support

Outputs are designed for sales leadership, coverage planning, campaign allocation, and next-best-action workflows.

Business Problem

The Coverage Challenge in Alternative Investment Distribution

Leadership needs visibility into funnel drop-off, RM productivity, product demand, campaign efficiency, and advisor prioritization so coverage resources can be aimed at the highest-impact segments.

Long Sales Cycles

Commitments take months to evaluate, making stage progression and time-to-close more important than top-line activity volume alone.

Concentrated Advisor Value

A small share of advisors and channels can represent a large share of committed capital, requiring disciplined segmentation.

Need for RM Prioritization

Relationship managers need clear next-best-action guidance to convert engagement into pipeline movement and commitments.

What I Built

A reproducible analytics workflow from synthetic CRM-style data to executive reporting.

Synthetic Data Generator

SQLite Database

SQL + Python Analytics

Dashboard-Ready CSV Marts

Executive Charts + Report

Advisor Priority Recommendations

7 raw tables 6 reporting marts 5 analysis modules 4 executive charts GitHub Actions validation

Analysis Modules

Five connected analyses that mirror sales strategy and enablement use cases.

Business Question

Where does the pipeline drop off?

Output File

sales_funnel_summary.csv

Key Metric

10.87% overall conversion rate

Why It Matters

Helps sales leadership focus on the stages and segments with the largest conversion gaps.

Selected Visuals

Executive-ready charts generated from the processed analytics marts.

Click any chart to review it in a larger format.

Sales funnel opportunities by stage — **Sales Funnel Conversion** Highlights where opportunities sit by stage and the capital attached to each step.

Relationship manager productivity leaderboard — **RM Productivity** Compares committed capital by RM while flagging coaching opportunities.

Campaign ROI by campaign type — **Campaign ROI** Shows modeled committed-capital-to-cost efficiency by campaign type.

Product demand by asset class — **Product Demand** Compares committed capital and engagement across alternative asset classes.

Key Insights

Business findings framed for sales leadership.

Northeast has the largest opportunity volume but weaker conversion.

Family Offices and Institutional Investors generate 59% of committed capital.

Private Equity and Infrastructure show stronger modeled conversion and capital efficiency.

Three RMs show high engagement but lower conversion, suggesting coaching around follow-up discipline.

Email campaigns show strongest modeled efficiency, but ROI magnitude should be interpreted directionally because the data is synthetic.

Data Model

CRM-style distribution analytics without confidential client data.

The data model mirrors advisor coverage, RM activity, product, opportunity, and campaign relationships while using only synthetic records.

Advisors Relationship Managers Funds Sales Activities Opportunities Campaigns Campaign Engagement

Quality and Reproducibility

Built to be inspectable, rerunnable, and safe to share publicly.

Deterministic Pipeline

Fixed random seed 42 ensures the raw data, processed marts, KPIs, and charts can be reproduced.

Validation Coverage

src/validate_outputs.py checks files, row counts, keys, KPI values, chart assets, and README embeds.

Public-Safe Data

No real client, advisor, fund, firm, or KKR data is used. The repo includes a clear synthetic data disclosure.

Read Data Quality Checks

Skills Demonstrated

Analytics, engineering, and executive communication.

Documentation & Review Materials

Supporting materials for technical reviewers, hiring managers, and business stakeholders.

Executive Report

Leadership-style summary of key findings, KPI outputs, and strategic recommendations.

Methodology & Assumptions

Explanation of synthetic data design, scoring logic, ROI interpretation, limitations, and production extensions.

Data Quality Checks

Validation coverage for row counts, key relationships, KPI outputs, chart assets, and reproducibility.

GitHub Repository

Full source code, SQL scripts, generated data, dashboard-ready marts, and CI validation workflow.

Full Repository

Explore the Full Analytics Project

Review the source code, SQL scripts, generated synthetic data, dashboard-ready marts, executive report, methodology, and validation workflow in the GitHub repository.

View GitHub Repository Read Executive Report