Case Study Overview

Company Profile

Founded: 2013

Headquarters: Portland, OR

Employees: 30

-2 Leadership

-10 Advisors

-7 Client Service Associates

-3 Planners

-3 Operational

-3 Compliance Reviewers

-2 Marketing

AUM: $1.8B and growing at 13-15% YoY

Number of Clients: 725

Client Base: Affluent & Emerging High Net Worth

• Avg Household = $1.1M

Service Model: Holistic financial planning & Discretionary Portfolio Management

Fee Structure: AUM-based, with optional planning subscription for younger, growing clients

Strategic Identity: Boutique RIA with a reputation for relationship-driven planning, trying to scale without losing “high-touch” standing


Growth Drivers

Advisor Recruitment Success: High-performing Advisors joined from captive wirehouse, bringing books to independent sector

Referral influx: Strong client satisfaction has led to accelerating organic referrals

Market successes: Tech-sector wealth in region and favorable equity markets

*Growth accelerating, but outpacing firm infrastructure


Growth Challenges

Capacity Issues

Advisors operation near full capacity due to task mix (intake, data entry, packet edits, meeting prep), not client volume

Client Service Advisors overwhelmed with onboarding, account transfers, and custodial paperwork

Planners backlogged on plan updates and annual reviews

Referral influx: Strong client satisfaction has led to accelerating organic referrals

Market successes: Tech-sector wealth in region and favorable equity markets

*Growth accelerating, but outpacing firm infrastructure


Case Study Context & Data Generation

Northwest Wealth Partners is a fictional Registered Investment Advisor (RIA) created for the purpose of demonstrating full‑stack operational analysis, workflow redesign, forecasting, workforce planning, and data governance capabilities. The firm’s characteristics (headcount, AUM, client base, growth rate, and organizational structure) were intentionally modeled to resemble a mid‑sized, fast‑growing boutique RIA facing real‑world operational strain.

To support the analysis, all underlying datasets used in this project were synthetically generated using a custom Python script. The script produced realistic but entirely fictional operational data, including household records, advisor assignments, onboarding workflows, planning inputs, custodial metadata, and compliance documentation indicators. This approach ensured that the project could demonstrate advanced analytical methods (data cleaning, reconciliation, workflow mapping, forecasting, and governance design) without relying on any proprietary or confidential information.