Timeline: H2 2019 - Q3 2020 | Focus: Internal Tool Development, Process Automation, Data Accessibility, User Enablement
Background
The Company & Need
PFM Inc. advises public sector clients on complex financial matters, including capital market transactions (healthcare, education). Analysts relied heavily on time-consuming, manual processes involving disparate spreadsheets and databases to analyze deal structures, market conditions, and generate client reports.
My Role
As Product Manager for Capital Markets Products, I observed these workflow bottlenecks firsthand. Recognizing the significant impact on productivity and potential for inconsistency, I initiated the project to develop a dedicated internal tool to streamline these critical analysis and reporting functions.
The Challenge
Manual Processes Hindering Efficiency and Scalability
The existing workflow suffered from several key pain points:
- Extreme Time Consumption: Analysts spent hours manually gathering, cleaning, and manipulating data from various sources for each deal analysis.
- Inconsistent Outputs: Manual processes led to variations in calculation methods and report formats across different analysts and deals.
- Limited Data Accessibility: Valuable historical data and market insights were siloed in individual files, hindering broader strategic analysis and trend spotting.
- High Risk of Error: Manual data entry and calculations increased the likelihood of errors in critical financial reports.
- Analyst Burnout: Repetitive, low-value tasks consumed significant analyst time, detracting from higher-value strategic work.
Key Workflow Bottlenecks:
The Opportunity
Automating Analysis, Empowering Analysts
The clear opportunity was to develop an internal platform (CMAP) that could automate the grunt work, ensure consistency, and provide easy access to unified data. This would not only save significant time but also empower analysts to focus on complex problem-solving, strategic advice, and client relationship management. The goal was to transform data analysis from a bottleneck into a strategic advantage.
The Work: Building the Solution
Phase 1: Discovery & Requirements (H2 2019)
- Conducted user research sessions with analysts and team leads to deeply understand existing workflows, pain points, and desired future state.
- Mapped key analysis processes and identified critical data sources (SQL databases, external APIs, spreadsheets).
- Defined core requirements for data ingestion, calculation logic, reporting outputs, and user interface needs. Gathered input via workshops and surveys.
- Developed initial product strategy and roadmap, prioritizing features based on impact and feasibility.
Phase 2: Design, Development & Iteration (Q1-Q2 2020)
- Collaborated with UX designers (using Figma for mockups/prototypes) to create an intuitive interface focused on streamlining core tasks.
- Led a cross-functional team (engineers, data specialists) using Agile/Scrum methodology (managed via Jira) for iterative development.
- Oversaw the build-out of backend data integration (leveraging SQL, Python scripts) and core calculation engines.
- Ran regular UAT sessions with key analysts, incorporating feedback into subsequent sprints to ensure the tool met practical needs.
Workflow Transformation: Before vs. After CMAP
Before (Manual Process)
- Download data from multiple sources
- Manually clean & merge spreadsheets
- Perform complex calculations in Excel
- Copy/paste results into report templates
- Manually check for errors
- Time per analysis: ~4-6 hours
After (CMAP Workflow)
- Select deal parameters in CMAP
- Platform automatically pulls & processes data
- Review system-generated calculations
- Generate standardized report with one click
- Focus on interpretation & insights
- Time per analysis: ~1-1.5 hours
Phase 3: Launch, Training & Support (Q3 2020)
- Developed comprehensive training materials including documentation on Confluence, video tutorials, and quick-start guides.
- Conducted role-specific training sessions for all Capital Markets analysts and relevant support staff.
- Managed a phased rollout, starting with a pilot group and gradually expanding access while collecting feedback.
- Established clear support channels and processes for bug reporting and feature requests, leading to a measured 10% reduction in related tickets.
User Enablement Activities
- Interactive Training Workshops
- On-Demand Video Tutorials
- Detailed Confluence Wiki
- Quick Reference Guides
- Pilot User Feedback Sessions
- Post-Launch Q&A Forums
- Office Hours Support
- Regular Usage Tips
Metrics + Analytics
Measuring Impact on Efficiency and Effectiveness
Key metrics tracked to evaluate CMAP's success included:
- Analysis Time per Deal: Measured via time studies and analyst self-reporting before and after launch. Goal: >50% reduction.
- User Adoption & Usage Frequency: Tracked logins and core feature usage via internal analytics. Goal: >90% weekly active users among analysts.
- User Satisfaction: Measured through surveys and qualitative feedback sessions. Goal: Improve satisfaction scores related to analysis tools/processes.
- Support Ticket Volume: Monitored tickets related to data analysis tasks. Goal: Reduction in volume post-training.
- Data Consistency Checks: Audited report outputs for standardization improvements.
The Outcome
Dramatically Improved Efficiency and Data Accessibility
The launch of the Capital Markets Analytics Platform yielded significant positive results:
- Achieved a 75% reduction in the average time required for manual data analysis and report generation per deal.
- Enabled the team to more efficiently support $1.8 billion in annual healthcare and education deal volume.
- Improved consistency and accuracy of analytical outputs across the entire team.
- Increased data accessibility, allowing for easier historical analysis and trend identification.
- Boosted analyst morale and satisfaction by automating tedious tasks and allowing focus on higher-value work.
- Reduced support burden with a 10% decrease in analysis-related support tickets following comprehensive training.
Average Analysis Time Per Deal (Hours)
CMAP reduced average analysis time by 75%.
Reflections + Learnings
Lessons from Building an Internal Efficiency Tool
- Deep User Understanding is Paramount: Investing time in observing and interviewing internal users was critical to identifying the right problems and designing a truly useful solution.
- Automation Frees Up Expertise: Automating repetitive tasks allows highly skilled employees (analysts) to apply their expertise to more strategic, impactful work.
- Internal Tools Need Product Management Rigor: Treating internal tools like external products (clear strategy, roadmap, user feedback loops, dedicated support) drives better adoption and outcomes.
- Training Drives Value Realization: Even the best tool provides little value if users don't know how to use it effectively. Comprehensive training was key to achieving the efficiency gains.
- Quantifying Internal ROI Matters: While sometimes harder to track than external revenue, measuring efficiency gains (like time saved) clearly demonstrates the value of internal product investments.
