PFM Weekly Update

Overview

A discovery research study uncovering how mobile banking users manage their personal finances manually — and how a weekly analytics feature could be built from scratch based on real user behavior, needs, and insights. This was a fast-turnaround project driven by a critical business need to understand the analytics page before making product investment decisions.

My Role

Sole UX Researcher on the PFM squad — responsible for full end-to-end research including planning, recruitment, survey design, interviews, competitive analysis, synthesis, and stakeholder presentation. Collaborated closely with product and engineering teams to translate insights into actionable, sprint-ready recommendations.

Timeline

Total duration: 7 working days — fast-tracked due to critical business priority

• Recruitment: 2 days
• Research & competitive analysis: 4 days
• Analysis & report preparation: 1 day

Research Goals

• Understand how users currently interact with the analytics section of the mobile banking app
• Uncover unmet user needs around personal finance management
• Identify what a new weekly analytics feature should contain and how it should be delivered
• Provide evidence to justify building a new feature from scratch

Research Methodology

Three methods were combined to answer both behavioral and attitudinal questions at scale:

Survey (100 participants) was chosen to quickly validate patterns across a large user base — understanding frequency of use, preferred information types, and timing preferences at scale.

In-depth interviews (10 participants) were used to go deeper — understanding the mental models, emotional context, and manual workarounds users had developed outside the app.

Competitive analysis (10+ products) was conducted to benchmark how leading banking and fintech products had responded to similar user needs — identifying best practices from Revolut, Monzo, Wise, Chase, Finshape, Personetics, and others.

Combining all three methods allowed quantitative patterns from the survey to be explained by qualitative depth from interviews, and enriched by real-world solutions from competitive analysis.

Recruitment

Participants were recruited through two channels: existing mobile banking user data and targeted social media groups with relevant audience members.

100 participants completed the survey. 10 participants ages 26–36 took part in in-depth interviews — selected to represent a range of banking experience and financial management behaviors.

Incentives were provided to interview participants. Budget was coordinated with the marketing team and incentive delivery logistics were managed personally.

First contact with participants was carefully managed — ensuring participants felt comfortable, informed that the product was being tested rather than them, and genuinely motivated to share honest experiences rather than desirable answers.

Screener Criteria

• Active mobile banking users
• Ages 26–36
• Range of financial management behaviors — from manual trackers to passive users
• Mix of high and low engagement with the existing analytics feature

Sample Interview Questions

• How do you currently keep track of your spending after receiving your salary?
• Have you ever calculated how much you can spend per day before your next salary? How did you do that?
• How often do you check your transaction history in the banking app?
• What information would make the analytics section more useful for you?
• What do you do when you want to compare this month's spending to last month?

Analysis & Synthesis

Data was analyzed using:

• Excel for survey data segmentation and pattern identification
• Miro for affinity mapping of interview findings
• Built-in survey analysis tools for quantitative visualization
• AI-assisted synthesis to identify recurring themes across qualitative interview notes
• Thematic analysis to group user behaviors into actionable insight clusters

Sessions were debriefed immediately after each interview. Patterns were identified by looking for recurring manual workarounds — behaviors users were performing outside the app that the app could replace.

Deliverables

A complete research report was delivered including:

• Research goals and context
• Methodology rationale
• Target audience definition and participant profiles
• Survey results with visualizations
• Interview insights organized by theme
• Competitive analysis findings
• Core insights and behavioral patterns
• Complete result analysis per insight area
• Summary and prioritized recommendations
• Next steps
• One core insight highlighted for executive attention

Key Survey Results

Key Findings

Users had 3 core manual financial behaviors performed entirely outside the app:

• Calculating weekly spending by category after salary — by hand
• Estimating daily budget remaining before next salary — manually
• Comparing this month vs last month spending — manually

Two user segments were identified:

• Inactive users— never engaged with analytics because it didn't serve their actual needs. Needed to be identified and onboarded through smart notifications and account activity monitoring
• Active users — used the feature but needed better guidance, clearer information flow, and a visual preview before fully engaging

Competitor Findings

• Monzo — weekly and monthly spending summaries via push notifications in a story-style format
• Finshape — weekly reports as stories, reducing financial stress by replacing raw numbers with narrative context
• Personetics — AI-powered personalized financial insights platform used by banks globally

The Solution Proposed

A weekly analytics story — inspired by the social media story format — delivered via SMS and push notifications. Each screen reveals one insight at a time:

Notification timing was data-driven: Monday and Sunday mornings based on survey results.

Outcome

The research directly shaped the creation of a new weekly analytics feature that did not exist before this study. The entire information architecture, notification strategy, content model, and delivery timing were built on research findings. The feature led to a measurable increase in monthly active users on the analytics page.

The screenshot below shows the released feature — a direct result of this research:

The team's speed in implementing recommendations — and the positive results that followed — validated both the quality of insights and the efficiency of the 7-day research process.

Next Steps & Recommendations

• Build a prioritization matrix for all proposed feature ideas to identify quick wins, long-term developments, and low-priority items
• Prepare a prototype of the weekly analytics story format
• Conduct usability testing on the prototype before full development begins

Reflections

Despite the compressed 7-day timeline, the research process ran smoothly and all team questions were answered without requiring additional rounds. The quality of findings was confirmed by outcomes — the analytics page saw measurable MAU growth following implementation, which exceeded initial expectations.

The most consistent challenge across discovery research is participant recruitment — specifically getting honest engagement rather than incentive-driven participation. Careful management of first contact, adapting tone and language to each participant's personality, and creating an environment where participants feel safe to share genuine experiences is what separates reliable insights from noise. Ensuring participants know they are evaluating a product — not being evaluated themselves — is always the foundation of a good research session.

Skills Used