Financial Advisor Feedback Intelligence Analysis
Transforming scattered feedback into actionable product insights
Project Duration
Q1 2023 - Q1 2025
My Role
As Lead UX Researcher, I strived to transform user feedback into actionable intelligence. My responsibilities included defining research objectives, designing the dashboard's information architecture, conducting user interviews with product managers, designers, executives and other stakeholders, and performing quantitative analysis. I translated these findings into actionable requirements, directly enabling data-informed product decisions and significant efficiency gains for the team.
Team
Project Overview
This quantitative UX research initiative addressed fragmented voice-of-customer data by delivering a feedback intelligence dashboard. Across 18,5k+ submissions (2020–2025), we combined user interviews, textual analysis, descriptive statistics, time series analysis, user segmentation, ML-driven sentiment, and CSAT surveys. The dashboard centralized insight generation and operationalized prioritization: 83% reduction in manual feedback processing, 47% improvement in feature prioritization accuracy (segmentation + 19K votes), 28% faster feature time-to-market, 32% shorter development cycles, stabilized CSAT around 88–90, and a 27% uplift in overall user sentiment.
Technologies & Methods
Tools
The Challenge: From Feedback Chaos to Clarity
Advisor360 faced a significant operational hurdle: user feedback, a goldmine of insights, was scattered across numerous channels—emails, support tickets, survey responses, and various internal systems. This disorganization made it nearly impossible to get a clear, holistic view of emerging needs/painpoints, user sentiment, and satisfaction.
Overwhelming & Siloed Feedback
How could we unify fragmented user voice?
Overwhelming & Siloed Feedback
Feedback was trapped in disparate systems (18,5k+ items), preventing a unified view and leading to missed insights and duplicated effort in manual collation.
Unquantified Sentiment & Trends
What did users *really* feel, and how was it changing?
Unquantified Sentiment & Trends
Without robust analysis, it was difficult to quantify overall user sentiment and satisfaction, track their evolution over time, or identify statistically significant trends versus anecdotal noise.
Reactive & Slow Insight Generation
Could we move from slow manual review to proactive insights?
Reactive & Slow Insight Generation
Product Managers spent excessive time (avg. 12hrs/week) manually sifting through feedback, delaying insight generation and leading to reactive rather than proactive product decisions.
The High Cost of Disconnected Feedback
- •Significant Team time consumed by manual feedback processing.
- •Delays in product decisions due to lack of timely feedback insights.
- •Missed opportunities to proactively address user pain points or capitalize on positive trends.
- •Inability to accurately gauge user satisfaction or the impact of product changes.
- •Product roadmap decisions were often based on assumptions or the loudest voices, not comprehensive data, costing effort and resources.
Our Research Aimed To:
- •Create a centralized, single source of truth for all user feedback.
- •Develop an interactive dashboard for Stakeholders to easily explore and understand feedback data.
- •Drastically reduce manual effort and empower data-driven prioritization.
- •Implement automated feedback sentiment analysis and trend identification.
- •Provide an overall CSAT metric for Stakeholders to track the impact of their product decisions, and compare that to the overall CSAT of other companies, inner products and services.
Orchestrating User Feedback into Actionable Intelligence
The core challenge was to transform this cacophony of fragmented feedback into a harmonized, intelligent system. This research initiative set out to build a solution that could not only collect and organize feedback but also analyze and visualize it, turning raw user comments into clear, actionable insights for strategic product development.
Key Research Questions
To address the challenge of fragmented feedback and build an effective intelligence dashboard, our research was guided by key questions aimed at understanding current processes, defining actionable insights, and leveraging technology.
Understanding the Current Feedback Landscape
To map existing feedback mechanisms and identify critical gaps and pain points for Stakeholders.
- Q1:
How are stakeholders currently collecting, processing, and utilizing user feedback across various channels?
- Q2:
What critical information or nuances are being missed, diluted, or misinterpreted in the current manual feedback process?
- Q3:
What are the primary frustrations and inefficiencies stakeholders experience when trying to synthesize and act on user feedback?
Defining Actionable Insights & Dashboard Needs
To determine the most valuable metrics and visualizations for a dashboard that empowers data-driven decisions.
- Q1:
Which specific feedback metrics (e.g., sentiment scores, trend velocity, CSAT levels) would provide the most actionable insights for product strategy and UX improvements?
- Q2:
How can feedback patterns, sentiment distributions, and CSAT movements be most effectively visualized to help PMs quickly identify priority areas and emerging trends?
- Q3:
What level of data granularity, filtering, and drill-down capability do stakeholders require to confidently analyze feedback related to specific product features or user segments?
Leveraging Technology for Enhanced Analysis
To explore how advanced technologies like Machine Learning could automate and elevate the feedback analysis process.
- Q1:
How can machine learning (e.g., NLP, sentiment analysis, CSAT trend modeling) be employed to automate the categorization, tagging, and summarization of large feedback volumes and quantify satisfaction impact over time?
- Q2:
What are the accuracy and reliability considerations for ML-driven feedback analysis, and how can these be validated?
- Q3:
Beyond automation, how can AI/ML surface deeper, predictive insights from feedback that might not be apparent through manual review alone?
From Questions to Methods: A Strategic Approach
These focused inquiries were crucial in shaping our research strategy. They ensured that the methods chosen—like Time Series Analysis for trends, Sentiment Analysis for user feelings, and CSAT benchmarking—were precisely targeted to deliver the comprehensive insights needed for the feedback intelligence dashboard and ultimately address the core challenge.
Research Methods
To transform scattered feedback into actionable insights, we employed these specialized quantitative techniques:
Time Series Analysis
Identifying patterns over time
Analysis of 18,5k+ feedback submissions from 2020 to 2025, showing peak volumes of 2.7K in 2023.
What this means: We analyzed how user feedback volume changed over several years. This helped us spot trends, like peak feedback times (e.g., after new releases), enabling us to anticipate loads and understand the impact of product updates.
Feedback Volume Trends (18,5k+ items)
User Segmentation
Categorizing feedback by user & topic
Categorization of feedback by user type (Advisors, BAA, Fee Only Advisors) and category (Client Services: 7.3K, Practice: 6.3K, Technology: 3.0K).
What this means: We grouped feedback based on who sent it and its subject. This showed if specific user groups had particular types of feedback more often, helping tailor solutions.
Feedback by Category Segments
CSAT Surveys
Measuring satisfaction with structured surveys
Customer Satisfaction (CSAT) surveys used to establish benchmarks, monitor weekly shifts, and contextualize changes with product releases.
What this means: We monitored CSAT to quantify how changes affected user satisfaction and prioritized work that moved the needle the most.
CSAT survey benchmarks & weekly shifts
Sentiment Analysis
Gauging emotion in feedback
Implementation of machine learning algorithms to categorize feedback as Neutral, Negative, Positive, Very Positive, and Very Negative.
What this means: We used AI to automatically determine the emotion in each piece of feedback. This gave a quick overview of user satisfaction and helped prioritize issues causing strong negative reactions.
Sentiment Score Distribution
Foundation for Process: These analytical methods were key to our research process. They enabled structured data discovery (Time Series, Segmentation), drove in-depth analysis (Sentiment, CSAT), and directly informed the actionable insights feeding into the dashboard design.
Research Process
From raw feedback to actionable insights: our data-driven approach
Challenge Identification
Data Source Mapping
SQL Querying & Cleaning
Data Visualization
Dashboard Implementation
Challenge Identification
Identified the key challenge of Stakeholders who were struggling to efficiently process large volumes of user feedback spread across multiple channels.
Data Source Mapping
Located and documented all feedback sources including feedback tickets, support tickets, idea tickets, user interviews, and internal feedback channels.
SQL Querying & Cleaning
Extracted raw feedback data using SQL queries, then cleaned and normalized the data to create a unified dataset for analysis.
Data Visualization
Developed visualizations to identify patterns, sentiment trends, and key insights from the aggregated feedback data.
Dashboard Implementation
Delivered a comprehensive feedback management dashboard that Stakeholders could use in their daily workflow to track, categorize, and act on user feedback.
Key Findings
Our analysis of 18,5k+ feedback submissions revealed these critical insights about advisor experience
User Experience Issues
43% of feedback pointed to experience issues (navigation, clarity, task flows) versus other issues. Focusing fixes here contributed to a measurable lift in overall sentiment.
Feedback Sentiment Gap
Negative feedback outweighed positive by 8 percentage points, with Neutral dominating. This gap narrowed after targeted UX fixes.
CSAT Benchmarks
Initial CSAT sat at a mean of 82, and a median of 85. Weekly monitoring and release guardrails reduced post‑release dips and improved stability.
Research Efficiency Gains
Our automated feedback analysis process reduced processing time from 12 hours to 2 hours weekly while improving categorization accuracy by 87%.
Key Insight
By implementing our advanced feedback analysis system, the product team was able to identify and prioritize improvements that led to a 27% increase in positive advisor sentiment and a more targeted product roadmap.
Impact & Outcomes
How our feedback analysis drove concrete engineering decisions, design changes, and measurable business results
Research Findings
43% of feedback was User Experience Issues
User segmentation revealed distinct needs across advisor types
Unclear goals and scattered requests caused extra engineering work and slow downs
Implementation
Experience-first fixes: navigation, clarity, task flows, customization
Segmentation-driven building initiatives/redesigns and roadmaps
Centralized feedback in a dashboard reduced miscommunication, handoffs and rework
User/Business Impact
Experience issues reduced; increasing positive feedback +27%
Feature prioritization accuracy improved by 47% (segmentation)
32% reduction in development cycle duration, measured by Engineers actively using the dashboard
Time Efficiency
By reducing workflow friction, each advisor gains more than a full workday back each week to focus on clients.
Operations Impact
Streamlined feedback processing improved confidence and cut the average concept-to-ship timeline from ~8 to ~6 weeks.
CSAT Stabilization
After actively analyzing feedback and addressing the root causes of dissatisfaction, the signal stabilized.
Impact on Product Roadmap
Before Research
Fragmented requests & unclear criteria caused rework and delays
Prioritization driven by anecdotes; votes for ideas scattered and unattainable; user/product segmentation unused
Manual collation and unclear understanding of the product increased decision latency and delayed releases
After Research
Centralized backlog + structured requirements + weekly triage reduced developement cycle by 32%
Segmentation-driven scoring + 19K‑vote ideas dashboard (47% prioritization accuracy)
Automated feedback data collection + weekly triage cut decision latency; 28% faster time‑to‑market
Visuals
Client Analysis Dashboard
Interactive time series visualizations tracking feedback volume across quarters from 2020-2025
Total of 18,5k+ feedback submissions with detailed client-based segmentation analysis
Breakdown showing 83% of feedback (~15.3K items) from one segment and 17% (~3.2K items) from another
