Smart Field Search
Improvement Focus:
Usability, intuitiveness, data discoverability.
​
My Responsibility:
* User research and design validation
* Cross-functional stakeholder management: engineer, designer, data analyst, marketing
* PRD & product road mapping
Context
PitchBook scales in the past years. Our data coverage started from private market, and now goes into M&A, public market, and debt & credit. As we keep adding more and more data point to Excel Plugin, the old field search experience can no longer meet the data discoverability demand today.
According to the user satisfaction survey, 39% of users felt either disappointed or very disappointed with Excel Plugin. User interview tells that 60% of these users felt annoyed at the data searching experience, which was used by 96% of active users every day. Thus, improving data discoverability and search experience is urgent to improve user acquisition and maintain a high retention rate in the long term.
Objectives
Enhance search experience in Excel Plugin to empower clients to improve work efficiency by using comprehensive PitchBook data quickly and easily.
Success Metrics
North Star: User satisfaction rate
Supporting Metrics: Session completion rate, Average search duration, Exit rate, User acquisition(MAU)
Do No Harm Metric: Uninstallation rate
Increased search accuracy by 200% and user satisfaction by 60% within 3 months post release.
Roadmap
Pain Points
- Users cannot find data points that are already available in Excel Plugin
- Users cannot tell out the differences when data points have similar naming.
- Users have difficulties applying data points in Formula Builder.
Solutions
1. Fuzzy Search & Data Glossary
Before
After
2. Formula Builder
Before
After
Key Takeaways
1. Keyword-Based Search vs. Semantic Search
Critical Thinking
-
User Lens -
-
What user pain point are we solving?​
-
What do users care about the most?
-
-
Technical Lens -
-
Is AI the only solution?​
-
Is AI the best solution?
-
Do we have enough data to train the model?
-
-
Business Lens -
-
Is the solution cost-effective?​
-
My Learnings -
-
Guide the team by "why to build" instead of "what to build" and "how to build"
-
Always being user-centric instead of solution-driven
-
Being considerate on implementing AI-solutions
2. Data-Driven Decision-Making
I established a product performance dashboard to track product performance, get user-centric insights, and guide product roadmap.
