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Smart Field Search

Improvement Focus:

Usability, intuitiveness, data discoverability.

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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

Data Glossary in XLP.png

After

2. Formula Builder

Before

Formula Builder Before.jpg

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.

Other Projects...

Auth0

Migration

12-Month

Roadmap

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