🎬 Poolhouse: Product Strategy & Churn Analysis

🎯 Project Goal

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Understand the high churn rate and low engagement metrics to create a research-backed strategy for improving user retention and community activity.

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📊 Data Analytics

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• Pulled backend user data (first seen, last seen, session counts)
• Calculated a 59.6% churn rate and found that 60.61% of users showed low engagement (1–5 sessions).
• Data required extensive cleaning and wrangling due to missing fields and irregular exports.

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Calculating User Engagement

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Mean (Average) sessions

On average, users have about 49.6 sessions. However, this average might be influenced by outliers (users with a very high number of sessions).

Median Sessions

The median number of sessions is 1, suggesting that at least half of the users have only one session. This is a strong indicator of low engagement for a significant portion of the user base.

Standard Deviation

A high standard deviation (127.27) indicates a wide variation in the number of sessions among users.

Maximum Sessions

The maximum number of sessions for a user is 796, which suggests that some users are highly engaged.

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What The Data Reveals

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Churn Rate: 59.6%

High Churn Rate: Indicates that a significant portion of the user base has not been active in 30 days. This high churn rate is a critical concern, as it suggests that more than half of the users are disengaging.

Low Engagement Rate: 60.61%

Low Engagement: The percentage of users classified as having low engagement (defined as having 1 to 5 sessions) reflects over half of the users on the platform.

Unusually high churn rates over a 30 day period

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🔍 Survey + Interview Insights

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• Launched a sentiment survey to poolhouse users for preliminary insights and recruitment.
• Conducted 15 user interviews across 3 user groups:
  • Cancelled users
  • Subscribed but inactive users
  • Super users (highly engaged)

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Key Themes Discovered:

• Users craved fresh content and better discovery tools.
• Notifications were perceived as either overwhelming or irrelevant.
• Profiles lacked personalization to showcase work and find meaningful connections.
• The forum was seen as valuable but difficult to navigate.

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👥 User Personas

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Monica – Community Builder:
Values thoughtful discussions and staying connected to peers.

Terry – Job Opportunist:
Wants better visibility for job leads and professional connections.

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💡 Strategic Recommendations

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1. Dynamic Highlight Feed: Surface new resources, projects, and active conversations automatically.
2. Smart Notifications: Give users full control over when and how they're notified.
3. Profile Personalization: Add fields for availability (e.g., looking for crew, mentorship, collaboration).
4. Community Manager Role: Assign a lead to drive conversations, host AMAs, and spark activity.
5. Forum Redesign: Introduce categories, pin FAQs, and improve onboarding for new users.
6. Pricing Model Tweaks: Create tiered options to make Poolhouse more accessible to emerging creatives.

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🏆 Outcome

• Identified a 59.6% churn rate and major user pain points through combined quantitative and qualitative research.
• Delivered 6 strategic product recommendations mapped to user needs and business goals.
• Stakeholders adopted the findings and launched an improved platform focused on user engagement.

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✨ Reflection

This project strengthened my ability to drive research independently, translate complex findings into actionable strategies, and influence key product decisions with clarity and user empathy.

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