Product Performance Dashboard

User Behavior & Product-Market Fit Analytics | Food Delivery Platform

Product Health Overview Real-time product-market fit signals

Product-Market Fit Score
73/100
↑ 8 pts vs last quarter
Engagement Health
82%
↑ 5.2% vs last month
Active User Momentum
+12.4%
↑ Strong growth
Feature Adoption Health
68%
→ Stable
User Satisfaction Index
4.3/5.0
↑ 0.2 vs last month
✓ Product Health: Strong
PMF score above 70 indicates strong product-market alignment. Engagement momentum is accelerating, driven by improved onboarding and new feature adoption. Continue monitoring feature health metrics for optimization opportunities.

User Engagement Metrics Activity patterns and stickiness indicators

Daily Active Users (DAU)
284K
↑ 14.2% vs last month
Monthly Active Users (MAU)
1.42M
↑ 9.8% vs last month
DAU/MAU Ratio (Stickiness)
20.0%
↑ 1.2 pts improvement
Weekly Active Users (WAU)
687K
↑ 11.5% vs last month
Avg Sessions per User/Week
3.8
↑ 0.4 sessions
Avg Session Duration
8m 24s
→ Stable
Engagement Depth Score
6.2
↑ 0.8 actions/session
Power User %
12.4%
↑ 1.1% growth
💡 Key Insight
DAU/MAU ratio improved to 20%, indicating stronger product stickiness. Power user percentage growing faster than overall user base suggests core value proposition is resonating. Focus on converting occasional users to frequent users through targeted engagement campaigns.

Activation & Onboarding First-time user experience and aha moment reach

New User Activation Rate
64.2%
↑ 7.5% vs last month
Time to First Order
18.4 hrs
↓ 3.2 hrs improvement
Onboarding Completion Rate
78.6%
↑ 4.8%
First Week Retention
52.1%
↑ 5.3%
Aha Moment Reach Rate
71.8%
↑ 6.2%

Onboarding Funnel Analysis

Sign Up 100%
—
Profile Setup 92.0%
-8.0%
Location Added 86.4%
-5.6%
Onboarding Complete 78.6%
-7.8%
First Browse 71.8%
-6.8%
First Order (Activation) 64.2%
-7.6%
⚠ Friction Point Detected
Largest drop-off occurs at Sign Up → Profile Setup (-8.0%). Users may perceive profile setup as too lengthy. Consider A/B testing a simplified profile flow or allowing users to skip optional fields and complete later.

Retention Analysis Cohort behavior and long-term engagement

Day 1 Retention
68.4%
↑ 3.2%
Day 7 Retention
42.8%
↑ 4.1%
Day 30 Retention
28.6%
↑ 2.8%
Day 90 Retention
18.2%
→ Stable
Resurrection Rate
14.6%
↑ 2.1%

Cohort Retention Heatmap

Signup Month Week 0 Week 1 Week 2 Week 4 Week 8 Week 12
Jan 2024 100% 52% 38% 31% 22% 18%
Feb 2024 100% 54% 40% 33% 24% 19%
Mar 2024 100% 56% 42% 35% 26% 20%
Apr 2024 100% 58% 44% 37% 28% 22%
May 2024 100% 60% 46% 39% 30% 24%
Jun 2024 100% 62% 48% 41% 32% —
✓ Improving Retention Trend
Recent cohorts (May-Jun 2024) showing 8-10% better Week 4 retention compared to Jan 2024 cohort. Product improvements and enhanced onboarding are having measurable impact. Focus on maintaining this momentum and identifying what's working for recent cohorts.

Feature Adoption & Engagement What users try vs what they keep using

Feature Engagement Matrix

⭐ Stars (High Adoption + High Engagement)
Favorites/Reorder - 84% adoption, 92% repeat usage
Order Tracking - 96% adoption, 88% repeat usage
Payment Methods - 78% adoption, 85% repeat usage
💎 Hidden Gems (Low Adoption + High Engagement)
Scheduled Orders - 28% adoption, 76% repeat usage
Group Ordering - 18% adoption, 68% repeat usage
Dietary Filters - 34% adoption, 71% repeat usage
📍 Casual Use (High Adoption + Low Engagement)
Restaurant Search - 88% adoption, 34% repeat usage
Cuisine Filters - 72% adoption, 28% repeat usage
Reviews - 64% adoption, 22% repeat usage
⚠ Zombies (Low Adoption + Low Engagement)
Loyalty Program - 42% adoption, 18% repeat usage
Social Sharing - 12% adoption, 8% repeat usage
Meal Planning - 16% adoption, 12% repeat usage
Grocery Delivery (New)
38.4%
↑ 12.1% adoption growth
Scheduled Orders (New)
28.2%
↑ 8.4% adoption growth
Group Ordering (New)
18.6%
→ Slow growth
💡 Strategic Recommendation
Scheduled Orders shows strong repeat usage (76%) but low adoption (28%) - classic "Hidden Gem". Increase discoverability through in-app prompts and onboarding. Conversely, Loyalty Program has moderate adoption but poor engagement - investigate value proposition and user experience issues before investing further.

Churn Analysis Understanding why users leave and early warning signals

Monthly Churn Rate
8.4%
↓ 1.2% improvement
At-Risk Users
142K
↑ 8.2% vs last month
Win-back Success Rate
22.4%
↑ 3.1%

Churn Reasons (Survey Data)

⚠ Churn Warning
At-risk user count increased 8.2%. Primary churn drivers: delivery time dissatisfaction (32%) and restaurant selection issues (24%). While overall churn is improving, proactive intervention needed for at-risk segment. Consider personalized retention campaigns focusing on delivery experience improvements.

User Segmentation Insights Behavioral cohorts and segment dynamics

Frequent Orderers
176K
↑ 14.2% growth
3+ orders/week
Occasional Users
684K
↑ 8.6% growth
1-2 orders/week
At-Risk Users
142K
↑ 8.2% (warning)
Declining activity
Dormant Users
418K
→ Stable
No orders 30+ days

Segment Migration Flow

✓ Positive Migration Trend
18.4% of Occasional Users migrated to Frequent Orderers this month - highest rate in 6 months. Key drivers: improved delivery times and personalized restaurant recommendations. Focus on replicating this success to convert more occasional users.

Product-Market Fit Indicators Core signals of product value and user sentiment

Net Promoter Score (NPS)
42
↑ 6 pts vs last quarter
Customer Satisfaction (CSAT)
4.3/5.0
↑ 0.2 improvement
Product Usage Intensity
58%
↑ 4% "very disappointed"
Organic Growth Rate
24.6%
↑ 3.2% referral growth
App Store Rating
4.5/5.0
→ Stable
✓ Strong PMF Signals
Product Usage Intensity at 58% (40%+ threshold indicates PMF). NPS improved to 42, moving into "good" territory. Organic growth accelerating suggests word-of-mouth is working. Continue doubling down on core value proposition while addressing detractor feedback.

Conversion Funnel Analysis Browse to purchase journey optimization

Browse Restaurants 100%
—
View Menu 72.0%
-28.0%
Add to Cart 58.4%
-13.6%
Proceed to Checkout 48.2%
-10.2%
Complete Order 41.6%
-6.6%

Funnel by Platform

⚠ Conversion Opportunity
Largest drop-off: Browse → View Menu (-28%). Users may not be finding appealing restaurants. Test improved restaurant discovery features, better filtering, and personalized recommendations. iOS conversion rate 8% higher than Android - investigate platform-specific friction points.

Engagement Quality Metrics Depth and frequency of product usage

Repeat Order Rate
68.4%
↑ 5.2%
Avg Orders per User/Month
3.8
↑ 0.4 orders
Cross-Category Usage
42.6%
↑ 3.8%
Avg Days Between Orders
8.2 days
↓ 0.6 days (better)
Reorder Rate (Same Restaurant)
34.8%
→ Stable
💡 Engagement Insight
Cross-category usage increased to 42.6% - users ordering from multiple cuisine types indicates product breadth is valued. Time between orders decreased to 8.2 days, suggesting higher habit formation. Consider loyalty incentives to push frequent orderers toward weekly ordering cadence.

Platform-Specific Insights Meal vs Grocery | iOS vs Android vs Web

Meal Delivery Users
1.18M
↑ 8.4% growth
Grocery Delivery Users
546K
↑ 18.2% growth
iOS App Users
624K
↑ 11.2%
Android App Users
582K
↑ 9.8%
Web Users
214K
→ Stable
✓ Grocery Delivery Momentum
Grocery delivery growing 2x faster than meal delivery (18.2% vs 8.4%). Strong product-market fit in this segment. iOS users show 12% higher engagement than Android - investigate Android UX improvements. Web users stable but low engagement - consider mobile-first strategy.

Product Insights & Alerts Automated anomaly detection and recommendations

🚨 Behavioral Anomaly Detected
At-Risk User Segment Growing: Users with declining activity increased 8.2% this month. Churn risk elevated. Recommend immediate win-back campaign targeting users with 15+ days since last order.
💡 Feature Performance Alert
Loyalty Program Underperforming: Only 18% repeat engagement despite 42% adoption. Users trying feature but not finding value. Investigate reward structure, redemption friction, and value communication.
✓ Opportunity Identified
Scheduled Orders - Hidden Gem: 76% repeat usage rate but only 28% adoption. High engagement indicates strong value proposition. Increase discoverability through onboarding flow and in-app prompts to drive adoption.
⚠ Cohort Health Warning
January 2024 Cohort Retention Declining: Week 12 retention at 18%, below target of 22%. Investigate onboarding experience and early engagement patterns for this cohort vs more recent high-performing cohorts.
💡 Friction Point Identified
Browse → View Menu Drop-off: 28% of users not progressing from browse to menu view. Potential restaurant discovery issue. Test improved filtering, search relevance, and personalized recommendations.

Roadmap Impact Analysis Feature performance and hypothesis validation

Recently Launched Features (Last 90 Days)

Grocery Delivery
38.4%
Adoption | Launch: 90 days ago
Scheduled Orders
28.2%
Adoption | Launch: 60 days ago
Dietary Filters
34.0%
Adoption | Launch: 45 days ago

A/B Test Results Summary

Test Name Hypothesis Result Impact Status
Simplified Checkout Reduce steps → Higher conversion +8.4% conversion +$2.4M ARR ✓ Shipped
Personalized Recommendations ML-based recs → Higher engagement +12.2% click-through +18% orders ✓ Shipped
Social Sharing Share orders → Viral growth -2% engagement No impact ✗ Killed
Loyalty Tier System Gamification → Retention +3.2% retention Marginal ⚠ Iterating
✓ Hypothesis Validated
Simplified Checkout: Expected +5% conversion, achieved +8.4%. Reducing checkout steps from 4 to 2 significantly reduced friction. Personalized Recommendations: ML-based restaurant suggestions drove 12.2% higher click-through and 18% more orders - exceeding expectations.
⚠ Hypothesis Invalidated
Social Sharing: Expected viral growth did not materialize. Feature saw -2% engagement and <5% usage. Users do not find value in sharing food orders socially. Recommend sunsetting feature and reallocating resources to higher-impact initiatives.
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