This guide shows how GrubGrab uses Squad after 6 months in market with 75,000 active users. Learn how to leverage existing data to find your next big opportunity and optimize what you’ve built.

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Overview

Your product is live, users are engaged, but growth is plateauing. This guide demonstrates how to use Squad to:
  • Analyze performance against original goals
  • Discover new opportunities from user behavior
  • Prioritize improvements based on impact
  • Evolve your strategy with market changes
Context: GrubGrab has been live for 6 months with:
  • 75,000 monthly active users
  • 32-minute average delivery time
  • $3.50 average fees
  • 4.1/5 app store rating

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Step 1: Performance Audit (10 mins)

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Connect all data sources

Your existing product generates rich data. Connect everything:
Google Analytics/Amplitude
  • User behavior funnels
  • Feature adoption rates
  • Cohort retention curves
  • Revenue metrics
Squad automatically imports and analyzes trends.

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Review original goals

Open Strategy view to see your initial goals vs. current performance:
Original GoalTargetCurrentStatus
Average delivery time25 min32 min⚠️ Behind
Total fees per orderless than $3$3.50⚠️ Behind
Monthly active users50K75K✅ Exceeded
Order frequency3x/week2.1x/week❌ Below
Squad shows you’re exceeding user acquisition but failing on engagement and core differentiators.

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Diagnose the gaps

Ask Squad: “Why are we missing our delivery time and frequency targets?” Squad analyzes all connected data and reports: 
DELIVERY TIME ANALYSIS:
- Restaurant prep delays account for 58% of excess time
- Driver routing inefficiency adds 3-4 minutes
- Multi-order batching actually increases individual delivery time

ORDER FREQUENCY ANALYSIS:
- Users who experience >35 min delivery order 50% less
- Limited restaurant variety in suburbs (main complaint)
- Dinner-only ordering pattern (missing lunch opportunity)

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Step 2: Opportunity Discovery (10 mins)

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Surface hidden opportunities

Navigate to Insights → View by Opportunities to see AI-discovered patterns:

Restaurant prep time

8,234 mentions“Food sits ready for 10+ minutes waiting for drivers”Impact: -7 min delivery time

Lunch availability

5,421 mentions“Why can’t I order from places near my office?”Impact: +0.8 orders/week

Group ordering

3,892 mentions“Impossible to coordinate office lunch orders”Impact: +$45 average order value

Vegetarian options

2,156 mentions“Can’t filter for veg restaurants”Impact: 12% user segment

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Validate with data

For each opportunity, Squad shows supporting evidence: Restaurant prep time opportunity:
  • Support tickets: 3,421 complaints last month
  • Delivery data: 11.3 min average wait at restaurant
  • Driver feedback: #1 frustration in surveys
  • Competitor analysis: DoorDash solved with “Ready timesʻ

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Step 3: Solution Generation (15 mins)

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Generate solutions for top opportunities

Select “Restaurant prep time” and click Generate Solutions:
1. Predictive prep time system ⭐ Recommended
  • ML model predicts when to start cooking
  • Based on driver location and historical data
  • Restaurants get tablet notifications
  • Impact: -6 min, Effort: 4 weeks
2. Dedicated pickup zones
  • Partner restaurants create driver waiting areas
  • Orders staged for quick pickup
  • Requires restaurant cooperation
  • Impact: -3 min, Effort: 8 weeks
3. Driver-restaurant chat
  • Direct communication channel
  • “Order ready” notifications
  • “Running late” alerts
  • Impact: -2 min, Effort: 2 weeks

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Update existing features

Squad identifies which current features need iteration: Underperforming features:
  1. Batch ordering - Actually slows delivery, needs algorithm update
  2. Route optimization - Not accounting for traffic patterns
  3. Restaurant search - Missing lunch-specific filters
High-performing features to expand:
  1. GrubGrab Plus - 34% adoption, add more benefits
  2. Real-time tracking - 4.8/5 rating, add ETA updates
  3. Quick reorder - 23% of orders, surface more prominently

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Step 4: Prioritization (20 mins)

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Score improvements vs. new features

Squad helps balance fixing existing issues vs. building new features:
TypeSolutionImpactEffortRICE ScorePriority
FixPrep time predictionHighMedium9,4001
NewGroup orderingHighHigh7,2002
FixRoute algorithm v2MediumLow6,8003
NewLunch partnershipsMediumMedium4,1004
FixBatch order logicLowLow2,3005

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Create iteration roadmap

Your roadmap balances quick wins with strategic bets: Sprint 1-2: Quick fixes
  • Route algorithm improvements
  • Lunch filter addition
  • Batch order optimization
Sprint 3-6: Core improvements
  • Predictive prep time system
  • Restaurant tablet app
  • Driver notification system
Sprint 7-10: New features
  • Group ordering
  • Lunch-specific partnerships
  • Dietary preference filters
Aim for 60/40 split between improving existing features and building new ones.

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Step 5: Measurement & Learning (Ongoing)

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Set up feedback loops

Configure Squad to track improvement impact:
1

Define success metrics

For prep time prediction:
  • Average wait at restaurant under 5 minutes
  • Delivery time reduction of 6+ minutes
  • Driver satisfaction score above 4.5
2

Create monitoring dashboard

Squad auto-generates dashboards showing:
  • Before/after metrics
  • User feedback themes
  • Feature adoption rates
  • Goal progress
3

Schedule reviews

Weekly: Check metrics and user feedback Bi-weekly: Team retrospective on learnings Monthly: Strategy adjustment based on data

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Iterate based on results

After launching prep time prediction: Week 1 results:
  • Delivery time: -3 minutes (not -6 expected)
  • Issue: Only 40% restaurant adoption
Squad’s analysis: “Low adoption due to complex tablet interface. Simplify to one-button system.” Iteration:
  • Redesign to show only “Start cooking” button
  • Add audio alerts
  • Result: 78% adoption, -5.5 minute improvement

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Avoiding common iteration mistakes

Don’t chase every complaintSquad helps you focus on patterns, not individual issues. If only 5 users want a feature, it’s not a priority.
Common pitfalls:
  1. Feature abandonment - Don’t give up on features too quickly
  2. Metric tunnel vision - Balance multiple success indicators
  3. Ignoring core users - Don’t optimize for edge cases
  4. Competition paranoia - Stick to your differentiation

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Advanced iteration techniques

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A/B testing strategy

Squad recommends what to test: 
High-confidence tests:
✓ Prep time alerts - strong user demand
✓ Group order UI - clear revenue upside

Low-confidence tests:
✗ Social features - no clear user need
✗ Gamification - doesn't align with mission

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Cohort analysis

Squad automatically segments users: Power users (top 20%)
  • Order 8x/month
  • Want: Faster delivery, meal scheduling
Regular users (middle 60%)
  • Order 2x/month
  • Want: Lower fees, more variety
Dormant users (bottom 20%)
  • Ordered once and churned
  • Issue: Bad first experience, high fees

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Measuring iteration success

Track these evolved KPIs: Efficiency metrics:
  • Delivery time improvement rate
  • Cost per delivery reduction
  • Driver utilization
Engagement metrics:
  • Feature adoption curves
  • Repeat usage rates
  • Cross-feature usage
Business metrics:
  • LTV/CAC ratio
  • Margin improvement
  • Market share growth

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When to pivot vs. persist

Squad helps identify when major strategy changes are needed: Persist signals:
  • Core metrics improving monthly
  • Clear path to profitability
  • Growing user love (NPS >50)
Pivot signals:
  • 3+ months of flat growth
  • Unit economics not improving
  • Competitors eating market share

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Next steps

1

Run the performance audit

Connect all your data sources and let Squad analyze
2

Pick 3 opportunities

Don’t try to fix everything at once
3

Set 30-day goals

Create specific, measurable targets
4

Ship weekly

Small iterations compound faster than big launches
Remember: Iteration is about learning velocity. Use Squad to shorten the feedback loop between shipping and understanding impact.

Building New Products

Starting fresh? See our guide for new product development