Analytics · User Insights

FOOD App User Insights

User Insights Analyst case study for FOOD, a web and app platform helping travelers discover personalised restaurant recommendations. Translated raw transactional user data into actionable product and marketing recommendations.

Role
User Insights Analyst
Client
ParkBee / FOOD App
Type
Analytics Case Study
Focus
Retention · ARPU · Re-engagement
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The Business Questions

Three things the business needed to understand.

  • How well FOOD was retaining users over time across different acquisition cohorts
  • Which acquisition channels brought the highest-value users based on average revenue per user
  • When first-time users were most likely to return for a second purchase, so re-engagement timing could be optimised

Data used: transaction-level dataset with user_id, transaction_date, revenue, platform, and acquisition_channel fields.

Retention Analysis

Earlier cohorts retained well. Later cohorts dropped sharply.

  • January to March 2024 cohorts showed retention rates above 90%, indicating strong engagement and successful onboarding strategies during that period
  • From May 2024 onwards, retention began declining steadily, with September to December cohorts showing significant drops
  • December 2024 cohort retention fell to approximately 10%, signalling weakening early engagement and possible onboarding or value communication issues for newer users
  • Seasonality may also be a factor, with certain months seeing less frequent app usage
90%+
Jan–Mar 2024 cohort retention
~10%
Dec 2024 cohort retention
Day 7
Recommended re-engagement trigger
Acquisition Channel Analysis

Google Ads led on ARPU. Organic and Email lagged.

  • Google Ads delivered the highest average revenue per user, making it the strongest acquisition channel in the dataset
  • Referral and Facebook Ads also performed well on ARPU metrics
  • Organic Search and Email Campaigns lagged behind on ARPU, indicating lower-value user acquisition through these channels
Re-engagement Timing

Day 18 is the strongest signal. Act before day 30.

  • Most users who made a second purchase returned within the first 30 days after their first transaction
  • The strongest re-engagement signal was around day 18 after first purchase
  • A secondary opportunity window existed between 30 and 90 days
  • Return rates dropped sharply after 90 days, making late re-engagement campaigns significantly less effective
  • App users may respond better to push-style engagement; web users may need email or retargeting support
Day 18
Peak re-engagement signal
30d
Primary action window
90d
Cliff after which return rates drop
Recommendations

Product, marketing, and growth recommendations.

Retention

  • Improve onboarding to ensure users understand the platform value proposition during first session
  • Re-engage users within 7 days of first transaction with personalised offers or content
  • Identify and replicate acquisition and engagement patterns from high-retention January to March cohorts
  • Offer loyalty rewards or discounts for users who make multiple purchases
  • Use segmented campaigns based on user preferences such as cuisine types and budget

Marketing and acquisition

  • Increase investment in Google Ads given its strong ARPU performance
  • Strengthen referral incentives to amplify the referral channel
  • Improve Facebook Ads creative and retargeting to close the ARPU gap
  • Optimise SEO and email personalisation for currently underperforming organic and email channels

Repeat purchase growth

  • Target users most aggressively in the first 30 days after their first transaction
  • Use reminders, personalised offers, urgency-led promotions, and tailored outreach by platform type
  • Deploy push notifications for app users and email or retargeting for web users in re-engagement sequences
Tools and Methods

How I worked.

Cohort AnalysisARPU AnalysisRetention ModellingTime-Gap AnalysisSQLPythonData VisualisationProduct AnalyticsUser Segmentation