Case study 04
Product & Growth Analytics
Behavior → Decisions
Using behavioral, product, and monetization data to understand what users do, why performance changes, and where product and growth opportunities exist.
01Context
Product analytics here spans the full lifecycle — connecting user behavior, product mechanics, the business model, growth, and monetization.
02Challenge
The work goes beyond reporting feature usage.
03My role
Supported and led product and growth analyses across marketplace behavior, feature performance, pricing, monetization, retention, and acquisition:
- Validated product tracking through Amplitude
- Analyzed feature adoption and performance
- Evaluated behavior across categories, plans, bundles, and add-ons
- Built retention, churn, and acquisition analyses
- Supported pricing and monetization decisions
- Identified upselling and growth opportunities
- Designed analytical approaches for experiments and A/B tests
- Built models and dashboards for recurring product decisions
- Translated findings into recommendations for product, marketing, commercial, and leadership teams
04Scope
User behavior, product mechanics, business model, growth, and monetization — connected.
Product features
- Platform features
- Reactions
- Booking & quotation flows
- Listing scores
- Feature adoption
Marketplace structure
- Categories
- Plans
- Bundles
- Add-ons
User lifecycle
- Acquisition
- Engagement
- Retention
- Churn
- Reactivation
Monetization
- Pricing
- Upselling
- Bundles
- Extra add-ons
- Segmented credits
- Visa / Mastercard analytics
Product measurement
- Amplitude event validation
- Behavioral analysis
- KPI design
- Experimentation design
- A/B testing
- Growth analytics
05Analytical lenses
One journey, read through different questions.
01
Users
02
Features
03
Behavior
04
Conversion · Engagement · Retention
05
Monetization
06
Product decision
Conceptual journey · choose a lens to see where it focuses.
06Questions behind the work
Decision-oriented, not reporting.
- ?Which features are actually being adopted?
- ?Where do users drop off?
- ?How does behavior differ across categories or user segments?
- ?Which users are retaining, churning, or re-engaging?
- ?How do pricing and bundles influence behavior?
- ?Where do upselling or monetization opportunities exist?
- ?Did an experiment meaningfully change user behavior?
07Approach
From measurement to decision.
01
Measure
Define reliable events, KPIs, and behavioral signals.
02
Understand
Analyze adoption, engagement, user journeys, and product performance.
03
Diagnose
Identify friction, drop-off, behavioral shifts, and root causes.
04
Evaluate
Assess pricing, monetization, experiments, segments, and growth opportunities.
05
Decide
Translate findings into clear recommendations for product and business teams.
08Impact
A clearer view of feature adoption and marketplace behavior
Analysis across acquisition, retention, churn, engagement, and reactivation
Pricing, bundles, add-ons, upselling, credits, and payment-related analysis
Complex user and product behavior translated into recommendations for product, business, marketing, and leadership teams
09What this demonstrates
Strong product analytics is not just measuring clicks or feature usage.
It requires understanding the product, the user journey, the business model, and the data well enough to explain what changed, why it changed, and what the team should do next.
Tools & capabilities