marketplace-pre-member-personalisation

v2026.09.24

Pre-member journey of a two-sided trust marketplace — from anonymous landing through onboarding, registration, and the paid-membership paywall. Covers anonymous signal inference, what pet owners specifically need to validate before paying (safety, availability, competence, effort, local cost comparison), what pet sitters specifically need to validate (opportunity, first-stay path, daily commitment, hidden costs), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Triggers on tasks involving visitor-to-member conversion, anonymous personalisation, onboarding flow design, paywall timing, pre-member ranking, or any question about what a pet owner or pet sitter needs to see before paying. Use this skill BEFORE marketplace-personalisation and marketplace-search-recsys-planning.

GitHub
Install command
npx skhub add pproenca/marketplace-pre-member-personalisation
Markdown
SKILL.md

Marketplace Engineering Two-Sided Pre-Member Personalisation Best Practices

Comprehensive design and diagnostic guide for the pre-member journey of a two-sided trust marketplace. Covers anonymous signal inference, side-specific validation (what pet owners and pet sitters each need to see before paying), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Contains 53 rules across 10 categories, ordered by cascade impact, every rule grounded in published consumer-trust and decision research.

When to Apply

Reference this skill when:

  • Designing or reviewing the anonymous landing page and first-render experience
  • Choosing what to show a visitor before they have registered or paid
  • Designing the onboarding flow and deciding which questions to ask in what order
  • Planning the paywall moment — timing, copy, triggers, price anchoring
  • Diagnosing a conversion funnel that is leaking between visit and paid membership
  • Choosing how to persist visitor state across the anonymous → registered → member transition
  • Measuring pre-member experiments and deciding whether to ship an intervention
  • Answering "what does a pet owner or sitter actually need to believe before paying?"

This skill is the precursor to marketplace-personalisation and marketplace-search-recsys-planning. Start here for anything pre-paid-membership; hand off to those two skills at the paid-member boundary.

Research foundations

Every rule in this skill is grounded in published research on consumer trust, decision-making under risk, marketplace economics, and experimentation:

Research sourceWhat it informs
Cialdini — InfluenceSocial proof (specific beats aggregate), similarity principle, commitment
Kahneman & Tversky — Prospect TheoryLoss aversion, price anchoring, risk framing
Roth — Who Gets What and WhyMatching-market dynamics, two-sided acceptance rates, cold-start penalty
Fogg — Behavior ModelMotivation × ability × trigger, paywall timing
Bandura — Self-Efficacy TheoryFirst-stay path design, concrete-step persuasion
Slovic — Affect HeuristicRisk overweighting, safety-signal prominence
Nielsen Norman GroupForm design, trust, review credibility
Trope & Liberman — Construal Level TheoryPsychological distance, local proof
Ein-Gar, Shiv, Tormala — Blemishing EffectMixed-review credibility
Small & Loewenstein — Identifiable Victim EffectNamed-person vs statistic evidence
Green & Brock — Narrative TransportationFirst-experience stories
Kohavi — Trustworthy Online ExperimentsPrimary outcomes, proxy metrics, segmentation
Radlinski & Craswell — Optimized InterleavingFast ranking experiments
Airbnb / DoorDash engineeringTwo-sided marketplace ranking and search

Rule Categories

Categories are ordered by cascade impact on the pre-member conversion journey:

#CategoryPrefixImpact
1Anonymous Signal Inferencesignal-CRITICAL
2Pet Owner Validation and Trustowner-CRITICAL
3Pet Sitter Validation and Opportunitysitter-HIGH
4Information-Asymmetry Closuregap-HIGH
5Progressive Profile Buildingprofile-MEDIUM-HIGH
6Social Proof and Lookalike Cohortsproof-MEDIUM-HIGH
7Personalised Conversion Triggersconvert-MEDIUM-HIGH
8Onboarding Intent Captureonboard-MEDIUM
9Identity Stitchingstitch-MEDIUM
10Pre-Member Measurement and Experimentationmeasure-MEDIUM

Quick Reference

1. Anonymous Signal Inference (CRITICAL)

2. Pet Owner Validation and Trust (CRITICAL)

3. Pet Sitter Validation and Opportunity (HIGH)

4. Information-Asymmetry Closure (HIGH)

5. Progressive Profile Building (MEDIUM-HIGH)

6. Social Proof and Lookalike Cohorts (MEDIUM-HIGH)

7. Personalised Conversion Triggers (MEDIUM-HIGH)

8. Onboarding Intent Capture (MEDIUM)

9. Identity Stitching (MEDIUM)

10. Pre-Member Measurement and Experimentation (MEDIUM)

Living Context

This skill treats the product as evolving. Three living artefacts carry context across sessions, releases and team changes:

  • gotchas.md — append-only diagnostic lessons from pre-member conversion incidents
  • Visitor-concern matrix — the side-by-side table of what each side needs to validate, extended as new concerns surface
  • Pre-member experiment log — every conversion experiment with hypothesis, cohort, intervention, outcome

Update all three after every shipped change.

How to Use

Related Skills

  • marketplace-search-recsys-planning — post-member retrieval planning (search, OpenSearch, ranking). Hand off after paid-member activation.
  • marketplace-personalisation — post-member personalisation (AWS Personalize, impression tracking, feedback loops, two-sided matching). Hand off after paid-member activation.

Reference Files

FileDescription
references/_sections.mdCategory definitions and cascade rationale
gotchas.mdAccumulated pre-member diagnostic lessons
assets/templates/_template.mdTemplate for authoring new rules
metadata.jsonVersion, discipline, research references
Discovery
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Version
Latest version metadata

Version

v2026.09.24

Published

Sep 24, 2026

Category

Uncategorized

License

MIT

Source path

skills/.experimental/marketplace-pre-member-personalisation

Default branch

master

Latest commit

cf93c57

Tree SHA

afbb575