| name | marketplace-pre-member-personalisation |
| description | 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. |
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 source | What it informs |
|---|
| Cialdini — Influence | Social proof (specific beats aggregate), similarity principle, commitment |
| Kahneman & Tversky — Prospect Theory | Loss aversion, price anchoring, risk framing |
| Roth — Who Gets What and Why | Matching-market dynamics, two-sided acceptance rates, cold-start penalty |
| Fogg — Behavior Model | Motivation × ability × trigger, paywall timing |
| Bandura — Self-Efficacy Theory | First-stay path design, concrete-step persuasion |
| Slovic — Affect Heuristic | Risk overweighting, safety-signal prominence |
| Nielsen Norman Group | Form design, trust, review credibility |
| Trope & Liberman — Construal Level Theory | Psychological distance, local proof |
| Ein-Gar, Shiv, Tormala — Blemishing Effect | Mixed-review credibility |
| Small & Loewenstein — Identifiable Victim Effect | Named-person vs statistic evidence |
| Green & Brock — Narrative Transportation | First-experience stories |
| Kohavi — Trustworthy Online Experiments | Primary outcomes, proxy metrics, segmentation |
| Radlinski & Craswell — Optimized Interleaving | Fast ranking experiments |
| Airbnb / DoorDash engineering | Two-sided marketplace ranking and search |
Rule Categories
Categories are ordered by cascade impact on the pre-member conversion journey:
| # | Category | Prefix | Impact |
|---|
| 1 | Anonymous Signal Inference | signal- | CRITICAL |
| 2 | Pet Owner Validation and Trust | owner- | CRITICAL |
| 3 | Pet Sitter Validation and Opportunity | sitter- | HIGH |
| 4 | Information-Asymmetry Closure | gap- | HIGH |
| 5 | Progressive Profile Building | profile- | MEDIUM-HIGH |
| 6 | Social Proof and Lookalike Cohorts | proof- | MEDIUM-HIGH |
| 7 | Personalised Conversion Triggers | convert- | MEDIUM-HIGH |
| 8 | Onboarding Intent Capture | onboard- | MEDIUM |
| 9 | Identity Stitching | stitch- | MEDIUM |
| 10 | Pre-Member Measurement and Experimentation | measure- | 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
