marketplace-pre-member-personalisation

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)

• signal-extract-role-from-url-and-referrer — side inferred from URL path before first render
• signal-infer-geography-with-confidence — geo-IP with confidence, not false certainty
• signal-capture-entry-point-metadata — UTM, referrer, landing path persisted per session
• signal-use-anonymous-session-tokens — session-level identity from the first request
• signal-classify-inbound-intent — transactional vs investigative vs curiosity
• signal-separate-raw-from-derived — raw signal plus versioned derived features

2. Pet Owner Validation and Trust (CRITICAL)

• owner-show-specific-local-reviews — identifiable-victim social proof, not aggregate stats
• owner-display-honest-local-availability — honest liquidity beats inflated counts (expectancy-violation research)
• owner-surface-safety-guarantees-prominently — insurance and coverage above the fold (Slovic affect heuristic)
• owner-rank-sitters-by-pet-match-experience — feasibility by pet type, not global popularity
• owner-demystify-effort-explicitly — explicit time budget beats aspirational copy (Fogg)
• owner-anchor-cost-against-local-alternative — local kennel price as anchor (Kahneman)

3. Pet Sitter Validation and Opportunity (HIGH)

• sitter-show-inventory-in-target-destinations — target-specific supply, not global counts
• sitter-be-honest-about-first-stay-competition — cohort-specific acceptance rates
• sitter-provide-concrete-first-stay-path — five-step path (Bandura self-efficacy)
• sitter-show-typical-daily-commitment — explicit hours and walks, not "varies"
• sitter-rank-stays-by-travel-goal — goal-aware ranking
• sitter-disclose-hidden-costs-transparently — food, utilities, transport (Edelman trust research)

4. Information-Asymmetry Closure (HIGH)

• gap-warn-about-cold-start-penalty — first transaction is the hardest; say so
• gap-surface-lead-time-reality — median booking advance per destination
• gap-display-acceptance-rate-for-profile-shape — cohort acceptance rate before paying
• gap-route-unworkable-segments-to-alternatives — decline payment rather than sell false hope
• gap-surface-seasonal-supply-constraints — seasonal curves with visitor month highlighted
• gap-link-to-realistic-first-experience-story — narrative transportation with honest friction

5. Progressive Profile Building (MEDIUM-HIGH)

• profile-build-incrementally-on-each-interaction — click updates profile, next page reranks
• profile-decay-features-with-inactivity — exponential decay, 5-minute half-life
• profile-persist-across-tabs-and-reloads — server-side session-keyed store
• profile-surface-confidence-alongside-predictions — confidence scores next to values
• profile-reset-on-explicit-role-change — role switch clears role-specific features

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

• proof-use-specific-peer-stories-not-aggregates — named people beat "4.9 stars"
• proof-match-peer-stories-to-inferred-cohort — similarity principle
• proof-source-stories-from-real-history-not-handpicked — data pipeline, not marketing
• proof-localise-social-proof-to-visitor-area — psychological distance reduction
• proof-surface-mixed-reviews-not-only-five-star — blemishing effect

7. Personalised Conversion Triggers (MEDIUM-HIGH)

• convert-trigger-paywall-on-specific-listings — specific object beats generic modal
• convert-use-loss-aversion-framing-on-soft-locks — "don't lose what you built" (Kahneman)
• convert-anchor-price-against-local-alternative — role-appropriate local anchor
• convert-never-interrupt-active-search — natural pause points only (Fogg)
• convert-re-engage-non-converting-registrants-personalised — personalised triggers beat generic

8. Onboarding Intent Capture (MEDIUM)

• onboard-ask-role-before-anything-else — role drives branching
• onboard-ask-highest-information-gain-first — information gain ordering
• onboard-prefill-from-inferred-signal — confirmation beats data entry
• onboard-make-optional-questions-genuinely-skippable — no dark-pattern required markers
• onboard-allow-answer-revision-without-restart — revision without losing progress

9. Identity Stitching (MEDIUM)

• stitch-preserve-profile-across-registration — no reset at signup
• stitch-use-deterministic-matching-for-returning-visitors — email hash beats fingerprinting
• stitch-avoid-cross-contamination-on-account-switch — household hygiene
• stitch-handle-multi-device-via-privacy-safe-signal — deterministic-only cross-device
• stitch-degrade-gracefully-on-low-confidence — fresh beats bad merge

10. Pre-Member Measurement and Experimentation (MEDIUM)

• measure-define-anonymous-to-member-as-primary-outcome — one primary metric, rest are diagnostics
• measure-attribute-conversion-to-signal-change — profile-diff attribution
• measure-segment-by-channel-and-visitor-profile — Simpson's paradox prevention
• measure-run-interleaving-for-fast-experiments — 10-100x less sample for ranking

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

• Read references/_sections.md for category structure and cascade rationale
• Read gotchas.md for accumulated lessons before suggesting interventions
• Read individual rule files when a specific task matches the rule title
• Use assets/templates/_template.md to author new rules as the skill grows

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

File	Description
references/_sections.md	Category definitions and cascade rationale
gotchas.md	Accumulated pre-member diagnostic lessons
assets/templates/_template.md	Template for authoring new rules
metadata.json	Version, discipline, research references