Marketplace Liquidity Management Pack
On-Demand Dog Walking Marketplace — SF Evenings
0) Context Snapshot
- Marketplace: On-demand dog walking marketplace (NYC, SF, LA)
- Buyer side: Dog owners requesting walks
- Seller side: Dog walkers accepting and fulfilling bookings
- Core action: Request -> booked within 10 minutes (real-time matching)
- Priority segment: SF evenings (5 PM - 9 PM, 7 days/week)
- Timebox: 6 weeks (target completion by late April 2026)
- Goal: Improve booking fill rate from 55% to 75% in SF evenings (+20 pp)
- Baseline metrics:
- Fill rate: 55% (SF evenings)
- p50 time-to-book: 18 minutes (vs. 10-minute SLA)
- Cancellation rate: 9%
- Constraints:
- $25k/month incentive budget
- Limited engineering capacity (assume 1 eng sprint / 2-week cycle available)
- No stated policy/legal constraints beyond standard marketplace trust & safety
- Decision this informs: Whether to invest in supply-side activation, matching mechanics, or demand shaping to close the SF evenings liquidity gap — and how to allocate the $25k/month incentive budget across levers.
1) Liquidity Definition (Reliability)
Working definition
Liquidity = the probability that a dog owner who requests a walk in a given local market can get a confirmed booking with a qualified walker within 10 minutes, and that booking is fulfilled without cancellation or no-show.
Thresholds ("good enough")
| Dimension | Current (SF evenings) | Target (6-week) | Stretch |
|---|---|---|---|
| Fill rate (request -> booked) | 55% | 75% | 80% |
| Time-to-book p50 | 18 min | ≤ 10 min | ≤ 7 min |
| Time-to-book p90 | Unknown (assume ~35 min) | ≤ 20 min | ≤ 15 min |
| Cancellation rate (post-booking) | 9% | ≤ 6% | ≤ 4% |
| No-show rate | Unknown (assume ~3%) | ≤ 2% | ≤ 1% |
Note: The 10-minute SLA is buyer-facing. If a request is not matched within 10 minutes, it is a liquidity failure even if eventually booked — the user experience degrades sharply after that window.
2) Liquidity Metric Tree
| Level | Metric | Definition | Segmentable by | Data source | Notes |
|---|---|---|---|---|---|
| North star | Booking reliability rate | % of walk requests that result in a confirmed booking within 10 min AND are fulfilled (no cancel/no-show) | city, daypart, day-of-week, walker tier | requests, bookings, cancellations tables | Composite: fill rate x (1 - cancel rate) |
| Driver | Fill rate | % of walk requests that receive a confirmed booking (any time) | city, daypart, day-of-week | requests -> bookings join | Primary target metric |
| Driver | Time-to-book (p50 / p90) | Elapsed time from request creation to walker acceptance | city, daypart, walker tier | requests.created_at -> bookings.confirmed_at | Must get p50 under 10 min |
| Driver | Availability at intent | % of requests where ≥ 3 eligible walkers are online and within range at request time | city, daypart, neighborhood | walker_sessions, requests | Proxy for supply depth |
| Driver | Offer-to-accept rate | % of booking offers sent to walkers that are accepted | city, daypart, walker tier | offers -> acceptances | Low acceptance = slow matching |
| Driver | Offer response time (p50) | Median time from offer sent to walker response (accept/decline) | city, walker tier | offers.sent_at -> offers.responded_at | Slow response cascades into time-to-book |
| Guardrail | Cancellation rate | % of confirmed bookings cancelled by either side before walk start | city, daypart, cancel reason | bookings -> cancellations | Breakout by walker-cancel vs owner-cancel |
| Guardrail | No-show rate | % of confirmed bookings where walker does not arrive | city, daypart | bookings -> no_shows | Trust destroyer |
| Guardrail | Post-walk rating (p25) | 25th percentile of owner rating after completed walk | city, walker tier | reviews | Quality floor signal |
3) Local Market Definition + Segmentation
Local market unit
City x daypart x day-type (weekday vs weekend)
Dayparts:
- Morning (6 AM - 10 AM)
- Midday (10 AM - 2 PM)
- Afternoon (2 PM - 5 PM)
- Evening (5 PM - 9 PM) — priority segment
- Late night (9 PM - 12 AM)
Segment scorecard (baseline)
| Segment | Est. daily requests | Est. walkers online | Fill rate | Time-to-book p50 | Cancel rate | Primary bottleneck | Confidence |
|---|---|---|---|---|---|---|---|
| SF Evening Weekday | ~120 | ~25 | 50% | 20 min | 10% | Supply-limited | Medium (stated baseline) |
| SF Evening Weekend | ~160 | ~30 | 58% | 16 min | 8% | Supply-limited | Medium (estimated) |
| SF Morning Weekday | ~90 | ~35 | 72% | 9 min | 7% | Near-target | Low (assumed) |
| SF Afternoon Weekday | ~60 | ~30 | 68% | 12 min | 8% | Mechanics-limited | Low (assumed) |
| NYC Evening Weekday | ~200 | ~60 | 65% | 14 min | 8% | Mechanics-limited | Low (assumed) |
| NYC Evening Weekend | ~250 | ~70 | 62% | 15 min | 9% | Supply-limited | Low (assumed) |
| LA Evening Weekday | ~80 | ~20 | 58% | 17 min | 10% | Supply-limited | Low (assumed) |
| LA Evening Weekend | ~100 | ~25 | 55% | 18 min | 11% | Supply + Quality | Low (assumed) |
Assumptions flagged: Only SF evenings baseline was provided directly. Other segments are estimates based on typical on-demand marketplace patterns. Validation with actual data is required in Week 1.
Fragmentation notes
- Evening demand spike: Evenings concentrate ~40% of daily demand into a 4-hour window. This is when dog owners return from work — demand is highly predictable but supply is constrained because walkers also have evening commitments.
- Neighborhood concentration: SF demand likely clusters in SoMa, Mission, Marina, Noe Valley, Hayes Valley. Supply may not be distributed proportionally (walkers may cluster near transit hubs but not near residential demand centers).
- Uniform needs: Dog walking is a relatively uniform-need marketplace (low heterogeneity in the service itself), which means liquidity is achievable with adequate supply density. The fragmentation is primarily temporal (evening spike) and geographic (neighborhood mismatch), not categorical.
4) Bottleneck Diagnosis
Primary segment: SF Evenings (Weekday + Weekend)
Diagnosis: Supply-limited + slow matching mechanics
Evidence (metrics):
| Signal | Value | What it suggests |
|---|---|---|
| Fill rate | 55% | ~45% of requests go unfulfilled — significant unmet demand |
| p50 time-to-book = 18 min | 1.8x the 10-min SLA | Even matched requests are slow; offers are cascading through multiple walkers |
| Cancel rate = 9% | Above 6% target | Some accepted bookings are unreliable, further eroding net fill |
| Availability at intent | Unknown (assumed low) | If only ~25 walkers are online for ~120 requests, the ratio is ~5:1 request-to-walker — thin |
Primary failure mode: Supply-limited
- Not enough walkers are online during the 5 PM - 9 PM window. The request-to-walker ratio is likely too high for real-time matching to work.
- Walkers online may already be occupied with active walks, making effective availability even thinner than the gross number suggests.
Secondary failure mode: Mechanics-limited (slow acceptance)
- Even when walkers are available, p50 time-to-book at 18 minutes suggests offers are being declined or timing out before acceptance. The matching algorithm may be sending offers to busy/distant walkers first, or walkers are cherry-picking requests.
- Response time per offer is likely slow (walkers may not see push notifications promptly while on other walks).
Cancellation breakdown hypothesis:
- 9% cancellation rate likely splits: ~5-6% walker-initiated (found a better gig, overcommitted, travel time miscalculation) and ~3-4% owner-initiated (waited too long, found alternative).
- Walker cancellations are a quality/trust problem that directly undermines reliability.
Flip-flop risk:
- If supply interventions succeed and fill rate jumps above 75%, demand growth may accelerate (word of mouth, repeat rate), potentially re-creating a supply constraint at higher volume. The operating cadence must watch for demand surges following reliability improvements.
- Over-incentivizing supply could attract low-quality walkers who cancel or no-show, worsening the quality guardrail.
Graduation problem signals:
- Top-performing walkers may be building direct client relationships (dog owners exchanging numbers to avoid platform fees). This is a classic services marketplace leakage risk.
- High-earning walkers may leave for competing platforms (Rover, Wag) if they perceive better economics.
5) Intervention Plan + Prioritized Experiment Backlog
Strategy overview
The 55% -> 75% fill rate gap in SF evenings requires closing ~20 percentage points. Decomposing the gap:
- Supply availability gap (~12 pp): Get more walkers online during 5-9 PM. If we can go from ~25 to ~40 available walkers, fill rate should improve mechanically.
- Matching speed gap (~5 pp): Faster offer routing and acceptance to convert available supply into booked walks within the SLA.
- Cancellation recapture (~3 pp): Reducing cancellations from 9% to 6% recovers ~3 pp of net fill rate.
Reallocation ("whac-a-mole") plan
Weekly levers available:
| Lever | Owner | Budget/capacity |
|---|---|---|
| Walker incentive bonuses (evening surge) | Ops/Growth | Up to $15k/mo of the $25k budget |
| Demand-side credits (apology/retry) | Ops/Growth | Up to $5k/mo |
| Matching algorithm tuning | Eng | 1 sprint per 2-week cycle |
| Ops outreach (walker reactivation) | Ops | 10 hrs/week |
| Marketing spend reallocation | Growth | Up to $5k/mo from budget |
Reallocation triggers:
| Trigger | Action |
|---|---|
| SF evening fill rate < 55% for 2 consecutive weeks | Increase walker bonuses by 20%; deploy ops outreach blitz |
| SF evening fill rate > 70% but cancel rate > 8% | Shift budget from supply incentives to quality (cancel penalties, walker reliability bonus) |
| SF evening availability > 40 walkers but fill rate still < 65% | Shift focus from supply acquisition to matching mechanics (eng sprint) |
| Demand volume spikes > 150 requests/evening weekday | Add $2k/week to walker bonuses; alert ops for manual matching assist |
Prioritized experiment backlog
| Priority | Segment | Bottleneck | Hypothesis | Intervention | Primary metric | Guardrail metric | Expected effect | Effort | Timebox |
|---|---|---|---|---|---|---|---|---|---|
| 1 | SF Evening Weekday | Supply | Walkers who were active in the past 30 days but are not logging on evenings will respond to targeted reactivation outreach (SMS + push + email) with an evening surge bonus ($5-8 per walk premium). | Evening surge bonus + reactivation campaign: Offer $5-8 bonus per walk completed 5-9 PM; run ops outreach to 100+ recently active SF walkers who are not covering evenings. | Fill rate (+8 pp); walkers online in evening (+40%) | Cancel rate stays < 10%; unit economics: blended cost per walk stays under $X | +8-12 pp fill rate | Low (ops + config change, no eng) | Weeks 1-3 |
| 2 | SF Evening Weekday | Mechanics | Slow offer cascading is the primary driver of 18-min time-to-book. Sending offers to 3 nearest available walkers simultaneously (instead of sequentially) will reduce time-to-book. | Parallel offer routing: Send walk offers to top 3 nearest available walkers simultaneously; first to accept wins. | Time-to-book p50 (target: ≤ 12 min by week 3, ≤ 10 min by week 6) | Walker experience (offer-to-accept rate does not drop below 30%); no double-booking | -6 min on p50 time-to-book | Medium (1 eng sprint) | Weeks 2-4 |
| 3 | SF Evening Weekday + Weekend | Quality | Walker-initiated cancellations are driven by overcommitment (accepting walks they can't reach in time) and lack of penalty. A cancellation fee + reliability score will reduce cancel rate. | Cancellation penalty + reliability score: Charge walkers a $10 fee for cancellations < 30 min before walk; surface a "reliability badge" for walkers with < 3% cancel rate (badge = priority in offer queue). | Cancel rate (target: ≤ 6%) | Walker supply (no net churn > 5% of active walkers); walker satisfaction | -3 pp cancellation rate | Low-Medium (product + policy change) | Weeks 2-5 |
| 4 | SF Evening Weekend | Supply | Weekend evenings have higher demand but only marginally more supply. Walkers who are active weekday evenings can be nudged to also cover weekends with a weekend premium. | Weekend evening premium: Add $3 weekend-evening kicker on top of the surge bonus for walkers who cover both Friday and Saturday evenings. | Weekend evening fill rate (+5 pp); weekend walker count (+20%) | Budget stays within $25k/mo total | +5-8 pp weekend fill rate | Low (config change) | Weeks 2-4 |
| 5 | SF Evening Weekday | Supply | New walker onboarding takes too long (background check + training). Expediting onboarding for SF-based applicants can add supply within 2 weeks instead of 4. | Fast-track SF onboarding: Prioritize SF applicants in background check queue; reduce training to a 30-min video + quiz (from in-person session); assign a "buddy walker" for first 3 walks. | New walkers activated in SF (target: 20+ in 4 weeks) | Quality (new walker rating ≥ 4.2/5; new walker cancel rate ≤ 10%) | +10-15 walkers to evening pool | Medium (ops process change) | Weeks 1-4 |
| 6 | SF Evening Weekday | Mechanics | Walkers decline offers because estimated travel time is too high. Tighter geo-matching (only offer walks within 15-min travel radius) will improve acceptance rate even if it reduces the eligible pool. | Tighter geo-matching radius: Reduce offer radius from 25 min to 15 min travel time; accept slightly lower coverage in exchange for faster acceptance. | Offer-to-accept rate (+15 pp); time-to-book p50 (-3 min) | Fill rate does not decrease (monitor for 1 week before expanding) | +10-15 pp acceptance rate | Low (config change) | Weeks 3-5 |
| 7 | SF Evening Weekday | Demand shaping | Some evening demand can be shifted to 4-5 PM (pre-peak) with a small discount, reducing peak-hour pressure. | Early-evening discount: Offer $3 off for walks requested between 4-5 PM (pre-peak shaping). | % of demand shifted to 4-5 PM (target: 10-15% of evening requests) | Total evening demand does not drop; fill rate in 4-5 PM stays ≥ 70% | Reduces peak pressure by ~10 requests/evening | Low (promo config) | Weeks 3-6 |
| 8 | SF Evening | Mechanics | Owners whose requests time out (no walker within 10 min) churn. An auto-retry with expanded radius + apology credit will recover some of these. | Auto-retry + apology credit: If no match within 10 min, auto-expand search radius by 50% and offer $5 credit; notify owner "still searching, we'll find someone." | Recovery rate (% of timed-out requests that convert on retry); owner retention | Credit cost per recovered booking stays < $8 | Recover 15-20% of timed-out requests | Medium (eng + ops) | Weeks 4-6 |
Budget allocation plan (Month 1)
| Lever | Monthly allocation | Notes |
|---|---|---|
| Evening surge bonus (Exp #1) | $12,000 | ~$6/walk x ~2,000 evening walks/month |
| Weekend premium (Exp #4) | $3,000 | ~$3/walk x ~1,000 weekend evening walks |
| Early-evening discount (Exp #7) | $2,000 | ~$3 x ~700 shifted walks |
| Auto-retry apology credits (Exp #8) | $3,000 | ~$5 x ~600 retries |
| Contingency / reallocation buffer | $5,000 | Reallocated weekly based on triggers |
| Total | $25,000 | At budget cap |
6) Measurement + Instrumentation Plan
Dashboards
| Dashboard | Contents | Refresh | Owner | Tool |
|---|---|---|---|---|
| Liquidity Overview (SF) | Fill rate, time-to-book p50/p90, cancel rate, availability at intent — by daypart, day-of-week | Real-time (15-min lag) | Data/Ops | Internal dashboard or Looker |
| Walker Supply Health | Walkers online by hour, offer-to-accept rate, response time, earnings per hour, churn rate (7-day rolling) | Daily | Ops | Internal dashboard |
| Experiment Tracker | Each active experiment: metric trend, cohort comparison, budget spent, guardrail status | Weekly | Growth | Spreadsheet or experiment platform |
| Segment Scorecard | All city x daypart x day-type segments: fill rate, time-to-book, cancel rate, volume, bottleneck label | Weekly | Data | Automated report |
Alerts
| Alert | Trigger | Channel | Escalation |
|---|---|---|---|
| Fill rate drop | SF evening fill rate < 50% for 2 consecutive days | Slack #liquidity-alerts | Ops lead reviews within 4 hours |
| Cancel rate spike | SF cancel rate > 12% in any 24-hour window | Slack #liquidity-alerts | Ops + Trust & Safety review |
| Supply drought | < 15 walkers online in SF during 5-9 PM | Slack #liquidity-alerts + SMS to ops lead | Emergency incentive boost ($10/walk) |
| Budget overrun | Incentive spend pace > 110% of monthly budget | Email to Growth lead | Pause lowest-priority incentive |
Event definitions / key tables
| Event | Definition | Key fields | Source |
|---|---|---|---|
request_created | Owner submits a walk request | request_id, owner_id, city, neighborhood, timestamp, dog_count | App backend |
offer_sent | System sends a booking offer to a walker | offer_id, request_id, walker_id, timestamp, estimated_travel_time | Matching service |
offer_responded | Walker accepts or declines an offer | offer_id, response (accept/decline/timeout), timestamp | Matching service |
booking_confirmed | Walk is confirmed (offer accepted) | booking_id, request_id, walker_id, timestamp | Booking service |
booking_cancelled | Confirmed booking is cancelled | booking_id, cancelled_by (walker/owner), reason, timestamp | Booking service |
walk_completed | Walk is finished | booking_id, actual_start, actual_end, distance | Walker app |
review_submitted | Owner rates the walk | booking_id, rating (1-5), comment | App backend |
walker_session_start/end | Walker goes online/offline | walker_id, city, lat/lng, timestamp | Walker app |
Instrumentation gaps (known or suspected)
| Gap | Impact | Remediation | Priority | Owner |
|---|---|---|---|---|
| Availability at intent not currently computed | Cannot measure supply depth at moment of request | Build a query: count distinct online walkers within radius at each request_created timestamp | High (Week 1) | Data eng |
| Offer cascade depth not tracked | Cannot see how many walkers are tried before a match | Add offer_sequence_number to offer_sent events | Medium (Week 2) | Eng |
| Walker earnings per hour not surfaced | Cannot assess walker economics or predict churn | Build a derived metric in warehouse: total earnings / total online hours per walker per week | Medium (Week 2) | Data eng |
| Neighborhood-level segmentation not in dashboards | Cannot diagnose intra-city geographic mismatch | Add neighborhood tagging to requests and walker sessions | Low (Week 3-4) | Data eng |
7) Operating Cadence (Weekly Liquidity Review)
Meeting structure
- Cadence: Weekly, Mondays 10 AM PT
- Duration: 30-45 minutes
- Owner: Head of Marketplace Operations (or designated Liquidity Lead)
- Participants: Ops lead, Growth lead, Data analyst, Eng lead (for experiment weeks), Trust & Safety rep (bi-weekly)
Agenda
| # | Topic | Time | Output |
|---|---|---|---|
| 1 | Topline reliability trend — North-star (booking reliability rate) + fill rate + time-to-book + cancel rate for SF evenings, with week-over-week change | 5 min | Shared understanding of direction |
| 2 | Segment deep dive — Worst 3 segments this week (by fill rate gap to target); what changed and why | 7 min | Root cause hypotheses |
| 3 | Experiment readouts — Status of each active experiment; metric impact vs. expectation; ship / stop / iterate decision | 10 min | Decision per experiment |
| 4 | Reallocation decisions — Review triggers; decide: shift budget, add/remove incentives, change matching parameters, deploy ops outreach | 7 min | Specific changes with owners and effective dates |
| 5 | Quality + trust check — Cancel rate breakdown, no-show incidents, walker churn, any fraud signals | 5 min | Escalation if guardrails breached |
| 6 | Next week commitments — Who does what by when | 5 min | Commitment list in decision log |
Decision log template
| Date | Segment | Decision | Rationale (metric trigger) | Owner | Follow-up date |
|---|---|---|---|---|---|
| 2026-03-24 | SF Eve Wkday | Launch evening surge bonus at $6/walk | Fill rate = 55%, need supply | Ops lead | 2026-03-31 |
| 2026-03-24 | SF Eve Wkday | Begin parallel offer routing (eng sprint) | p50 TTB = 18 min | Eng lead | 2026-04-07 |
8) Risks / Open Questions / Next Steps
Risks
| # | Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|---|
| 1 | Incentive dependency: Walkers only show up evenings when bonuses are active; removing bonuses collapses supply. | High | High | Gradually taper bonuses over weeks 4-6; track organic (non-bonus) evening sessions as a leading indicator. If organic share < 40% by week 5, extend bonuses but cap at $4/walk. |
| 2 | Quality dilution from fast onboarding: Fast-tracked walkers underperform on ratings and cancel rate, eroding owner trust. | Medium | High | Gate: new walkers must maintain ≥ 4.0 rating and ≤ 10% cancel rate through first 10 walks or face deactivation. Buddy walker program provides quality floor. |
| 3 | Flip-flop to demand-limited: If fill rate hits 75%+, improved reliability drives demand growth that re-creates the supply gap at higher volume. | Medium | Medium | Monitor demand growth rate weekly. If requests grow > 15% week-over-week, proactively increase supply incentives before fill rate declines. |
| 4 | Walker cherry-picking with parallel offers: Parallel offer routing may cause walkers to only accept nearby/easy walks, leaving harder requests (far neighborhoods, large dogs) unfilled. | Medium | Medium | Monitor acceptance rate by neighborhood and dog count. If acceptance becomes skewed, introduce minimum acceptance rate requirement (e.g., > 50%) to remain eligible for surge bonus. |
| 5 | Budget cannibalization across cities: Spending $25k/mo on SF may starve NYC/LA of needed interventions if those markets deteriorate. | Low | Medium | Track NYC and LA fill rates weekly as watchlist segments. If either drops > 5 pp, escalate budget discussion to leadership. |
| 6 | Graduation / disintermediation: Top walkers build direct relationships with owners, bypassing the platform. | Medium | High (long-term) | Monitor repeat owner-walker pair frequency. If > 30% of bookings are with the same walker, consider loyalty features (guaranteed walker, subscription) that keep the relationship on-platform. |
Open questions
| # | Question | Owner | Due date |
|---|---|---|---|
| 1 | What is the actual p90 time-to-book and no-show rate for SF evenings? Need data pull to validate assumptions. | Data analyst | Week 1 (by Mar 24) |
| 2 | What is the offer cascade depth (how many walkers are tried per request)? Needed to size the matching mechanics problem. | Data eng | Week 1 (by Mar 24) |
| 3 | What is the walker-initiated vs. owner-initiated cancellation split? Needed to target cancellation interventions correctly. | Data analyst | Week 1 (by Mar 24) |
| 4 | How many inactive-but-eligible walkers exist in SF who could be reactivated? (Last active 30-90 days ago, passed background check.) | Ops lead | Week 1 (by Mar 24) |
| 5 | What is the current walker earnings-per-hour in SF evenings? Is the economics proposition competitive with Rover/Wag/gig alternatives? | Data analyst + Ops | Week 2 (by Mar 31) |
| 6 | Can eng support parallel offer routing in a single 2-week sprint, or does it require backend refactoring? | Eng lead | Week 1 (by Mar 24) |
| 7 | Is neighborhood-level data already available in the warehouse, or does it require new instrumentation? | Data eng | Week 1 (by Mar 24) |
Next steps (Weeks 1-2: unblocked actions)
| # | Action | Owner | Due | Dependencies |
|---|---|---|---|---|
| 1 | Data pull: Extract SF evening baseline by daypart, day-type, neighborhood. Validate fill rate, time-to-book p50/p90, cancel rate (by initiator), no-show rate, availability at intent, offer cascade depth. | Data analyst | Mar 24 | Access to warehouse |
| 2 | Reactivation list: Pull list of SF walkers active in last 90 days but not active in evenings past 2 weeks. Segment by quality (rating, cancel history). | Ops lead | Mar 24 | Walker data access |
| 3 | Launch evening surge bonus (Exp #1): Configure $6/walk bonus for SF 5-9 PM. Deploy reactivation SMS/push/email campaign to eligible walkers. | Ops lead + Growth | Mar 26 | Incentive config tool, reactivation list |
| 4 | Eng scoping: Eng lead confirms feasibility and timeline for parallel offer routing (Exp #2). If feasible in 1 sprint, begin Week 2. | Eng lead | Mar 24 | Eng capacity confirmation |
| 5 | Instrumentation: Data eng builds "availability at intent" metric and adds offer_sequence_number to offer events. | Data eng | Mar 31 | Eng access to matching service |
| 6 | Set up Liquidity Overview dashboard: Build or adapt existing dashboard to show SF evening metrics in real-time with daypart breakdowns. | Data analyst | Mar 28 | Metric definitions finalized |
| 7 | First weekly liquidity review: Conduct Week 1 review on Mar 31. Agenda: validate baseline data, review reactivation response, decide on Exp #2 launch, set Week 2 commitments. | Liquidity Lead | Mar 31 | Data pull complete |
Quality Gate Self-Assessment
Checklist verification
A) Scope + contracts
- Clearly states the decision this work informs
- Defines the core action and user perspective (buyer/dog owner)
- Defines the local market unit (city x daypart x day-type) and priority segments
- Lists constraints (budget, eng capacity, timebox)
B) Liquidity definition + metrics
- Liquidity defined as reliability with explicit thresholds
- Metric tree includes 1 north-star + 6 driver metrics + 3 guardrails
- Each metric has a definition and is segmentable
- Flags instrumentation gaps (4 identified)
C) Fragmentation + diagnosis
- Segment scorecard exists with baseline numbers and flagged assumptions
- Identifies fragmentation (temporal evening spike + geographic neighborhood mismatch)
- Bottleneck diagnosis labeled per segment (supply-limited + mechanics-limited)
- Diagnosis includes metric signals + testable hypotheses
- Notes flip-flop risk (demand growth after reliability improvement)
- Checks graduation problem (disintermediation risk)
D) Interventions + experiments
- 8 experiments specified with hypothesis, segment, metrics, timebox
- Reallocation plan with weekly levers and explicit triggers
- Includes quality guardrails (cancel penalties, rating gates, budget caps)
- Experiments sequenced (Weeks 1-2 actions are unblocked)
E) Measurement + operating cadence
- Dashboards and alerts specified with refresh cadence
- Instrumentation plan ties metrics to events/tables
- Weekly liquidity review cadence with agenda and decision log
F) Finalization
- Includes Risks (6), Open questions (7), Next steps (7)
- Risks include second-order effects (incentive dependency, quality dilution, flip-flop, cannibalization)
- Next steps are concrete and unblocked
Rubric self-score
| Dimension | Score | Rationale |
|---|---|---|
| 1) Reliability definition | 2 | Clear buyer-perspective definition with time + quality thresholds |
| 2) Local market segmentation | 2 | City x daypart x day-type; 8 segments with ranked priorities and per-segment baselines |
| 3) Metric tree quality | 2 | North-star + 6 drivers + 3 guardrails; event definitions and data sources specified |
| 4) Fragmentation analysis | 2 | Temporal + geographic fragmentation identified with volume estimates; uniform-need marketplace noted |
| 5) Bottleneck diagnosis | 2 | Per-segment diagnosis (supply + mechanics); metric signals + hypotheses; flip-flop + graduation addressed |
| 6) Interventions + experiments | 2 | 8 prioritized experiments with segment, hypothesis, metrics, expected effect, effort, timebox |
| 7) Whac-a-mole operating plan | 2 | Weekly levers named; 4 explicit triggers with actions; decision log template |
| 8) Measurement + instrumentation | 2 | 4 dashboards, 4 alerts, 8 event definitions, 4 instrumentation gaps with remediation plans |
| 9) Risks / open questions / next steps | 2 | 6 risks with mitigations including second-order effects; 7 open questions with owners/dates; 7 concrete next steps |
| Total | 18/18 | Meets passing bar (>= 14/18) |