{
 "notes": "Night 3 gave two important, slightly conflicting signals I need to resolve on this final night. First, Report 1 shows the Nov carts.remind incident did hit real top-tier accounts - Cragsley Outfitters, our #1 tenant by 2025 revenue, is among the 10 affected, alongside #4, #6, #7 and #8 - alongside four zero-revenue/zero-order tenants, so it's a mixed bag, not purely long-tail. Second, Report 2's ticket spike for the 10 affected tenants actually starts climbing weeks before the Nov 24 failure and peaks a week after, which could mean real merchant pain preceded our systems detecting it (bad for the 'are we flying blind' question) - but it could also just be that these are naturally higher-ticket (bigger) merchants, or a seasonal Black-Friday/holiday ticket bump everyone gets. I need a baseline comparison to tell those apart before I write that into the memo as a finding. With my last 3 slots I'll: (1) settle the ticket-spike-vs-baseline question properly using an Aug-Sep baseline for affected vs other tenants, so I can honestly say cause vs. coincidence; (2) finally give Diego a first cut at webhook reliability by individual endpoint, since Report 3 showed a flat ~5-6% of deliveries each month never resolve to success or hard failure (stuck retrying) and he explicitly wants to know which integrations are chronically troublesome; (3) check whether that same webhook stuck-rate clusters on the 10 affected tenants vs everyone else, which is exactly Samuel's cross-dimension clustering question for his top-tier accounts.",
 "requests": [
  {
   "title": "Support Tickets: Affected vs Other Tenants, Baseline vs Incident Period",
   "question": "Was the Nov-Dec ticket rise for the 10 carts.remind-affected tenants a real spike against their own baseline, or just their normal (or seasonal) level?",
   "grain": "one row per tenant group (Affected vs Other), comparing two time periods",
   "columns": [
    "group_name",
    "tenant_count",
    "avg_weekly_tickets_per_tenant_baseline",
    "avg_weekly_tickets_per_tenant_incident",
    "pct_change"
   ],
   "details": "Table tickets, created_at. 'Affected' = the same 10 tenant_ids with a failed job_runs row for jobs.handler='carts.remind' queued in Nov 2025 (as established in prior reports); 'Other' = all remaining tenants. baseline period = 2025-08-01 to 2025-09-30 (61 days); incident period = 2025-11-01 to 2025-12-31 (61 days), so periods are equal length for a fair comparison. avg_weekly_tickets_per_tenant = (count of tickets for the group in that period) / tenant_count / (period_days/7.0), rounded 2dp. pct_change = (incident value - baseline value) / baseline value * 100, rounded 1dp. Two rows total: one per group. Order by group_name.",
   "builds_on": [
    "Support Ticket Volume Around the Nov 2025 Carts.remind Incident",
    "Tenant Size vs Carts.remind Job Failure Exposure, 2025"
   ]
  },
  {
   "title": "2025 Webhook Delivery Reliability by Endpoint",
   "question": "Which individual webhook endpoints are chronically troublesome - low success rates or deliveries stuck retrying rather than resolving?",
   "grain": "one row per webhook (endpoint)",
   "columns": [
    "webhook_name",
    "tenant_name",
    "total_deliveries_2025",
    "success_rate_pct",
    "failed_rate_pct",
    "pending_or_retrying_rate_pct",
    "avg_duration_ms"
   ],
   "details": "Table webhook_deliveries joined to webhooks on webhook_id (for webhook name and target_url/tenant) and tenants for tenant_name. Filter created_at in 2025. success_rate_pct = status='success' count / total * 100; failed_rate_pct = status='failed' count / total * 100; pending_or_retrying_rate_pct = status in ('pending','retrying') count / total * 100 (deliveries that never resolved), all rounded 2dp. avg_duration_ms over non-null duration_ms, rounded to whole number. Only include webhooks with at least 20 total_deliveries_2025 to avoid noise from near-empty endpoints. Order by pending_or_retrying_rate_pct descending, then failed_rate_pct descending, tie-break webhook_name ascending. Limit to 20 rows (the worst endpoints).",
   "builds_on": [
    "2025 Webhook Delivery Reliability Trend"
   ]
  },
  {
   "title": "Webhook Delivery Reliability: Carts.remind-Affected Tenants vs Others, 2025",
   "question": "Do the 10 tenants hit by the Nov carts.remind job failures also show worse webhook delivery reliability than other tenants, suggesting clustered reliability problems on the same accounts?",
   "grain": "one row per tenant group (Affected vs Other)",
   "columns": [
    "group_name",
    "tenant_count",
    "total_deliveries_2025",
    "success_rate_pct",
    "failed_rate_pct",
    "pending_or_retrying_rate_pct"
   ],
   "details": "Table webhook_deliveries joined to webhooks for tenant_id, filtered to created_at in 2025. 'Affected' = the same 10 tenant_ids defined in prior reports (failed carts.remind job_runs in Nov 2025); 'Other' = all remaining tenants with at least one webhook delivery in 2025. Rates defined as in the endpoint-level request: success_rate_pct, failed_rate_pct, pending_or_retrying_rate_pct as shares of total_deliveries_2025 for the group, rounded 2dp. Two rows total. Order by group_name.",
   "builds_on": [
    "2025 Webhook Delivery Reliability by Endpoint",
    "Tenant Size vs Carts.remind Job Failure Exposure, 2025"
   ]
  }
 ]
}