feat: Playtomic pricing/occupancy pipeline + email i18n + audience restructure

Three workstreams:

1. Playtomic full data extraction & transform pipeline:
   - Expand venue bounding boxes from 4 to 23 regions (global coverage)
   - New staging models for court resources, opening hours, and slot-level
     availability with real prices from the Playtomic API
   - Foundation fact tables for venue capacity and daily occupancy/revenue
   - City-level pricing benchmarks replacing hardcoded country estimates
   - Planner defaults now use 3-tier cascade: city data → country → fallback

2. Transactional email i18n:
   - _t() helper in worker.py with ~70 translation keys (EN + DE)
   - All 8 email handlers translated, lang passed in task payloads

3. Resend audiences restructured to 3 named audiences (free plan limit)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql

@@ -1,11 +1,13 @@
 -- Per-city planner defaults for the financial calculator.
 -- When a user selects a city in the planner, these values pre-fill the inputs.
--- Consumed by: padelnomics.planner.routes — city_defaults(city_slug) lookup.
+-- Consumed by: padelnomics.planner.routes — /api/market-data endpoint.
 --
--- Values are derived from market data where available, otherwise fall back to
--- country-level medians, then to global fallbacks from market research report.
+-- 3-tier data cascade:
+--   1. City-level: real pricing/occupancy from Playtomic availability snapshots
+--   2. Country-level: median across cities in same country
+--   3. Hardcoded fallback: market research estimates (only when no Playtomic data)
 --
--- Units are explicit in column names (EUR, %, h). All monetary values in EUR.
+-- Units are explicit in column names. Monetary values in local currency.
 
 MODEL (
   name serving.planner_defaults,
@@ -14,59 +16,120 @@ MODEL (
   grain city_slug
 );
 
-WITH country_medians AS (
-  -- Country-level fallback values from market research (hardcoded until we
-  -- have richer pricing data from Playtomic or direct scraping).
-  SELECT * FROM (VALUES
-    -- (country_code, hourly_rate_peak_eur, monthly_rent_eur_sqm, capex_court_eur,
-    --  avg_utilisation_pct, courts_typical)
-    ('DE', 22.0,  14.0, 42000.0, 0.55, 4),
-    ('ES', 16.0,   9.0, 32000.0, 0.62, 6),
-    ('GB', 24.0,  18.0, 48000.0, 0.52, 4),
-    ('FR', 18.0,  12.0, 36000.0, 0.58, 5),
-    ('IT', 15.0,  10.0, 30000.0, 0.60, 6),
-    ('PT', 12.0,   8.0, 28000.0, 0.65, 6),
-    ('AT', 20.0,  13.0, 40000.0, 0.54, 4),
-    ('CH', 28.0,  22.0, 55000.0, 0.50, 4),
-    ('NL', 20.0,  15.0, 40000.0, 0.56, 4),
-    ('BE', 18.0,  13.0, 36000.0, 0.57, 4),
-    ('SE', 22.0,  14.0, 42000.0, 0.50, 4),
-    ('US', 20.0,  12.0, 38000.0, 0.58, 6)
-  ) AS t(country_code, hourly_rate_peak_eur, monthly_rent_eur_sqm, capex_court_eur,
-         avg_utilisation_pct, courts_typical)
+WITH -- Real city-level benchmarks from Playtomic
+city_benchmarks AS (
+  SELECT
+    country_code,
+    city,
+    median_peak_rate,
+    median_offpeak_rate,
+    median_occupancy_rate,
+    median_daily_revenue_per_venue,
+    median_court_count,
+    venue_count,
+    total_venue_days_observed,
+    price_currency
+  FROM serving.venue_pricing_benchmarks
 ),
-city_venue_density AS (
+-- Country-level medians (fallback when a city has no availability data)
+country_benchmarks AS (
+  SELECT
+    country_code,
+    MEDIAN(median_peak_rate)                    AS median_peak_rate,
+    MEDIAN(median_offpeak_rate)                 AS median_offpeak_rate,
+    MEDIAN(median_occupancy_rate)               AS median_occupancy_rate,
+    MEDIAN(median_court_count)                  AS median_court_count,
+    SUM(venue_count)                            AS total_venues,
+    MIN(price_currency)                         AS price_currency
+  FROM city_benchmarks
+  GROUP BY country_code
+),
+-- Hardcoded global fallbacks (only for countries with zero Playtomic coverage)
+hardcoded_fallbacks AS (
+  SELECT * FROM (VALUES
+    ('DE', 22.0, 16.5, 0.55, 4, 'EUR'),
+    ('ES', 16.0, 12.0, 0.62, 6, 'EUR'),
+    ('GB', 24.0, 18.0, 0.52, 4, 'GBP'),
+    ('FR', 18.0, 13.5, 0.58, 5, 'EUR'),
+    ('IT', 15.0, 11.0, 0.60, 6, 'EUR'),
+    ('PT', 12.0,  9.0, 0.65, 6, 'EUR'),
+    ('AT', 20.0, 15.0, 0.54, 4, 'EUR'),
+    ('CH', 28.0, 21.0, 0.50, 4, 'CHF'),
+    ('NL', 20.0, 15.0, 0.56, 4, 'EUR'),
+    ('BE', 18.0, 13.5, 0.57, 4, 'EUR'),
+    ('SE', 22.0, 16.5, 0.50, 4, 'SEK'),
+    ('US', 20.0, 15.0, 0.58, 6, 'USD'),
+    ('MX', 12.0,  9.0, 0.55, 4, 'MXN'),
+    ('AR', 10.0,  7.5, 0.60, 4, 'ARS'),
+    ('DK', 24.0, 18.0, 0.48, 4, 'DKK'),
+    ('NO', 26.0, 19.5, 0.45, 4, 'NOK'),
+    ('FI', 22.0, 16.5, 0.48, 4, 'EUR')
+  ) AS t(country_code, peak_rate, offpeak_rate, occupancy, courts, currency)
+),
+city_profiles AS (
   SELECT
     city_slug,
     country_code,
+    city_name,
     padel_venue_count,
     population,
-    venues_per_100k,
-    market_score
+    market_score,
+    venues_per_100k
   FROM serving.city_market_profile
 )
 SELECT
-  cvd.city_slug,
-  cvd.country_code,
-  cvd.padel_venue_count,
-  cvd.population,
-  cvd.market_score,
-  -- Hourly rate: adjust country median by market maturity
-  -- (high-density markets → slightly lower rates from competition)
-  ROUND(
-    cm.hourly_rate_peak_eur
-    * CASE
-        WHEN cvd.venues_per_100k > 4  THEN 0.90  -- very competitive
-        WHEN cvd.venues_per_100k > 2  THEN 0.95  -- competitive
-        WHEN cvd.venues_per_100k < 0.5 THEN 1.10 -- underserved premium
-        ELSE 1.0
-      END
-  , 2)                                         AS hourly_rate_peak_eur,
-  ROUND(cm.hourly_rate_peak_eur * 0.75, 2)     AS hourly_rate_offpeak_eur,
-  cm.monthly_rent_eur_sqm,
-  cm.capex_court_eur,
-  cm.avg_utilisation_pct,
-  cm.courts_typical,
-  CURRENT_DATE                                 AS refreshed_date
-FROM city_venue_density cvd
-LEFT JOIN country_medians cm ON cvd.country_code = cm.country_code
+  cp.city_slug,
+  cp.country_code,
+  cp.city_name,
+  cp.padel_venue_count,
+  cp.population,
+  cp.market_score,
+  -- Peak rate: city → country → hardcoded
+  ROUND(COALESCE(
+    cb.median_peak_rate,
+    ctb.median_peak_rate,
+    hf.peak_rate
+  ), 2)                                         AS rate_peak,
+  -- Off-peak rate
+  ROUND(COALESCE(
+    cb.median_offpeak_rate,
+    ctb.median_offpeak_rate,
+    hf.offpeak_rate
+  ), 2)                                         AS rate_off_peak,
+  -- Occupancy (utilisation)
+  ROUND(COALESCE(
+    cb.median_occupancy_rate,
+    ctb.median_occupancy_rate,
+    hf.occupancy
+  ), 4)                                         AS avg_utilisation_pct,
+  -- Typical court count
+  COALESCE(
+    cb.median_court_count,
+    ctb.median_court_count,
+    hf.courts
+  )                                             AS courts_typical,
+  -- Revenue estimate (city-level only)
+  cb.median_daily_revenue_per_venue             AS daily_revenue_per_venue,
+  -- Data provenance
+  CASE
+    WHEN cb.venue_count IS NOT NULL THEN 'city_data'
+    WHEN ctb.total_venues IS NOT NULL THEN 'country_data'
+    ELSE 'hardcoded'
+  END                                           AS data_source,
+  CASE
+    WHEN cb.total_venue_days_observed >= 100 THEN 1.0
+    WHEN cb.total_venue_days_observed >= 30  THEN 0.8
+    WHEN cb.venue_count IS NOT NULL          THEN 0.6
+    WHEN ctb.total_venues IS NOT NULL        THEN 0.4
+    ELSE 0.2
+  END                                           AS data_confidence,
+  COALESCE(cb.price_currency, ctb.price_currency, hf.currency, 'EUR') AS price_currency,
+  CURRENT_DATE                                  AS refreshed_date
+FROM city_profiles cp
+LEFT JOIN city_benchmarks cb
+  ON cp.country_code = cb.country_code
+  AND LOWER(TRIM(cp.city_name)) = LOWER(TRIM(cb.city))
+LEFT JOIN country_benchmarks ctb
+  ON cp.country_code = ctb.country_code
+LEFT JOIN hardcoded_fallbacks hf
+  ON cp.country_code = hf.country_code