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

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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>
This commit is contained in:
Deeman
2026-02-23 00:54:53 +01:00
parent c25e20f83a
commit 79f7fc6fad
24 changed files with 1318 additions and 324 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
View File

@@ -24,6 +24,8 @@ WITH base AS (
c.population,
c.population_year,
c.padel_venue_count,
c.median_income_pps,
c.income_year,
-- Venue density: padel venues per 100K residents
CASE WHEN c.population > 0
THEN ROUND(c.padel_venue_count::DOUBLE / c.population * 100000, 2)
@@ -51,18 +53,30 @@ scored AS (
FROM base
)
SELECT
city_code,
country_code,
city_name,
city_slug,
lat,
lon,
population,
population_year,
padel_venue_count,
venues_per_100k,
data_confidence,
market_score,
s.city_code,
s.country_code,
s.city_name,
s.city_slug,
s.lat,
s.lon,
s.population,
s.population_year,
s.padel_venue_count,
s.venues_per_100k,
s.data_confidence,
s.market_score,
s.median_income_pps,
s.income_year,
-- Playtomic pricing/occupancy (NULL when no availability data)
vpb.median_hourly_rate,
vpb.median_peak_rate,
vpb.median_offpeak_rate,
vpb.median_occupancy_rate,
vpb.median_daily_revenue_per_venue,
vpb.price_currency,
CURRENT_DATE AS refreshed_date
FROM scored
ORDER BY market_score DESC
FROM scored s
LEFT JOIN serving.venue_pricing_benchmarks vpb
ON s.country_code = vpb.country_code
AND LOWER(TRIM(s.city_name)) = LOWER(TRIM(vpb.city))
ORDER BY s.market_score DESC

165
transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql
View File

@@ -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

57
transform/sqlmesh_padelnomics/models/serving/venue_pricing_benchmarks.sql Normal file
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@@ -0,0 +1,57 @@
-- Per-city pricing and occupancy benchmarks from Playtomic availability data.
-- Aggregates venue-level daily metrics (last 30 days) into city-level benchmarks.
-- Consumed by: planner defaults (pre-fill), city market profile, SEO articles.
--
-- Minimum data threshold: venues with >= 3 days of observations.
MODEL (
name serving.venue_pricing_benchmarks,
kind FULL,
cron '@daily',
grain (country_code, city)
);
WITH venue_stats AS (
-- Aggregate last 30 days per venue
SELECT
da.tenant_id,
da.country_code,
da.city,
da.price_currency,
AVG(da.occupancy_rate) AS avg_occupancy_rate,
MEDIAN(da.median_price) AS median_hourly_rate,
MEDIAN(da.median_price_peak) AS median_peak_rate,
MEDIAN(da.median_price_offpeak) AS median_offpeak_rate,
AVG(da.estimated_revenue_eur) AS avg_daily_revenue,
MAX(da.active_court_count) AS court_count,
COUNT(DISTINCT da.snapshot_date) AS days_observed
FROM foundation.fct_daily_availability da
WHERE da.snapshot_date >= CURRENT_DATE - INTERVAL '30 days'
AND da.occupancy_rate IS NOT NULL
AND da.occupancy_rate BETWEEN 0 AND 1.5
GROUP BY da.tenant_id, da.country_code, da.city, da.price_currency
HAVING COUNT(DISTINCT da.snapshot_date) >= 3
)
SELECT
country_code,
city,
price_currency,
COUNT(*) AS venue_count,
-- Pricing benchmarks
ROUND(MEDIAN(median_hourly_rate), 2) AS median_hourly_rate,
ROUND(MEDIAN(median_peak_rate), 2) AS median_peak_rate,
ROUND(MEDIAN(median_offpeak_rate), 2) AS median_offpeak_rate,
ROUND(PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY median_hourly_rate), 2) AS hourly_rate_p25,
ROUND(PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY median_hourly_rate), 2) AS hourly_rate_p75,
-- Occupancy benchmarks
ROUND(MEDIAN(avg_occupancy_rate), 4) AS median_occupancy_rate,
ROUND(AVG(avg_occupancy_rate), 4) AS avg_occupancy_rate,
-- Revenue benchmarks (per venue per day)
ROUND(MEDIAN(avg_daily_revenue), 2) AS median_daily_revenue_per_venue,
-- Court mix
ROUND(MEDIAN(court_count), 0)::INTEGER AS median_court_count,
-- Data quality
SUM(days_observed) AS total_venue_days_observed,
CURRENT_DATE AS refreshed_date
FROM venue_stats
GROUP BY country_code, city, price_currency