new-site/scripts/coupon_ab_scoreboard.py
justin 60d5d3b9d8 analytics: attribute price-test arm per click + A/B/C scoreboard
So we can see whether a discount helps the CTA (clicks) AND the sale (paid
orders), per arm:

1. pw-analytics.js: include the ?code= (the price-test arm) on the campaign-click
   Umami event, and fire it when EITHER a utm OR a code is present (coupon links
   often carry code with no utm, so those human clicks were being dropped). Also
   strip a stray @TrackLink suffix Listmonk glues onto the last query value.

2. coupon_ab_scoreboard.py: one report combining HUMAN clicks (Umami, already
   bot-filtered) and PAID conversions (compliance_orders) per arm. Discount arms
   map by code->pct (campaign-daily:<date>:<pct> marker); the no-code control arm
   is recovered by re-hashing customer_email the same way the builder bucketed it.
   Prints clicks, paid orders, revenue, and click->order per arm.
2026-06-30 16:39:26 -05:00

170 lines
7.6 KiB
Python

#!/usr/bin/env python3
"""
coupon_ab_scoreboard.py — The trucking price A/B/C scoreboard.
Answers two questions per discount arm (20% / 30% / 40% / full-price control):
1. Does the discount drive the CTA click? -> HUMAN clicks from Umami
(campaign-click events, already bot-filtered by pw-bot-filter.js), attributed
to an arm by the ?code= the link carried.
2. Does it drive the sale? -> PAID orders from compliance_orders,
attributed to an arm by discount_code (discount arms) or, for the control
arm (no code), by re-hashing customer_email the SAME way the builder bucketed
it (sha256(email) % len(arms)).
Both DBs live on the same Postgres. Reads:
- umami DB: website_event (event_name='campaign-click', has url_query/event_data)
- performancewest DB: discount_codes (campaign-daily:<date>:<pct> marker -> arm),
compliance_orders (paid conversions + customer_email)
Usage (run on the host, or anywhere with psql access to the containers):
python3 scripts/coupon_ab_scoreboard.py # last 14 days
python3 scripts/coupon_ab_scoreboard.py --days 30
python3 scripts/coupon_ab_scoreboard.py --arms 20,30,40,0
The arm set MUST match CAMPAIGN_COUPON_AB_PCTS so the control re-hash lines up.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import subprocess
import sys
from collections import defaultdict
# How to reach the two databases. On the host we exec into the postgres
# container; override with env if running elsewhere.
PG_CONTAINER = os.getenv("PW_PG_CONTAINER", "performancewest-api-postgres-1")
UMAMI_CONTAINER = os.getenv("PW_UMAMI_CONTAINER", "performancewest-umami-postgres-1")
PW_DB = os.getenv("PW_DB", "performancewest")
UMAMI_DB = os.getenv("PW_UMAMI_DB", "umami")
PW_USER = os.getenv("PW_DB_USER", "pw")
UMAMI_USER = os.getenv("PW_UMAMI_USER", "umami")
def psql(container: str, db: str, user: str, sql: str) -> list[list[str]]:
"""Run SQL via `docker exec ... psql -tAF$'\\t'` and return rows of fields."""
out = subprocess.run(
["docker", "exec", "-i", container, "psql", "-U", user, "-d", db,
"-tAF", "\t", "-c", sql],
capture_output=True, text=True, timeout=60,
)
if out.returncode != 0:
raise RuntimeError(f"psql {db} failed: {out.stderr[:300]}")
return [line.split("\t") for line in out.stdout.splitlines() if line.strip()]
def arm_for_email(email: str, arms: list[int]) -> int:
"""Reproduce the builder's deterministic bucketing (pick_coupon_for_email).
pcts are sorted; bucket = sha256(lower(email)) % len(pcts).
"""
pcts = sorted(arms)
if len(pcts) == 1:
return pcts[0]
h = hashlib.sha256((email or "").strip().lower().encode()).hexdigest()
return pcts[int(h, 16) % len(pcts)]
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--days", type=int, default=14)
ap.add_argument("--arms", default=os.getenv("CAMPAIGN_COUPON_AB_PCTS", "20,30,40,0"),
help="comma list matching CAMPAIGN_COUPON_AB_PCTS, e.g. 20,30,40,0")
args = ap.parse_args()
arms = [int(a) for a in args.arms.split(",") if a.strip().lstrip("-").isdigit()]
if not arms:
print("no arms parsed", file=sys.stderr)
return 1
days = args.days
# ── 1. Map every daily A/B code -> its arm pct (from the description marker) ──
code_rows = psql(PG_CONTAINER, PW_DB, PW_USER, f"""
SELECT upper(code), discount_value
FROM discount_codes
WHERE description LIKE 'campaign-daily:%'
AND created_at > now() - interval '{days} days'
""")
code_to_pct = {c: int(v) for c, v in code_rows}
# ── 2. HUMAN clicks per arm (Umami campaign-click, bot-filtered already) ──────
# The arm rides in event_data 'code' (new) OR the url_query ?code= (older). We
# read both and map code->pct. Clicks with no code = control arm (pct 0) IF the
# control arm exists, but a no-code click could also just be a non-coupon link;
# we only count no-code clicks toward control when they hit an /order/ page.
click_rows = psql(UMAMI_CONTAINER, UMAMI_DB, UMAMI_USER, f"""
SELECT lower(coalesce(url_path,'')) AS path,
upper(coalesce(url_query,'')) AS q
FROM website_event
WHERE event_name = 'campaign-click'
AND created_at > now() - interval '{days} days'
""")
clicks = defaultdict(int)
for path, q in click_rows:
code = ""
if "CODE=" in q:
# extract CODE=XXXXX from the query string
seg = q.split("CODE=", 1)[1]
for sep in ("&", "@", " "):
seg = seg.split(sep, 1)[0]
code = seg
if code and code in code_to_pct:
clicks[code_to_pct[code]] += 1
elif not code and "/ORDER/" in path.upper() and 0 in arms:
clicks[0] += 1 # no-code order click = full-price control arm
# ── 3. PAID conversions per arm ──────────────────────────────────────────────
order_rows = psql(PG_CONTAINER, PW_DB, PW_USER, f"""
SELECT upper(coalesce(discount_code,'')) AS code,
lower(coalesce(customer_email,'')) AS email,
service_fee_cents
FROM compliance_orders
WHERE payment_status = 'paid'
AND created_at > now() - interval '{days} days'
AND customer_email !~* 'performancewest|carrierone|e2e|test|@example|synthetic|pipeline'
""")
paid = defaultdict(int)
revenue = defaultdict(int)
for code, email, fee in order_rows:
fee_cents = int(fee) if fee and fee.isdigit() else 0
if code and code in code_to_pct:
arm = code_to_pct[code]
elif code and code.isalpha() and len(code) == 5:
# an A/B code from outside the window; skip rather than misattribute
continue
else:
# control arm (no code) -> recover the arm by re-hashing the email
arm = arm_for_email(email, arms)
paid[arm] += 1
revenue[arm] += fee_cents
# ── 4. Print the scoreboard ──────────────────────────────────────────────────
print(f"\n=== Trucking price A/B/C scoreboard (last {days} days) ===")
print(f"arms: {arms} (control = full price, no code)\n")
hdr = f"{'arm':>8} {'human_clicks':>13} {'paid_orders':>12} {'revenue':>10} {'click→order':>12}"
print(hdr)
print("-" * len(hdr))
for pct in sorted(arms):
label = f"{pct}% off" if pct > 0 else "control"
c = clicks.get(pct, 0)
o = paid.get(pct, 0)
rev = revenue.get(pct, 0) / 100.0
cvr = f"{(o / c * 100):.1f}%" if c else "-"
print(f"{label:>8} {c:>13} {o:>12} {('$%.0f' % rev):>10} {cvr:>12}")
tot_c = sum(clicks.values()); tot_o = sum(paid.values()); tot_r = sum(revenue.values()) / 100.0
print("-" * len(hdr))
print(f"{'TOTAL':>8} {tot_c:>13} {tot_o:>12} {('$%.0f' % tot_r):>10} "
f"{((tot_o/tot_c*100) if tot_c else 0):>11.1f}%")
print("\nClicks are HUMAN (bot-filtered via Umami before-send). Conversions are")
print("PAID Stripe orders. Control conversions recovered by email re-hash.")
if tot_o < 30:
print(f"\n NOTE: only {tot_o} paid conversions in window — not yet enough for a")
print(" statistically meaningful price comparison (want ~hundreds/arm). Clicks")
print(" are the early CTA signal; conversions are the real answer once volume builds.")
return 0
if __name__ == "__main__":
raise SystemExit(main())