update
This commit is contained in:
803
.sync/Archive/scripts/reports/fetch_monthly_report_data.py
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803
.sync/Archive/scripts/reports/fetch_monthly_report_data.py
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#!/usr/bin/env python3
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"""
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Pobiera dane z Google Ads API + GA4 za wskazany miesiąc i zapisuje jako JSON.
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Użycie:
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python scripts/reports/fetch_monthly_report_data.py --customer studio-zoe.pl --month 2026-02
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python scripts/reports/fetch_monthly_report_data.py --customer 3871661050 --month 2026-02 --output output/report.json
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"""
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import argparse
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import calendar
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import csv
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import json
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import os
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import re
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import sys
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import io
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import tomllib
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from datetime import datetime, timedelta
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from pathlib import Path
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from urllib.parse import parse_qs, urlparse
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from lib.gads_client import get_client, get_customer_id, run_query
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import requests
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ROOT = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(ROOT))
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from src.gads_v2.config import load_env
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load_env(ROOT / ".env")
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def load_client_report_config(domain):
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"""Load scalar report settings for a client from config/clients.toml."""
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config_path = ROOT / "config" / "clients.toml"
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if not config_path.exists():
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return {}
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data = tomllib.loads(config_path.read_text(encoding="utf-8"))
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return data.get("clients", {}).get(domain, {})
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def parse_month(month_str):
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"""Parse YYYY-MM to (year, month) and calculate date range."""
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year, month = map(int, month_str.split("-"))
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last_day = calendar.monthrange(year, month)[1]
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start = f"{year}-{month:02d}-01"
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end = f"{year}-{month:02d}-{last_day:02d}"
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return year, month, start, end
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def prev_month(year, month):
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"""Calculate previous month's date range."""
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if month == 1:
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py, pm = year - 1, 12
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else:
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py, pm = year, month - 1
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last_day = calendar.monthrange(py, pm)[1]
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start = f"{py}-{pm:02d}-01"
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end = f"{py}-{pm:02d}-{last_day:02d}"
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return py, pm, start, end
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def pct_change(current, previous):
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"""Calculate percentage change, handling zero division."""
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if previous == 0:
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return 100.0 if current > 0 else 0.0
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return round(((current - previous) / previous) * 100, 1)
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def normalize_header(value):
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value = (value or "").strip().lower()
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replacements = {
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"ą": "a",
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"ć": "c",
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"ę": "e",
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"ł": "l",
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"ń": "n",
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"ó": "o",
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"ś": "s",
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"ź": "z",
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"ż": "z",
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}
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for src, dst in replacements.items():
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value = value.replace(src, dst)
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return re.sub(r"[^a-z0-9]+", "", value)
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def parse_money(value):
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text = str(value or "").strip()
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if not text:
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return 0.0
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text = text.replace("PLN", "").replace("zl", "").replace("zł", "")
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text = text.replace("\u00a0", " ").replace(" ", "")
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if "," in text and "." in text:
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text = text.replace(".", "").replace(",", ".")
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elif "," in text:
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text = text.replace(",", ".")
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text = re.sub(r"[^0-9.\-]", "", text)
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return round(float(text), 2) if text else 0.0
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def parse_int_value(value):
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return int(round(parse_money(value)))
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def parse_history_month(value):
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text = str(value or "").strip()
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if not text:
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return ""
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if re.fullmatch(r"\d{4}-\d{2}", text):
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return text
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if re.fullmatch(r"\d{2}[.-]\d{4}", text):
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month, year = re.split(r"[.-]", text)
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return f"{int(year):04d}-{int(month):02d}"
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if re.fullmatch(r"\d{4}[./-]\d{1,2}[./-]\d{1,2}", text):
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year, month, _day = re.split(r"[./-]", text)
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return f"{int(year):04d}-{int(month):02d}"
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if re.fullmatch(r"\d{1,2}[./-]\d{1,2}[./-]\d{4}", text):
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_day, month, year = re.split(r"[./-]", text)
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return f"{int(year):04d}-{int(month):02d}"
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return text
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def parse_sheet_config(sheet_config):
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value = str(sheet_config or "").strip()
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if not value:
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return "", "0"
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if value.startswith("http"):
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parsed = urlparse(value)
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match = re.search(r"/spreadsheets/d/([^/]+)", parsed.path)
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spreadsheet_id = match.group(1) if match else value
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query_gid = parse_qs(parsed.query).get("gid", [None])[0]
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fragment_gid = parse_qs(parsed.fragment).get("gid", [None])[0]
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return spreadsheet_id, query_gid or fragment_gid or ""
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if ":" in value:
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return value.split(":", 1)
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return value, ""
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def fetch_sales_history_from_sheet(domain, sheet_config):
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"""Fetch monthly sales history from a public Google Sheet CSV export."""
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spreadsheet_id, gid = parse_sheet_config(sheet_config)
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export_url = f"https://docs.google.com/spreadsheets/d/{spreadsheet_id}/gviz/tq?tqx=out:csv"
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if gid:
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export_url += f"&gid={gid}"
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response = requests.get(export_url, timeout=30)
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response.raise_for_status()
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response.encoding = "utf-8"
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reader = csv.DictReader(io.StringIO(response.text))
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history = []
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for row in reader:
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normalized = {normalize_header(key): value for key, value in row.items()}
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month = parse_history_month(
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normalized.get("month")
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or normalized.get("miesiac")
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or normalized.get("data")
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or normalized.get("date")
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)
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revenue = parse_money(
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normalized.get("revenue")
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or normalized.get("przychod")
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or normalized.get("przychody")
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or normalized.get("sprzedaz")
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or normalized.get("wartosc")
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)
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transactions = parse_int_value(
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normalized.get("transactions")
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or normalized.get("transakcje")
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or normalized.get("zamowienia")
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or normalized.get("orders")
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)
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if not month or not revenue:
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continue
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aov = parse_money(
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normalized.get("aov")
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or normalized.get("sredniakoszyka")
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or normalized.get("sredniawartosckoszyka")
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or normalized.get("sredniawartosczamowienia")
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)
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if not aov and transactions:
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aov = round(revenue / transactions, 2)
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history.append({
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"month": month,
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"transactions": transactions,
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"revenue": revenue,
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"aov": aov,
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"source": "google_sheet",
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})
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return sorted(history, key=lambda item: item["month"])
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def apply_sheet_ecommerce(report, sales_history, month, previous_month):
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"""Use Google Sheet sales data for e-commerce KPI cards."""
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by_month = {row["month"]: row for row in sales_history}
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current = by_month.get(month)
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if not current:
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return False
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previous = by_month.get(previous_month, {"transactions": 0, "revenue": 0.0, "aov": 0.0})
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has_previous = previous_month in by_month
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ecommerce = (report.get("ga4") or {}).get("ecommerce") or {}
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ecommerce["current"] = {
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"transactions": current.get("transactions", 0),
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"revenue": current.get("revenue", 0.0),
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"aov": current.get("aov", 0.0),
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}
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ecommerce["previous"] = {
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"transactions": previous.get("transactions", 0),
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"revenue": previous.get("revenue", 0.0),
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"aov": previous.get("aov", 0.0),
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}
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ecommerce["mom_change"] = {
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"transactions_pct": pct_change(ecommerce["current"]["transactions"], ecommerce["previous"]["transactions"]) if has_previous else None,
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"revenue_pct": pct_change(ecommerce["current"]["revenue"], ecommerce["previous"]["revenue"]) if has_previous else None,
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"aov_pct": pct_change(ecommerce["current"]["aov"], ecommerce["previous"]["aov"]) if has_previous else None,
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}
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ecommerce["source"] = "google_sheet"
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if report.get("ga4") is None:
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report["ga4"] = {}
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report["ga4"]["ecommerce"] = ecommerce
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return True
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def fetch_google_ads_data(client, customer_id, start_date, end_date):
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"""Fetch campaign metrics for a date range."""
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query = f"""
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SELECT campaign.id, campaign.name, campaign.status,
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campaign.advertising_channel_type,
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metrics.impressions, metrics.clicks,
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metrics.cost_micros, metrics.conversions,
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metrics.conversions_value,
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metrics.ctr, metrics.average_cpc
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FROM campaign
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WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
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AND campaign.status != 'REMOVED'
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"""
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rows = run_query(client, customer_id, query)
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campaigns = {}
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for r in rows:
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cid = str(r.campaign.id)
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if cid not in campaigns:
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campaigns[cid] = {
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"id": cid,
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"name": r.campaign.name,
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"status": r.campaign.status.name,
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"type": r.campaign.advertising_channel_type.name,
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"impressions": 0, "clicks": 0, "cost": 0.0,
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"conversions": 0.0, "conversion_value": 0.0,
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"ctr": 0.0, "cpc": 0.0,
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}
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c = campaigns[cid]
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c["impressions"] += r.metrics.impressions
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c["clicks"] += r.metrics.clicks
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c["cost"] += r.metrics.cost_micros / 1_000_000
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c["conversions"] += r.metrics.conversions
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c["conversion_value"] += r.metrics.conversions_value
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# Calculate derived metrics
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for c in campaigns.values():
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c["cost"] = round(c["cost"], 2)
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c["conversions"] = round(c["conversions"], 1)
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c["conversion_value"] = round(c["conversion_value"], 2)
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c["ctr"] = round((c["clicks"] / c["impressions"] * 100) if c["impressions"] else 0, 2)
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c["cpc"] = round((c["cost"] / c["clicks"]) if c["clicks"] else 0, 2)
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c["cpa"] = round((c["cost"] / c["conversions"]) if c["conversions"] else 0, 2)
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c["roas"] = round((c["conversion_value"] / c["cost"]) if c["cost"] else 0, 2)
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return list(campaigns.values())
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def calc_totals(campaigns):
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"""Sum up totals across campaigns."""
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t = {"impressions": 0, "clicks": 0, "cost": 0.0, "conversions": 0.0, "conversion_value": 0.0}
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for c in campaigns:
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t["impressions"] += c["impressions"]
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t["clicks"] += c["clicks"]
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t["cost"] += c["cost"]
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t["conversions"] += c["conversions"]
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t["conversion_value"] += c.get("conversion_value", 0.0)
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t["cost"] = round(t["cost"], 2)
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t["conversions"] = round(t["conversions"], 1)
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t["conversion_value"] = round(t["conversion_value"], 2)
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t["ctr"] = round((t["clicks"] / t["impressions"] * 100) if t["impressions"] else 0, 2)
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t["cpc"] = round((t["cost"] / t["clicks"]) if t["clicks"] else 0, 2)
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t["cpa"] = round((t["cost"] / t["conversions"]) if t["conversions"] else 0, 2)
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t["roas"] = round((t["conversion_value"] / t["cost"]) if t["cost"] else 0, 2)
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return t
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def fetch_daily_data(client, customer_id, start_date, end_date):
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"""Fetch daily breakdown for charts."""
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query = f"""
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SELECT segments.date,
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metrics.impressions, metrics.clicks, metrics.cost_micros
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FROM campaign
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WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
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AND campaign.status != 'REMOVED'
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"""
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rows = run_query(client, customer_id, query)
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daily = {}
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for r in rows:
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d = r.segments.date
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if d not in daily:
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daily[d] = {"date": d, "impressions": 0, "clicks": 0, "cost": 0.0}
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daily[d]["impressions"] += r.metrics.impressions
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daily[d]["clicks"] += r.metrics.clicks
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daily[d]["cost"] += r.metrics.cost_micros / 1_000_000
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result = sorted(daily.values(), key=lambda x: x["date"])
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for d in result:
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d["cost"] = round(d["cost"], 2)
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return result
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def fetch_search_terms(client, customer_id, start_date, end_date, limit=15):
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"""Fetch top search terms by clicks."""
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query = f"""
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SELECT search_term_view.search_term,
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metrics.impressions, metrics.clicks,
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metrics.cost_micros, metrics.conversions
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FROM search_term_view
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WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
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ORDER BY metrics.clicks DESC
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LIMIT {limit}
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"""
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rows = run_query(client, customer_id, query)
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terms = []
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for r in rows:
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clicks = r.metrics.clicks
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impressions = r.metrics.impressions
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terms.append({
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"term": r.search_term_view.search_term,
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"impressions": impressions,
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"clicks": clicks,
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"cost": round(r.metrics.cost_micros / 1_000_000, 2),
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"conversions": round(r.metrics.conversions, 1),
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"ctr": round((clicks / impressions * 100) if impressions else 0, 2),
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})
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return terms
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|
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def fetch_ga4_data(property_id, start_date, end_date, prev_start, prev_end):
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"""Fetch GA4 data: sessions, users, traffic sources, devices."""
|
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from google.oauth2.credentials import Credentials
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from google.analytics.data_v1beta import BetaAnalyticsDataClient
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from google.analytics.data_v1beta.types import (
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RunReportRequest, DateRange, Metric, Dimension, OrderBy,
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)
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credentials = Credentials(
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token=None,
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refresh_token=os.environ["GA4_REFRESH_TOKEN"],
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client_id=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_ID"],
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client_secret=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_SECRET"],
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token_uri="https://oauth2.googleapis.com/token",
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)
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client = BetaAnalyticsDataClient(credentials=credentials)
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prop = f"properties/{property_id}"
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# 1. Sessions & Users (current + previous month)
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def get_totals(sd, ed):
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resp = client.run_report(RunReportRequest(
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property=prop,
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date_ranges=[DateRange(start_date=sd, end_date=ed)],
|
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metrics=[
|
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Metric(name="sessions"),
|
||||
Metric(name="totalUsers"),
|
||||
Metric(name="newUsers"),
|
||||
Metric(name="screenPageViews"),
|
||||
Metric(name="averageSessionDuration"),
|
||||
Metric(name="bounceRate"),
|
||||
],
|
||||
))
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row = resp.rows[0] if resp.rows else None
|
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if not row:
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||||
return {"sessions": 0, "users": 0, "new_users": 0, "pageviews": 0, "avg_duration": 0, "bounce_rate": 0}
|
||||
return {
|
||||
"sessions": int(row.metric_values[0].value),
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||||
"users": int(row.metric_values[1].value),
|
||||
"new_users": int(row.metric_values[2].value),
|
||||
"pageviews": int(row.metric_values[3].value),
|
||||
"avg_duration": round(float(row.metric_values[4].value), 1),
|
||||
"bounce_rate": round(float(row.metric_values[5].value) * 100, 1),
|
||||
}
|
||||
|
||||
current = get_totals(start_date, end_date)
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||||
previous = get_totals(prev_start, prev_end)
|
||||
|
||||
# 2. Traffic sources
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
|
||||
dimensions=[Dimension(name="sessionSourceMedium")],
|
||||
metrics=[Metric(name="sessions")],
|
||||
order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="sessions"), desc=True)],
|
||||
limit=10,
|
||||
))
|
||||
sources = []
|
||||
for row in resp.rows:
|
||||
sources.append({
|
||||
"source_medium": row.dimension_values[0].value,
|
||||
"sessions": int(row.metric_values[0].value),
|
||||
})
|
||||
|
||||
# 3. Devices
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
|
||||
dimensions=[Dimension(name="deviceCategory")],
|
||||
metrics=[Metric(name="sessions")],
|
||||
order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="sessions"), desc=True)],
|
||||
))
|
||||
devices = []
|
||||
for row in resp.rows:
|
||||
devices.append({
|
||||
"device": row.dimension_values[0].value,
|
||||
"sessions": int(row.metric_values[0].value),
|
||||
})
|
||||
|
||||
# 4. Daily sessions (for chart)
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
|
||||
dimensions=[Dimension(name="date")],
|
||||
metrics=[Metric(name="sessions"), Metric(name="totalUsers")],
|
||||
order_bys=[OrderBy(dimension=OrderBy.DimensionOrderBy(dimension_name="date"))],
|
||||
))
|
||||
daily_sessions = []
|
||||
for row in resp.rows:
|
||||
raw = row.dimension_values[0].value
|
||||
formatted = f"{raw[:4]}-{raw[4:6]}-{raw[6:]}"
|
||||
daily_sessions.append({
|
||||
"date": formatted,
|
||||
"sessions": int(row.metric_values[0].value),
|
||||
"users": int(row.metric_values[1].value),
|
||||
})
|
||||
|
||||
return {
|
||||
"current": current,
|
||||
"previous": previous,
|
||||
"mom_change": {
|
||||
"sessions_pct": pct_change(current["sessions"], previous["sessions"]),
|
||||
"users_pct": pct_change(current["users"], previous["users"]),
|
||||
"new_users_pct": pct_change(current["new_users"], previous["new_users"]),
|
||||
"pageviews_pct": pct_change(current["pageviews"], previous["pageviews"]),
|
||||
"avg_duration_pct": pct_change(current["avg_duration"], previous["avg_duration"]),
|
||||
"bounce_rate_pct": pct_change(current["bounce_rate"], previous["bounce_rate"]),
|
||||
},
|
||||
"sources": sources,
|
||||
"devices": devices,
|
||||
"daily": daily_sessions,
|
||||
}
|
||||
|
||||
|
||||
def fetch_ga4_ecommerce(property_id, start_date, end_date, prev_start, prev_end):
|
||||
"""Fetch GA4 e-commerce data: transactions, revenue, AOV."""
|
||||
from google.oauth2.credentials import Credentials
|
||||
from google.analytics.data_v1beta import BetaAnalyticsDataClient
|
||||
from google.analytics.data_v1beta.types import (
|
||||
RunReportRequest, DateRange, Metric, Dimension, OrderBy,
|
||||
)
|
||||
|
||||
credentials = Credentials(
|
||||
token=None,
|
||||
refresh_token=os.environ["GA4_REFRESH_TOKEN"],
|
||||
client_id=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_ID"],
|
||||
client_secret=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_SECRET"],
|
||||
token_uri="https://oauth2.googleapis.com/token",
|
||||
)
|
||||
client = BetaAnalyticsDataClient(credentials=credentials)
|
||||
prop = f"properties/{property_id}"
|
||||
|
||||
def get_ecom(sd, ed):
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=sd, end_date=ed)],
|
||||
metrics=[
|
||||
Metric(name="transactions"),
|
||||
Metric(name="purchaseRevenue"),
|
||||
Metric(name="averagePurchaseRevenue"),
|
||||
],
|
||||
))
|
||||
row = resp.rows[0] if resp.rows else None
|
||||
if not row:
|
||||
return {"transactions": 0, "revenue": 0.0, "aov": 0.0}
|
||||
return {
|
||||
"transactions": int(row.metric_values[0].value),
|
||||
"revenue": round(float(row.metric_values[1].value), 2),
|
||||
"aov": round(float(row.metric_values[2].value), 2),
|
||||
}
|
||||
|
||||
current = get_ecom(start_date, end_date)
|
||||
previous = get_ecom(prev_start, prev_end)
|
||||
|
||||
# Daily revenue chart
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
|
||||
dimensions=[Dimension(name="date")],
|
||||
metrics=[Metric(name="purchaseRevenue"), Metric(name="transactions")],
|
||||
order_bys=[OrderBy(dimension=OrderBy.DimensionOrderBy(dimension_name="date"))],
|
||||
))
|
||||
daily_revenue = []
|
||||
for row in resp.rows:
|
||||
raw = row.dimension_values[0].value
|
||||
formatted = f"{raw[:4]}-{raw[4:6]}-{raw[6:]}"
|
||||
daily_revenue.append({
|
||||
"date": formatted,
|
||||
"revenue": round(float(row.metric_values[0].value), 2),
|
||||
"transactions": int(row.metric_values[1].value),
|
||||
})
|
||||
|
||||
# Revenue by source
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
|
||||
dimensions=[Dimension(name="sessionSourceMedium")],
|
||||
metrics=[Metric(name="purchaseRevenue"), Metric(name="transactions")],
|
||||
order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="purchaseRevenue"), desc=True)],
|
||||
limit=10,
|
||||
))
|
||||
revenue_by_source = []
|
||||
for row in resp.rows:
|
||||
revenue_by_source.append({
|
||||
"source_medium": row.dimension_values[0].value,
|
||||
"revenue": round(float(row.metric_values[0].value), 2),
|
||||
"transactions": int(row.metric_values[1].value),
|
||||
})
|
||||
|
||||
# Top products by revenue
|
||||
resp = client.run_report(RunReportRequest(
|
||||
property=prop,
|
||||
date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
|
||||
dimensions=[Dimension(name="itemName")],
|
||||
metrics=[
|
||||
Metric(name="itemRevenue"),
|
||||
Metric(name="itemsPurchased"),
|
||||
],
|
||||
order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="itemRevenue"), desc=True)],
|
||||
limit=10,
|
||||
))
|
||||
top_products = []
|
||||
for row in resp.rows:
|
||||
top_products.append({
|
||||
"name": row.dimension_values[0].value,
|
||||
"revenue": round(float(row.metric_values[0].value), 2),
|
||||
"quantity": int(row.metric_values[1].value),
|
||||
})
|
||||
|
||||
return {
|
||||
"current": current,
|
||||
"previous": previous,
|
||||
"mom_change": {
|
||||
"transactions_pct": pct_change(current["transactions"], previous["transactions"]),
|
||||
"revenue_pct": pct_change(current["revenue"], previous["revenue"]),
|
||||
"aov_pct": pct_change(current["aov"], previous["aov"]),
|
||||
},
|
||||
"daily": daily_revenue,
|
||||
"revenue_by_source": revenue_by_source,
|
||||
"top_products": top_products,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Pobierz dane do raportu miesięcznego")
|
||||
parser.add_argument("--customer", required=True, help="Domena lub Google Ads customer ID")
|
||||
parser.add_argument("--month", required=True, help="Miesiąc raportu (YYYY-MM)")
|
||||
parser.add_argument("--output", help="Ścieżka do pliku JSON")
|
||||
parser.add_argument("--ga4-property", help="GA4 Property ID (domyślnie z .env)")
|
||||
parser.add_argument("--skip-ga4", action="store_true", help="Pomiń dane GA4")
|
||||
args = parser.parse_args()
|
||||
|
||||
customer_id = get_customer_id(args.customer)
|
||||
client = get_client(use_proto_plus=True)
|
||||
|
||||
year, month, start_date, end_date = parse_month(args.month)
|
||||
py, pm, prev_start, prev_end = prev_month(year, month)
|
||||
month_names_pl = {
|
||||
1: "Styczeń", 2: "Luty", 3: "Marzec", 4: "Kwiecień",
|
||||
5: "Maj", 6: "Czerwiec", 7: "Lipiec", 8: "Sierpień",
|
||||
9: "Wrzesień", 10: "Październik", 11: "Listopad", 12: "Grudzień",
|
||||
}
|
||||
|
||||
# Resolve domain name for output
|
||||
domain = args.customer if not args.customer.replace("-", "").isdigit() else args.customer
|
||||
|
||||
print(f"Pobieram dane Google Ads: {domain} za {args.month}...")
|
||||
|
||||
# Google Ads data
|
||||
campaigns = fetch_google_ads_data(client, customer_id, start_date, end_date)
|
||||
prev_campaigns = fetch_google_ads_data(client, customer_id, prev_start, prev_end)
|
||||
totals = calc_totals(campaigns)
|
||||
prev_totals = calc_totals(prev_campaigns)
|
||||
daily = fetch_daily_data(client, customer_id, start_date, end_date)
|
||||
search_terms = fetch_search_terms(client, customer_id, start_date, end_date)
|
||||
|
||||
mom_change = {
|
||||
"impressions_pct": pct_change(totals["impressions"], prev_totals["impressions"]),
|
||||
"clicks_pct": pct_change(totals["clicks"], prev_totals["clicks"]),
|
||||
"cost_pct": pct_change(totals["cost"], prev_totals["cost"]),
|
||||
"conversions_pct": pct_change(totals["conversions"], prev_totals["conversions"]),
|
||||
"conversion_value_pct": pct_change(totals["conversion_value"], prev_totals["conversion_value"]),
|
||||
"ctr_pct": pct_change(totals["ctr"], prev_totals["ctr"]),
|
||||
"cpc_pct": pct_change(totals["cpc"], prev_totals["cpc"]),
|
||||
"cpa_pct": pct_change(totals["cpa"], prev_totals["cpa"]),
|
||||
"roas_pct": pct_change(totals["roas"], prev_totals["roas"]),
|
||||
}
|
||||
|
||||
report = {
|
||||
"client": domain,
|
||||
"month": args.month,
|
||||
"month_name": month_names_pl[month],
|
||||
"year": year,
|
||||
"prev_month": f"{py}-{pm:02d}",
|
||||
"prev_month_name": month_names_pl[pm],
|
||||
"generated_at": datetime.now().isoformat(),
|
||||
"google_ads": {
|
||||
"campaigns": campaigns,
|
||||
"totals": totals,
|
||||
"prev_totals": prev_totals,
|
||||
"mom_change": mom_change,
|
||||
"daily": daily,
|
||||
"search_terms": search_terms,
|
||||
},
|
||||
}
|
||||
|
||||
# GA4 data
|
||||
if not args.skip_ga4:
|
||||
ga4_property = args.ga4_property
|
||||
if not ga4_property:
|
||||
# Try to find GA4 property in .env
|
||||
env_key = f"GA4_PROPERTY_ID_{domain}"
|
||||
ga4_property = os.environ.get(env_key)
|
||||
|
||||
if ga4_property:
|
||||
print(f"Pobieram dane GA4 (property: {ga4_property})...")
|
||||
try:
|
||||
ga4 = fetch_ga4_data(ga4_property, start_date, end_date, prev_start, prev_end)
|
||||
report["ga4"] = ga4
|
||||
print(f" GA4: {ga4['current']['sessions']} sesji, {ga4['current']['users']} uzytkownikow")
|
||||
except Exception as e:
|
||||
print(f" UWAGA: Blad GA4: {e}")
|
||||
report["ga4"] = None
|
||||
else:
|
||||
print(f" Brak GA4 Property ID w .env ({env_key}) - pomijam GA4")
|
||||
report["ga4"] = None
|
||||
else:
|
||||
report["ga4"] = None
|
||||
|
||||
# Semstorm SEO data
|
||||
semstorm_login = os.environ.get("SEMSTORM_LOGIN", "")
|
||||
if semstorm_login:
|
||||
print(f"Pobieram dane Semstorm...")
|
||||
try:
|
||||
sys.path.insert(0, str(Path(__file__).parent))
|
||||
from fetch_semstorm_data import fetch_domain_stats
|
||||
semstorm = fetch_domain_stats(domain, args.month)
|
||||
report["semstorm"] = semstorm
|
||||
if semstorm and semstorm.get("current"):
|
||||
cur = semstorm["current"]
|
||||
print(f" Semstorm: TOP3={cur['top3']}, TOP10={cur['top10']}, TOP50={cur['top50']}, traffic={cur['traffic']}")
|
||||
except Exception as e:
|
||||
print(f" UWAGA: Blad Semstorm: {e}")
|
||||
report["semstorm"] = None
|
||||
else:
|
||||
report["semstorm"] = None
|
||||
|
||||
# E-commerce data: Shoper (primary) or GA4 (fallback)
|
||||
shoper_key = f"SHOPER_API_URL_{domain}"
|
||||
if os.environ.get(shoper_key):
|
||||
print(f"Pobieram dane e-commerce ze Shoper...")
|
||||
try:
|
||||
from fetch_shoper_data import fetch_shoper_ecommerce
|
||||
shoper_ecom = fetch_shoper_ecommerce(domain, args.month, f"{py}-{pm:02d}")
|
||||
if shoper_ecom and shoper_ecom["current"]["transactions"] > 0:
|
||||
# Get revenue_by_source and top_products from GA4
|
||||
if report.get("ga4") and ga4_property:
|
||||
try:
|
||||
ga4_ecom = fetch_ga4_ecommerce(ga4_property, start_date, end_date, prev_start, prev_end)
|
||||
if ga4_ecom:
|
||||
shoper_ecom["revenue_by_source"] = ga4_ecom.get("revenue_by_source", [])
|
||||
shoper_ecom["top_products"] = ga4_ecom.get("top_products", [])
|
||||
except Exception as e:
|
||||
print(f" UWAGA: GA4 revenue_by_source/top_products: {e}")
|
||||
shoper_ecom["revenue_by_source"] = []
|
||||
shoper_ecom["top_products"] = []
|
||||
if report.get("ga4") is None:
|
||||
report["ga4"] = {}
|
||||
report["ga4"]["ecommerce"] = shoper_ecom
|
||||
cur = shoper_ecom["current"]
|
||||
print(f" Shoper: {cur['transactions']} zamówień, {cur['revenue']:.2f} PLN, AOV {cur['aov']:.2f} PLN")
|
||||
else:
|
||||
if report.get("ga4"):
|
||||
report["ga4"]["ecommerce"] = None
|
||||
except Exception as e:
|
||||
print(f" UWAGA: Blad Shoper: {e}")
|
||||
if report.get("ga4"):
|
||||
report["ga4"]["ecommerce"] = None
|
||||
elif report.get("ga4") and ga4_property:
|
||||
print(f"Pobieram dane GA4 e-commerce...")
|
||||
try:
|
||||
ecom = fetch_ga4_ecommerce(ga4_property, start_date, end_date, prev_start, prev_end)
|
||||
if ecom and ecom["current"]["transactions"] > 0:
|
||||
report["ga4"]["ecommerce"] = ecom
|
||||
cur = ecom["current"]
|
||||
print(f" E-commerce (GA4): {cur['transactions']} transakcji, {cur['revenue']:.2f} PLN przychodu")
|
||||
else:
|
||||
report["ga4"]["ecommerce"] = None
|
||||
except Exception as e:
|
||||
print(f" UWAGA: Blad GA4 e-commerce: {e}")
|
||||
if report.get("ga4"):
|
||||
report["ga4"]["ecommerce"] = None
|
||||
|
||||
# Monthly sales history for chart. Prefer client Google Sheet when configured.
|
||||
client_report_config = load_client_report_config(domain)
|
||||
sales_history_sheet = client_report_config.get("sales_history_sheet") or os.environ.get(f"GSHEET_SALES_HISTORY_{domain}")
|
||||
report_start = os.environ.get(f"REPORT_START_DATE_{domain}")
|
||||
if sales_history_sheet:
|
||||
try:
|
||||
sales_history = fetch_sales_history_from_sheet(domain, sales_history_sheet)
|
||||
if apply_sheet_ecommerce(report, sales_history, args.month, f"{py}-{pm:02d}"):
|
||||
current_sheet = report["ga4"]["ecommerce"]["current"]
|
||||
print(
|
||||
f" E-commerce (Google Sheet): {current_sheet['transactions']} transakcji, "
|
||||
f"{current_sheet['revenue']:.2f} PLN przychodu"
|
||||
)
|
||||
filtered = [e for e in sales_history if not report_start or e["month"] >= report_start]
|
||||
report["sales_history"] = filtered
|
||||
print(f" Historia sprzedaży z Google Sheet: {len(filtered)} miesięcy")
|
||||
except Exception as e:
|
||||
report["sales_history"] = []
|
||||
print(f" UWAGA: Nie udalo sie pobrac historii sprzedazy z Google Sheet: {e}")
|
||||
else:
|
||||
ecom_data = report.get("ga4", {}).get("ecommerce") if report.get("ga4") else None
|
||||
if ecom_data and ecom_data.get("current", {}).get("transactions", 0) > 0:
|
||||
history_path = ROOT / "clients" / domain / "sales_history.json"
|
||||
history_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
sales_history = []
|
||||
if history_path.exists():
|
||||
with open(history_path, "r", encoding="utf-8") as f:
|
||||
sales_history = json.load(f)
|
||||
cur_entry = {
|
||||
"month": args.month,
|
||||
"transactions": ecom_data["current"]["transactions"],
|
||||
"revenue": ecom_data["current"]["revenue"],
|
||||
"aov": ecom_data["current"]["aov"],
|
||||
"source": ecom_data.get("source", "ga4"),
|
||||
}
|
||||
by_month = {e["month"]: e for e in sales_history}
|
||||
by_month[args.month] = cur_entry
|
||||
sales_history = sorted(by_month.values(), key=lambda x: x["month"])
|
||||
with open(history_path, "w", encoding="utf-8") as f:
|
||||
json.dump(sales_history, f, indent=2, ensure_ascii=False)
|
||||
filtered = [e for e in sales_history if not report_start or e["month"] >= report_start]
|
||||
report["sales_history"] = filtered
|
||||
print(f" Historia sprzedaży: {len(filtered)} miesięcy zapisanych")
|
||||
else:
|
||||
report["sales_history"] = []
|
||||
|
||||
# SEO links from Google Sheets
|
||||
seo_links_key = f"GSHEET_SEO_LINKS_{domain}"
|
||||
if os.environ.get(seo_links_key):
|
||||
print(f"Pobieram linki SEO...")
|
||||
try:
|
||||
from fetch_seo_links import fetch_seo_links, fetch_seo_activities
|
||||
seo_links = fetch_seo_links(domain, args.month)
|
||||
report["seo_links"] = seo_links or []
|
||||
print(f" Linki SEO: {len(report['seo_links'])} w {args.month}")
|
||||
# SEO activities (text box)
|
||||
seo_act_key = f"GSHEET_SEO_ACTIVITIES_{domain}"
|
||||
if os.environ.get(seo_act_key):
|
||||
seo_activities = fetch_seo_activities(domain, args.month)
|
||||
report["seo_activities"] = seo_activities
|
||||
if seo_activities:
|
||||
print(f" Działania SEO: {len(seo_activities)} znaków")
|
||||
except Exception as e:
|
||||
print(f" UWAGA: Blad SEO links: {e}")
|
||||
report["seo_links"] = []
|
||||
else:
|
||||
report["seo_links"] = []
|
||||
|
||||
# Output
|
||||
if args.output:
|
||||
output_path = Path(args.output)
|
||||
else:
|
||||
output_path = ROOT / "scripts" / "reports" / "output" / f"{domain}_{args.month}.json"
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
json.dump(report, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print(f"\nZapisano: {output_path}")
|
||||
print(f"Google Ads: {totals['clicks']} klikniec, {totals['conversions']} konwersji, {totals['cost']:.2f} PLN")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,716 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="pl">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Raport Kwiecień 2026 — Aruba Rzeszow</title>
|
||||
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
|
||||
<style>
|
||||
:root {
|
||||
--primary: #0d8b8b;
|
||||
--primary-dark: #065a5a;
|
||||
--primary-light: #e8f5f5;
|
||||
--green: #27ae60;
|
||||
--red: #e74c3c;
|
||||
--gray: #6c757d;
|
||||
--light-gray: #f8f9fa;
|
||||
--border: #e9ecef;
|
||||
}
|
||||
|
||||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||||
|
||||
body {
|
||||
font-family: system-ui, -apple-system, 'Segoe UI', Roboto, sans-serif;
|
||||
color: #333;
|
||||
background: #f5f5f5;
|
||||
line-height: 1.6;
|
||||
}
|
||||
|
||||
.hero {
|
||||
background: linear-gradient(135deg, var(--primary-dark) 0%, var(--primary) 60%, #10a5a5 100%);
|
||||
color: white;
|
||||
padding: 60px 40px;
|
||||
position: relative;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.hero::before {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 0; right: 0;
|
||||
width: 50%;
|
||||
height: 100%;
|
||||
background: url("data:image/svg+xml,%3Csvg width='400' height='400' xmlns='http://www.w3.org/2000/svg'%3E%3Cdefs%3E%3Cpattern id='grid' width='40' height='40' patternUnits='userSpaceOnUse'%3E%3Ccircle cx='20' cy='20' r='1.5' fill='rgba(255,255,255,0.15)'/%3E%3C/pattern%3E%3C/defs%3E%3Crect width='400' height='400' fill='url(%23grid)'/%3E%3Cline x1='20' y1='60' x2='100' y2='20' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='100' y1='20' x2='180' y2='80' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='180' y1='80' x2='260' y2='40' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='260' y1='40' x2='340' y2='100' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='60' y1='140' x2='140' y2='120' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='140' y1='120' x2='220' y2='180' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='220' y1='180' x2='300' y2='140' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='300' y1='140' x2='380' y2='200' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3C/svg%3E") repeat;
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
.hero-content {
|
||||
max-width: 1400px;
|
||||
margin: 0 auto;
|
||||
position: relative;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
.logo {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
margin-bottom: 50px;
|
||||
}
|
||||
|
||||
.logo svg {
|
||||
height: 40px;
|
||||
}
|
||||
|
||||
.logo-text {
|
||||
font-size: 22px;
|
||||
font-weight: 700;
|
||||
letter-spacing: 3px;
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
.logo-subtitle {
|
||||
font-size: 11px;
|
||||
letter-spacing: 2px;
|
||||
opacity: 0.85;
|
||||
text-transform: lowercase;
|
||||
}
|
||||
|
||||
.hero h1 {
|
||||
font-size: 42px;
|
||||
font-weight: 700;
|
||||
line-height: 1.2;
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
|
||||
.hero .meta {
|
||||
font-size: 18px;
|
||||
opacity: 0.9;
|
||||
}
|
||||
|
||||
.container {
|
||||
max-width: 1400px;
|
||||
margin: 0 auto;
|
||||
padding: 0 20px;
|
||||
}
|
||||
|
||||
.report-section {
|
||||
background: white;
|
||||
border-radius: 12px;
|
||||
padding: 32px;
|
||||
margin: 24px auto;
|
||||
max-width: 1400px;
|
||||
box-shadow: 0 1px 3px rgba(0,0,0,0.08);
|
||||
}
|
||||
|
||||
.section-title {
|
||||
color: var(--primary-dark);
|
||||
font-size: 24px;
|
||||
font-weight: 700;
|
||||
margin-bottom: 24px;
|
||||
padding-bottom: 12px;
|
||||
border-bottom: 3px solid var(--primary);
|
||||
}
|
||||
|
||||
.kpi-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(170px, 1fr));
|
||||
gap: 16px;
|
||||
margin-bottom: 32px;
|
||||
}
|
||||
|
||||
.kpi-card {
|
||||
background: var(--light-gray);
|
||||
border-radius: 10px;
|
||||
padding: 20px;
|
||||
text-align: center;
|
||||
border: 1px solid var(--border);
|
||||
}
|
||||
|
||||
.kpi-label {
|
||||
font-size: 12px;
|
||||
color: var(--primary);
|
||||
text-transform: uppercase;
|
||||
font-weight: 600;
|
||||
letter-spacing: 0.5px;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
|
||||
.kpi-value {
|
||||
font-size: 28px;
|
||||
font-weight: 700;
|
||||
color: #2c3e50;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.kpi-unit {
|
||||
font-size: 14px;
|
||||
font-weight: 400;
|
||||
color: var(--gray);
|
||||
}
|
||||
|
||||
.kpi-change {
|
||||
font-size: 13px;
|
||||
margin-top: 6px;
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
.chart-container {
|
||||
margin: 24px 0;
|
||||
}
|
||||
|
||||
.chart-container h3 {
|
||||
color: var(--primary-dark);
|
||||
margin-bottom: 12px;
|
||||
font-size: 16px;
|
||||
}
|
||||
|
||||
.chart-container canvas {
|
||||
max-height: 300px;
|
||||
}
|
||||
|
||||
.data-table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.data-table thead {
|
||||
background: var(--primary);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.data-table th {
|
||||
padding: 10px 12px;
|
||||
text-align: left;
|
||||
font-weight: 600;
|
||||
font-size: 12px;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.5px;
|
||||
}
|
||||
|
||||
.data-table td {
|
||||
padding: 10px 12px;
|
||||
border-bottom: 1px solid var(--border);
|
||||
}
|
||||
|
||||
.data-table tbody tr:hover {
|
||||
background: var(--primary-light);
|
||||
}
|
||||
|
||||
.data-table .num {
|
||||
text-align: right;
|
||||
font-variant-numeric: tabular-nums;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.badge {
|
||||
display: inline-block;
|
||||
padding: 2px 8px;
|
||||
border-radius: 4px;
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.badge-search { background: #dbeafe; color: #1e40af; }
|
||||
.badge-performance_max { background: #fef3c7; color: #92400e; }
|
||||
.badge-shopping { background: #d1fae5; color: #065f46; }
|
||||
.badge-display { background: #ede9fe; color: #5b21b6; }
|
||||
.badge-demand_gen { background: #fce7f3; color: #9d174d; }
|
||||
|
||||
.summary-box {
|
||||
background: var(--primary-light);
|
||||
border-left: 4px solid var(--primary);
|
||||
padding: 24px;
|
||||
border-radius: 0 10px 10px 0;
|
||||
font-size: 15px;
|
||||
line-height: 1.8;
|
||||
}
|
||||
|
||||
.two-col {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr;
|
||||
gap: 24px;
|
||||
margin-top: 24px;
|
||||
}
|
||||
|
||||
.bar {
|
||||
height: 8px;
|
||||
background: var(--primary);
|
||||
border-radius: 4px;
|
||||
min-width: 4px;
|
||||
}
|
||||
|
||||
.recommendations-list {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 16px;
|
||||
}
|
||||
|
||||
.rec-item {
|
||||
display: flex;
|
||||
gap: 16px;
|
||||
padding: 16px;
|
||||
background: var(--light-gray);
|
||||
border-radius: 8px;
|
||||
border-left: 4px solid var(--primary);
|
||||
}
|
||||
|
||||
.rec-icon {
|
||||
font-size: 20px;
|
||||
flex-shrink: 0;
|
||||
margin-top: 2px;
|
||||
}
|
||||
|
||||
.rec-item p {
|
||||
margin-top: 4px;
|
||||
color: var(--gray);
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.questions-box {
|
||||
background: linear-gradient(135deg, #fff8e1 0%, #fff3cd 100%);
|
||||
border-left: 4px solid #f59e0b;
|
||||
border-radius: 10px;
|
||||
padding: 24px 28px;
|
||||
margin-top: 16px;
|
||||
}
|
||||
.questions-box h3 {
|
||||
margin: 0 0 8px 0;
|
||||
color: #92400e;
|
||||
font-size: 18px;
|
||||
}
|
||||
.questions-box .intro {
|
||||
color: #78350f;
|
||||
font-size: 14px;
|
||||
margin-bottom: 16px;
|
||||
}
|
||||
.questions-list {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 14px;
|
||||
}
|
||||
.q-item {
|
||||
display: flex;
|
||||
gap: 14px;
|
||||
padding: 14px 16px;
|
||||
background: rgba(255,255,255,0.7);
|
||||
border-radius: 8px;
|
||||
}
|
||||
.q-num {
|
||||
flex-shrink: 0;
|
||||
width: 28px;
|
||||
height: 28px;
|
||||
border-radius: 50%;
|
||||
background: #f59e0b;
|
||||
color: white;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-weight: 700;
|
||||
font-size: 14px;
|
||||
}
|
||||
.q-item strong { color: #92400e; display: block; margin-bottom: 4px; }
|
||||
.q-item p { margin: 0; color: #57534e; font-size: 14px; line-height: 1.6; }
|
||||
|
||||
.footer {
|
||||
text-align: center;
|
||||
padding: 32px;
|
||||
color: var(--gray);
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.footer a {
|
||||
color: var(--primary);
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
.hero { padding: 40px 20px; }
|
||||
.hero h1 { font-size: 28px; }
|
||||
.kpi-grid { grid-template-columns: repeat(2, 1fr); }
|
||||
.two-col { grid-template-columns: 1fr; }
|
||||
.report-section { padding: 20px; margin: 12px; }
|
||||
}
|
||||
|
||||
@media print {
|
||||
body { background: white; }
|
||||
.report-section { box-shadow: none; page-break-inside: avoid; }
|
||||
.hero { padding: 30px; }
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<!-- HERO / TITLE -->
|
||||
<header class="hero">
|
||||
<div class="hero-content">
|
||||
<div class="logo">
|
||||
<img src="https://www.project-pro.pl/upload/filemanager/Project-Design/logos/project-pro/logo-white.svg" alt="Project-Pro" style="height: 40px;">
|
||||
</div>
|
||||
<h1>Raport z działań marketingowych</h1>
|
||||
<div class="meta">Aruba Rzeszow — Kwiecień 2026</div>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<!-- RECOMMENDATIONS -->
|
||||
|
||||
<section class="report-section" id="recommendations">
|
||||
<h2 class="section-title">Wnioski i rekomendacje</h2>
|
||||
<div class="recommendations-list">
|
||||
<div class="rec-item"><span class="rec-icon">⚠</span><div><strong>Spadek konwersji do obserwacji</strong><p>Liczba konwersji spadla o 8.2% miesiac do miesiaca. Rekomendujemy sprawdzenie kampanii o najwiekszym spadku wolumenu.</p></div></div><div class="rec-item"><span class="rec-icon">📈</span><div><strong>ROAS liczony z Google Ads</strong><p>ROAS z Google Ads wyniosl 8.47. Ten wskaznik liczymy z wartosci konwersji Google Ads, nie z przychodow sklepu.</p></div></div><div class="rec-item"><span class="rec-icon">🔍</span><div><strong>Kontrola wzrostu kosztu</strong><p>Koszt reklam wzrosl o 12.2% miesiac do miesiaca. Warto porownac wzrost kosztu ze wzrostem konwersji i wartosci konwersji.</p></div></div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
|
||||
|
||||
|
||||
<!-- GA4 SECTION -->
|
||||
|
||||
|
||||
<!-- E-COMMERCE -->
|
||||
|
||||
|
||||
<!-- PRODUCT OPTIMIZATIONS -->
|
||||
|
||||
|
||||
<!-- TOP ADS PRODUCTS -->
|
||||
|
||||
|
||||
<!-- YEAR OVER YEAR -->
|
||||
|
||||
|
||||
<!-- GOOGLE ADS KPIs -->
|
||||
<section class="report-section" id="ads-kpi">
|
||||
<h2 class="section-title">Google Ads — Podsumowanie</h2>
|
||||
<div class="kpi-grid">
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">Wyświetlenia</div>
|
||||
<div class="kpi-value">172 277<span class="kpi-unit"></span></div>
|
||||
<div class="kpi-change" style="color: #e74c3c">
|
||||
▼ -0.6% vs Marzec
|
||||
</div>
|
||||
</div>
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">Kliknięcia</div>
|
||||
<div class="kpi-value">3 826<span class="kpi-unit"></span></div>
|
||||
<div class="kpi-change" style="color: #27ae60">
|
||||
▲ +2.5% vs Marzec
|
||||
</div>
|
||||
</div>
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">CTR</div>
|
||||
<div class="kpi-value">2.2<span class="kpi-unit">%</span></div>
|
||||
<div class="kpi-change" style="color: #27ae60">
|
||||
▲ +3.3% vs Marzec
|
||||
</div>
|
||||
</div>
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">Konwersje</div>
|
||||
<div class="kpi-value">199<span class="kpi-unit"></span></div>
|
||||
<div class="kpi-change" style="color: #e74c3c">
|
||||
▼ -8.2% vs Marzec
|
||||
</div>
|
||||
</div>
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">Koszt</div>
|
||||
<div class="kpi-value">4880.74<span class="kpi-unit"> PLN</span></div>
|
||||
<div class="kpi-change" style="color: #e74c3c">
|
||||
▲ +12.2% vs Marzec
|
||||
</div>
|
||||
</div>
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">CPA</div>
|
||||
<div class="kpi-value">24.46<span class="kpi-unit"> PLN</span></div>
|
||||
<div class="kpi-change" style="color: #e74c3c">
|
||||
▲ +22.2% vs Marzec
|
||||
</div>
|
||||
</div>
|
||||
<div class="kpi-card">
|
||||
<div class="kpi-label">ROAS</div>
|
||||
<div class="kpi-value">8.47<span class="kpi-unit">x</span></div>
|
||||
<div class="kpi-change" style="color: #e74c3c">
|
||||
▼ -1.5% vs Marzec
|
||||
</div>
|
||||
</div></div>
|
||||
</section>
|
||||
|
||||
<!-- DAILY CHART -->
|
||||
<section class="report-section" id="ads-chart">
|
||||
<h2 class="section-title">Google Ads — Aktywność dzienna</h2>
|
||||
<div class="chart-container">
|
||||
<canvas id="dailyClicksChart"></canvas>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- CAMPAIGNS TABLE -->
|
||||
<section class="report-section" id="campaigns">
|
||||
<h2 class="section-title">Kampanie</h2>
|
||||
<table class="data-table">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Kampania</th>
|
||||
<th>Typ</th>
|
||||
<th>Wyświetlenia</th>
|
||||
<th>Kliknięcia</th>
|
||||
<th>CTR</th>
|
||||
<th>Konwersje</th>
|
||||
<th>Koszt</th>
|
||||
<th>CPA</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>[Search] brand</td>
|
||||
<td><span class="badge badge-search">SEARCH</span></td>
|
||||
<td class="num">1 614</td>
|
||||
<td class="num">483</td>
|
||||
<td class="num">29.9%</td>
|
||||
<td class="num">27</td>
|
||||
<td class="num">337.96 PLN</td>
|
||||
<td class="num">12.52 PLN</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>[DSA] produkty</td>
|
||||
<td><span class="badge badge-search">SEARCH</span></td>
|
||||
<td class="num">9 984</td>
|
||||
<td class="num">694</td>
|
||||
<td class="num">7.0%</td>
|
||||
<td class="num">23</td>
|
||||
<td class="num">1098.15 PLN</td>
|
||||
<td class="num">47.75 PLN</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>[PMax] products (catch-all)</td>
|
||||
<td><span class="badge badge-performance_max">PERFORMANCE_MAX</span></td>
|
||||
<td class="num">138 921</td>
|
||||
<td class="num">2 164</td>
|
||||
<td class="num">1.6%</td>
|
||||
<td class="num">106</td>
|
||||
<td class="num">2762.99 PLN</td>
|
||||
<td class="num">25.94 PLN</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>[PLA] produkty (bestsellers)</td>
|
||||
<td><span class="badge badge-shopping">SHOPPING</span></td>
|
||||
<td class="num">21 758</td>
|
||||
<td class="num">485</td>
|
||||
<td class="num">2.2%</td>
|
||||
<td class="num">43</td>
|
||||
<td class="num">681.64 PLN</td>
|
||||
<td class="num">15.85 PLN</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
</section>
|
||||
|
||||
<!-- SEARCH TERMS -->
|
||||
<section class="report-section" id="search-terms">
|
||||
<h2 class="section-title">Najpopularniejsze frazy wyszukiwania</h2>
|
||||
<table class="data-table">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>#</th>
|
||||
<th>Fraza</th>
|
||||
<th>Wyświetlenia</th>
|
||||
<th>Kliknięcia</th>
|
||||
<th>CTR</th>
|
||||
<th>Konwersje</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td class="num">1</td>
|
||||
<td>aruba rzeszów</td>
|
||||
<td class="num">836</td>
|
||||
<td class="num">246</td>
|
||||
<td class="num">29.4%</td>
|
||||
<td class="num">16</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">2</td>
|
||||
<td>gen factor</td>
|
||||
<td class="num">858</td>
|
||||
<td class="num">59</td>
|
||||
<td class="num">6.9%</td>
|
||||
<td class="num">1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">3</td>
|
||||
<td>aruba hurtownia</td>
|
||||
<td class="num">122</td>
|
||||
<td class="num">48</td>
|
||||
<td class="num">39.3%</td>
|
||||
<td class="num">4</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">4</td>
|
||||
<td>aruba rzeszow</td>
|
||||
<td class="num">127</td>
|
||||
<td class="num">39</td>
|
||||
<td class="num">30.7%</td>
|
||||
<td class="num">0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">5</td>
|
||||
<td>gen factor green</td>
|
||||
<td class="num">207</td>
|
||||
<td class="num">21</td>
|
||||
<td class="num">10.1%</td>
|
||||
<td class="num">2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">6</td>
|
||||
<td>gen factor</td>
|
||||
<td class="num">604</td>
|
||||
<td class="num">21</td>
|
||||
<td class="num">3.5%</td>
|
||||
<td class="num">1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">7</td>
|
||||
<td>verru immuno</td>
|
||||
<td class="num">495</td>
|
||||
<td class="num">19</td>
|
||||
<td class="num">3.8%</td>
|
||||
<td class="num">2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">8</td>
|
||||
<td>aruba sklep</td>
|
||||
<td class="num">48</td>
|
||||
<td class="num">17</td>
|
||||
<td class="num">35.4%</td>
|
||||
<td class="num">1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">9</td>
|
||||
<td>aurumaris</td>
|
||||
<td class="num">113</td>
|
||||
<td class="num">13</td>
|
||||
<td class="num">11.5%</td>
|
||||
<td class="num">0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">10</td>
|
||||
<td>aruba hurtownia kosmetyczna</td>
|
||||
<td class="num">25</td>
|
||||
<td class="num">12</td>
|
||||
<td class="num">48.0%</td>
|
||||
<td class="num">1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">11</td>
|
||||
<td>aruba kosmetyki</td>
|
||||
<td class="num">33</td>
|
||||
<td class="num">12</td>
|
||||
<td class="num">36.4%</td>
|
||||
<td class="num">1</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">12</td>
|
||||
<td>gen factor 09</td>
|
||||
<td class="num">47</td>
|
||||
<td class="num">11</td>
|
||||
<td class="num">23.4%</td>
|
||||
<td class="num">0</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">13</td>
|
||||
<td>genfactor</td>
|
||||
<td class="num">111</td>
|
||||
<td class="num">11</td>
|
||||
<td class="num">9.9%</td>
|
||||
<td class="num">2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">14</td>
|
||||
<td>podopharm verru immuno</td>
|
||||
<td class="num">230</td>
|
||||
<td class="num">11</td>
|
||||
<td class="num">4.8%</td>
|
||||
<td class="num">2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td class="num">15</td>
|
||||
<td>hurtownia aruba</td>
|
||||
<td class="num">32</td>
|
||||
<td class="num">10</td>
|
||||
<td class="num">31.2%</td>
|
||||
<td class="num">0</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
</section>
|
||||
|
||||
<!-- NEGATIVE KEYWORDS ADDED -->
|
||||
|
||||
|
||||
<!-- SEMSTORM SEO -->
|
||||
|
||||
|
||||
<!-- SEO ACTIVITIES -->
|
||||
|
||||
|
||||
<!-- SEO LINKS -->
|
||||
|
||||
|
||||
<!-- SUMMARY -->
|
||||
<section class="report-section" id="summary">
|
||||
<h2 class="section-title">Podsumowanie miesiąca</h2>
|
||||
<div class="summary-box">
|
||||
Odnotowano 199 konwersji w tym miesiącu. Ruch z reklam wzrósł o 2.5% (3826 kliknięć).
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- RECOMMENDATIONS moved to top -->
|
||||
|
||||
<!-- FOOTER -->
|
||||
<div class="footer">
|
||||
Raport wygenerowany przez <a href="https://www.project-pro.pl">Project-Pro</a> — marketing w wersji PRO
|
||||
</div>
|
||||
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||||
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||||
|
||||
|
||||
|
||||
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|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,429 @@
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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||||
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||||
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||||
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||||
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||||
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||||
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|
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"name": "[PLA] produkty (bestsellers)",
|
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"status": "ENABLED",
|
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"type": "SHOPPING",
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|
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|
||||
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|
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|
||||
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|
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|
||||
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||||
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|
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||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"date": "2026-04-06",
|
||||
"impressions": 3976,
|
||||
"clicks": 96,
|
||||
"cost": 112.82
|
||||
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|
||||
{
|
||||
"date": "2026-04-07",
|
||||
"impressions": 5853,
|
||||
"clicks": 130,
|
||||
"cost": 138.02
|
||||
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|
||||
{
|
||||
"date": "2026-04-08",
|
||||
"impressions": 7519,
|
||||
"clicks": 166,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
{
|
||||
"date": "2026-04-10",
|
||||
"impressions": 4340,
|
||||
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|
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|
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|
||||
{
|
||||
"date": "2026-04-11",
|
||||
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|
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|
||||
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|
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|
||||
{
|
||||
"date": "2026-04-12",
|
||||
"impressions": 4104,
|
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|
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"cost": 116.26
|
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|
||||
{
|
||||
"date": "2026-04-13",
|
||||
"impressions": 7332,
|
||||
"clicks": 185,
|
||||
"cost": 201.76
|
||||
},
|
||||
{
|
||||
"date": "2026-04-14",
|
||||
"impressions": 7941,
|
||||
"clicks": 176,
|
||||
"cost": 232.59
|
||||
},
|
||||
{
|
||||
"date": "2026-04-15",
|
||||
"impressions": 7296,
|
||||
"clicks": 164,
|
||||
"cost": 186.57
|
||||
},
|
||||
{
|
||||
"date": "2026-04-16",
|
||||
"impressions": 6191,
|
||||
"clicks": 149,
|
||||
"cost": 165.26
|
||||
},
|
||||
{
|
||||
"date": "2026-04-17",
|
||||
"impressions": 4557,
|
||||
"clicks": 107,
|
||||
"cost": 95.56
|
||||
},
|
||||
{
|
||||
"date": "2026-04-18",
|
||||
"impressions": 3621,
|
||||
"clicks": 101,
|
||||
"cost": 118.02
|
||||
},
|
||||
{
|
||||
"date": "2026-04-19",
|
||||
"impressions": 5409,
|
||||
"clicks": 114,
|
||||
"cost": 175.25
|
||||
},
|
||||
{
|
||||
"date": "2026-04-20",
|
||||
"impressions": 7762,
|
||||
"clicks": 196,
|
||||
"cost": 239.2
|
||||
},
|
||||
{
|
||||
"date": "2026-04-21",
|
||||
"impressions": 7615,
|
||||
"clicks": 163,
|
||||
"cost": 262.91
|
||||
},
|
||||
{
|
||||
"date": "2026-04-22",
|
||||
"impressions": 9246,
|
||||
"clicks": 210,
|
||||
"cost": 265.25
|
||||
},
|
||||
{
|
||||
"date": "2026-04-23",
|
||||
"impressions": 9234,
|
||||
"clicks": 170,
|
||||
"cost": 222.45
|
||||
},
|
||||
{
|
||||
"date": "2026-04-24",
|
||||
"impressions": 5931,
|
||||
"clicks": 116,
|
||||
"cost": 202.37
|
||||
},
|
||||
{
|
||||
"date": "2026-04-25",
|
||||
"impressions": 5078,
|
||||
"clicks": 112,
|
||||
"cost": 174.69
|
||||
},
|
||||
{
|
||||
"date": "2026-04-26",
|
||||
"impressions": 5786,
|
||||
"clicks": 131,
|
||||
"cost": 162.94
|
||||
},
|
||||
{
|
||||
"date": "2026-04-27",
|
||||
"impressions": 6014,
|
||||
"clicks": 144,
|
||||
"cost": 191.42
|
||||
},
|
||||
{
|
||||
"date": "2026-04-28",
|
||||
"impressions": 6078,
|
||||
"clicks": 132,
|
||||
"cost": 181.99
|
||||
},
|
||||
{
|
||||
"date": "2026-04-29",
|
||||
"impressions": 5629,
|
||||
"clicks": 135,
|
||||
"cost": 166.02
|
||||
},
|
||||
{
|
||||
"date": "2026-04-30",
|
||||
"impressions": 4099,
|
||||
"clicks": 96,
|
||||
"cost": 121.72
|
||||
}
|
||||
],
|
||||
"search_terms": [
|
||||
{
|
||||
"term": "aruba rzeszów",
|
||||
"impressions": 836,
|
||||
"clicks": 246,
|
||||
"cost": 131.67,
|
||||
"conversions": 16.0,
|
||||
"ctr": 29.43
|
||||
},
|
||||
{
|
||||
"term": "gen factor",
|
||||
"impressions": 858,
|
||||
"clicks": 59,
|
||||
"cost": 134.33,
|
||||
"conversions": 1.0,
|
||||
"ctr": 6.88
|
||||
},
|
||||
{
|
||||
"term": "aruba hurtownia",
|
||||
"impressions": 122,
|
||||
"clicks": 48,
|
||||
"cost": 26.45,
|
||||
"conversions": 4.0,
|
||||
"ctr": 39.34
|
||||
},
|
||||
{
|
||||
"term": "aruba rzeszow",
|
||||
"impressions": 127,
|
||||
"clicks": 39,
|
||||
"cost": 24.46,
|
||||
"conversions": 0.0,
|
||||
"ctr": 30.71
|
||||
},
|
||||
{
|
||||
"term": "gen factor green",
|
||||
"impressions": 207,
|
||||
"clicks": 21,
|
||||
"cost": 46.65,
|
||||
"conversions": 2.0,
|
||||
"ctr": 10.14
|
||||
},
|
||||
{
|
||||
"term": "gen factor",
|
||||
"impressions": 604,
|
||||
"clicks": 21,
|
||||
"cost": 25.05,
|
||||
"conversions": 1.0,
|
||||
"ctr": 3.48
|
||||
},
|
||||
{
|
||||
"term": "verru immuno",
|
||||
"impressions": 495,
|
||||
"clicks": 19,
|
||||
"cost": 27.24,
|
||||
"conversions": 2.0,
|
||||
"ctr": 3.84
|
||||
},
|
||||
{
|
||||
"term": "aruba sklep",
|
||||
"impressions": 48,
|
||||
"clicks": 17,
|
||||
"cost": 3.4,
|
||||
"conversions": 1.0,
|
||||
"ctr": 35.42
|
||||
},
|
||||
{
|
||||
"term": "aurumaris",
|
||||
"impressions": 113,
|
||||
"clicks": 13,
|
||||
"cost": 14.52,
|
||||
"conversions": 0.0,
|
||||
"ctr": 11.5
|
||||
},
|
||||
{
|
||||
"term": "aruba hurtownia kosmetyczna",
|
||||
"impressions": 25,
|
||||
"clicks": 12,
|
||||
"cost": 7.85,
|
||||
"conversions": 1.0,
|
||||
"ctr": 48.0
|
||||
},
|
||||
{
|
||||
"term": "aruba kosmetyki",
|
||||
"impressions": 33,
|
||||
"clicks": 12,
|
||||
"cost": 3.27,
|
||||
"conversions": 1.0,
|
||||
"ctr": 36.36
|
||||
},
|
||||
{
|
||||
"term": "gen factor 09",
|
||||
"impressions": 47,
|
||||
"clicks": 11,
|
||||
"cost": 15.43,
|
||||
"conversions": 0.0,
|
||||
"ctr": 23.4
|
||||
},
|
||||
{
|
||||
"term": "genfactor",
|
||||
"impressions": 111,
|
||||
"clicks": 11,
|
||||
"cost": 27.84,
|
||||
"conversions": 2.0,
|
||||
"ctr": 9.91
|
||||
},
|
||||
{
|
||||
"term": "podopharm verru immuno",
|
||||
"impressions": 230,
|
||||
"clicks": 11,
|
||||
"cost": 15.75,
|
||||
"conversions": 2.0,
|
||||
"ctr": 4.78
|
||||
},
|
||||
{
|
||||
"term": "hurtownia aruba",
|
||||
"impressions": 32,
|
||||
"clicks": 10,
|
||||
"cost": 7.31,
|
||||
"conversions": 0.0,
|
||||
"ctr": 31.25
|
||||
}
|
||||
]
|
||||
},
|
||||
"ga4": null,
|
||||
"semstorm": null,
|
||||
"sales_history": [],
|
||||
"seo_links": [],
|
||||
"recommendations": [
|
||||
{
|
||||
"icon": "⚠",
|
||||
"title": "Spadek konwersji do obserwacji",
|
||||
"text": "Liczba konwersji spadla o 8.2% miesiac do miesiaca. Rekomendujemy sprawdzenie kampanii o najwiekszym spadku wolumenu."
|
||||
},
|
||||
{
|
||||
"icon": "📈",
|
||||
"title": "ROAS liczony z Google Ads",
|
||||
"text": "ROAS z Google Ads wyniosl 8.47. Ten wskaznik liczymy z wartosci konwersji Google Ads, nie z przychodow sklepu."
|
||||
},
|
||||
{
|
||||
"icon": "🔍",
|
||||
"title": "Kontrola wzrostu kosztu",
|
||||
"text": "Koszt reklam wzrosl o 12.2% miesiac do miesiaca. Warto porownac wzrost kosztu ze wzrostem konwersji i wartosci konwersji."
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user