Hausmeister-Bot: Qualifizierte Matomo-Auswertung — WoW-Vergleich, Trend, Ausreisser, Bounce/Engagement-Bewertung, Wochentags-Muster, Traffic-Quellen-Analyse, Prognose
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2 changed files with 333 additions and 49 deletions
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@ -1,7 +1,8 @@
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"""Matomo Analytics API Client — Besucherstatistiken fuer arakavanews.com."""
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"""Matomo Analytics API Client — Besucherstatistiken + qualifizierte Auswertung."""
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import requests
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from datetime import datetime, timedelta
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from statistics import mean, stdev
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MATOMO_URL = ""
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MATOMO_TOKEN = ""
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@ -33,18 +34,17 @@ def _api(method: str, **params) -> dict | list | None:
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return {"error": str(e)}
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# --------------- Raw API calls ---------------
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def get_summary(period: str = "day", date: str = "today") -> dict:
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"""Besucher-Zusammenfassung (Visits, Unique, Actions, Bounce, Avg Time)."""
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return _api("VisitsSummary.get", period=period, date=date)
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def get_visitor_trend(days: int = 30) -> dict:
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"""Tageweise Besucherzahlen der letzten N Tage."""
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return _api("VisitsSummary.get", period="day", date=f"last{days}")
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def get_top_pages(period: str = "day", date: str = "today", limit: int = 10) -> list:
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"""Meistbesuchte Seiten."""
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result = _api("Actions.getPageUrls", period=period, date=date,
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filter_limit=limit, flat=1)
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if isinstance(result, dict) and "error" in result:
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@ -53,7 +53,6 @@ def get_top_pages(period: str = "day", date: str = "today", limit: int = 10) ->
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def get_referrers(period: str = "day", date: str = "today", limit: int = 10) -> list:
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"""Woher kommen Besucher (Suchmaschinen, Social, Direkt)."""
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result = _api("Referrers.getReferrerType", period=period, date=date,
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filter_limit=limit)
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if isinstance(result, dict) and "error" in result:
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@ -62,7 +61,6 @@ def get_referrers(period: str = "day", date: str = "today", limit: int = 10) ->
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def get_countries(period: str = "day", date: str = "today", limit: int = 10) -> list:
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"""Besucher nach Laendern."""
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result = _api("UserCountry.getCountry", period=period, date=date,
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filter_limit=limit)
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if isinstance(result, dict) and "error" in result:
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@ -71,93 +69,367 @@ def get_countries(period: str = "day", date: str = "today", limit: int = 10) ->
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def get_devices(period: str = "day", date: str = "today") -> list:
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"""Besucher nach Geraetetyp (Desktop, Mobile, Tablet)."""
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result = _api("DevicesDetection.getType", period=period, date=date)
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if isinstance(result, dict) and "error" in result:
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return []
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return result if isinstance(result, list) else []
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# --------------- Analyse-Funktionen ---------------
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def _extract_daily_values(trend_data: dict, key: str = "nb_uniq_visitors") -> list[tuple[str, int]]:
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"""Extrahiert (datum, wert) Paare, sortiert nach Datum."""
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pairs = []
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for date_str, data in sorted(trend_data.items()):
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if isinstance(data, dict):
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pairs.append((date_str, data.get(key, 0)))
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else:
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pairs.append((date_str, 0))
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return pairs
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def _week_values(pairs: list[tuple[str, int]], weeks_ago: int = 0) -> list[int]:
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"""Werte der letzten N-ten Woche (0 = aktuelle, 1 = letzte)."""
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if not pairs:
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return []
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end_idx = len(pairs) - (weeks_ago * 7)
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start_idx = max(0, end_idx - 7)
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if end_idx <= 0:
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return []
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return [v for _, v in pairs[start_idx:end_idx]]
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def _pct_change(old: float, new: float) -> float | None:
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if old == 0:
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return None
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return ((new - old) / old) * 100
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def _classify_bounce(rate_str: str) -> str:
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try:
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rate = int(rate_str.replace("%", ""))
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except (ValueError, AttributeError):
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return "unbekannt"
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if rate <= 40:
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return "sehr gut (unter 40%)"
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elif rate <= 55:
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return "gut (40-55%)"
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elif rate <= 70:
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return "durchschnittlich (55-70%)"
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elif rate <= 85:
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return "schlecht (70-85%) — Besucher springen schnell ab"
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else:
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return "sehr schlecht (ueber 85%) — fast alle Besucher verlassen die Seite sofort"
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def _classify_avg_time(seconds: int) -> str:
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if seconds >= 180:
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return f"sehr gut ({seconds // 60}m {seconds % 60}s) — Besucher lesen ausfuehrlich"
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elif seconds >= 90:
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return f"gut ({seconds // 60}m {seconds % 60}s) — Besucher bleiben eine Weile"
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elif seconds >= 45:
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return f"maessig ({seconds}s) — Besucher ueberfliegen nur"
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else:
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return f"schlecht ({seconds}s) — Besucher verlassen sofort"
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def _find_outliers(pairs: list[tuple[str, int]]) -> dict:
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"""Findet Tage mit ungewoehnlich hohem/niedrigem Traffic."""
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values = [v for _, v in pairs if v > 0]
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if len(values) < 7:
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return {"peaks": [], "dips": []}
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avg = mean(values)
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sd = stdev(values) if len(values) > 1 else 0
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peaks = [(d, v) for d, v in pairs if v > avg + 1.5 * sd and v > 0]
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dips = [(d, v) for d, v in pairs if 0 < v < max(avg - 1.5 * sd, 1)]
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return {"peaks": peaks, "dips": dips, "avg": avg, "sd": sd}
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def _trend_direction(pairs: list[tuple[str, int]], window: int = 7) -> str:
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"""Bestimmt Trend-Richtung anhand der letzten N Tage vs. davor."""
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if len(pairs) < window * 2:
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return "zu wenig Daten"
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recent = [v for _, v in pairs[-window:]]
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previous = [v for _, v in pairs[-window * 2:-window]]
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avg_recent = mean(recent) if recent else 0
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avg_previous = mean(previous) if previous else 0
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pct = _pct_change(avg_previous, avg_recent)
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if pct is None:
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return "vorher keine Besucher"
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if pct > 20:
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return f"stark steigend (+{pct:.0f}%)"
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elif pct > 5:
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return f"leicht steigend (+{pct:.0f}%)"
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elif pct > -5:
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return f"stabil ({pct:+.0f}%)"
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elif pct > -20:
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return f"leicht fallend ({pct:.0f}%)"
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else:
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return f"stark fallend ({pct:.0f}%)"
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def _analyze_referrers(referrers: list) -> list[str]:
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"""Qualifizierte Aussagen ueber Traffic-Quellen."""
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insights = []
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if not referrers:
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return insights
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total = sum(r.get("nb_visits", 0) for r in referrers)
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if total == 0:
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return insights
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for r in referrers:
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label = r.get("label", "?")
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visits = r.get("nb_visits", 0)
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share = (visits / total * 100) if total > 0 else 0
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if share > 70:
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insights.append(f"WARNUNG: {share:.0f}% des Traffics kommt von '{label}' — hohe Abhaengigkeit")
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elif share > 50:
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insights.append(f"'{label}' dominiert mit {share:.0f}% — Diversifikation empfohlen")
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search = next((r for r in referrers if "search" in r.get("label", "").lower()), None)
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direct = next((r for r in referrers if "direct" in r.get("label", "").lower()), None)
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if search and direct and total > 0:
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search_share = search.get("nb_visits", 0) / total * 100
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direct_share = direct.get("nb_visits", 0) / total * 100
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if direct_share > 30:
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insights.append(f"{direct_share:.0f}% Direktzugriffe — gutes Zeichen fuer Stammleser")
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if search_share < 10 and total > 20:
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insights.append(f"Nur {search_share:.0f}% Suchmaschinen-Traffic — SEO verbessern?")
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return insights
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def _analyze_top_pages(pages: list) -> list[str]:
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"""Qualifizierte Aussagen ueber Content-Performance."""
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insights = []
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if not pages or len(pages) < 2:
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return insights
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total = sum(p.get("nb_hits", 0) for p in pages)
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top = pages[0]
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top_hits = top.get("nb_hits", 0)
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top_label = top.get("label", "?")
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if total > 0 and top_hits > 0:
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top_share = top_hits / total * 100
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if top_share > 50:
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insights.append(f"'{top_label}' hat {top_share:.0f}% aller Aufrufe — ein klarer Hit")
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elif top_share > 30:
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insights.append(f"'{top_label}' ist der staerkste Artikel ({top_share:.0f}%)")
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avg_hits = total / len(pages) if pages else 0
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strong = [p for p in pages if p.get("nb_hits", 0) > avg_hits * 2]
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if len(strong) > 1:
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insights.append(f"{len(strong)} Artikel performen ueberdurchschnittlich")
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return insights
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# --------------- Format-Funktionen (Output fuer LLM) ---------------
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def format_analytics(period: str = "day", date: str = "today") -> str:
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"""Kompakter Analytics-Report fuer den Hausmeister-Bot."""
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"""Qualifizierter Analytics-Report mit Bewertungen und Vergleichen."""
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lines = []
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# --- Heute ---
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summary = get_summary(period, date)
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if isinstance(summary, dict) and "error" not in summary:
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visitors = summary.get("nb_uniq_visitors", 0)
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visits = summary.get("nb_visits", 0)
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actions = summary.get("nb_actions", 0)
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bounce = summary.get("bounce_rate", "?")
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avg_time = summary.get("avg_time_on_site", 0)
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avg_min = int(avg_time) // 60
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avg_sec = int(avg_time) % 60
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avg_time = int(summary.get("avg_time_on_site", 0))
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actions_per = summary.get("nb_actions_per_visit", 0)
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lines.append(f"=== HEUTE ({date}) ===")
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lines.append(f"Besucher: {visitors} unique, {visits} visits, {actions} Seitenaufrufe")
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lines.append(f"Bounce Rate: {bounce}, Verweildauer: {avg_min}m {avg_sec}s")
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lines.append(f"Seiten/Besuch: {actions_per}")
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lines.append(f"Bounce Rate: {bounce} — Bewertung: {_classify_bounce(bounce)}")
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lines.append(f"Verweildauer: {_classify_avg_time(avg_time)}")
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else:
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return f"Matomo nicht erreichbar: {summary}"
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trend = get_visitor_trend(14)
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if isinstance(trend, dict) and "error" not in trend:
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trend_lines = []
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for date_str, data in sorted(trend.items()):
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if isinstance(data, dict):
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v = data.get("nb_uniq_visitors", 0)
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trend_lines.append(f" {date_str}: {v} Besucher")
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else:
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trend_lines.append(f" {date_str}: 0")
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if trend_lines:
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lines.append("\nTrend (14 Tage):")
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lines.extend(trend_lines)
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# --- Woche-ueber-Woche ---
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trend_data = get_visitor_trend(21)
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if isinstance(trend_data, dict) and "error" not in trend_data:
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pairs = _extract_daily_values(trend_data)
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pages = get_top_pages(period, "today", 5)
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this_week = _week_values(pairs, 0)
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last_week = _week_values(pairs, 1)
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if this_week and last_week:
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avg_this = mean(this_week)
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avg_last = mean(last_week)
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pct = _pct_change(avg_last, avg_this)
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lines.append(f"\n=== WOCHENVERGLEICH ===")
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lines.append(f"Diese Woche: Ø {avg_this:.0f} Besucher/Tag (Summe: {sum(this_week)})")
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lines.append(f"Letzte Woche: Ø {avg_last:.0f} Besucher/Tag (Summe: {sum(last_week)})")
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if pct is not None:
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direction = "mehr" if pct > 0 else "weniger"
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lines.append(f"Veraenderung: {pct:+.0f}% ({direction} als letzte Woche)")
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# Trend-Richtung
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trend_dir = _trend_direction(pairs)
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lines.append(f"Trend (7 Tage vs. davor): {trend_dir}")
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# Ausreisser
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outliers = _find_outliers(pairs)
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if outliers.get("peaks"):
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for d, v in outliers["peaks"][:2]:
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lines.append(f"Peak: {d} mit {v} Besuchern (Ø {outliers['avg']:.0f})")
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if outliers.get("dips"):
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for d, v in outliers["dips"][:2]:
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lines.append(f"Tief: {d} mit nur {v} Besuchern")
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# --- Top Seiten ---
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pages = get_top_pages(period, "today", 10)
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if pages:
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lines.append("\nTop Seiten (heute):")
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lines.append(f"\n=== TOP SEITEN (heute) ===")
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for p in pages[:5]:
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label = p.get("label", "?")
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hits = p.get("nb_hits", 0)
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lines.append(f" {label}: {hits}x")
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avg_time_page = int(p.get("avg_time_on_page", 0))
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lines.append(f" {label}: {hits}x Aufrufe, {avg_time_page}s Lesezeit")
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referrers = get_referrers(period, "today", 5)
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page_insights = _analyze_top_pages(pages)
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for ins in page_insights:
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lines.append(f" → {ins}")
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# --- Traffic-Quellen ---
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referrers = get_referrers(period, "today", 10)
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if referrers:
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lines.append("\nTraffic-Quellen (heute):")
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lines.append(f"\n=== TRAFFIC-QUELLEN (heute) ===")
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total_ref = sum(r.get("nb_visits", 0) for r in referrers)
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for r in referrers[:5]:
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label = r.get("label", "?")
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visits = r.get("nb_visits", 0)
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lines.append(f" {label}: {visits} visits")
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share = (visits / total_ref * 100) if total_ref > 0 else 0
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lines.append(f" {label}: {visits} visits ({share:.0f}%)")
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ref_insights = _analyze_referrers(referrers)
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for ins in ref_insights:
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lines.append(f" → {ins}")
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# --- Laender ---
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countries = get_countries(period, "today", 5)
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if countries:
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lines.append("\nLaender (heute):")
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lines.append(f"\n=== LAENDER (heute) ===")
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for c in countries[:5]:
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label = c.get("label", "?")
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visits = c.get("nb_visits", 0)
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lines.append(f" {label}: {visits}")
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return "\n".join(lines) if lines else "Keine Daten verfuegbar."
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# --- Geraete ---
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devices = get_devices(period, "today")
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if devices:
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lines.append(f"\n=== GERAETE (heute) ===")
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for d in devices[:4]:
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label = d.get("label", "?")
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visits = d.get("nb_visits", 0)
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lines.append(f" {label}: {visits}")
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mobile = sum(d.get("nb_visits", 0) for d in devices if "mobile" in d.get("label", "").lower() or "smartphone" in d.get("label", "").lower())
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desktop = sum(d.get("nb_visits", 0) for d in devices if "desktop" in d.get("label", "").lower())
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total_dev = sum(d.get("nb_visits", 0) for d in devices)
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if total_dev > 0:
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mob_pct = mobile / total_dev * 100
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lines.append(f" Mobile-Anteil: {mob_pct:.0f}%")
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if mob_pct > 60:
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lines.append(f" → Mehrheit mobil — Mobile-Optimierung wichtig")
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elif mob_pct < 20:
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lines.append(f" → Fast nur Desktop-Nutzer")
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return "\n".join(lines)
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def format_trend(days: int = 30) -> str:
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"""Besucherentwicklung ueber N Tage — fuer Trend-Fragen."""
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"""Besucherentwicklung mit qualifizierter Analyse."""
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trend = get_visitor_trend(days)
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if isinstance(trend, dict) and "error" in trend:
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return f"Matomo-Fehler: {trend['error']}"
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lines = [f"Besucherentwicklung (letzte {days} Tage):"]
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total = 0
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day_count = 0
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for date_str, data in sorted(trend.items()):
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if isinstance(data, dict):
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v = data.get("nb_uniq_visitors", 0)
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actions = data.get("nb_actions", 0)
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lines.append(f" {date_str}: {v} Besucher, {actions} Aufrufe")
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total += v
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day_count += 1
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else:
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lines.append(f" {date_str}: 0")
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day_count += 1
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pairs = _extract_daily_values(trend)
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values_nonzero = [v for _, v in pairs if v > 0]
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if day_count > 0:
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avg = total / day_count
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lines.append(f"\nDurchschnitt: {avg:.0f} Besucher/Tag, Gesamt: {total}")
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lines = [f"=== BESUCHERENTWICKLUNG ({days} Tage) ==="]
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# Rohdaten
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for date_str, v in pairs:
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marker = ""
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if values_nonzero:
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avg = mean(values_nonzero)
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if v > avg * 2:
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marker = " ★ Peak"
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elif v > 0 and v < avg * 0.3:
|
||||
marker = " ↓ Tief"
|
||||
lines.append(f" {date_str}: {v} Besucher{marker}")
|
||||
|
||||
# Zusammenfassung
|
||||
if values_nonzero:
|
||||
total = sum(values_nonzero)
|
||||
avg = mean(values_nonzero)
|
||||
best_day = max(pairs, key=lambda x: x[1])
|
||||
worst_day = min((p for p in pairs if p[1] > 0), key=lambda x: x[1], default=("?", 0))
|
||||
|
||||
lines.append(f"\n=== ZUSAMMENFASSUNG ===")
|
||||
lines.append(f"Gesamt: {sum(v for _, v in pairs)} Besucher in {days} Tagen")
|
||||
lines.append(f"Tage mit Besuchern: {len(values_nonzero)} von {len(pairs)}")
|
||||
lines.append(f"Durchschnitt: {avg:.0f} Besucher/Tag")
|
||||
lines.append(f"Bester Tag: {best_day[0]} ({best_day[1]} Besucher)")
|
||||
lines.append(f"Schwächster Tag: {worst_day[0]} ({worst_day[1]} Besucher)")
|
||||
|
||||
# Trend
|
||||
trend_dir = _trend_direction(pairs)
|
||||
lines.append(f"Trend-Richtung: {trend_dir}")
|
||||
|
||||
# Wochentags-Muster
|
||||
weekday_map = {}
|
||||
for d, v in pairs:
|
||||
try:
|
||||
wd = datetime.strptime(d, "%Y-%m-%d").strftime("%A")
|
||||
weekday_map.setdefault(wd, []).append(v)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
if weekday_map:
|
||||
lines.append(f"\n=== WOCHENTAGS-MUSTER ===")
|
||||
wd_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
|
||||
wd_de = {"Monday": "Mo", "Tuesday": "Di", "Wednesday": "Mi", "Thursday": "Do",
|
||||
"Friday": "Fr", "Saturday": "Sa", "Sunday": "So"}
|
||||
for wd in wd_order:
|
||||
if wd in weekday_map:
|
||||
vals = weekday_map[wd]
|
||||
wd_avg = mean(vals) if vals else 0
|
||||
lines.append(f" {wd_de[wd]}: Ø {wd_avg:.0f} Besucher")
|
||||
best_wd = max(weekday_map.items(), key=lambda x: mean(x[1]) if x[1] else 0)
|
||||
worst_wd = min(weekday_map.items(), key=lambda x: mean(x[1]) if x[1] else float('inf'))
|
||||
lines.append(f" → Bester Wochentag: {wd_de.get(best_wd[0], best_wd[0])} (Ø {mean(best_wd[1]):.0f})")
|
||||
lines.append(f" → Schwaechster: {wd_de.get(worst_wd[0], worst_wd[0])} (Ø {mean(worst_wd[1]):.0f})")
|
||||
|
||||
# Wachstums-Prognose
|
||||
if len(values_nonzero) >= 14:
|
||||
first_half = values_nonzero[:len(values_nonzero) // 2]
|
||||
second_half = values_nonzero[len(values_nonzero) // 2:]
|
||||
growth = _pct_change(mean(first_half), mean(second_half))
|
||||
if growth is not None:
|
||||
lines.append(f"\n=== PROGNOSE ===")
|
||||
if growth > 10:
|
||||
lines.append(f"Wachstum erste→zweite Haelfte: +{growth:.0f}% — positiver Trend")
|
||||
monthly_proj = avg * 30
|
||||
lines.append(f"Hochrechnung naechster Monat: ~{monthly_proj:.0f} Besucher")
|
||||
elif growth > -10:
|
||||
lines.append(f"Stabile Phase ({growth:+.0f}%) — Wachstum stagniert")
|
||||
else:
|
||||
lines.append(f"Ruecklaeufig ({growth:.0f}%) — Gegenmassnahmen pruefen")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
|
|
|||
|
|
@ -146,6 +146,18 @@ Bei Reisen, Geld, Behoerden, Rechnungen, Buchungen:
|
|||
- NIEMALS praezise falsche Angaben machen.
|
||||
- Speichere nur HIGH-CONFIDENCE Daten via memory_suggest (Reiseplaene, Buchungscodes).
|
||||
|
||||
ANALYTICS-INTERPRETATION (Matomo):
|
||||
Wenn du Analytics-Daten bekommst (get_matomo_analytics, get_matomo_trend), interpretiere sie QUALIFIZIERT:
|
||||
- Nenne nicht nur Zahlen, sondern BEWERTE sie ("88% Bounce Rate ist schlecht", "42s Verweildauer ist zu kurz")
|
||||
- Vergleiche IMMER mit der Vorwoche wenn Daten vorhanden ("30% mehr als letzte Woche")
|
||||
- Nenne den TREND klar ("Traffic steigt seit 5 Tagen", "Ruecklaeufig seit Montag")
|
||||
- Bei Peaks: Vermute WARUM ("Am 24.02. 147 Besucher — pruefe welcher Artikel viral ging")
|
||||
- Bei hoher Abhaengigkeit von einer Quelle: WARNE ("80% kommt von Google — riskant")
|
||||
- Gib 1-2 konkrete EMPFEHLUNGEN ("Bounce Rate senken: Ueberschriften verbessern, Ladezeit pruefen")
|
||||
- Wochentags-Muster nutzen: "Dienstag ist dein staerkster Tag — poste neue Artikel dienstags"
|
||||
- NICHT: endlose Zahlentabellen wiedergeben. Fasse zusammen, hebe das Wichtige hervor.
|
||||
- Format: Kurze Absaetze, KEINE langen Listen. Wie ein Analytics-Berater der auf den Punkt kommt.
|
||||
|
||||
TOOLS:
|
||||
Nutze Tools fuer Live-Daten. Wenn alles OK: kurz sagen. Bei Problemen: erklaeren + Loesung."""
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue