Workflow n8n
Automatisation Google Analytics avec n8n : rapport hebdomadaire
Ce workflow n8n a pour objectif de générer un rapport hebdomadaire des performances de votre site web à partir de Google Analytics. Il s'adresse aux équipes marketing et aux responsables de la performance digitale qui souhaitent suivre l'évolution de leur trafic et de leurs conversions. Grâce à cette automatisation n8n, vous pouvez facilement obtenir des données sur les sept derniers jours et les comparer avec la même période de l'année précédente, le tout sans intervention manuelle.
- Étape 1 : le workflow commence par un déclencheur programmé qui active le processus chaque semaine.
- Étape 2 : il utilise le nœud Google Analytics pour récupérer les métriques des sept derniers jours et celles de l'année précédente.
- Étape 3 : les données sont ensuite traitées et résumées à l'aide de nœuds de calcul et de résumé, permettant de générer des insights précieux.
- Étape 4 : les résultats sont envoyés par email et via Telegram, assurant ainsi une diffusion rapide et efficace des informations. Cette automatisation permet non seulement de gagner du temps, mais aussi d'améliorer la prise de décision basée sur des données précises et actualisées.
Tags clés :Google Analyticsautomatisationemail marketingTelegramreporting
Catégorie: Scheduled · Tags: Google Analytics, automatisation, email marketing, Telegram, reporting❤ 0
Workflow n8n Google Analytics, email marketing, Telegram, reporting : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n Google Analytics, email marketing, Telegram, reporting : détail des nœuds
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"id": "AAjX1BuwhyXpo8xP",
"meta": {
"instanceId": "558d88703fb65b2d0e44613bc35916258b0f0bf983c5d4730c00c424b77ca36a"
},
"name": "Google Analytics: Weekly Report",
"tags": [],
"nodes": [
{
"id": "91ba5982-e226-4f0b-af0d-8c9a44b08279",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
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300
],
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
1
],
"triggerAtHour": 7
}
]
}
},
"typeVersion": 1.2
},
{
"id": "62c38eaf-2222-4d22-8589-677f36bce10d",
"name": "Google Analytics Letzte 7 Tage",
"type": "n8n-nodes-base.googleAnalytics",
"position": [
-1540,
300
],
"parameters": {
"metricsGA4": {
"metricValues": [
{
"listName": "screenPageViews"
},
{},
{
"listName": "sessions"
},
{
"listName": "sessionsPerUser"
},
{
"name": "averageSessionDuration",
"listName": "other"
},
{
"name": "ecommercePurchases",
"listName": "other"
},
{
"name": "averagePurchaseRevenue",
"listName": "other"
},
{
"name": "purchaseRevenue",
"listName": "other"
}
]
},
"propertyId": {
"__rl": true,
"mode": "list",
"value": "345060083",
"cachedResultUrl": "https://analytics.google.com/analytics/web/#/p345060083/",
"cachedResultName": "https://www.ep-reisen.de – GA4"
},
"dimensionsGA4": {
"dimensionValues": [
{}
]
},
"additionalFields": {}
},
"credentials": {
"googleAnalyticsOAuth2": {
"id": "onRKXREI8izfGzv0",
"name": "Google Analytics account"
}
},
"typeVersion": 2
},
{
"id": "0a51c2f3-a487-4226-884f-63d4cb2bf4e4",
"name": "Send Email",
"type": "n8n-nodes-base.emailSend",
"position": [
420,
80
],
"parameters": {
"html": "={{ $json.message.content }}",
"options": {},
"subject": "Weekly Report: Google Analytics: Last 7 days",
"toEmail": "friedemann.schuetz@ep-reisen.de",
"fromEmail": "friedemann.schuetz@posteo.de"
},
"credentials": {
"smtp": {
"id": "A71x7hx6lKj7nxp1",
"name": "SMTP account"
}
},
"typeVersion": 2.1
},
{
"id": "04963783-f455-4983-afea-e94b316d8532",
"name": "Telegram",
"type": "n8n-nodes-base.telegram",
"position": [
420,
420
],
"parameters": {
"text": "={{ $json.message.content }}",
"chatId": "1810565648",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"id": "0hnyvxyUMN77sBmU",
"name": "Telegram account"
}
},
"typeVersion": 1.2
},
{
"id": "3b6b4902-15b3-4bbc-8427-c35471a7431b",
"name": "Processing for Telegram",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
60,
420
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "GPT-4O-MINI"
},
"options": {},
"messages": {
"values": [
{
"content": "=Convert the following text from HTML to normal text:\n\n{{ $json.message.content }}\n\nPlease format the table so that each metric is a separate paragraph!\n\nExample:\n\nTotal views: xx.xxx\nTotal views previous year: xx,xxx\nDifference: x.xx %\n\nTotal users: xx,xxx\nTotal users previous year: xx,xxx\nDifference: -x.xx %"
}
]
}
},
"credentials": {
"openAiApi": {
"id": "niikB3HA4fT5WAqt",
"name": "OpenAi account"
}
},
"typeVersion": 1.7
},
{
"id": "d761980c-0327-4d4e-92aa-d0342b2e249e",
"name": "Calculator",
"type": "@n8n/n8n-nodes-langchain.toolCalculator",
"position": [
140,
300
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ce7ba356-80bb-4b17-9445-fb535267cdf0",
"name": "Google Analytics: Past 7 days of the previous year",
"type": "n8n-nodes-base.googleAnalytics",
"position": [
-600,
300
],
"parameters": {
"endDate": "={{ $json.endDate }}",
"dateRange": "custom",
"startDate": "={{ $json.startDate }}",
"metricsGA4": {
"metricValues": [
{
"listName": "screenPageViews"
},
{},
{
"listName": "sessions"
},
{
"listName": "sessionsPerUser"
},
{
"name": "averageSessionDuration",
"listName": "other"
},
{
"name": "ecommercePurchases",
"listName": "other"
},
{
"name": "averagePurchaseRevenue",
"listName": "other"
},
{
"name": "purchaseRevenue",
"listName": "other"
}
]
},
"propertyId": {
"__rl": true,
"mode": "list",
"value": "345060083",
"cachedResultUrl": "https://analytics.google.com/analytics/web/#/p345060083/",
"cachedResultName": "https://www.ep-reisen.de – GA4"
},
"dimensionsGA4": {
"dimensionValues": [
{}
]
},
"additionalFields": {}
},
"credentials": {
"googleAnalyticsOAuth2": {
"id": "onRKXREI8izfGzv0",
"name": "Google Analytics account"
}
},
"typeVersion": 2
},
{
"id": "d2062aaa-e41b-4405-8470-9e7b4cd77245",
"name": "Summarize Data",
"type": "n8n-nodes-base.summarize",
"position": [
-1080,
300
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "Aufrufe",
"aggregation": "sum"
},
{
"field": "Nutzer",
"aggregation": "sum"
},
{
"field": "Sitzungen",
"aggregation": "sum"
},
{
"field": "Sitzungen pro Nutzer",
"aggregation": "average"
},
{
"field": "Sitzungsdauer",
"aggregation": "average"
},
{
"field": "Käufe",
"aggregation": "sum"
},
{
"field": "Revenue pro Kauf",
"aggregation": "average"
},
{
"field": "Revenue",
"aggregation": "sum"
},
{
"field": "date"
}
]
}
},
"typeVersion": 1
},
{
"id": "d1f48d36-9f27-4cda-af53-e6d430d1a8db",
"name": "Summarize Data1",
"type": "n8n-nodes-base.summarize",
"position": [
-220,
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],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "Aufrufe",
"aggregation": "sum"
},
{
"field": "Nutzer",
"aggregation": "sum"
},
{
"field": "Sitzungen",
"aggregation": "sum"
},
{
"field": "Sitzungen pro Nutzer",
"aggregation": "average"
},
{
"field": "Sitzungsdauer",
"aggregation": "average"
},
{
"field": "Käufe",
"aggregation": "sum"
},
{
"field": "Revenue pro Kauf",
"aggregation": "average"
},
{
"field": "Revenue",
"aggregation": "sum"
},
{
"field": "date"
}
]
}
},
"typeVersion": 1
},
{
"id": "5b6a0644-3839-4a62-8ff3-bf866aa4568c",
"name": "Calculation same period previous year",
"type": "n8n-nodes-base.code",
"position": [
-840,
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],
"parameters": {
"jsCode": "return {\n // Berechnung des Startdatums: Vorjahr, gleiche Woche, 7 Tage zurück\n startDate: (() => {\n const date = new Date();\n date.setFullYear(date.getFullYear() - 1); // Zurück ins Vorjahr\n date.setDate(date.getDate() - 7); // 7 Tage zurück\n return date.toISOString().split('T')[0];\n })(),\n \n // Berechnung des Enddatums: Vorjahr, heutiges Datum\n endDate: (() => {\n const date = new Date();\n date.setFullYear(date.getFullYear() - 1); // Zurück ins Vorjahr\n return date.toISOString().split('T')[0];\n })(),\n};\n"
},
"typeVersion": 2
},
{
"id": "ab813532-cbe6-4c41-b20b-7efaa1ae4389",
"name": "Assign data",
"type": "n8n-nodes-base.set",
"position": [
-1300,
300
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9c2f8b9a-e964-49a0-8837-efb0dfd7bcae",
"name": "Aufrufe",
"type": "number",
"value": "={{ $json.screenPageViews }}"
},
{
"id": "8b524518-1268-4971-b5c9-ae7da09d94f9",
"name": "Nutzer",
"type": "number",
"value": "={{ $json.totalUsers }}"
},
{
"id": "ca7279b9-c643-425f-aa99-cb17146e9994",
"name": "Sitzungen",
"type": "number",
"value": "={{ $json.sessions }}"
},
{
"id": "591288f7-e8cf-445e-872a-5b83f997b825",
"name": "Sitzungen pro Nutzer",
"type": "number",
"value": "={{ $json.sessionsPerUser }}"
},
{
"id": "dc1a43da-3f3a-4dca-bbde-904222d7f693",
"name": "Sitzungsdauer",
"type": "number",
"value": "={{ $json.averageSessionDuration }}"
},
{
"id": "eac0b53e-c452-40b8-92bc-8af8ea349984",
"name": "=Käufe",
"type": "number",
"value": "={{ $json.ecommercePurchases }}"
},
{
"id": "b96439be-189d-4ebe-b49e-d5c31fefe9f0",
"name": "Revenue pro Kauf",
"type": "number",
"value": "={{ $json.averagePurchaseRevenue }}"
},
{
"id": "94835d43-2fc8-49c0-97f0-6f0f8699337a",
"name": "Revenue",
"type": "number",
"value": "={{ $json.purchaseRevenue }}"
},
{
"id": "d70f8138-3b84-4b88-a98f-eb929e1cc29a",
"name": "date",
"type": "string",
"value": "={{ $json.date }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2454fe8a-005d-46dc-ae22-1044c1b793b7",
"name": "Assign data1",
"type": "n8n-nodes-base.set",
"position": [
-400,
300
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9c2f8b9a-e964-49a0-8837-efb0dfd7bcae",
"name": "Aufrufe",
"type": "number",
"value": "={{ $json.screenPageViews }}"
},
{
"id": "8b524518-1268-4971-b5c9-ae7da09d94f9",
"name": "Nutzer",
"type": "number",
"value": "={{ $json.totalUsers }}"
},
{
"id": "ca7279b9-c643-425f-aa99-cb17146e9994",
"name": "Sitzungen",
"type": "number",
"value": "={{ $json.sessions }}"
},
{
"id": "591288f7-e8cf-445e-872a-5b83f997b825",
"name": "Sitzungen pro Nutzer",
"type": "number",
"value": "={{ $json.sessionsPerUser }}"
},
{
"id": "dc1a43da-3f3a-4dca-bbde-904222d7f693",
"name": "Sitzungsdauer",
"type": "number",
"value": "={{ $json.averageSessionDuration }}"
},
{
"id": "eac0b53e-c452-40b8-92bc-8af8ea349984",
"name": "=Käufe",
"type": "number",
"value": "={{ $json.ecommercePurchases }}"
},
{
"id": "b96439be-189d-4ebe-b49e-d5c31fefe9f0",
"name": "Revenue pro Kauf",
"type": "number",
"value": "={{ $json.averagePurchaseRevenue }}"
},
{
"id": "94835d43-2fc8-49c0-97f0-6f0f8699337a",
"name": "Revenue",
"type": "number",
"value": "={{ $json.purchaseRevenue }}"
},
{
"id": "dd8255c6-65b1-41ce-b596-70c09108d6e2",
"name": "=date",
"type": "string",
"value": "={{ $json.date }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0a48cbb0-3d4c-4ac8-8dba-08213f7fc430",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2220,
80
],
"parameters": {
"width": 440,
"height": 560,
"content": "Welcome to my Google Analytics Weekly Report Workflow!\n\nThis workflow has the following sequence:\n\n1. time trigger (e.g. every Monday at 7 a.m.)\n2. retrieval of Google Analytics data from the last 7 days\n3. assignment and summary of the data\n4. retrieval of Google Analytics data from the last 7 days of the previous year\n5. allocation and summary of the data\n6. preparation in tabular form and brief analysis by AI.\n7. sending the report as an email\n8. preparation in short form by AI for Telegram (optional)\n9. sending as Telegram message.\n\nThe following accesses are required for the workflow:\n- Google Analytics (via Google Analytics API): https://docs.n8n.io/integrations/builtin/credentials/google/\n- AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)\n- SMTP access data (for sending the mail)\n- Telegram access data (optional for sending as Telegram message): https://docs.n8n.io/integrations/builtin/credentials/telegram/\n\nYou can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz"
},
"typeVersion": 1
},
{
"id": "c87bc648-8fe8-4cec-84d4-2742060f9c53",
"name": "Processing for email",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
60,
80
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "GPT-4O"
},
"options": {},
"messages": {
"values": [
{
"content": "=Please analyze the following data and output the results in tabular form:\n\n| Metrics | Last 7 days | Previous year | Percentage change |\n|-------------------------------|---------------|---------|\n| Total page views | {{ $('Summarize Data').item.json.sum_Aufrufe }} | {{ $('Summarize Data1').item.json.sum_Aufrufe }} | Percentage change |\n| total users | {{ $('Summarize Data').item.json.sum_Nutzer }} | {{ $('Summarize Data1').item.json.sum_Nutzer }} | Percentage change |\n| Total sessions | {{ $('Summarize Data').item.json.sum_Sitzungen }} | {{ $('Summarize Data1').item.json.sum_Sitzungen }} | Percentage change |\n| Average sessions/user | {{ $('Summarize Data').item.json.average_Sitzungen_pro_Nutzer }} | {{ $('Summarize Data1').item.json.average_Sitzungen_pro_Nutzer }} | Percentage change |\n| Average session duration | {{ $('Summarize Data').item.json.average_Sitzungsdauer }} | {{ $('Summarize Data1').item.json.average_Sitzungsdauer }} | Percentage change |\n| Total purchases | {{ $('Summarize Data').item.json['sum_Käufe'] }} | {{ $('Summarize Data1').item.json['sum_Käufe'] }} | Percentage change |\n| Average revenue/purchase | {{ $('Summarize Data').item.json.average_Revenue_pro_Kauf }} | {{ $('Summarize Data1').item.json.average_Revenue_pro_Kauf }} | Percentage change |\n| Total revenue | {{ $('Summarize Data').item.json.sum_Revenue }} | {{ $('Summarize Data1').item.json.sum_Revenue }} | Percentage change |\n\nFormat for numbers:\n- Dot (.) for numbers in thousands (e.g. 4,000)\n- Comma (,) for decimal numbers (e.g. 3.4)\n- Conversion of average session duration in minutes instead of seconds\n- Average turnover/purchase and total turnover in €\n\nPlease write a short summary of the analyzed data above the table (in a maximum of 3 sentences!)\n\nPlease format to a sleek and modern HTML format so that the result can be sent as HTML mail!\n\nStructure of the e-mail:\n\n“Hello! Here is the Weekly Report: Google Analytics of the last 7 days!\n[Summary]\n[Table]”"
}
]
}
},
"credentials": {
"openAiApi": {
"id": "niikB3HA4fT5WAqt",
"name": "OpenAi account"
}
},
"typeVersion": 1.7
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "556c3292-0d40-4c75-8037-90bacf1b2ccb",
"connections": {
"Telegram": {
"main": [
[]
]
},
"Calculator": {
"ai_tool": [
[
{
"node": "Processing for email",
"type": "ai_tool",
"index": 0
}
]
]
},
"Assign data": {
"main": [
[
{
"node": "Summarize Data",
"type": "main",
"index": 0
}
]
]
},
"Assign data1": {
"main": [
[
{
"node": "Summarize Data1",
"type": "main",
"index": 0
}
]
]
},
"Summarize Data": {
"main": [
[
{
"node": "Calculation same period previous year",
"type": "main",
"index": 0
}
]
]
},
"Summarize Data1": {
"main": [
[
{
"node": "Processing for email",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Google Analytics Letzte 7 Tage",
"type": "main",
"index": 0
}
]
]
},
"Processing for email": {
"main": [
[
{
"node": "Send Email",
"type": "main",
"index": 0
},
{
"node": "Processing for Telegram",
"type": "main",
"index": 0
}
]
]
},
"Processing for Telegram": {
"main": [
[
{
"node": "Telegram",
"type": "main",
"index": 0
}
]
]
},
"Google Analytics Letzte 7 Tage": {
"main": [
[
{
"node": "Assign data",
"type": "main",
"index": 0
}
]
]
},
"Calculation same period previous year": {
"main": [
[
{
"node": "Google Analytics: Past 7 days of the previous year",
"type": "main",
"index": 0
}
]
]
},
"Google Analytics: Past 7 days of the previous year": {
"main": [
[
{
"node": "Assign data1",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n Google Analytics, email marketing, Telegram, reporting : pour qui est ce workflow ?
Ce workflow s'adresse principalement aux équipes marketing, aux analystes de données et aux responsables de la performance digitale dans les entreprises de taille moyenne à grande. Un niveau technique intermédiaire est recommandé pour la personnalisation de ce workflow.
Workflow n8n Google Analytics, email marketing, Telegram, reporting : problème résolu
Ce workflow résout le problème de la collecte manuelle des données de performance hebdomadaire, qui peut être chronophage et sujette à des erreurs. En automatisant ce processus, les utilisateurs éliminent les frustrations liées à la recherche et à la compilation des données, tout en réduisant le risque d'erreurs humaines. À la suite de la mise en place de ce workflow, les utilisateurs obtiennent des rapports précis et réguliers, leur permettant de prendre des décisions éclairées et rapides.
Workflow n8n Google Analytics, email marketing, Telegram, reporting : étapes du workflow
Étape 1 : Le workflow est déclenché par un nœud de planification qui s'active chaque semaine.
- Étape 1 : Il interroge Google Analytics pour obtenir les métriques des sept derniers jours.
- Étape 2 : Les données de l'année précédente sont également récupérées pour comparaison.
- Étape 3 : Les résultats sont traités par des nœuds de calcul et de résumé pour générer des insights.
- Étape 4 : Les données sont ensuite envoyées par email et via Telegram pour une diffusion rapide.
Workflow n8n Google Analytics, email marketing, Telegram, reporting : guide de personnalisation
Pour personnaliser ce workflow, commencez par ajuster le déclencheur de planification selon vos besoins (par exemple, changer la fréquence d'envoi). Modifiez les paramètres du nœud Google Analytics pour inclure les métriques spécifiques que vous souhaitez suivre. Vous pouvez également personnaliser le contenu des emails et des messages Telegram en fonction de votre audience. Assurez-vous que les identifiants de propriété Google Analytics et les adresses email sont correctement configurés. Enfin, vous pouvez ajouter d'autres nœuds pour intégrer des outils supplémentaires selon vos besoins.