Werbeanalyse
Experte
Dies ist ein Market Research, AI Summarization-Bereich Automatisierungsworkflow mit 25 Nodes. Hauptsächlich werden If, Set, Code, Merge, SplitOut und andere Nodes verwendet. Analyse der Meta-Werbewirkung basierend auf dem Vergleich zwischen GPT-4 und Gemini AI
Voraussetzungen
- •Google Sheets API-Anmeldedaten
- •OpenAI API Key
- •Facebook Graph API-Anmeldedaten
- •Google Gemini API Key
Verwendete Nodes (25)
Kategorie
Workflow-Vorschau
Visualisierung der Node-Verbindungen, mit Zoom und Pan
Workflow exportieren
Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n
{
"id": "YrhmqkmhYl2ghAKN",
"meta": {
"instanceId": "d5e1aee00419f7d76c99b6815681c983a5e0b4d87bfcf461d2e125e2786ef532",
"templateCredsSetupCompleted": true
},
"name": "Ads Analysis",
"tags": [
{
"id": "tITAFvUSOpVVIBvI",
"name": "MetaAds",
"createdAt": "2025-07-05T02:24:26.705Z",
"updatedAt": "2025-07-05T02:24:26.705Z"
},
{
"id": "8ZOxg8cY0fPL8g0s",
"name": "ReadyToUse",
"createdAt": "2025-07-10T04:08:19.089Z",
"updatedAt": "2025-07-10T04:08:19.089Z"
}
],
"nodes": [
{
"id": "02512611-49a2-4319-9423-2a8c84cda262",
"name": "Zeitplan-Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
0,
-432
],
"parameters": {
"rule": {
"interval": [
{}
]
}
},
"typeVersion": 1.2
},
{
"id": "46d2cd63-dd9b-41de-a050-a7b0106ec6c7",
"name": "Anzeigen abrufen",
"type": "n8n-nodes-base.facebookGraphApi",
"position": [
672,
-544
],
"parameters": {
"edge": "ads",
"node": "={{ $json.campaign_id ?? $('Set parameters').item.json.campaign_id }}",
"options": {
"fields": {
"field": [
{
"name": "=id,name,adset_id,creative{id,object_story_id,object_type,image_url,video_id,object_story_spec{video_data{video_id,image_url}}}"
}
]
}
},
"graphApiVersion": "v22.0"
},
"credentials": {
"facebookGraphApi": {
"id": "jF7Lc9HyK76Jr9Xm",
"name": "KH | Facebook Graph"
}
},
"typeVersion": 1
},
{
"id": "90adc41b-f6bf-4a03-894d-3ae50067f64d",
"name": "Kennzahlen abrufen",
"type": "n8n-nodes-base.facebookGraphApi",
"position": [
1120,
-480
],
"parameters": {
"edge": "insights",
"node": "={{ $json.id }}",
"options": {
"fields": {
"field": [
{
"name": "ad_id,ad_name,adset_id,campaign_id,spend,impressions,clicks,reach,frequency,actions,action_values,video_p25_watched_actions,video_p50_watched_actions,video_p75_watched_actions,video_p100_watched_actions"
}
]
}
},
"graphApiVersion": "v22.0"
},
"credentials": {
"facebookGraphApi": {
"id": "jF7Lc9HyK76Jr9Xm",
"name": "KH | Facebook Graph"
}
},
"typeVersion": 1
},
{
"id": "e9ef5bb7-b017-4068-abe2-937c077d9630",
"name": "Anzeigen aufteilen",
"type": "n8n-nodes-base.splitOut",
"position": [
896,
-544
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"executeOnce": false,
"typeVersion": 1
},
{
"id": "bdff8dcb-4319-484a-b7b9-6a25c3a957df",
"name": "Kennzahlen aufteilen",
"type": "n8n-nodes-base.splitOut",
"position": [
1344,
-480
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1
},
{
"id": "a4b5eb06-0a3b-4092-b682-dbb77ef9f7a6",
"name": "Metriken festlegen",
"type": "n8n-nodes-base.set",
"position": [
1792,
-672
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "8bb35599-d421-4f61-a4c7-f77c60233ba9",
"name": "spend",
"type": "string",
"value": "={{ $json.spend }}"
},
{
"id": "28321e91-fd2f-49e7-92e1-c45a89f67702",
"name": "impressions",
"type": "string",
"value": "={{ $json.impressions }}"
},
{
"id": "0c9319e6-b233-456b-878b-d30515473326",
"name": "link_click",
"type": "string",
"value": "={{ $json.actions.find(action => action.action_type.includes('link_click'))?.value || 0 }}"
},
{
"id": "52248e0e-c428-4d5d-a593-5c43d84398ee",
"name": "app_installs",
"type": "string",
"value": "={{ $json.actions.find(action => action.action_type.includes('app_install'))?.value || 0 }}"
},
{
"id": "0e8282f2-a9cd-4209-87f7-1f10d794b65b",
"name": "complete_registration",
"type": "string",
"value": "={{ $json.actions.find(action => action.action_type.includes('complete_registration'))?.value || 0 }}"
},
{
"id": "709915f4-b44b-434e-9193-39ea390ccbb0",
"name": "add_to_cart",
"type": "string",
"value": "={{ $json.actions.find(action => action.action_type.includes('add_to_cart'))?.value || 0 }}"
},
{
"id": "62420b81-8f39-4c88-ae1e-8a20c4d1a13f",
"name": "initiate_checkout",
"type": "string",
"value": "={{ $json.actions.find(action => action.action_type.includes('initiate_checkout'))?.value || 0 }}"
},
{
"id": "9bc0b5fe-6639-4b6d-b31d-924b25b6818d",
"name": "purchase",
"type": "string",
"value": "={{ $json.actions.find(action => action.action_type.includes('purchase'))?.value || 0 }}"
},
{
"id": "96859718-a1f3-4678-8b40-110e0e38964f",
"name": "value",
"type": "string",
"value": "={{ $json.action_values ? $json.action_values.find(action => action.action_type.includes('purchase'))?.value || 0 : 0 }}"
},
{
"id": "82193e6a-e80c-429c-9066-56ac3985647f",
"name": "ad_id",
"type": "string",
"value": "={{ $json.ad_id }}"
},
{
"id": "04316c4e-1417-46d5-9368-5335f9899dd6",
"name": "ad_name",
"type": "string",
"value": "={{ $json.ad_name }}"
},
{
"id": "114d6153-66ff-4087-b82e-df19abbd52dc",
"name": "campaign_id",
"type": "string",
"value": "={{ $json.campaign_id }}"
},
{
"id": "58d3f46a-ecd5-4590-9194-76cfe4541dc3",
"name": "adset_id",
"type": "string",
"value": "={{ $json.adset_id }}"
},
{
"id": "fa3c43ea-17f4-41ab-94e5-d75d9b386c23",
"name": "video_image_url",
"type": "string",
"value": "={{ $json.creative.object_story_spec.video_data.image_url }}"
},
{
"id": "000a2f08-6fca-4790-bcaf-d0937c2b70f0",
"name": "image_url",
"type": "string",
"value": "={{ $json.creative.image_url }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f891f10b-02c3-4eab-ad22-e01f8c6d3e20",
"name": "Parameter festlegen",
"type": "n8n-nodes-base.set",
"position": [
224,
-432
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a2f4de51-3c9b-41dd-94bd-cdab162261ba",
"name": "source",
"type": "string",
"value": "Sheets"
},
{
"id": "3c3bb838-3411-4347-b98c-f05d1ff60a8d",
"name": "campaign_id",
"type": "string",
"value": ""
},
{
"id": "418cf02d-3573-4f78-8495-553b5a0f47e3",
"name": "benchmarks_data",
"type": "string",
"value": "=Ad ID,Ad name,Ad delivery,Frequency,Reach,Impressions,CTR (link click-through rate),\"CPM (cost per 1,000 impressions) (USD)\",Link clicks,CPC (cost per link click) (USD),IPM,App Installs,Cost per App Install (USD),CR install to reg,Registrations completed,Cost per registration completed (USD),Results,Result indicator,Cost per results,Amount spent (USD),Adds to cart,Cost per add to cart (USD),Checkouts initiated,Cost per checkout initiated (USD),Purchases,Searches,Checkouts initiated conversion value\n120222559945080510,How-to-fly-earn.mp4,not_delivering,2.253171,1312196,2956602,0.787593,2.876305,23286,0.365202,5.80598944,17166,0.495403,0.25381568,4357,1.951822,4357,actions:app_custom_event.fb_mobile_complete_registration,1.95182235,8504.09,515,16.512796,136,62.530074,,19042,7666249.29\n120227998563210510,EASY JUNE 13.jpg,active,2.044556,892309,1824376,1.141212,2.786471,20820,0.244168,3.96354699,7231,0.703024,0.19941917,1442,3.525361,82,actions:app_custom_event.fb_mobile_initiated_checkout,61.9947561,5083.57,360,14.121028,83,61.247831,,9336,4367509.01\n120218673707160510,EASY MARCH ITALY - 1.jpg,not_delivering,1.800396,818312,1473286,0.84505,1.963522,12450,0.232356,2.63356877,3880,0.745575,0.23840206,925,3.127384,925,actions:app_custom_event.fb_mobile_complete_registration,3.12738378,2892.83,194,14.911495,61,47.423443,,5721,2333824.7\n120216673487350510,Flying20Private.mp4,not_delivering,1.915076,301010,576457,0.468899,2.564927,2703,0.547011,2.84496502,1640,0.901567,0.28414634,466,3.172897,459,actions:app_custom_event.fb_mobile_complete_registration,3.2212854,1478.57,121,12.219587,35,42.244857,,2904,1238511.1\n120226828283920510,DJoPkbKzfcU.mp4,not_delivering,1.713265,517876,887259,0.837636,2.505728,7432,0.299143,4.41133874,3914,0.56802,0.25038324,980,2.268602,980,actions:app_custom_event.fb_mobile_complete_registration,2.26860204,2223.23,110,20.211182,29,76.663103,,4559,1405339.92\n120215360661600510,EASY-1 New.jpg,inactive,1.796525,382927,687938,0.715035,2.079548,4919,0.290831,1.89697327,1305,1.096245,0.27126437,354,4.041243,354,actions:app_custom_event.fb_mobile_complete_registration,4.04124294,1430.6,75,19.074667,29,49.331034,,2295,688904.04"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "423b6343-a63c-42b3-9804-4088e8e1694a",
"name": "Wenn",
"type": "n8n-nodes-base.if",
"position": [
448,
-432
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "e682854e-6f62-4016-8406-897dc46e2837",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.source }}",
"rightValue": "Meta"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "104f7e12-094c-4f7f-8ec4-420866771334",
"name": "Anzeigendetails abrufen",
"type": "n8n-nodes-base.facebookGraphApi",
"position": [
896,
-288
],
"parameters": {
"node": "={{ $json.AdID }}",
"options": {
"fields": {
"field": [
{
"name": "id,name,adset_id,creative{id,object_story_id,object_type,image_url,video_id,object_story_spec{video_data{video_id,image_url}}}"
}
]
}
},
"graphApiVersion": "v22.0"
},
"credentials": {
"facebookGraphApi": {
"id": "jF7Lc9HyK76Jr9Xm",
"name": "KH | Facebook Graph"
}
},
"typeVersion": 1
},
{
"id": "b1111c61-bb8a-4f7d-958f-8a09dc75c910",
"name": "Zusammenführen",
"type": "n8n-nodes-base.merge",
"position": [
1568,
-480
],
"parameters": {
"mode": "combine",
"options": {},
"advanced": true,
"mergeByFields": {
"values": [
{
"field1": "id",
"field2": "ad_id"
}
]
}
},
"typeVersion": 3.2
},
{
"id": "b5b5eb35-4402-471e-a15c-7b6f38a46c18",
"name": "Aufteilen",
"type": "n8n-nodes-base.splitOut",
"position": [
2416,
-480
],
"parameters": {
"options": {},
"fieldToSplitOut": "message.content.creatives"
},
"typeVersion": 1
},
{
"id": "2a067c01-1113-41f1-8165-484f83e2c86c",
"name": "Strukturierter Ausgabe-Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
2224,
64
],
"parameters": {
"jsonSchemaExample": "{\n \"ad_id\": \"9847213\",\n \"evaluation\": \"high\",\n \"significance\": \"yes\",\n \"summary\": \"CTR and CPC are significantly better than benchmarks, with a strong funnel down to Checkouts Initiated.\",\n \"recommendation\": \"scale\"\n}"
},
"typeVersion": 1.3
},
{
"id": "62321b02-c786-4397-96dd-c86532968664",
"name": "Google Gemini Chat-Modell",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
2032,
48
],
"parameters": {
"options": {
"temperature": 0.4
}
},
"credentials": {
"googlePalmApi": {
"id": "ZIfu0CPV4qEzGAfw",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "b599b087-9f76-4183-8b39-c15cba123e0c",
"name": "CSV für OpenAI vorbereiten",
"type": "n8n-nodes-base.code",
"position": [
1792,
-480
],
"parameters": {
"jsCode": "// Определяем заголовки CSV один раз, они будут одинаковыми для всех строк.\nconst header = \"ad_name,ad_id,spend,impressions,clicks,app_installs,registrations,purchases,purchase_value\";\n\n// Используем .map() для преобразования КАЖДОГО входящего элемента\n// Метод .map() вернет новый массив той же длины, что и items\nreturn items.map(item => {\n // Внутри .map() мы работаем с одним 'item' за раз.\n // Получаем его json-содержимое\n const currentJson = item.json;\n\n // Извлекаем данные для ТЕКУЩЕГО элемента, создавая строку со значениями\n const row = [\n `\"${currentJson.name.replace(/\"/g, '\"\"')}\"`, // Экранируем кавычки\n currentJson.ad_id || 0,\n currentJson.spend || 0,\n currentJson.impressions || 0,\n currentJson.clicks || 0,\n currentJson.actions?.find(a => a.action_type.includes('app_install'))?.value || 0,\n currentJson.actions?.find(a => a.action_type.includes('complete_registration'))?.value || 0,\n currentJson.actions?.find(a => a.action_type.includes('purchase'))?.value || 0,\n currentJson.action_values?.find(a => a.action_type.includes('purchase'))?.value || 0\n ].join(',');\n\n // Возвращаем новую структуру для ТЕКУЩЕГО элемента.\n // Она будет содержать все старые данные + новое поле с CSV.\n return {\n json: {\n ...currentJson, // Сохраняем все исходные данные\n creative_csv_data: `${header}\\n${row}` // Добавляем новое поле с CSV\n }\n };\n});"
},
"typeVersion": 2
},
{
"id": "5dac1e5f-a814-48a7-beaf-b0657d1ff926",
"name": "CSV für Gemini vorbereiten",
"type": "n8n-nodes-base.code",
"position": [
1808,
-160
],
"parameters": {
"jsCode": "// Определяем заголовки CSV один раз, они будут одинаковыми для всех строк.\nconst header = \"ad_name,ad_id,spend,impressions,clicks,app_installs,registrations,purchases,purchase_value\";\n\n// Используем .map() для преобразования КАЖДОГО входящего элемента\n// Метод .map() вернет новый массив той же длины, что и items\nreturn items.map(item => {\n // Внутри .map() мы работаем с одним 'item' за раз.\n // Получаем его json-содержимое\n const currentJson = item.json;\n\n // Извлекаем данные для ТЕКУЩЕГО элемента, создавая строку со значениями\n const row = [\n `\"${currentJson.name.replace(/\"/g, '\"\"')}\"`, // Экранируем кавычки\n currentJson.ad_id || 0,\n currentJson.spend || 0,\n currentJson.impressions || 0,\n currentJson.clicks || 0,\n currentJson.actions?.find(a => a.action_type.includes('app_install'))?.value || 0,\n currentJson.actions?.find(a => a.action_type.includes('complete_registration'))?.value || 0,\n currentJson.actions?.find(a => a.action_type.includes('purchase'))?.value || 0,\n currentJson.action_values?.find(a => a.action_type.includes('purchase'))?.value || 0\n ].join(',');\n\n // Возвращаем новую структуру для ТЕКУЩЕГО элемента.\n // Она будет содержать все старые данные + новое поле с CSV.\n return {\n json: {\n ...currentJson, // Сохраняем все исходные данные\n creative_csv_data: `${header}\\n${row}` // Добавляем новое поле с CSV\n }\n };\n});"
},
"typeVersion": 2
},
{
"id": "9d019e72-f72f-4e52-8ec2-cdf83fb57700",
"name": "Anzeigendaten von Gemini",
"type": "n8n-nodes-base.googleSheets",
"position": [
2432,
-160
],
"parameters": {
"columns": {
"value": {
"AdID": "={{ $json.output.ad_id }}",
"summary G": "={{ $json.output.summary }}",
"evaluation G": "={{ $json.output.evaluation }}",
"significance G": "={{ $json.output.significance }}",
"recommendation G": "={{ $json.output.recommendation }}"
},
"schema": [
{
"id": "FileName",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "FileName",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "CreativeID",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "CreativeID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Type",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "Type",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Timestamp",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "Timestamp",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "campaign_id",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "campaign_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "AdsetID",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "AdsetID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "AdID",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "AdID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Image",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "Image",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Impressions",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "Impressions",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "spend",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "spend",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "link_click",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "link_click",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "app_install",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "app_install",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "complete_registration",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "complete_registration",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "add_to_cart",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "add_to_cart",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "initiate_checkout",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "initiate_checkout",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "purchase",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "purchase",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "value",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "value",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "evaluation",
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"content": "=**BENCHMARKS_DATA (top-performing creatives with calculated metrics like CTR, CPC, CPA, ROAS, etc.)**\n*This is a CSV string with data from your top-performing creatives. All key metrics are pre-calculated here.*\n{{ $('Set parameters').item.json.benchmarks_data }}\n\n**ANALYSIS_DATA (raw test data without calculated metrics)**:\n\n{{ $json.creative_csv_data }}\n\n*Context*:\n\t•\tEasyBusy is a business aggregator.\n\t•\tDue to a long sales cycle (~30 days) and high average order value, there are no Purchases in the data.\n\t•\tWe use Checkouts Initiated as a proxy conversion metric and also track the full funnel.\n\n**Your task**:\n\t1.\tCalculate the same metrics for the test creatives as in the benchmark data (CTR, CPC, CPA, ROAS, etc.).\n\t2.\tCompare each test creative against the benchmarks:\n\t•\tMetric-by-metric (e.g. higher CTR but worse CPC)\n\t•\tFunnel quality (is there drop-off or strength at any step?)\n\t3.\tAnswer the following:\n\t•\tDo we have enough data to draw conclusions for each creative?\n\t•\tWhich creatives are ready to scale now? Why?\n\t•\tWhich creatives should continue testing?\n\t•\tWhich creatives should be stopped?\n\nOUTPUT FORMAT — ONLY JSON ARRAY OF CREATIVES:\n\nDo not write any explanation, markdown, or formatting outside of the array. Just return a single JSON array like this:\nYou must include ALL creatives in a SINGLE JSON array — not split across multiple responses. \nRespond in ONE single message. Do NOT return one object per message.\n\n**Each object must follow this structure**:\n{\n \"ad_id\": \"string (ad_id from ANALYSIS_DATA dataset)\",\n \"evaluation\": \"high | medium | low\",\n \"significance\": \"yes | no\",\n \"summary\": \"2–3 sentence explanation of why this creative got this score\",\n \"recommendation\": \"scale | optimize | stop\"\n}\n\n*Example*:\n[\n {\n \"ad_id\": \"1202153606616005\",\n \"evaluation\": \"high\",\n \"significance\": \"yes\",\n \"summary\": \"CTR and CPC are significantly better than benchmarks, with a strong funnel down to Checkouts Initiated.\",\n \"recommendation\": \"scale\"\n },\n {\n \"ad_id\": \"92021533361600510\",\n \"evaluation\": \"low\",\n \"significance\": \"yes\",\n \"summary\": \"Low CTR and high CPC. Almost no meaningful downstream activity.\",\n \"recommendation\": \"stop\"\n }\n]\n\nIf there isn’t enough data, set \"significance\": \"no\" and clearly explain the reason in summary. Still provide the other fields.\n\n**Important**:\n\t•\tNo markdown\n\t•\tNo extra text\n\t•\tOutput = only one clean JSON array"
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"content": "### Ads Performance Analysis with AI\n\nThis workflow automates the in-depth analysis of Meta Ads creatives by leveraging the analytical power of AI models (OpenAI and Google Gemini). It fetches performance data, compares it against historical benchmarks, and provides actionable recommendations (\"scale\", \"optimize\", \"stop\") for each creative.\n\nThe system is designed for high flexibility and can be used in two primary ways:\n1. **As a standalone tool:** By setting the `source` to \"Meta\" and providing a `campaign_id` in the configuration node, it will automatically analyze all active ads in a campaign.\n2. **As a continuation of the \"Creative Testing Automation\" workflow:** By setting the `source` to \"Sheets\", it can read a list of newly created `AdID`s from a Google Sheet and analyze them.\n\nThe core of this workflow is its AI-driven analysis. It feeds raw performance metrics and pre-defined benchmarks (stored in the configuration node) to AI models, which then act as expert performance marketers to evaluate each creative's potential.\n\n**Key Features:**\n- **Dual AI Analysis:** Runs parallel analyses using both OpenAI and Google Gemini for comparative insights.\n- **Dynamic Benchmarking:** Easily update performance benchmarks in a central configuration node to keep the AI's analysis relevant.\n- **Resilient & Fault-Tolerant:** The workflow is architected to be robust. It first logs raw metrics to a Google Sheet and then updates the same row with AI analysis. If an AI model fails, the core data is still saved.\n- **Flexible Data Sourcing:** Pulls data directly from Meta Ads or from a preparatory Google Sheet."
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"content": "**⚙️ 1. Main Configuration**\n\nThis is the control center for the entire workflow.\n\n**1. Choose a Source (source):**\n- **`Meta`** - to analyze an entire ad campaign (you'll need to set the `campaign_id` below).\n- **`sheets`** - to analyze creatives from a Google Sheet (you'll need to configure the \"Get Ads from Sheet\" node).\n\n**2. Add Your Benchmarks (benchmarks_data):**\n- Paste your top-performing creative data here in CSV format(a standard export from Ads Manager is perfect for this). The AI will use this for comparison and analysis.\n- **The higher the quality of your benchmarks, the more accurate the results.**"
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"content": "**📈 2. Metrics Preparation**\n\nThis node \"unpacks\" the data from the Meta API response, preparing it for the Google Sheet and for the AI.\n\n- The main metrics (spend, impressions, clicks, actions) are already set up.\n- You can add extraction for **custom conversions** or any other fields you need from the Facebook response here.\n\nAll fields added here will be written to the Google Sheet."
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"content": "**🤖 3. AI Scoring **\n\nDon't be afraid to **experiment with the prompts!**\n\n- The template includes a quality base prompt, but it can and should be adapted to your specific tasks, KPIs, and business context.\n- Try different phrasing to find the most effective combination for your goals. **The quality of the analysis directly depends on the quality of the prompt!**"
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}Häufig gestellte Fragen
Wie verwende ich diesen Workflow?
Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.
Für welche Szenarien ist dieser Workflow geeignet?
Experte - Marktforschung, KI-Zusammenfassung
Ist es kostenpflichtig?
Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.
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