01 使用AI媒体买家分析Facebook广告表现并将洞察发送到Google Sheets
高级
这是一个Market Research, AI Summarization领域的自动化工作流，包含 34 个节点。主要使用 If, Set, Code, Sort, Merge 等节点。 使用Gemini AI分析Facebook广告并将洞察发送到Google Sheets
前置要求
- •可能需要目标 API 的认证凭证
- •Google Sheets API 凭证
- •Google Gemini API Key
使用的节点 (34)
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
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"name": "01 使用AI媒体买家分析Facebook广告表现并将洞察发送到Google Sheets",
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"parameters": {
"jsCode": "// --- START OF CODE NODE 4 SCRIPT (Stringify Output) ---\n// Mode: Run Once for All Items\n// Input: The array of aggregated ad creative performance objects\n\nconst allAggregatedItems = $input.all(); // This is an array of n8n items\n\n// Extract the .json part from each n8n item to get the actual data objects\nconst dataToConvert = allAggregatedItems.map(item => item.json);\n\n// Stringify the array of data objects\n// The 'null, 2' arguments pretty-print the JSON string with an indent of 2 spaces,\n// which can be helpful for readability if you're inspecting it, but not strictly necessary for an LLM.\n// If the LLM prefers a more compact JSON, you can omit 'null, 2'.\nconst jsonString = JSON.stringify(dataToConvert, null, 2);\n\n// Output a single item containing this string\nreturn [{ json: { all_ads_data_string: jsonString } }];\n// --- END OF CODE NODE 4 SCRIPT ---"
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"jsonSchemaExample": "[\n {\n \"ad_id\": \"XXX\",\n \"ad_name\": \"Insert Ad Name\",\n \"total_spend_creative\": 490.01,\n \"performance_category\": \"HELL YES\",\n \"justification\": \"This ad is a 'HELL YES' performer. With a significant spend of $490.01 driving 93 purchases, its ROAS of 19.82 massively outperforms the benchmark ROAS of 8.5. Furthermore, its Cost Per Purchase of $5.27 is well below the benchmark $7.50, and its 5.26% conversion rate is substantially higher than the benchmark 3.1%. The combination of high volume, exceptional ROAS, and efficiency makes it a clear top driver.\",\n \"key_performance_indicators\": {\n \"ad_spend\": 490.01,\n \"ad_total_purchases\": 93,\n \"ad_total_purchase_value\": 9709.6,\n \"ad_roas\": 19.82,\n \"benchmark_roas\": 8.5,\n \"ad_conversion_rate\": \"5.26%\",\n \"benchmark_conversion_rate\": \"3.1%\",\n \"ad_cost_per_purchase\": 5.27,\n \"benchmark_cost_per_purchase\": 7.50\n },\n \"recommendation\": \"Aggressively Scale Budget & Explore New Audiences\"\n }\n]"
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{
"id": "aa9a05cc-532f-4f6f-844b-5511178b1722",
"name": "将原始数据发送到Google Sheet",
"type": "n8n-nodes-base.googleSheets",
"position": [
272,
-16
],
"parameters": {
"operation": "appendOrUpdate",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "",
"cachedResultUrl": "",
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"documentId": {
"__rl": true,
"mode": "url",
"value": "XXXX"
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"credentials": {
"googleSheetsOAuth2Api": {
"id": "8dzgEvzMeMhDaVGY",
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"parameters": {
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"content": "### 步骤1：安全管理您的Facebook API令牌"
},
"typeVersion": 1
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{
"id": "7b2463ab-0eac-4bd5-8185-744ea231ef1d",
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"table": "mlkymmbtoa2iz72",
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"jsCode": "// Get the first input item (assumes NocoDB returns an array)\nconst tokenData = $input.first()?.json || {};\n\n// Get current time in milliseconds\nconst now = new Date().getTime();\n\n// Parse the token's end date from NocoDB\nconst endDate = new Date(tokenData.end_date).getTime();\n\n// Define 3 days in milliseconds\nconst threeDaysInMs = 3 * 24 * 60 * 60 * 1000;\n\n// Calculate the difference between end date and now\nconst timeToExpiry = endDate - now;\n\n// Determine if the token is within 3 days of expiry and not already expired\nconst needsRefresh = timeToExpiry > 0 && timeToExpiry <= threeDaysInMs;\n\n// Return both the result and debug info\nreturn [{\n json: {\n needs_refresh: needsRefresh,\n time_to_expiry_ms: timeToExpiry,\n expires_at: tokenData.end_date,\n checked_at: new Date().toISOString(),\n }\n}];\n"
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"parameters": {
"url": "https://graph.facebook.com/v22.0/oauth/access_token",
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"queryParameters": {
"parameters": [
{
"name": "grant_type",
"value": "fb_exchange_token"
},
{
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{
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"table": "mlkymmbtoa2iz72",
"fieldsUi": {
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{
"fieldName": "=Id",
"fieldValue": "={{ $('Getting Long-Term Token').item.json.Id }}"
},
{
"fieldName": "longTermAccessToken",
"fieldValue": "={{ $json.access_token }}"
},
{
"fieldName": "end_date",
"fieldValue": "={{ $json.end_date }}"
},
{
"fieldName": "current_time",
"fieldValue": "={{ $json.current_time }}"
}
]
},
"operation": "update",
"projectId": "pfhk9mz7b66t5s2",
"authentication": "nocoDbApiToken"
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{
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"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"width": 752,
"height": 540,
"content": "### 步骤3a：将处理后的数据记录到Google Sheets"
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"typeVersion": 1
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{
"id": "fe5643c3-3012-4573-84b7-d11c93261bee",
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"assignments": [
{
"id": "f3604846-252c-438b-8266-36ec0819d3fa",
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"value": "={{ $('Getting Long-Term Token').item.json.longTermAccessToken }}"
}
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{
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"type": "n8n-nodes-base.code",
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"jsCode": "// --- START OF CODE NODE 1 SCRIPT (Process Raw Data) ---\n// Mode: Run Once for All Items\n\nconst allN8NInputItems = $input.all(); // Gets all n8n items (usually just one from HTTP Request if pagination combines, or one per page)\nconst finalOutputItems = [];\n\n// Iterate over each n8n item received (typically one, or one per page if HTTP node outputs that way)\nfor (const n8nItem of allN8NInputItems) {\n const facebookResponse = n8nItem.json; // This is an object like: { \"data\": [...], \"paging\": ... }\n\n // Check if the 'data' array exists in the Facebook response\n if (facebookResponse && facebookResponse.data && Array.isArray(facebookResponse.data)) {\n const adsInsightsArray = facebookResponse.data; // This is the array of actual ad insight objects\n\n // Loop through each ad insight object within the 'data' array\n for (const fbData of adsInsightsArray) {\n const output = {};\n\n // Basic Info\n output.ad_id = fbData.ad_id; // <<< --- ADDING AD_ID HERE ---\n output.date_start = fbData.date_start;\n output.date_stop = fbData.date_stop;\n output.campaign_name = fbData.campaign_name;\n output.adset_name = fbData.adset_name;\n output.ad_name = fbData.ad_name;\n output.objective = fbData.objective;\n output.spend = parseFloat(fbData.spend || 0);\n output.impressions = parseInt(fbData.impressions || 0);\n output.clicks = parseInt(fbData.clicks || 0);\n\n // Initialize specific e-commerce metrics\n output.add_to_carts_count = 0;\n output.checkouts_initiated_count = 0;\n output.purchases_count = 0;\n output.purchase_value_total = 0.0;\n\n // --- Define your PREFERRED canonical action_types in order of preference ---\n const PREFERRED_ACTION_ORDER = {\n atc: ['omni_add_to_cart', 'offsite_conversion.fb_pixel_add_to_cart', 'add_to_cart', 'onsite_web_app_add_to_cart', 'onsite_web_add_to_cart'],\n checkout: ['omni_initiated_checkout', 'offsite_conversion.fb_pixel_initiate_checkout', 'initiate_checkout', 'onsite_web_initiate_checkout'],\n purchase_count: ['omni_purchase', 'offsite_conversion.fb_pixel_purchase', 'purchase', 'web_in_store_purchase', 'onsite_web_purchase', 'onsite_web_app_purchase'],\n purchase_value: ['omni_purchase', 'offsite_conversion.fb_pixel_purchase', 'purchase', 'web_in_store_purchase', 'onsite_web_purchase', 'onsite_web_app_purchase']\n };\n\n // --- Helper function to get COUNT for a specific metric from 'actions' ---\n function getActionCount(actionsData, preferredTypes) {\n if (!actionsData) return 0;\n let actionsArray = [];\n if (Array.isArray(actionsData)) {\n actionsArray = actionsData;\n } else if (typeof actionsData === 'object' && actionsData !== null && typeof actionsData.action_type !== 'undefined') {\n actionsArray = [actionsData]; // Handle if 'actions' is a single object\n } else {\n return 0; // Invalid actionsData structure or empty\n }\n\n for (const type of preferredTypes) {\n const action = actionsArray.find(a => a.action_type === type);\n if (action) {\n return parseInt(action.value || 0);\n }\n }\n return 0;\n }\n\n // --- Helper function to get MONETARY VALUE for purchases from 'action_values' ---\n function getPurchaseMonetaryValue(actionValuesData, preferredTypes) {\n if (!actionValuesData || !Array.isArray(actionValuesData)) return 0.0;\n for (const type of preferredTypes) {\n const action = actionValuesData.find(a => a.action_type === type);\n if (action) {\n if (type === 'web_app_in_store_purchase' && parseFloat(action.value || 0) < 1.00) {\n const hasOtherMoreValuablePurchaseType = preferredTypes.some(pt =>\n pt !== 'web_app_in_store_purchase' &&\n actionValuesData.find(av => av.action_type === pt && parseFloat(av.value || 0) >= 1.00)\n );\n if (hasOtherMoreValuablePurchaseType) {\n continue;\n }\n }\n return parseFloat(action.value || 0);\n }\n }\n return 0.0;\n }\n\n // Populate the e-commerce metrics\n output.add_to_carts_count = getActionCount(fbData.actions, PREFERRED_ACTION_ORDER.atc);\n output.checkouts_initiated_count = getActionCount(fbData.actions, PREFERRED_ACTION_ORDER.checkout);\n output.purchases_count = getActionCount(fbData.actions, PREFERRED_ACTION_ORDER.purchase_count);\n output.purchase_value_total = getPurchaseMonetaryValue(fbData.action_values, PREFERRED_ACTION_ORDER.purchase_value);\n \n finalOutputItems.push({ json: output });\n }\n } else {\n console.error(\"Input data structure is not as expected from HTTP Request. 'data' array not found or not an array.\", facebookResponse);\n finalOutputItems.push({ json: { error: \"Facebook response parsing error\", details: \"Input 'data' array not found.\" } });\n }\n}\n\nreturn finalOutputItems;\n// --- END OF CODE NODE 1 SCRIPT (Process Raw Data) ---"
},
"typeVersion": 2
},
{
"id": "83a6b0a8-8422-4705-9576-deca22f7f092",
"name": "基于支出排序",
"type": "n8n-nodes-base.sort",
"position": [
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],
"parameters": {
"options": {},
"sortFieldsUi": {
"sortField": [
{
"order": "descending",
"fieldName": "total_spend"
}
]
}
},
"typeVersion": 1
},
{
"id": "c8bda278-bb77-423b-b2bc-49f23d6d3eb5",
"name": "便签3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1072,
272
],
"parameters": {
"width": 512,
"height": 620,
"content": "### 步骤6：使用AI洞察更新Google Sheets"
},
"typeVersion": 1
},
{
"id": "fdb93589-5381-4dfc-a0ec-7dbd01b2bc64",
"name": "便签4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1232,
272
],
"parameters": {
"color": 3,
"width": 320,
"height": 524,
"content": "### 步骤2：获取Facebook广告数据"
},
"typeVersion": 1
},
{
"id": "5b88a75e-945c-46b2-92d0-a8ddd399433a",
"name": "便签6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-592,
208
],
"parameters": {
"width": 680,
"height": 744,
"content": "### 步骤3：计算广告和基准KPI"
},
"typeVersion": 1
},
{
"id": "2fdd3e2f-92fa-4034-841a-500dad9f91bc",
"name": "仅筛选销售活动",
"type": "n8n-nodes-base.filter",
"position": [
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"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
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"combinator": "and",
"conditions": [
{
"id": "7e4103af-52c4-4035-97e5-4f4356312ed6",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.objective }}",
"rightValue": "OUTCOME_SALES"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "b2f42335-8b1e-4d00-a9a8-f7f631f1927d",
"name": "获取过去28天的数据，按活动、广告组和广告分段",
"type": "n8n-nodes-base.httpRequest",
"position": [
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],
"parameters": {
"url": "=https://graph.facebook.com/v22.0/act_XXXXXX/insights",
"options": {
"response": {
"response": {
"responseFormat": "json"
}
}
},
"sendQuery": true,
"sendHeaders": true,
"queryParameters": {
"parameters": [
{
"name": "level",
"value": "ad"
},
{
"name": "fields",
"value": "campaign_name,adset_name,ad_name,ad_id,objective,spend,impressions,clicks,actions,action_values,date_start,date_stop"
},
{
"name": "date_preset",
"value": "=last_28d"
},
{
"name": "limit",
"value": "500"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "=Bearer {{ $json.accessToken }}"
}
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}
},
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{
"id": "2dfd3d0c-0988-42df-8dfc-c545188d4ea2",
"name": "高级Facebook广告媒体买家",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
608,
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"parameters": {
"text": "=**Role:** You are a Senior Facebook Ads Media Buyer with extensive experience in e-commerce campaign optimization. Your primary goal is to maximize Return on Ad Spend (ROAS) and overall profitability by identifying high-impact ad creatives and categorizing their performance.\n\n**Context:**\nYou are analyzing ad creative performance for an e-commerce client. You have been provided with two sets of data for the past 14 days:\n1. **\"Ad Creative Performance Data\"**: A JSON string representing an array of objects. Each object details a unique ad creative and its performance metrics (including `total_spend`, `total_purchases`, `total_purchase_value`).\n2. **\"Overall Account Benchmark Data\"**: A JSON string representing a single object. This object contains the aggregated performance and KPIs for all ads in the account over the same period, serving as crucial benchmark values.\n\n**Task:**\nYour task is to rigorously evaluate each ad creative from the \"Ad Creative Performance Data\" against the \"Overall Account Benchmark Data.\" **For every ad creative you evaluate, you MUST compare its key calculated metrics (CTR, CPC, CPM, Cost Per Add To Cart, Cost Per Checkout, Cost Per Purchase, Conversion Rate, ROAS, Average Order Value) directly against the corresponding values in the benchmark data.**\n\nBased on this comparison and the spend rules below, categorize the performance of **each ad creative**.\n\n**Performance Categorization Rules & Spend Thresholds:**\n\n1. **Spend Significance:**\n * Any ad creative with a `total_spend` of **less than $50** CANNOT be categorized as \"HELL YES\", \"YES\", or even \"MAYBE\", regardless of its other metrics. At best, it can be \"NOT REALLY\" if ratios are poor, or \"INSUFFICIENT DATA/SPEND\" if ratios look promising but spend is too low for a definitive positive categorization.\n * To be considered for \"HELL YES\" or \"YES\" categories, an ad creative **MUST have a `total_spend` of $100 or more.**\n\n2. **Performance Categories (evaluate against benchmarks AFTER spend rules):**\n * **\"HELL YES\":** Significant spend (>= $100) AND dramatically outperforms benchmarks across ROAS, CVR, and Cost Per Purchase. Clear 80/20 driver.\n * **\"YES\":** Significant spend (>= $100) AND clearly outperforms benchmarks on ROAS, CVR, and Cost Per Purchase. Reliably contributing.\n * **\"MAYBE\":** Shows promise or mixed results. Spend >= $50. Marginally outperforms benchmarks, or strong on some key metrics but not others. Or, spend < $100 but > $50 with very strong ratios. Requires observation/optimization.\n * **\"NOT REALLY\":** Underperforming. Metrics generally at/below benchmarks. Or, spend < $50 with unpromising ratios. Not a clear winner.\n * **\"WE WASTED MONEY\":** Performing very poorly. Likely spent > $30-$50 (use judgment) with significantly worse metrics than benchmarks (very low ROAS, very high CPP). Pause candidate.\n * **\"INSUFFICIENT DATA/SPEND\":** Spend < $50. Ratios might look okay/good, but not enough data for confident positive categorization (\"MAYBE\", \"YES\", \"HELL YES\").\n\n**Input Data:**\n\n**1. Ad Creative Performance Data (JSON String - LLM needs to parse this):**\n\n{{ $json.data[0].all_ads_data_string }}\n\n2. Overall Account Benchmark Data (JSON String - LLM needs to parse this):\n{{ $json.data[1].benchmarkData }}\n",
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.6
},
{
"id": "6256c78d-67fb-4bc1-afb9-07b6191e254d",
"name": "合并广告数据和基准数据",
"type": "n8n-nodes-base.merge",
"position": [
224,
512
],
"parameters": {},
"typeVersion": 3.1
},
{
"id": "88276317-cd5c-4529-8329-51f1fc9bcfb0",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
160,
272
],
"parameters": {
"color": 3,
"width": 356,
"height": 544,
"content": "### 步骤4：准备AI分析数据"
},
"typeVersion": 1
},
{
"id": "f44bb155-47a1-422d-b857-6007bbcb9844",
"name": "将广告洞察更新到Google Sheets",
"type": "n8n-nodes-base.googleSheets",
"position": [
1184,
528
],
"parameters": {
"columns": {
"value": {
"ad_id": "={{ $json.ad_id }}",
"Justification": "={{ $json.justification }}",
"Recommendation": "={{ $json.recommendation }}",
"Best Performing Ad": "={{ $json.performance_category }}"
},
"schema": [
{
"id": "ad_name",
"type": "string",
"display": true,
"required": false,
"displayName": "ad_name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "ad_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "ad_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "objective",
"type": "string",
"display": true,
"required": false,
"displayName": "objective",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_spend",
"type": "string",
"display": true,
"required": false,
"displayName": "total_spend",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_impressions",
"type": "string",
"display": true,
"required": false,
"displayName": "total_impressions",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_clicks",
"type": "string",
"display": true,
"required": false,
"displayName": "total_clicks",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_add_to_carts",
"type": "string",
"display": true,
"required": false,
"displayName": "total_add_to_carts",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_checkouts_initiated",
"type": "string",
"display": true,
"required": false,
"displayName": "total_checkouts_initiated",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_purchases",
"type": "string",
"display": true,
"required": false,
"displayName": "total_purchases",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "total_purchase_value",
"type": "string",
"display": true,
"required": false,
"displayName": "total_purchase_value",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "ctr",
"type": "string",
"display": true,
"required": false,
"displayName": "ctr",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "cpc",
"type": "string",
"display": true,
"required": false,
"displayName": "cpc",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "cpm",
"type": "string",
"display": true,
"required": false,
"displayName": "cpm",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "cost_per_add_to_cart",
"type": "string",
"display": true,
"required": false,
"displayName": "cost_per_add_to_cart",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "cost_per_checkout",
"type": "string",
"display": true,
"required": false,
"displayName": "cost_per_checkout",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "cost_per_purchase",
"type": "string",
"display": true,
"required": false,
"displayName": "cost_per_purchase",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "roas",
"type": "string",
"display": true,
"required": false,
"displayName": "roas",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "average_order_value",
"type": "string",
"display": true,
"required": false,
"displayName": "average_order_value",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "conversion_rate",
"type": "string",
"display": true,
"required": false,
"displayName": "conversion_rate",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Best Performing Ad",
"type": "string",
"display": true,
"required": false,
"displayName": "Best Performing Ad",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Justification",
"type": "string",
"display": true,
"required": false,
"displayName": "Justification",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Recommendation",
"type": "string",
"display": true,
"required": false,
"displayName": "Recommendation",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Source_URL",
"type": "string",
"display": true,
"required": false,
"displayName": "Source_URL",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Asset Type",
"type": "string",
"display": true,
"required": false,
"displayName": "Asset Type",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Transcription",
"type": "string",
"display": true,
"required": false,
"displayName": "Transcription",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "hook",
"type": "string",
"display": true,
"required": false,
"displayName": "hook",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "purpose",
"type": "string",
"display": true,
"required": false,
"displayName": "purpose",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "text_captions",
"type": "string",
"display": true,
"required": false,
"displayName": "text_captions",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "disclaimer",
"type": "string",
"display": true,
"required": false,
"displayName": "disclaimer",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Status",
"type": "string",
"display": true,
"required": false,
"displayName": "Status",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "focal_point",
"type": "string",
"display": true,
"required": false,
"displayName": "focal_point",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "colours",
"type": "string",
"display": true,
"required": false,
"displayName": "colours",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "layout",
"type": "string",
"display": true,
"required": false,
"displayName": "layout",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "text_elements",
"type": "string",
"display": true,
"required": false,
"displayName": "text_elements",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "row_number",
"type": "string",
"display": true,
"removed": true,
"readOnly": true,
"required": false,
"displayName": "row_number",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"ad_id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "update",
"sheetName": {
"__rl": true,
"mode": "list",
"value": "gid=0",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1_9fnWQm3ipnWg3DvP6XD-EnT2e5vZRvVlQyVA0rbNMQ/edit#gid=0",
"cachedResultName": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "url",
"value": "XXXXXX"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "8dzgEvzMeMhDaVGY",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "21dcc80e-ac2f-40ce-9890-6c8ba2ee463c",
"name": "令牌需要刷新吗？",
"type": "n8n-nodes-base.if",
"position": [
-2464,
768
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "73f5e309-6f35-413f-a891-a0bc7185ca60",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json.needs_refresh }}",
"rightValue": "true"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "96584b1b-2f7f-46d1-96c7-268c0ea9603b",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3856,
176
],
"parameters": {
"width": 608,
"height": 1024,
"content": "## AI驱动的电子商务Facebook广告分析"
},
"typeVersion": 1
},
{
"id": "5ce47343-8189-4d1d-9038-f0e0bcaf9802",
"name": "提取长期令牌",
"type": "n8n-nodes-base.set",
"position": [
-1600,
704
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fe9ab593-0caa-415b-958c-5afea549b1d2",
"name": "longAccessToken",
"type": "string",
"value": "={{ $json.longTermAccessToken }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "5e3dc574-4049-4bee-ae81-8a899b553615",
"name": "便签5",
"type": "n8n-nodes-base.stickyNote",
"position": [
528,
272
],
"parameters": {
"color": 7,
"width": 528,
"height": 624,
"content": "### 步骤5：AI驱动的广告创意分析"
},
"typeVersion": 1
},
{
"id": "2eaabe3c-fec3-4804-8f2b-db39a00cb0c4",
"name": "计算账户基准",
"type": "n8n-nodes-base.code",
"position": [
-464,
784
],
"parameters": {
"jsCode": "// --- START OF CODE NODE 3 SCRIPT ---\n// Input: Output from Code Node 1 (array of processed ad records)\n// Mode: Run Once for All Items\n\nconst allProcessedAdRecords = $input.all(); // Array of n8n items from Code Node 1\n\n// Initialize overall totals\nconst overallTotals = {\n total_spend: 0,\n total_impressions: 0,\n total_clicks: 0,\n total_add_to_carts: 0,\n total_checkouts_initiated: 0,\n total_purchases: 0,\n total_purchase_value: 0,\n // We can't really sum 'objective' or 'ad_name' in a meaningful way for an overall summary,\n // so we'll omit them or set them to a generic value.\n // We also won't sum date_start/date_stop, but we can determine the overall period.\n};\n\nlet minDateStart = null;\nlet maxDateStop = null;\n\n// Step 1: Sum up fundamental metrics from all processed ad records\nfor (const n8nItem of allProcessedAdRecords) {\n const adRecord = n8nItem.json; // Each adRecord is an output from Code Node 1\n\n overallTotals.total_spend += parseFloat(adRecord.spend || 0);\n overallTotals.total_impressions += parseInt(adRecord.impressions || 0);\n overallTotals.total_clicks += parseInt(adRecord.clicks || 0);\n overallTotals.total_add_to_carts += parseInt(adRecord.add_to_carts_count || 0);\n overallTotals.total_checkouts_initiated += parseInt(adRecord.checkouts_initiated_count || 0);\n overallTotals.total_purchases += parseInt(adRecord.purchases_count || 0);\n overallTotals.total_purchase_value += parseFloat(adRecord.purchase_value_total || 0);\n\n // Determine overall date range\n if (adRecord.date_start) {\n const currentStartDate = new Date(adRecord.date_start);\n if (!minDateStart || currentStartDate < minDateStart) {\n minDateStart = currentStartDate;\n }\n }\n if (adRecord.date_stop) {\n const currentStopDate = new Date(adRecord.date_stop);\n if (!maxDateStop || currentStopDate > maxDateStop) {\n maxDateStop = currentStopDate;\n }\n }\n}\n\n// Add overall date range to the output\nif (minDateStart) {\n overallTotals.overall_date_start = minDateStart.toISOString().split('T')[0]; // YYYY-MM-DD\n}\nif (maxDateStop) {\n overallTotals.overall_date_stop = maxDateStop.toISOString().split('T')[0]; // YYYY-MM-DD\n}\n\n\n// Step 2: Calculate overall derived KPIs based on the grand totals\nconst overallKPIs = { ...overallTotals }; // Start with the summed totals\n\noverallKPIs.ctr = overallTotals.total_impressions > 0 ? (overallTotals.total_clicks / overallTotals.total_impressions) : 0;\noverallKPIs.cpc = overallTotals.total_clicks > 0 ? (overallTotals.total_spend / overallTotals.total_clicks) : 0;\noverallKPIs.cpm = overallTotals.total_impressions > 0 ? (overallTotals.total_spend / overallTotals.total_impressions * 1000) : 0;\n\noverallKPIs.cost_per_add_to_cart = overallTotals.total_add_to_carts > 0 ? (overallTotals.total_spend / overallTotals.total_add_to_carts) : 0;\noverallKPIs.cost_per_checkout = overallTotals.total_checkouts_initiated > 0 ? (overallTotals.total_spend / overallTotals.total_checkouts_initiated) : 0;\noverallKPIs.cost_per_purchase = overallTotals.total_purchases > 0 ? (overallTotals.total_spend / overallTotals.total_purchases) : 0;\n\noverallKPIs.roas = overallTotals.total_spend > 0 ? (overallTotals.total_purchase_value / overallTotals.total_spend) : 0;\n\nlet conversionRateDecimal = overallTotals.total_clicks > 0 ? (overallTotals.total_purchases / overallTotals.total_clicks) : 0;\noverallKPIs.average_order_value = overallTotals.total_purchases > 0 ? (overallTotals.total_purchase_value / overallTotals.total_purchases) : 0;\n\n// --- Format numbers for the overall summary ---\noverallKPIs.total_spend = parseFloat(overallKPIs.total_spend.toFixed(2));\noverallKPIs.total_purchase_value = parseFloat(overallKPIs.total_purchase_value.toFixed(2));\n\noverallKPIs.ctr = parseFloat(overallKPIs.ctr.toFixed(4));\noverallKPIs.cpc = parseFloat(overallKPIs.cpc.toFixed(2));\noverallKPIs.cpm = parseFloat(overallKPIs.cpm.toFixed(2));\n\noverallKPIs.cost_per_add_to_cart = parseFloat(overallKPIs.cost_per_add_to_cart.toFixed(2));\noverallKPIs.cost_per_checkout = parseFloat(overallKPIs.cost_per_checkout.toFixed(2));\noverallKPIs.cost_per_purchase = parseFloat(overallKPIs.cost_per_purchase.toFixed(2));\n\noverallKPIs.roas = parseFloat(overallKPIs.roas.toFixed(2));\n\noverallKPIs.conversion_rate = (conversionRateDecimal * 100).toFixed(2) + '%'; // As percentage string\noverallKPIs.average_order_value = parseFloat(overallKPIs.average_order_value.toFixed(2));\n\n// This node will output a single item containing the overall summary\nreturn [{ json: overallKPIs }];\n// --- END OF CODE NODE 3 SCRIPT ---"
},
"typeVersion": 2
},
{
"id": "23411043-d16f-40f4-8fcc-3c6653f3595c",
"name": "字符串化基准数据",
"type": "n8n-nodes-base.code",
"position": [
-48,
784
],
"parameters": {
"jsCode": "// --- START OF CODE NODE 4 SCRIPT (Stringify Output) ---\n// Mode: Run Once for All Items\n// Input: The array of aggregated ad creative performance objects\n\nconst allAggregatedItems = $input.all(); // This is an array of n8n items\n\n// Extract the .json part from each n8n item to get the actual data objects\nconst dataToConvert = allAggregatedItems.map(item => item.json);\n\n// Stringify the array of data objects\n// The 'null, 2' arguments pretty-print the JSON string with an indent of 2 spaces,\n// which can be helpful for readability if you're inspecting it, but not strictly necessary for an LLM.\n// If the LLM prefers a more compact JSON, you can omit 'null, 2'.\nconst jsonString = JSON.stringify(dataToConvert, null, 2);\n\n// Output a single item containing this string\nreturn [{ json: { benchmarkData: jsonString } }];\n// --- END OF CODE NODE 4 SCRIPT ---"
},
"typeVersion": 2
},
{
"id": "cf388ebd-dff0-4658-b13a-59619c8cf701",
"name": "按广告创意聚合指标",
"type": "n8n-nodes-base.code",
"position": [
-496,
448
],
"parameters": {
"jsCode": "// --- START OF CODE NODE 2 SCRIPT (Aggregate by Ad Creative & KPIs) ---\n// Mode: Run Once for All Items\n// Input: Output from Code Node 1 (which now includes ad_id)\n\nconst allInputItems = $input.all();\nconst aggregatedByAdName = {};\n\n// Step 1: Aggregate metrics by ad_name\nfor (const n8nItem of allInputItems) {\n const itemData = n8nItem.json; // Data from Code Node 1, includes ad_id\n const key = itemData.ad_name;\n\n if (!aggregatedByAdName[key]) {\n aggregatedByAdName[key] = {\n ad_name: itemData.ad_name,\n ad_id: itemData.ad_id, // <<< STORE THE AD_ID OF THE FIRST ENCOUNTERED ITEM\n objective: itemData.objective,\n // Initialize all sums\n total_spend: 0,\n total_impressions: 0,\n total_clicks: 0,\n total_add_to_carts: 0,\n total_checkouts_initiated: 0,\n total_purchases: 0,\n total_purchase_value: 0,\n };\n }\n\n // If the first itemData for an ad_name somehow missed an ad_id (unlikely if Code Node 1 is correct),\n // but a subsequent one has it, this ensures it gets populated.\n if (itemData.ad_id && !aggregatedByAdName[key].ad_id) {\n aggregatedByAdName[key].ad_id = itemData.ad_id;\n }\n // Same for objective, if needed\n if (itemData.objective && !aggregatedByAdName[key].objective) {\n aggregatedByAdName[key].objective = itemData.objective;\n }\n\n // Sum up the metrics\n aggregatedByAdName[key].total_spend += parseFloat(itemData.spend || 0);\n aggregatedByAdName[key].total_impressions += parseInt(itemData.impressions || 0);\n aggregatedByAdName[key].total_clicks += parseInt(itemData.clicks || 0);\n aggregatedByAdName[key].total_add_to_carts += parseInt(itemData.add_to_carts_count || 0);\n aggregatedByAdName[key].total_checkouts_initiated += parseInt(itemData.checkouts_initiated_count || 0);\n aggregatedByAdName[key].total_purchases += parseInt(itemData.purchases_count || 0);\n aggregatedByAdName[key].total_purchase_value += parseFloat(itemData.purchase_value_total || 0);\n}\n\n// Step 2: Calculate derived metrics and prepare final output\nconst finalOutputArray = [];\nfor (const key in aggregatedByAdName) {\n const adCreativeData = aggregatedByAdName[key];\n const outputItem = { ...adCreativeData }; // Includes ad_name, ad_id, objective, and all total_... fields\n\n // Calculate derived metrics, handling division by zero\n outputItem.ctr = adCreativeData.total_impressions > 0 ? (adCreativeData.total_clicks / adCreativeData.total_impressions) : 0;\n outputItem.cpc = adCreativeData.total_clicks > 0 ? (adCreativeData.total_spend / adCreativeData.total_clicks) : 0;\n outputItem.cpm = adCreativeData.total_impressions > 0 ? (adCreativeData.total_spend / adCreativeData.total_impressions * 1000) : 0;\n \n outputItem.cost_per_add_to_cart = adCreativeData.total_add_to_carts > 0 ? (adCreativeData.total_spend / adCreativeData.total_add_to_carts) : 0;\n outputItem.cost_per_checkout = adCreativeData.total_checkouts_initiated > 0 ? (adCreativeData.total_spend / adCreativeData.total_checkouts_initiated) : 0;\n outputItem.cost_per_purchase = adCreativeData.total_purchases > 0 ? (adCreativeData.total_spend / adCreativeData.total_purchases) : 0;\n \n outputItem.roas = adCreativeData.total_spend > 0 ? (adCreativeData.total_purchase_value / adCreativeData.total_spend) : 0;\n\n let conversionRateDecimal = adCreativeData.total_clicks > 0 ? (adCreativeData.total_purchases / adCreativeData.total_clicks) : 0;\n outputItem.average_order_value = adCreativeData.total_purchases > 0 ? (adCreativeData.total_purchase_value / adCreativeData.total_purchases) : 0;\n\n // Format numbers\n outputItem.total_spend = parseFloat(outputItem.total_spend.toFixed(2));\n outputItem.total_purchase_value = parseFloat(outputItem.total_purchase_value.toFixed(2));\n \n outputItem.ctr = parseFloat(outputItem.ctr.toFixed(4));\n outputItem.cpc = parseFloat(outputItem.cpc.toFixed(2));\n outputItem.cpm = parseFloat(outputItem.cpm.toFixed(2));\n \n outputItem.cost_per_add_to_cart = parseFloat(outputItem.cost_per_add_to_cart.toFixed(2));\n outputItem.cost_per_checkout = parseFloat(outputItem.cost_per_checkout.toFixed(2));\n outputItem.cost_per_purchase = parseFloat(outputItem.cost_per_purchase.toFixed(2));\n \n outputItem.roas = parseFloat(outputItem.roas.toFixed(2));\n\n outputItem.conversion_rate = (conversionRateDecimal * 100).toFixed(2) + '%';\n outputItem.average_order_value = parseFloat(outputItem.average_order_value.toFixed(2));\n\n finalOutputArray.push({ json: outputItem });\n}\n\nreturn finalOutputArray;\n// --- END OF CODE NODE 2 SCRIPT ---"
},
"typeVersion": 2
},
{
"id": "3b80f5a8-4aa8-4a65-882b-ebc391da066c",
"name": "为LLM合并广告和基准数据",
"type": "n8n-nodes-base.aggregate",
"position": [
400,
512
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "dbc2be05-df91-463b-bd18-d935809393a1",
"name": "提取访问令牌",
"type": "n8n-nodes-base.set",
"position": [
-1360,
800
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a6e2f5e6-eebb-43ea-9c51-9016c673d4da",
"name": "accessToken",
"type": "string",
"value": "={{ $json.longAccessToken }}"
}
]
}
},
"typeVersion": 3.4
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "13e4e553-de65-4188-9e59-6517c3cdcb5c",
"connections": {
"Split Out": {
"main": [
[
{
"node": "Updating Ad Insights Into Google Sheets",
"type": "main",
"index": 0
}
]
]
},
"Updating Token": {
"main": [
[
{
"node": "Extracting Long Term Token",
"type": "main",
"index": 0
}
]
]
},
"Stringify Everything": {
"main": [
[
{
"node": "Combining Ad Data and Benchmarking Data",
"type": "main",
"index": 0
}
]
]
},
"Extracting Access Token": {
"main": [
[
{
"node": "Getting Data For the Past 28 Days Segemented Per Campaign Adset and Ad",
"type": "main",
"index": 0
}
]
]
},
"Getting Long-Term Token": {
"main": [
[
{
"node": "Does it Need A Token Refresh?",
"type": "main",
"index": 0
}
]
]
},
"Sorting Based on Spends": {
"main": [
[
{
"node": "Sending Raw Data To A Google Sheet",
"type": "main",
"index": 0
},
{
"node": "Stringify Everything",
"type": "main",
"index": 0
}
]
]
},
"Getting Long-Term Token1": {
"main": [
[
{
"node": "Extracting Access Token",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Senior Facebook Ads Media Buyer",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Stringify Benchmark Data": {
"main": [
[
{
"node": "Combining Ad Data and Benchmarking Data",
"type": "main",
"index": 1
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Senior Facebook Ads Media Buyer",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Extracting Long Term Token": {
"main": [
[
{
"node": "Extracting Access Token",
"type": "main",
"index": 0
}
]
]
},
"Calculate Account Benchmarks": {
"main": [
[
{
"node": "Stringify Benchmark Data",
"type": "main",
"index": 0
}
]
]
},
"Does Token Needs Refreshing?": {
"main": [
[
{
"node": "Getting Long-Lived Access Token1",
"type": "main",
"index": 0
}
],
[
{
"node": "Getting Long-Term Token1",
"type": "main",
"index": 0
}
]
]
},
"Does it Need A Token Refresh?": {
"main": [
[
{
"node": "Does Token Needs Refreshing?",
"type": "main",
"index": 0
}
]
]
},
"Splitting Out In Table Format": {
"main": [
[
{
"node": "Filtering Only For Sales Campaigns",
"type": "main",
"index": 0
}
]
]
},
"Calculating End Date of Token1": {
"main": [
[
{
"node": "Updating Token",
"type": "main",
"index": 0
}
]
]
},
"Senior Facebook Ads Media Buyer": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Aggregate Metrics by Ad Creative": {
"main": [
[
{
"node": "Sorting Based on Spends",
"type": "main",
"index": 0
}
]
]
},
"Getting Long-Lived Access Token1": {
"main": [
[
{
"node": "Calculating End Date of Token1",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Getting Long-Term Token",
"type": "main",
"index": 0
}
]
]
},
"Filtering Only For Sales Campaigns": {
"main": [
[
{
"node": "Aggregate Metrics by Ad Creative",
"type": "main",
"index": 0
},
{
"node": "Calculate Account Benchmarks",
"type": "main",
"index": 0
}
]
]
},
"Combine Ad & Benchmark Data for LLM": {
"main": [
[
{
"node": "Senior Facebook Ads Media Buyer",
"type": "main",
"index": 0
}
]
]
},
"Combining Ad Data and Benchmarking Data": {
"main": [
[
{
"node": "Combine Ad & Benchmark Data for LLM",
"type": "main",
"index": 0
}
]
]
},
"Getting Data For the Past 28 Days Segemented Per Campaign Adset and Ad": {
"main": [
[
{
"node": "Splitting Out In Table Format",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级 - 市场调研, AI 摘要总结
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
高级
节点数量34
分类2
节点类型16
作者
JJ Tham
@jj-thamFounder of Osinity. I build AI-powered n8n automations that save businesses 10+ hours a week and grow their revenue. We guarantee results with a 30-day risk-free trial—you only pay when we hit your targets.
外部链接
在 n8n.io 查看 →
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