用 n8n 并行调用 GPT-4 与 Gemini 自动分析 Meta Ads 创意表现

输入 Meta Ads 数据或 Google Sheets 清单 → 提取指标 → 并行送 OpenAI/Gemini 对比基准 → 输出评分与操作建议到表格

给谁

有 n8n 经验、需批量评估 Meta 广告创意的投放团队

你会得到

自动为每个创意生成 high/medium/low 评级、显著性判断与 scale/optimize/stop 建议，并汇总到 Google Sheets

你需要

n8n · facebook-graph-api · google-sheets · openai · gemini · schedule-trigger

步骤

1. 配置凭证与中心参数节点

   在 n8n 中导入模板后，先连接四个凭证：Meta Ads、Google 账号（用于 Sheets）、OpenAI、Google Gemini（Palm）。然后打开 ⚙️ Set parameters 节点，设置三项：source 选 'Meta'（直接拉取广告系列）或 'sheets'（从 Google Sheet 读取 AdID 列表）；campaign_id 填入 Meta 广告系列 ID（若 source=Meta）；benchmarks_data 粘贴你的历史高表现创意数据 CSV，字段需与模板示例对齐。

2. 配置三个 Google Sheets 节点

   将三个写入节点（Ad metrics / Ad data from OpenAI / Ad data from Gemini）指向同一个 spreadsheet 与 sheet。Ad metrics 的 Operation 设为 Append or Update；后两个设为 Update，以 AdID 为匹配列。确保表格已包含 AdID、FileName、spend、impressions、evaluation、summary、recommendation、evaluation G、summary G 等列。

3. 设置触发器并激活工作流

   在 Schedule Trigger 节点设定运行频率（如每小时或每天）。保存并激活工作流。首次运行前建议用 Execute Workflow 手动测试，确认数据能正确写入 Sheets。【首次测试安全】首次测试前，先创建独立的测试 Google Sheet，将「Ad metrics」「Ad data from OpenAI」「Ad data from Gemini」三个 Sheets 节点临时指向该测试表，或仅用少量脱敏数据执行；确认列名匹配与写入内容正确后，再切回生产表格并激活 Schedule Trigger。

4. 执行并行 AI 分析（OpenAI + Gemini）

   工作流运行时，Merge 后的创意数据会经 Code 节点转成 CSV，同时进入两条并行分支：一条送 OpenAI（gpt-4.1-nano）生成 JSON 数组评级；另一条送 Gemini AI Agent（带 Structured Output Parser）生成独立评级。两路结果分别回写到同一行的 OpenAI 列与 Gemini 列。

   运行提示词 · 来源原文。OpenAI 节点 Send data to 4.1-NANO 的 system message

   复制提示词

   =You are the lead performance marketer at Digital Agency, a Meta Ads specialist.
   Your core strength is deep, objective analysis of creatives using funnel metrics (Impressions → Clicks → Installs → Registrations → Add to Cart → Checkouts Initiated).
   You compare test creatives with internal benchmarks and give clear recommendations on whether to scale, keep testing, or stop a creative.
   Your goal is to help make decisions, not just repeat the data.

   运行提示词 · 来源原文。OpenAI 节点 Send data to 4.1-NANO 的 user message

   复制提示词

   =**BENCHMARKS_DATA (top-performing creatives with calculated metrics like CTR, CPC, CPA, ROAS, etc.)**
   *This is a CSV string with data from your top-performing creatives. All key metrics are pre-calculated here.*
   {{ $('Set parameters').item.json.benchmarks_data }}
   
   **ANALYSIS_DATA (raw test data without calculated metrics)**:
   
   {{ $json.creative_csv_data }}
   
   *Context*:
   	•	EasyBusy is a business aggregator.
   	•	Due to a long sales cycle (~30 days) and high average order value, there are no Purchases in the data.
   	•	We use Checkouts Initiated as a proxy conversion metric and also track the full funnel.
   
   **Your task**:
   	1.	Calculate the same metrics for the test creatives as in the benchmark data (CTR, CPC, CPA, ROAS, etc.).
   	2.	Compare each test creative against the benchmarks:
   	•	Metric-by-metric (e.g. higher CTR but worse CPC)
   	•	Funnel quality (is there drop-off or strength at any step?)
   	3.	Answer the following:
   	•	Do we have enough data to draw conclusions for each creative?
   	•	Which creatives are ready to scale now? Why?
   	•	Which creatives should continue testing?
   	•	Which creatives should be stopped?
   
   OUTPUT FORMAT — ONLY JSON ARRAY OF CREATIVES:
   
   Do not write any explanation, markdown, or formatting outside of the array. Just return a single JSON array like this:
   You must include ALL creatives in a SINGLE JSON array — not split across multiple responses. 
   Respond in ONE single message. Do NOT return one object per message.
   
   **Each object must follow this structure**:
   {
     "ad_id": "string (ad_id from ANALYSIS_DATA dataset)",
     "evaluation": "high | medium | low",
     "significance": "yes | no",
     "summary": "2–3 sentence explanation of why this creative got this score",
     "recommendation": "scale | optimize | stop"
   }
   
   *Example*:
   [
     {
       "ad_id": "1202153606616005",
       "evaluation": "high",
       "significance": "yes",
       "summary": "CTR and CPC are significantly better than benchmarks, with a strong funnel down to Checkouts Initiated.",
       "recommendation": "scale"
     },
     {
       "ad_id": "92021533361600510",
       "evaluation": "low",
       "significance": "yes",
       "summary": "Low CTR and high CPC. Almost no meaningful downstream activity.",
       "recommendation": "stop"
     }
   ]
   
   If there isn’t enough data, set "significance": "no" and clearly explain the reason in summary. Still provide the other fields.
   
   **Important**:
   	•	No markdown
   	•	No extra text
   	•	Output = only one clean JSON array

   运行提示词 · 来源原文。OpenAI 节点 Send data to 4.1-NANO 的 assistant message

   复制提示词

   =Do not return each creative in a separate message. You must return a SINGLE JSON array that includes ALL creatives from the dataset.

   运行提示词 · 来源原文。Gemini AI Agent 节点 Send data to Gemini 的 text prompt

   复制提示词

   =You are the lead performance marketer at Digital Agency, a Meta Ads specialist.
   Your core strength is deep, objective analysis of creatives using funnel metrics (Impressions → Clicks → Installs → Registrations → Add to Cart → Checkouts Initiated). You compare test creatives with internal benchmarks and give clear recommendations on whether to scale, keep testing, or stop a creative.
   Your goal is to help make decisions, not just repeat the data.
   
   INPUT DATA
   **1. BENCHMARKS_DATA**:
   *This is a CSV string with data from your top-performing creatives. All key metrics are pre-calculated here.*
   {{ $('Set parameters').item.json.benchmarks_data }}
   
   **2. ANALYSIS_DATA (raw test data without calculated metrics)**:
   {{ $json.creative_csv_data }}
   
   Context:
   	•	EasyBusy is a business aggregator.
   	•	Due to a long sales cycle (~30 days) and high average order value, there are no Purchases in the data.
   	•	We use Checkouts Initiated as a proxy conversion metric and also track the full funnel.
   
   **Your task:**
   1. Calculate metrics for each creative in ANALYSIS_DATA. Use the following formulas:
   - CTR (link click-through rate) = Link clicks / Impressions
   - CPC (cost per link click) = Amount spent (USD) / Link clicks
   - IPM (Installs per 1000 impressions) = App Installs / (Impressions / 1000)
   - Cost per App Install = Amount spent (USD) / App Installs
   - CR install to reg = Registrations completed / App Installs
   - Cost per registration completed = Amount spent (USD) / Registrations completed
   - Cost per add to cart = Amount spent (USD) / Adds to cart
   - Cost per checkout initiated = Amount spent (USD) / Checkouts initiated
   - If a denominator is zero, the resulting metric should be 0 or null.
   2. Analyze each creative from ANALYSIS_DATA according to the following rules:
   - Rule for determining statistical significance (significance):
   --Consider the data sufficient ("significance": "yes") if BOTH of the following conditions are met:
   --- Amount spent (USD) > 49
   --- Impressions > 10,000
   Otherwise, set "significance": "no".
   -Rule for evaluation (evaluation):
   -- Compare the key metrics of the test creative (CTR, CPC, Cost per App Install, Cost per checkout initiated) against the average values of the same metrics from the BENCHMARKS_DATA file.
   -- "high": The creative significantly outperforms the benchmark averages on key funnel metrics (e.g., higher CTR, and lower CPC and Cost per checkout initiated).
   -- "medium": The creative shows mixed results or performs on par with the benchmarks. For example, a good CTR but an expensive Cost per checkout initiated.
   -- "low": The creative significantly underperforms the benchmarks on most key metrics.
   3. Based on your analysis, provide a final JSON object containing the ad_id from the input data, and your calculated values for evaluation, significance, summary, and recommendation. For the summary, provide a 2-3 sentence explanation. If there is not enough data, set "significance" to "no" and explain why in the summary.

本模板为搭建启动词：请确认你已配置 facebookGraphApi、googleSheetsOAuth2Api、openAiApi、googlePalmApi 四项凭证，并在 Set parameters 节点中填入 source、campaign_id（如适用）及 benchmarks_data CSV。然后按步骤配置三个 Google Sheets 节点指向同一表格，最后设定 Schedule Trigger 并激活。