{ "id": "DEmOnEbgsR5bEoAH", "name": "Daily paid acquisition intelligence report", "tags": [ { "id": "ZLNp4q7bLInAPzlr", "name": "Databox MCP", "createdAt": "2026-02-12T05:49:19.149Z", "updatedAt": "2026-02-12T05:49:19.149Z" }, { "id": "OITWk9JNRp1srhj6", "name": "Paid Ads", "createdAt": "2026-02-26T16:50:04.993Z", "updatedAt": "2026-02-26T16:50:04.993Z" }, { "id": "fjJWe0P5GkqnbIXj", "name": "Automation", "createdAt": "2026-02-12T05:49:19.177Z", "updatedAt": "2026-02-12T05:49:19.177Z" } ], "nodes": [ { "id": "51b9a6e9-5a6f-4815-9c7a-43fb276e0bee", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ 16, 16 ], "parameters": { "color": 7, "width": 1680, "height": 400, "content": "## Daily Paid Acquisition Intelligence Report via Databox MCP\n\nGet a daily intelligence briefing on your paid acquisition performance. Three specialized AI agents run in sequence: Agent 1 analyzes website behavior, Agent 2 analyzes paid channel performance across all connected platforms, and Agent 3 runs cross-channel correlation analysis - surfacing insights like cost per quality visit, channel efficiency rankings, and concrete budget reallocation recommendations.\n\n### How it works\n`Schedule Trigger` - `Agent 1: Website Analysis (Databox MCP)` - `Agent 2: Paid Acquisition Analysis (Databox MCP)` - `Agent 3: Correlations + Recommendations` - `HTML Email Report`\n\n### What you need\n- Databox account with website analytics and at least one paid ads platform connected - Free plan available: https://databox.com/?ref=n8n\n- OpenAI API key\n- Gmail account\n\n### Supported Data Sources\nGoogle Analytics - Google Ads - Facebook Ads - LinkedIn Ads - TikTok Ads - YouTube Ads - Microsoft Ads - Reddit Ads - And 100+ more - https://databox.com/integrations" }, "typeVersion": 1 }, { "id": "82c15713-2ebc-4b54-8d9d-f05c91200446", "name": "Sticky Note 1", "type": "n8n-nodes-base.stickyNote", "position": [ 16, 448 ], "parameters": { "color": 4, "width": 440, "height": 772, "content": "## 1 - Schedule\n\n### What this section does\nTriggers the workflow every day at 8 AM and captures today's date so all three agents use consistent reporting windows.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### Change the schedule (optional)\n- Click the \"Every Monday 8 AM\" node\n- Click on the Cron Expression field\n- Modify the schedule (e.g., change to daily, weekly on different day, or custom time)" }, "typeVersion": 1 }, { "id": "312cd311-e63a-4ca2-897e-dc69564ecfb1", "name": "Sticky Note 2", "type": "n8n-nodes-base.stickyNote", "position": [ 496, 448 ], "parameters": { "color": 6, "width": 1204, "height": 772, "content": "## 2 - AI Agents + Databox MCP Setup\n\n### What this section does\nThree agents run in sequence:\n- **Agent 1 - Website Analyst**: fetches sessions, bounce rate, pages per session, goal completions from your website analytics source\n- **Agent 2 - Paid Acquisition Analyst**: fetches spend, CPC, CTR, ROAS and impressions for every connected paid platform\n- **Agent 3 - Correlation Analyst**: receives both outputs, finds cross-channel patterns, ranks channel efficiency, and writes the final HTML report (no MCP calls needed)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### What you need to do\n1. Click each **OpenAI Chat Model** node and add your API key\n - You can replace with an **Anthropic Chat Model** node\n2. Click each **Databox MCP Tool** node - set Authentication to `OAuth2` - authorize with your Databox account\n3. Ensure at least one paid ads platform and a website analytics source are connected in Databox" }, "typeVersion": 1 }, { "id": "cf0ecb3b-06b4-4e6a-853d-fb4a01d85df8", "name": "Sticky Note 3", "type": "n8n-nodes-base.stickyNote", "position": [ 1728, 448 ], "parameters": { "color": 5, "width": 492, "height": 772, "content": "## 3 - Email Report Output\n\n### What this section does\nFormats the correlation analysis as a styled HTML email and delivers it to the configured recipient every morning.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### What you need to do\n- Click the **Send Email** node and add your **Gmail credential**\n- Update the **To** field with the recipient email address\n- **Optional**: Add a Slack node after Prepare Email for an additional Slack notification" }, "typeVersion": 1 }, { "id": "184c61e2-e4b9-496d-90fc-1d2fcfce9a15", "name": "Every Day 8 AM", "type": "n8n-nodes-base.scheduleTrigger", "notes": "Triggers every day at 8 AM - adjust the cron expression to change frequency", "position": [ 80, 704 ], "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "0 8 * * *" } ] } }, "typeVersion": 1.2 }, { "id": "e7a6fbb9-ce74-49a1-a21b-e883e008d388", "name": "Get Current Date", "type": "n8n-nodes-base.dateTime", "position": [ 272, 704 ], "parameters": { "options": {} }, "typeVersion": 2 }, { "id": "8cb84507-2f08-45eb-940e-bee02b6f06a3", "name": "Website Analysis Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 608, 704 ], "parameters": { "text": "=Today's Date: {{ $json.formattedDate }}\n\nFetch website performance data from Databox for the last 7 days compared to the previous 7 days. Calculate the exact date ranges based on today's date.\n\nFocus on: traffic volume, engagement quality (bounce rate, pages per session, session duration), and conversion metrics (goal completions, conversion rate).", "options": { "systemMessage": "You are a website analytics specialist. Fetch website performance data from Databox via MCP and produce a structured plain-text analysis.\n\nSTEP-BY-STEP:\n1. Call list_accounts to get the account ID.\n2. Call list_data_sources to find connected website analytics sources (Google Analytics, GA4, Adobe Analytics, etc.).\n3. For each website analytics source found, fetch these metrics for both the last 7 days and the previous 7 days:\n - Sessions or Users\n - Bounce Rate\n - Pages per Session\n - Average Session Duration\n - Goal Completions or Conversions\n - Conversion Rate\n4. Calculate week-over-week (WoW) percentage change for each metric.\n\nOUTPUT FORMAT:\nProduce a structured plain-text summary (NOT HTML) with:\n- Data source name\n- Sessions/Users: current value, WoW change %\n- Bounce Rate: current value, WoW change %\n- Pages per Session: current value, WoW change %\n- Avg Session Duration: current value, WoW change %\n- Goal Completions: current value, WoW change %\n- Conversion Rate: current value, WoW change %\n- 2-3 short observations about website behavior patterns\n\nIf no website analytics source is connected, output: \"No website analytics data available.\"\nDo NOT produce HTML - output plain text only." }, "promptType": "define" }, "retryOnFail": false, "typeVersion": 3, "alwaysOutputData": true }, { "id": "17afd3f1-7697-46bf-a303-7a91ba14b4c8", "name": "Databox MCP Tool", "type": "@n8n/n8n-nodes-langchain.mcpClientTool", "position": [ 752, 896 ], "parameters": { "options": {}, "endpointUrl": "https://mcp.databox.com/mcp", "authentication": "mcpOAuth2Api" }, "credentials": { "mcpOAuth2Api": { "name": "" } }, "typeVersion": 1.2 }, { "id": "7843456d-dba3-484c-8982-f76640ac7ce6", "name": "Paid Acquisition Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 960, 704 ], "parameters": { "text": "=Today's Date: {{ $('Get Current Date').item.json.formattedDate }}\n\nWebsite analysis from Agent 1:\n{{ $json.output }}\n\nNow fetch paid acquisition performance data from Databox for the same period (last 7 days vs previous 7 days). Focus on all connected paid advertising platforms - fetch spend efficiency (CPC, CTR, ROAS), volume metrics (impressions, clicks), and conversion data per platform.", "options": { "systemMessage": "You are a paid acquisition analyst. Fetch paid channel performance data from Databox via MCP and produce a structured plain-text analysis.\n\nSTEP-BY-STEP:\n1. Call list_accounts to get the account ID.\n2. Call list_data_sources to find all connected paid advertising platforms (Google Ads, Facebook Ads, LinkedIn Ads, TikTok Ads, Microsoft Ads, Reddit Ads, Snapchat Ads, Pinterest Ads, X/Twitter Ads, YouTube Ads).\n3. For EACH connected paid platform, fetch these metrics for both the last 7 days and the previous 7 days:\n - Spend or Cost\n - Clicks\n - CPC (Cost Per Click)\n - CTR (Click-Through Rate)\n - Impressions\n - ROAS or Conversions\n4. Calculate WoW percentage change for each metric on each platform.\n5. Calculate aggregated totals across all platforms: total spend, total clicks, total impressions, total conversions, average CPC, average CTR.\n\nOUTPUT FORMAT:\nProduce a structured plain-text summary (NOT HTML) with:\n- For EACH connected platform: name, spend, clicks, CPC, CTR, impressions, ROAS/conversions with WoW changes\n- Aggregated totals across all platforms with WoW changes\n- 2-3 short observations about paid performance patterns\n\nIf no paid ads platforms are connected, output: \"No paid advertising data available.\"\nDo NOT produce HTML - output plain text only." }, "promptType": "define" }, "retryOnFail": false, "typeVersion": 3, "alwaysOutputData": true }, { "id": "e6de4884-bf7c-45f1-a5c8-3481b9ed5f5b", "name": "Correlation Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 1328, 704 ], "parameters": { "text": "=Today's Date: {{ $('Get Current Date').item.json.formattedDate }}\n\nWEBSITE ANALYSIS (from Agent 1):\n{{ $('Website Analysis Agent').item.json.output }}\n\nPAID ACQUISITION ANALYSIS (from Agent 2):\n{{ $json.output }}\n\nUsing ONLY the data above (do not call any external tools), produce a complete daily intelligence report as an HTML email body. Structure it as follows:\n\n1. Executive Summary - 2-3 sentences on the single most important cross-channel finding\n2. Website Performance - table with key metrics and WoW changes\n3. Paid Acquisition - per-platform tables with spend, clicks, CPC, CTR, ROAS and WoW changes\n4. Correlation Analysis - the unique insight layer:\n - Does this week's paid spend correlate with website traffic volume?\n - Which paid channels drive the highest-quality visitors (lowest bounce rate, highest conversion rate)?\n - Estimated cost per website conversion by channel (total paid spend divided by goal completions)\n - Efficiency ranking of paid channels (best to worst)\n5. Recommendations - exactly 3 specific, actionable recommendations based on the data\n6. Footer\n\nOutput ONLY the HTML body content starting with a
tag. Do not include , , or tags.", "options": { "systemMessage": "You are a marketing intelligence analyst specializing in cross-channel correlation analysis. You do NOT call any tools - you analyze only the data provided in the prompt.\n\nYour job is to synthesize website analytics and paid acquisition data into a single HTML report that surfaces insights neither dataset could reveal on its own.\n\nHTML FORMATTING RULES:\n- Use Databox brand color #3164FA for all headings and table headers\n- Color WoW changes: green (#059669) for improvements, red (#dc2626) for declines\n- Font: 'Helvetica Neue', Arial, sans-serif throughout\n- Table headers: background #3164FA, white text, padding 12px\n- Alternating table rows: #ffffff and #f9fafb\n- Section headings:

with color #3164FA, font-weight 600\n- Executive summary box: background #F7F9FC, left border 4px solid #3164FA, padding 15px, border-radius 4px\n- Correlation insights box: background #FFF8E7, left border 4px solid #F59E0B, padding 15px, border-radius 4px\n- Recommendations box: background #F0FDF4, left border 4px solid #059669, padding 15px, border-radius 4px\n- Footer: small gray text, border-top, font-size 11px\n- No em-dashes - use hyphens instead\n- Format numbers with thousand separators (commas)\n- Currency as $X,XXX.XX, percentages as X.XX%\n- If data for a section is missing, note it gracefully rather than showing an error" }, "promptType": "define" }, "retryOnFail": false, "typeVersion": 3, "alwaysOutputData": true }, { "id": "7ff62ebf-8eca-4085-ab2d-aa3ad1656f22", "name": "Prepare Email", "type": "n8n-nodes-base.code", "position": [ 1808, 704 ], "parameters": { "jsCode": "const agentOutput = $input.first().json.output || '';\n\nconst today = new Date();\nconst dateStr = today.toLocaleDateString('en-US', {\n month: 'long',\n day: 'numeric',\n year: 'numeric'\n});\n\nreturn [{\n json: {\n subject: `Daily Paid Acquisition Report - ${dateStr}`,\n htmlBody: agentOutput\n }\n}];" }, "typeVersion": 2 }, { "id": "f19469a6-7667-4595-93ae-750695e0b3d6", "name": "Send Email", "type": "n8n-nodes-base.gmail", "notes": "Update the To field with the recipient email address", "position": [ 2000, 704 ], "parameters": { "sendTo": "user@example.com", "message": "={{ $json.htmlBody }}", "options": {}, "subject": "={{ $json.subject }}" }, "credentials": { "gmailOAuth2": { "name": "" } }, "typeVersion": 2.2 }, { "id": "5801443e-8b91-4c50-9c39-4a51ea8981aa", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 1728, 16 ], "parameters": { "color": 7, "width": 496, "height": 400, "content": "### How to connect Databox MCP Tool in n8n\n@[youtube](892KtXhv-vI)" }, "typeVersion": 1 }, { "id": "3a24b5cd-a138-4b86-845a-055af932339a", "name": "Databox MCP Tool 2", "type": "@n8n/n8n-nodes-langchain.mcpClientTool", "position": [ 1136, 896 ], "parameters": { "options": {}, "endpointUrl": "https://mcp.databox.com/mcp", "authentication": "mcpOAuth2Api" }, "credentials": { "mcpOAuth2Api": { "name": "" } }, "typeVersion": 1.2 }, { "id": "ec486f27-d925-477d-b2e3-bccbdcd08b4a", "name": "OpenAI Chat Model 3", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 1328, 896 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o", "cachedResultName": "gpt-4o" }, "options": { "maxTokens": 4096 }, "builtInTools": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 }, { "id": "26492b87-50d3-4104-9b3b-9aa3a4ca10f3", "name": "OpenAI Chat Model 2", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 928, 896 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o", "cachedResultName": "gpt-4o" }, "options": { "maxTokens": 2048 }, "builtInTools": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 }, { "id": "6644413f-f864-4e8a-b85f-de5c3794cfbf", "name": "OpenAI Chat Model 1", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 560, 896 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o", "cachedResultName": "gpt-4o" }, "options": { "maxTokens": 2048 }, "builtInTools": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "39bced5e-61fc-44d0-956b-acd8337a6cf5", "connections": { "Prepare Email": { "main": [ [ { "node": "Send Email", "type": "main", "index": 0 } ] ] }, "Every Day 8 AM": { "main": [ [ { "node": "Get Current Date", "type": "main", "index": 0 } ] ] }, "Databox MCP Tool": { "ai_tool": [ [ { "node": "Website Analysis Agent", "type": "ai_tool", "index": 0 } ] ] }, "Get Current Date": { "main": [ [ { "node": "Website Analysis Agent", "type": "main", "index": 0 } ] ] }, "Correlation Agent": { "main": [ [ { "node": "Prepare Email", "type": "main", "index": 0 } ] ] }, "Databox MCP Tool 2": { "ai_tool": [ [ { "node": "Paid Acquisition Agent", "type": "ai_tool", "index": 0 } ] ] }, "OpenAI Chat Model 1": { "ai_languageModel": [ [ { "node": "Website Analysis Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenAI Chat Model 2": { "ai_languageModel": [ [ { "node": "Paid Acquisition Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "OpenAI Chat Model 3": { "ai_languageModel": [ [ { "node": "Correlation Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Paid Acquisition Agent": { "main": [ [ { "node": "Correlation Agent", "type": "main", "index": 0 } ] ] }, "Website Analysis Agent": { "main": [ [ { "node": "Paid Acquisition Agent", "type": "main", "index": 0 } ] ] } } }