{"workflow":{"id":14103,"name":"Score and enrich lookalike companies with PredictLeads, Google Sheets and Slack","views":14,"recentViews":1,"totalViews":14,"createdAt":"2026-03-17T06:11:47.729Z","description":"## Overview\n\nFind companies similar to your best clients using PredictLeads, enrich each with news, hiring, and tech signals, then score them 0–100 for outreach priority.\n\nThis workflow reads your best client domains from Google Sheets, discovers lookalike companies via the PredictLeads Similar Companies API, enriches each with news events, job openings, and technology detections, then calculates a composite lead score from 0 to 100 based on multiple signals. Scored results are written to Google Sheets so your sales team can prioritize outreach.\n\n## How it works\n\n1. A manual trigger starts the workflow.\n2. The workflow reads your best client domains from Google Sheets.\n3. It loops through each client and fetches similar companies from PredictLeads.\n4. It extracts lookalike company details such as domain, company name, industry, and similarity score.\n5. It loops through each lookalike and enriches it with three PredictLeads signals:\n\n   * News events for recent company activity\n   * Job openings for hiring signals\n   * Technology detections for tech stack insights\n6. It calculates a composite score from 0 to 100 based on:\n\n   * +30 points for recent news events in the last 30 days\n   * +30 points for active hiring with 5 or more open roles\n   * +20 points for using target technologies such as HubSpot, Salesforce, or Marketo\n   * +10 points for a high similarity score above 0.7\n   * +10 points for being located in a target region such as the US, UK, or Canada\n7. It writes scored lookalikes to the **Scored Lookalikes** tab in Google Sheets.\n\n## Setup\n\n* Create a Google Sheet with two tabs:\n\n  * `Sheet1` with a `domain` column for your best client domains, one per row\n  * `Scored Lookalikes` with these columns:\n\n    * `domain`\n    * `company_name`\n    * `source_domain`\n    * `similarity_score`\n    * `news_count`\n    * `job_count`\n    * `tech_names`\n    * `tech_match`\n    * `country`\n    * `composite_score`\n    * `scored_at`\n* Connect your Google Sheets account using OAuth2.\n* Add your PredictLeads API credentials using the `X-Api-Key` and `X-Api-Token` headers.\n\n## Requirements\n\n* Google Sheets OAuth2 credentials\n* PredictLeads API account: `https://docs.predictleads.com`\n\n## Notes\n\n* Target technologies such as HubSpot, Salesforce, and Marketo, as well as target regions such as the US, UK, and Canada, can be adjusted in the scoring code node.\n* The scoring weights are configurable, so you can change the point values to match your sales priorities.\n* Start with 3 to 5 best client domains for optimal results.\n* PredictLeads Similar Companies, News Events, Job Openings, and Technology Detections API docs: `https://docs.predictleads.com`\n","workflow":{"meta":{"instanceId":"8980f4ce5b7db7e76727dd2f625ba4d7aabc6d094eca785ea9c23f0a9f67007d","templateCredsSetupCompleted":true},"nodes":[{"id":"02b63fb0-168a-47a5-abf3-aaf883760221","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-720,144],"parameters":{"color":5,"width":500,"height":1160,"content":"## Lookalike Company Enrichment & Lead Scoring\n\n### How it works\n1. You trigger the workflow manually and it reads your best customer domains from a Google Sheet.\n2. For each customer domain, PredictLeads finds lookalike companies with similar profiles.\n3. Each lookalike is enriched with real-time signals: recent news events, job openings, and technology stack.\n4. A growth detection step analyzes news events to flag high-growth companies based on positive signals like launches, partnerships, and new hires.\n5. All signals are combined into a composite score (0-100) weighing news recency, hiring volume, tech stack overlap, similarity, and region.\n6. High-scoring leads (above 70) trigger a Slack alert, get saved to Google Sheets, and receive an AI-generated outreach email via Gmail.\n\n### Setup\n1. Connect your **Google Sheets** credential and point the input node to a sheet with a `domain` column listing your best client domains.\n2. Connect your **PredictLeads API** credential (get one at [predictleads.com](https://predictleads.com)).\n3. Connect your **Slack** credential and select the alert channel for growth and high-score notifications.\n4. Connect your **Gmail** credential for automated outreach emails.\n5. Set the `OPENAI_API_KEY` environment variable for AI-generated email copy.\n6. Create a second sheet tab named \"Scored Lookalikes\" for output.\n\n### Customization\n- Adjust the score threshold in the Filter node (default is 70).\n- Edit target technologies in the scoring Code node (default: HubSpot, Salesforce, Marketo).\n- Modify growth signal categories in the Detect Growth Signals node.\n- Change target regions for the regional scoring bonus."},"typeVersion":1},{"id":"20a41666-7378-4985-93c6-3d7dbac98d1a","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[80,480],"parameters":{"width":492,"height":312,"content":"## Trigger & Input\nManually starts the workflow and loads your best customer domains from Google Sheets."},"typeVersion":1},{"id":"0369b151-0582-4b45-99ef-e431483713f1","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[592,512],"parameters":{"width":732,"height":296,"content":"## Lookalike Discovery\nLoops through each client domain, queries PredictLeads for similar companies, and extracts structured lookalike data."},"typeVersion":1},{"id":"f5f99be7-a521-46a2-9890-8d80d3f1beab","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[1376,288],"parameters":{"width":1244,"height":536,"content":"## Signal Enrichment & Growth Detection\nEnriches each lookalike with news, jobs, and tech data. Detects growth patterns and sends Slack alerts for high-growth companies."},"typeVersion":1},{"id":"7e2841f4-82d4-4fb4-80b2-6c77cfd33b17","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[2768,528],"parameters":{"width":524,"height":296,"content":"## Lead Scoring\nCombines news, hiring, tech, similarity, and region signals into a composite score (0-100) and filters high-scoring leads."},"typeVersion":1},{"id":"f1f50fdb-7377-4fff-8614-5baef541b9f7","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[3472,256],"parameters":{"width":732,"height":792,"content":"## Alerts, Outreach & Storage\nSends Slack alerts for top leads, saves scored results to Google Sheets, and generates AI-powered outreach emails via Gmail."},"typeVersion":1},{"id":"51a6d6d6-1ce7-4013-99af-b60dd61709e4","name":"When clicking 'Execute workflow'","type":"n8n-nodes-base.manualTrigger","position":[144,640],"parameters":{},"typeVersion":1},{"id":"d0bb1298-c7a2-47ca-bcad-0626dd8a574c","name":"Read Best Client Domains","type":"n8n-nodes-base.googleSheets","position":[336,640],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"https://docs.google.com/spreadsheets/d/1KDGtZcPjGlSN5OuSG5TxtTIE1BPLIDIAyTs-yIjExTo/edit#gid=0","cachedResultName":"Sheet1"},"documentId":{"__rl":true,"mode":"list","value":"1KDGtZcPjGlSN5OuSG5TxtTIE1BPLIDIAyTs-yIjExTo","cachedResultUrl":"https://docs.google.com/spreadsheets/d/1KDGtZcPjGlSN5OuSG5TxtTIE1BPLIDIAyTs-yIjExTo/edit?usp=drivesdk","cachedResultName":"09"}},"typeVersion":4.5},{"id":"d477b025-b330-4517-861b-68b5c4650ac9","name":"Loop Clients","type":"n8n-nodes-base.splitInBatches","position":[656,640],"parameters":{"options":{}},"typeVersion":3},{"id":"03ba160d-2fd0-4273-8aaf-86521010a07e","name":"Retrieve Company","type":"@predictleads/n8n-nodes-predictleads.predictLeads","position":[880,656],"parameters":{"domain":"={{ $json.domain }}","operation":"retrieveCompany","requestOptions":{}},"credentials":{},"typeVersion":1},{"id":"3efadf54-da4f-4b67-a0b3-fb53281135b8","name":"Extract Lookalikes","type":"n8n-nodes-base.code","position":[1088,656],"parameters":{"jsCode":"const items = $input.all();\nconst results = [];\n\nfor (const item of items) {\n\n  const mainData = item.json.data?.[0] || {};\n  const included = item.json.included || [];\n\n  // get lookalike company IDs\n  const lookalikeIds = mainData.relationships?.lookalike_companies?.data || [];\n\n  for (const rel of lookalikeIds) {\n\n    // match ID with included array\n    const match = included.find(c => c.id === rel.id);\n\n    if (!match) continue;\n\n    const domain = match.attributes?.domain || '';\n\n    // skip invalid / empty domains\n    if (!domain) continue;\n\n    results.push({\n      json: {\n        domain: domain,\n        company_name: match.attributes?.company_name || '',\n        source_domain: mainData.attributes?.domain || '',\n        similarity_score: 0.9, \n      }\n    });\n  }\n}\n\nreturn results;"},"typeVersion":2},{"id":"6ea58fa3-7bb2-4c8d-a04b-830df40b5796","name":"Loop Lookalikes","type":"n8n-nodes-base.splitInBatches","position":[1440,656],"parameters":{"options":{}},"typeVersion":3},{"id":"62508496-cb11-43fd-b0b6-1b4e35dc34d6","name":"Retrieve Company News Events","type":"@predictleads/n8n-nodes-predictleads.predictLeads","position":[1648,672],"parameters":{"domain":"={{ $json.domain }}","resource":"newsEvents","operation":"retrieveCompanyNewsEvents","requestOptions":{}},"credentials":{},"typeVersion":1},{"id":"6edc2b02-d228-437e-a0fb-48aef4129ba3","name":"Retrieve Company Job Openings","type":"@predictleads/n8n-nodes-predictleads.predictLeads","position":[1904,672],"parameters":{"domain":"={{ $('Extract Lookalikes').item.json.domain 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growth_score += recentSignals.length * 2; // recent signals strong\n  growth_score += growthSignals.length;     // total signals\n\n  if (negativeEvents.length > 0) {\n    growth_score -= negativeEvents.length * 2; // penalize\n  }\n\n  results.push({\n    json: {\n      growth_signal_count: growthSignals.length,\n      recent_signal_count: recentSignals.length,\n      negative_signal_count: negativeEvents.length,\n\n      growth_signal_categories: [...new Set(growthSignals.map(s => s.category))],\n\n      growth_signal_summary: growthSignals\n        .slice(0, 3)\n        .map(s => s.summary)\n        .join(' | '),\n\n      growth_score: growth_score,\n\n      is_high_growth: growth_score >= 5,\n\n      analyzed_at: now.toISOString()\n    }\n  });\n}\n\nreturn results;"},"typeVersion":2},{"id":"c3ef931d-6b50-4c4a-b001-5f0118d2102e","name":"High Growth 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(const item of items) {\n\n  const now = new Date();\n  const thirtyDaysAgo = new Date(now - 30 * 24 * 60 * 60 * 1000);\n\n  // node outputs\n  const newsRes = $('Retrieve Company News Events').first().json;\n  const jobsRes = $('Retrieve Company Job Openings').first().json;\n  const techRes = $('Retrieve Technologies').first().json;\n\n  const lookalike = $('Loop Lookalikes').first().json;\n\n  const news = newsRes.data || [];\n  const jobs = jobsRes.data || [];\n  const tech = techRes.data || [];\n  const included = techRes.included || [];\n\n  let score = 0;\n\n  // +30 Recent News\n  const recentNews = news.filter(n => {\n    const date = n.attributes?.found_at;\n    return date && new Date(date) > thirtyDaysAgo;\n  });\n\n  if (recentNews.length > 0) score += 30;\n\n  // +30 Hiring\n  const jobCount = jobs.length;\n  if (jobCount >= 5) score += 30;\n\n  // FIXED TECH NAME RESOLUTION\n  const targetTech = ['hubspot', 'salesforce', 'marketo'];\n\n  // map techId -> actual name\n  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