Describe the problem/error/question
I am having an issue while creating/updating leads in our Zoho CRM. The issue only seems to arise when trying to add custom fields.
What is the error message (if any)?
{
“errorMessage”: “There was an unknown issue while executing the node”,
“errorDescription”: “Double-check the node configuration and the service it connects to. Check the error details below and refer to the n8n documentation to troubleshoot the issue.“,
“errorDetails”: {},
“n8nDetails”: {
“nodeName”: “Create or update a lead”,
“nodeType”: “n8n-nodes-base.zohoCrm”,
“nodeVersion”: 1,
“resource”: “lead”,
“operation”: “upsert”,
“time”: “4/29/2026, 11:54:43 AM”,
“n8nVersion”: “2.17.5 (Cloud)”,
“binaryDataMode”: “filesystem”,
“stackTrace”: [
“NodeApiError: There was an unknown issue while executing the node”,
" at ExecuteContext.zohoApiRequest (/usr/local/lib/node_modules/n8n/node_modules/.pnpm/n8n-nodes-base@file+packages+nodes-base*@aws-sdk+credential-providers@3.808.0_asn1.js@5_8da18263ca0574b0db58d4fefd8173ce/node_modules/n8n-nodes-base/nodes/Zoho/GenericFunctions.ts:82:9)”,
" at processTicksAndRejections (node:internal/process/task_queues:104:5)“,
" at ExecuteContext.execute (/usr/local/lib/node_modules/n8n/node_modules/.pnpm/n8n-nodes-base@file+packages+nodes-base_@aws-sdk+credential-providers@3.808.0_asn1.js@5_8da18263ca0574b0db58d4fefd8173ce/node_modules/n8n-nodes-base/nodes/Zoho/ZohoCrm.node.ts:828:22)”,
" at WorkflowExecute.executeNode (/usr/local/lib/node_modules/n8n/node_modules/.pnpm/n8n-core@file+packages+core_@opentelemetry+api@1.9.0_@opentelemetry+exporter-trace-otlp_2d19a9be2839cb42cd2e8c9cacd05d5a/node_modules/n8n-core/src/execution-engine/workflow-execute.ts:1048:9)“,
" at WorkflowExecute.runNode (/usr/local/lib/node_modules/n8n/node_modules/.pnpm/n8n-core@file+packages+core_@opentelemetry+api@1.9.0_@opentelemetry+exporter-trace-otlp_2d19a9be2839cb42cd2e8c9cacd05d5a/node_modules/n8n-core/src/execution-engine/workflow-execute.ts:1239:11)”,
" at /usr/local/lib/node_modules/n8n/node_modules/.pnpm/n8n-core@file+packages+core_@opentelemetry+api@1.9.0_@opentelemetry+exporter-trace-otlp_2d19a9be2839cb42cd2e8c9cacd05d5a/node_modules/n8n-core/src/execution-engine/workflow-execute.ts:1687:27”,
" at /usr/local/lib/node_modules/n8n/node_modules/.pnpm/n8n-core@file+packages+core_@opentelemetry+api@1.9.0_@opentelemetry+exporter-trace-otlp_2d19a9be2839cb42cd2e8c9cacd05d5a/node_modules/n8n-core/src/execution-engine/workflow-execute.ts:2339:11"
]
}
}
Please share your workflow
(Select the nodes on your canvas and use the keyboard shortcuts CMD+C/CTRL+C and CMD+V/CTRL+V to copy and paste the workflow.)
{
"nodes": [
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Message a model in Perplexity",
"messages": {
"message": [
{
"content": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('message0_Text', ``, 'string') }}",
"role": "system"
},
{
"content": "=Company Domain: {{ $json['Company Domain'] }}"
}
]
},
"options": {},
"requestOptions": {}
},
"id": "87e68835-2a7a-448f-afcb-660ecb4874ce",
"name": "Perplexity Research Tool",
"type": "n8n-nodes-base.perplexityTool",
"typeVersion": 1,
"position": [
480,
336
],
"credentials": {
"perplexityApi": {
"id": "5JJhQfjXLPJvKzjT",
"name": "Perplexity account"
}
}
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Research Lead on LinkedIn Perplexity",
"messages": {
"message": [
{
"content": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('message0_Text', ``, 'string') }}",
"role": "system"
},
{
"content": "=LinkedIN URL: {{ $json['LinkedIn Profile URL'] }}"
}
]
},
"options": {},
"requestOptions": {}
},
"id": "21f09e90-00ce-4fdd-844a-03953f369f22",
"name": "Perplexity for LinkedIn Research",
"type": "n8n-nodes-base.perplexityTool",
"typeVersion": 1,
"position": [
656,
336
],
"credentials": {
"perplexityApi": {
"id": "5JJhQfjXLPJvKzjT",
"name": "Perplexity account"
}
}
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "1wmNeu8bkUP6e0ewHb4E4GIGBsdyzHl00fxcamq7VJnQ",
"mode": "list",
"cachedResultName": "Lead Research Analyst",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1wmNeu8bkUP6e0ewHb4E4GIGBsdyzHl00fxcamq7VJnQ/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "Sheet1",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/1wmNeu8bkUP6e0ewHb4E4GIGBsdyzHl00fxcamq7VJnQ/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"Date": "={{ $('Research Agent').item.json.output.date }}",
"Email": "={{ $('Research Agent').item.json.output.email }}",
"Name": "={{ $('Research Agent').item.json.output.leadName }}",
"Decision maker?": "={{ $('Research Agent').item.json.output.decisionMaker }}",
"Company Name": "={{ $('Research Agent').item.json.output.companyName }}",
"Revenue": "={{ $('Research Agent').item.json.output.monthlyRevenue }}",
"Interest Summary": "={{ $('Research Agent').item.json.output.CPPInterestSummary }}",
"Potential Company Solutions": "={{ $('Research Agent').item.json.output.potentialCompanySolutions }}",
"Score": "={{ $json.output.score }}",
"Phone Number": "={{ $('Research Agent').item.json.output.phonenumber }}"
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"matchingColumns": [],
"schema": [
{
"id": "Date",
"displayName": "Date",
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"type": "string",
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{
"id": "Email",
"displayName": "Email",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Name",
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"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
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{
"id": "Decision maker?",
"displayName": "Decision maker?",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Company Name",
"displayName": "Company Name",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Revenue",
"displayName": "Revenue",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Interest Summary",
"displayName": "Interest Summary",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Potential Company Solutions",
"displayName": "Potential Company Solutions",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Score",
"displayName": "Score",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "Phone Number",
"displayName": "Phone Number",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.7,
"position": [
1456,
128
],
"id": "e227ee83-6a19-4bf4-8561-d428c31648b7",
"name": "Append row in sheet",
"credentials": {
"googleSheetsOAuth2Api": {
"id": "jAtkSnPzs7rCgiD8",
"name": "Google Sheets OAuth2 API"
}
}
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
336,
320
],
"id": "f8f17995-6d87-478a-8d1e-b00742d4269c",
"name": "Brain1",
"credentials": {
"openAiApi": {
"id": "GoiPh1Fp6oeQ6J6i",
"name": "OpenAI account"
}
}
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"builtInTools": {},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
992,
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],
"id": "72da7f3d-8c57-4b95-bf10-bdbcc2870431",
"name": "Brain2",
"credentials": {
"openAiApi": {
"id": "GoiPh1Fp6oeQ6J6i",
"name": "OpenAI account"
}
}
},
{
"parameters": {
"formTitle": "Lead Intelligence Research Request",
"formDescription": "Submit lead information to get comprehensive intelligence and insights",
"formFields": {
"values": [
{
"fieldLabel": "Full Name",
"placeholder": "Eg. John Smith"
},
{
"fieldLabel": "Company Domain",
"placeholder": "Company website domain (e.g., example.com)"
},
{
"fieldLabel": "Company Name"
},
{
"fieldLabel": "LinkedIn Profile URL",
"placeholder": "LinkedIn profile URL (optional)"
},
{
"fieldLabel": "Company City"
},
{
"fieldLabel": "Industry Type"
},
{
"fieldLabel": "Phone Number"
},
{
"fieldLabel": "Email Address"
}
]
},
"options": {
"buttonLabel": "Start Research"
}
},
"id": "75dcc00a-8191-4f71-b37e-ca7709f95d5f",
"name": "New Lead",
"type": "n8n-nodes-base.formTrigger",
"typeVersion": 2.3,
"position": [
80,
128
],
"webhookId": "18fa90a4-12bd-4c85-a1aa-694a01b7bffc"
},
{
"parameters": {
"promptType": "define",
"text": "=\n{{ $('New Lead').item.json['Company Domain'] }}\n\n{{ $json['Full Name'] }}\n\n{{ $json['LinkedIn Profile URL'] }}\n\n{{ $json['Company City'] }}\n\n{{ $json['Industry Type'] }}\n\n{{ $json['Company Name'] }}\n\n{{ $json['Phone Number'] }}\n\n{{ $json['Email Address'] }}\n",
"hasOutputParser": true,
"options": {
"systemMessage": "=## Overview\n\nYou are an **expert lead intelligence analyst** specializing in **Credit Card Processing Services (Merchant Services) sales, underwriting, and implementation readiness**.\n\nYour goal is to transform all available data about a lead and their company into a **concise, highly actionable intelligence report** that helps a **Merchant Services ISO, Payment Processor, or Sales Organization. Max Iterations is 9 **:\n\n* Win the deal\n* Optimize pricing (interchange + margin)\n* Reduce risk exposure\n* Accelerate onboarding and implementation\n* Identify long-term revenue opportunities\n\n---\n\n## 1. Research Tools & Mandatory Usage\n\n**Always use external research tools (Perplexity, LinkedIn, job boards, review platforms)** to gather intelligence on:\n\n### Business & Operational Signals\n\n* **Recent job postings**\n\n * High-volume hiring (cashiers, support, billing, finance)\n * Manual processing roles (AP/AR, reconciliation, bookkeeping)\n* **Revenue model & transaction flow**\n\n * Online vs in-person vs hybrid\n * Subscription, one-time, high-ticket, or recurring billing\n\n### Payment & Infrastructure Insights\n\n* Current or likely **POS systems** (Toast, Clover, Square, Lightspeed, Shopify, etc.)\n* **Payment processors** (Stripe, Square, PayPal, legacy processors)\n* E-commerce platforms (Shopify, WooCommerce, Magento)\n* Billing systems, invoicing tools, or ERP integrations\n\n### Customer & Experience Signals\n\n* Reviews (Google, Yelp, Glassdoor, Trustpilot)\n\n * Complaints about:\n\n * Checkout friction\n * Payment failures\n * Refund delays\n * Customer service issues\n\n### Industry & Competitive Trends\n\n* Payment trends in their vertical (e.g., high-risk, card-present, card-not-present)\n* Competitor adoption of:\n\n * POS upgrades\n * Omnichannel payments\n * Subscription billing\n * Buy Now Pay Later (BNPL)\n\n### Merchant Services-Specific Indicators\n\n* Mentions of:\n\n * High credit card fees\n * Chargebacks\n * Fraud issues\n * Payment disputes\n * Outdated terminals or systems\n\n---\n\n## 2. Internal Data Sources to Use\n\nSystematically synthesize:\n\n1. **Merchant Interest Signals**\n\n * Any indication they are exploring:\n\n * Lower fees\n * New processors\n * POS upgrades\n * Payment optimization\n\n2. **Firmographic Data (Clearbit, etc.)**\n\n * Industry, size, revenue band\n * Physical locations vs online presence\n * Growth stage\n\n3. **Contact Intelligence (Hunter, LinkedIn)**\n\n * Decision-makers:\n\n * Owner, CFO, COO\n * Head of Finance\n * Operations Manager\n * IT / Systems Manager\n * Identify who influences:\n\n * Payment decisions\n * Vendor selection\n * Financial operations\n\n4. **Recent Company Developments**\n\n * Expansion, new locations\n * E-commerce launches\n * Pricing changes\n * Operational scaling\n\n5. **Lead Score & Service Interest**\n\n * Use all scoring inputs to determine:\n\n * Likelihood to switch processors\n * Revenue potential\n * Implementation complexity\n\n---\n\n## 3. Core Analytic Tasks\n\n---\n\n### 3.1 Merchant Readiness Assessment (Score 1–10)\n\nProvide a **Merchant Services Readiness Score (1–10)** based on:\n\n* **Current payment infrastructure maturity**\n\n * Modern POS vs outdated terminals\n * Integrated vs fragmented systems\n\n* **Transaction complexity**\n\n * Volume, ticket size, recurring vs one-time\n\n* **Fee sensitivity**\n\n * Likelihood they are overpaying on interchange or markup\n\n* **Operational efficiency**\n\n * Manual reconciliation, invoicing, reporting\n\n* **Growth stage**\n\n * Expansion signals = higher need for scalable payments\n\n* **Lead Score & Interest Signals**\n\n * Explicitly incorporate into reasoning\n\n---\n\n### 3.2 Revenue & Optimization Opportunity Discovery\n\nIdentify **merchant services opportunities**, including:\n\n#### Cost Reduction\n\n* Interchange optimization\n* Eliminating hidden fees\n* Reducing processor markups\n\n#### Revenue Expansion\n\n* Upselling:\n\n * POS systems\n * Payment terminals\n * Gateway services\n * Subscription billing tools\n\n#### Operational Efficiency\n\n* Faster settlements\n* Automated reconciliation\n* Integrated reporting\n\n#### Time Horizons\n\n* **Quick wins (≤ 30–60 days)**\n* **Mid-term (2–6 months)**\n* **Long-term (> 6 months)**\n\nFor each opportunity:\n\n* Estimate **revenue potential**\n* Complexity\n* Close probability\n\n---\n\n### 3.3 Decision-Maker & Champion Identification\n\nIdentify key stakeholders:\n\n* **Economic Buyer**\n\n * Owner, CFO\n\n* **Operational Influencers**\n\n * Operations Manager\n * Finance / Accounting Lead\n\n* **Technical Gatekeepers**\n\n * IT / Systems\n\nFor each:\n\n* Name, title\n* Influence level\n* Role in decision process\n* Likelihood to support switching processors\n\n---\n\n### 3.4 Pain Point → Merchant Solution Mapping\n\nIdentify pain points such as:\n\n* High processing fees\n* Chargebacks and fraud\n* Slow settlements\n* Poor POS experience\n* Lack of reporting/visibility\n* Checkout friction (online or in-store)\n\nMap to solutions:\n\n* Fee reduction / interchange optimization\n* Chargeback mitigation tools\n* Faster funding (next-day or same-day)\n* POS upgrades\n* Omnichannel payment solutions\n* Subscription or recurring billing systems\n\nAdd **personalized pitch angles**, e.g.:\n\n> “Based on your transaction volume, switching processors could reduce fees by X% annually.”\n\n---\n\n### 3.5 Competitive Pressure Analysis (Score 1–10)\n\nEvaluate urgency based on:\n\n* Competitors adopting:\n\n * Modern POS systems\n * Seamless checkout experiences\n * Flexible payment options\n\n* Industry shifts:\n\n * Cashless trends\n * E-commerce growth\n * Customer expectations for speed & convenience\n\nProvide a **Competitive Pressure Score (1–10)** with reasoning.\n\n---\n\n### 3.6 Industry-Specific Merchant Use Case Matcher\n\nRecommend **3–5 merchant service use cases**, tailored to:\n\n* Industry (restaurant, retail, SaaS, healthcare, etc.)\n* Company size\n\nInclude:\n\n* POS recommendations\n* Payment flow improvements\n* Revenue optimization strategies\n\nHighlight **quick wins with fast ROI**.\n\n---\n\n## 4. Required Report Structure (Markdown Output)\n\n---\n\n### **EXECUTIVE SUMMARY**\n\n* Lead Score & Priority\n* Company Overview & Health Score (1–10)\n* Merchant Readiness Score (1–10)\n* Estimated Monthly Processing Volume (Based on industry type and location)\n* Revenue Opportunity Estimate\n* Recommended Timeline\n* Engagement Priority\n\n---\n\n### **KEY STAKEHOLDERS**\n\n* Decision Makers\n* Influencers\n* Recommended Primary Contact\n\n---\n\n### **COMPANY INTELLIGENCE**\n\n* Recent News & Developments\n* Industry Position\n* Payment Infrastructure Insights\n* Customer Experience Signals\n* Merchant Service Indicators\n\n---\n\n### **USE CASE RECOMMENDATIONS**\n\n* Industry-Specific Payment Solutions\n* Quick Wins (≤ 60 days)\n* Implementation Scope\n* Upsell Opportunities\n\n---\n\n### **PAIN POINT ANALYSIS**\n\n* Key Pain Points\n* Mapped Merchant Solutions\n* Personalized Sales Angles\n\n---\n\n### **OPPORTUNITY ANALYSIS**\n\n* Competitive Pressure Score (1–10)\n* Revenue Opportunities\n* Cost Reduction Potential\n* ROI Estimates\n\n---\n\n### **ENGAGEMENT STRATEGY**\n\n* Recommended Sales Approach\n* Value Propositions\n* Objection Handling\n* Quick Wins to Lead With\n* Suggested Discovery Call Agenda\n\n---\n\n### **RISK FACTORS & CONSIDERATIONS**\n\n* High-risk industry indicators\n* Chargeback exposure\n* Pricing sensitivity\n* Contract lock-in risks\n* Technical limitations\n\n---\n\n## 5. Style & Output Guidelines\n\n* Use **clear, structured Markdown**\n* Keep insights **sales-focused and actionable**\n* Quantify:\n\n * Savings\n * Revenue potential\n * Volume estimates\n* Do **only fabricate unkown or missing data, based on quality info from you research, you can also use main company emails or phone numbers for contact info** — state “Not found” if needed\n* Continuously reference:\n\n * Lead Score\n * Merchant Readiness\n * Revenue opportunity\n\n---"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3,
"position": [
496,
128
],
"id": "a0ad7b17-15e4-4ebe-bf97-6b4665f26f54",
"name": "Research Agent"
},
{
"parameters": {
"content": "# 1. New Lead\n",
"height": 496,
"width": 256
},
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
0
],
"typeVersion": 1,
"id": "da015274-e2e2-46c6-acfa-ca7ca6be93e8",
"name": "Sticky Note"
},
{
"parameters": {
"content": "# 2. Lead & Company Research Agent\n",
"height": 496,
"width": 672,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"position": [
272,
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],
"typeVersion": 1,
"id": "f567b5b5-57c6-4c10-a3f6-69f2e284a21d",
"name": "Sticky Note1"
},
{
"parameters": {
"jsonSchemaExample": "{\n \"date\": \"2024-01-15\",\n \"email\": \"john@example.com\",\n \"leadName\": \"John Doe\",\n \"companyName\": \"Example Corp\",\n \"monthlyRevenue\": \"$75,000\",\n \"CPPInterestSummary\": \"High interest in new Credit Card Processing services, currently exploring POS solutions\",\n \"potentialCompanySolutions\": \"New POS, workflow automation, Payment Automation, fee elimination\",\n \"decisionMaker\": true,\n \"phonenumber\": \"770-391-9191\"\n\n \n}"
},
"id": "f5cb8ba5-ecbb-4f9d-992c-cc0ac1abff9b",
"name": "Output Structure",
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"typeVersion": 1.3,
"position": [
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},
{
"parameters": {
"content": "# 3. Lead Scoring Agent\n",
"height": 496,
"width": 432,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"position": [
960,
0
],
"typeVersion": 1,
"id": "dd0594ab-c58a-4209-b652-b90e75e2aedd",
"name": "Sticky Note2"
},
{
"parameters": {
"jsonSchemaExample": "{\n\t\"score\": 50\n}"
},
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
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"position": [
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],
"id": "3e6025be-406a-4d75-bd5b-2805e274d921",
"name": "Output Structure1"
},
{
"parameters": {
"content": "# 4. Notify Team / Contact\n",
"height": 496,
"width": 688,
"color": 6
},
"type": "n8n-nodes-base.stickyNote",
"position": [
1408,
0
],
"typeVersion": 1,
"id": "5b561725-c11f-447d-8648-44c0014d6cac",
"name": "Sticky Note3"
},
{
"parameters": {
"promptType": "define",
"text": "={{ $json.output }}\n{{ $('New Lead').item.json['Company Domain'] }}\n{{ $json.output.CPPInterestSummary }}\n{{ $json.output.potentialCompanySolutions }}\n{{ $json.output.monthlyRevenue }}",
"hasOutputParser": true,
"options": {
"systemMessage": "=You are a lead qualification specialist for a Credit Card Processing Services company (Merchant Services / Payment Processing). Score this lead based on their fit for our merchant services offerings.\n\nScoring Criteria:\n1. Processing Volume / Revenue Potential (0-30 points): Must be generating meaningful monthly card volume or business revenue. Award full points for businesses likely processing at a strong level (for example $50k+/month in revenue or equivalent card volume), scale down proportionally for lower volume, and assign 0 points if the business appears too small to be commercially viable.\n2. Business Maturity (0-25 points): Well-established business with stable operations, longevity, real market presence, and signs they can support a processor relationship and implementation, brand new business's like ones opened within the past 3 months should also score high.\n3. Merchant Services Need & Switch Potential (0-25 points): Based on available research findings, score for signals such as high processing fees, outdated POS/payment systems, chargeback pain, growth requiring better infrastructure, dissatisfaction with current provider, or clear need for better payment solutions.\n4. Company Size & Decision Structure (0-10 points): Team size, operational complexity, number of locations, and whether there is a clear owner/operator, finance leader, or decision-maker who can approve a processor switch.\n5. Industry Fit (0-10 points): Prioritize industries where merchant services has high value and clear use cases, such as retail, restaurants, healthcare, hospitality, automotive, professional services, e-commerce, and subscription-based businesses.\n\nProvide:\n- Overall Lead Score (0-100)\n- Only output the single number, no notes or text.\n\nBe data-driven and specific in your scoring."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3,
"position": [
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],
"id": "da377977-525e-47a0-aff5-42a88c1960b0",
"name": "Lead Scoring Agent"
},
{
"parameters": {
"rules": {
"values": [
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
"conditions": [
{
"leftValue": "={{ $('Lead Scoring Agent').item.json.output.score }}",
"rightValue": 70,
"operator": {
"type": "number",
"operation": "gte"
},
"id": "b16d4f0a-f52b-4b1b-85da-d6e1d406ca45"
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "Hot Lead (70-100)"
},
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict",
"version": 2
},
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