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Best prompts for ChatGPT for customer support using CRM integration
12 practical, copy-ready prompts for customer support agents using CRM integrations. Each prompt includes a concise explanation, a ready-to-use prompt with placeholders, a realistic example, and suggested AI models that work best.
Claude Sonnet 4
GPT-5
Claude Opus 4
Gemini 2.5 Pro
Gemini 2.5 Flash
You know that sinking feeling when a frustrated customer is on the line, you're staring at scattered CRM data, and ChatGPT gives you yet another generic response that sounds like a robot wrote it? You're not alone in wondering why AI feels so disconnected from the real tools and workflows that make customer support actually work. Most support agents are drowning in manual tasks, switching between systems, and crafting responses from scratch while customers wait.
These 12 battle-tested prompts transform your AI interactions from generic helpers into CRM-powered support machines that actually understand your workflow. From instantly pulling complete customer snapshots to detecting churn risks and generating knowledge base articles, each prompt is designed to work seamlessly with your existing CRM systems and support processes. Instead of spending precious minutes piecing together customer context and crafting responses, you'll have ready-to-use prompts that deliver personalized, accurate support in seconds.
1
Retrieve complete customer snapshot
Given this CRM payload for customer_id={{customer_id}}:
{{crm_payload}}
Return a concise customer snapshot in bullet form (1) identity and contact, (2) account status and tier, (3) last 5 interactions with date, channel, brief summary, and ticket IDs, (4) open issues and SLA deadlines, (5) relevant subscriptions or entitlements. Highlight items requiring immediate attention at the top.
Fetch and summarize key CRM fields and recent interactions for a specific customer so an agent can quickly prepare a response.
2
Draft SLA-compliant reply with CRM context
Using customer record: {{crm_record}} and ticket: {{ticket_text}}, draft a professional reply under 200 words that (1) greets the customer by name, (2) acknowledges the issue, (3) references their account/tier or recent activity from CRM, (4) provides clear next steps or workaround, (5) lists expected resolution time within SLA, and (6) ends with a call-to-action for confirmation. Keep tone empathetic and aligned with 'brand_voice': {{brand_voice}}.
Compose a customer-facing message that meets SLA, uses CRM context, and suggests next steps. Include personalization and a short troubleshooting checklist if relevant.
3
Summarize recent interactions for handoff
Given ticket_id={{ticket_id}} and interaction_history={{interactions}}, produce a 6–8 line handoff summary including: (1) current status, (2) what agent attempted, (3) what customer tried, (4) open diagnostics/logs, (5) next recommended step, and (6) ideal agent assignment or skills required.
Create a short, structured handoff note that lists what was done, pending items, and recommended next actions for the next agent.
4
Create CRM task and templated follow-up
Using ticket {{ticket_id}}, create a CRM task payload with fields: title, description, due_date (ISO), priority (Low/Medium/High), assignee_role, and call_outcome_template. Also generate a 2-sentence follow-up message to send after the task completes. Use context: {{ticket_summary}} and SLA: {{sla_hours}}.
Generate a CRM task entry and a short templated follow-up message for the customer, including due date and priority.
5
Identify upsell / cross-sell opportunities from CRM data
Given customer_profile={{profile}} and recent_usage={{usage_metrics}}, list up to three relevant upsell or cross-sell recommendations. For each recommendation include: product_name, reason (based on data), expected benefit, risk/objection, and a 1-line outreach message personalized for the customer name {{customer_name}}.
Analyze a customer’s CRM profile and activity to recommend tailored upsell or cross-sell offers and provide suggested messaging for outreach.
6
Auto-tag and classify incoming messages
For this incoming_message={{message_text}}, output JSON with fields: tags (list of up to 5 concise tags), category (one of: Billing, Technical, Account, Sales, Feedback), urgency (Low/Medium/High), and a one-sentence summary. Use context: customer_tier={{tier}}.
Produce tags and a single-sentence classification for an incoming customer message to support routing and metrics.
7
Extract structured fields from a support conversation
Given conversation_text={{conversation}} extract and return JSON with fields: name, email, phone, issue_type (one of Technical/Billing/Account/Other), product, purchase_date (YYYY-MM-DD or null), attachments_present (true/false), and customer_consent_for_followup (yes/no). If field cannot be determined, return null.
Parse a freeform conversation and return structured CRM fields for quick import (name, email, phone, issue_type, product, purchase_date, attachments_present).
Stop Losing Your AI Work
Tired of rewriting the same prompts, juggling ChatGPT and Claude in multiple tabs, and watching your best AI conversations disappear forever?
AI Flow Chat lets you save winning prompts to a reusable library, test all models in one workspace, and convert great chats into automated workflows that scale.
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