HubSpot Deal Analysis - Agent.ai Documentation

Automatically analyze deals using AI, review timeline history, and update records with health scores and next-step recommendations. What it does: Finds deals in a specific stage, analyzes each one using AI and historical data, then updates the deal with insights. Common uses:

Daily deal health checks
Pipeline review automation
Identify at-risk deals
Generate AI-powered next steps
Scale deal analysis across entire pipeline

Complexity: Intermediate - Uses search, loops, AI analysis, and updates

Overview

This workflow combines HubSpot data with AI analysis to automatically assess deal health. For each deal in your target stage, it:

Searches for deals in a specific stage
Loops through each deal
Gets timeline events for historical context
Sends deal data + timeline to AI for analysis
Updates the deal with AI-generated insights

Result: Every deal gets a health score, risk assessment, and recommended next steps automatically.

What You’ll Need

HubSpot Setup

Custom Properties (create these in HubSpot → Settings → Properties → Deals):

ai_health_score (Number) - Stores 1-10 health rating
ai_risks (Multi-line text) - Stores identified risks
ai_next_steps (Multi-line text) - Stores recommended actions
ai_close_likelihood (Single-line text) - Stores probability assessment

Permissions:

Read Deals
Write Deals
Read Timeline Events

Agent.AI Setup

Actions needed:

Search HubSpot (V2)
For Loop
Get Timeline Events (V2)
Invoke LLM (or Generate Content)
Update HubSpot Object (V2)
End Loop

LLM Access:

OpenAI, Anthropic, or other LLM provider configured

Step-by-Step Setup

Step 1: Add a Trigger

Choose how to run this workflow: Option A: Scheduled (Recommended)

Trigger: Schedule
Frequency: Daily at 9:00 AM
Use for: Regular pipeline health checks

Option B: Manual

Trigger: Manual
Use for: On-demand analysis

Step 2: Search for Target Deals

Add action: Search HubSpot (V2) Configuration:

Object Type: Deals
Search Filters: Click ”+ Add Property”
- Property: Deal Stage
- Operator: Equals
- Value: “presentationscheduled” (or your target stage)
Retrieve Properties: Click ”+ Add Property” and select:
- dealname
- dealstage
- amount
- closedate
- hs_object_id
- pipeline
- hubspot_owner_id
Sort: -createdate (newest first)
Limit: 50 (adjust based on your needs)
Output Variable: target_deals

What this does: Finds all deals in “Presentation Scheduled” stage (or whatever stage you chose), gets their key details.

Step 3: Start Loop

Add action: For Loop Configuration:

Loop through: Click {} → select target_deals
Current item variable: current_deal

What this does: Processes each deal one at a time.

Step 4: Get Timeline Events

Add action: Get Timeline Events (V2) Configuration:

Object Type: Deals
Object ID: Click {} → current_deal → hs_object_id
Event Type Filter: Leave blank (get all events)
Output Variable: deal_timeline

What this does: Gets the complete timeline history for the current deal (emails, calls, meetings, notes, custom events).

Step 5: AI Analysis

Add action: Invoke LLM (or Generate Content) Configuration:

Prompt:

Analyze this deal and provide insights:

Deal Name: {{current_deal.dealname}}
Stage: {{current_deal.dealstage}}
Amount: ${{current_deal.amount}}
Close Date: {{current_deal.closedate}}

Timeline History:
{{deal_timeline}}

Please provide:
1. Deal health score (1-10, where 10 is healthiest)
2. Key risks or concerns
3. Recommended next steps (3-5 specific actions)
4. Likelihood to close (percentage or descriptive)

Return as JSON with keys: health_score, risks, next_steps, close_likelihood

Model: gpt-4 (or your preferred LLM)
Output Variable: deal_insights

What this does: AI analyzes the deal using all available context and generates actionable insights. Tip: Adjust the prompt to match your sales process. Ask about specific things that matter to your team.

Step 6: Update Deal with Insights

Add action: Update HubSpot Object (V2) Configuration:

Object Type: Deals
Identify by: Lookup by Object ID
Identifier: Click {} → current_deal → hs_object_id
Update Properties: Click ”+ Add Property” and select your custom properties:
- ai_health_score: Click {} → deal_insights → health_score
- ai_risks: Click {} → deal_insights → risks
- ai_next_steps: Click {} → deal_insights → next_steps
- ai_close_likelihood: Click {} → deal_insights → close_likelihood
Output Variable: updated_deal

What this does: Saves AI insights back to the deal record so your team can see them in HubSpot.

Step 7: Close the Loop

Add action: End Loop What this does: Marks the end of the loop. Workflow jumps back to Step 3 and processes the next deal.

Step 8 (Optional): Send Summary

Add action: Send Email (after the loop) Configuration:

To: Your email or sales team email
Subject: “Deal Analysis Complete”
Body: “Analyzed and updated insights for all deals in Presentation Scheduled stage.”

What this does: Notifies you when the workflow finishes.

How It Works

Execution flow:

Search finds 50 deals in “Presentation Scheduled” stage → saves to target_deals
For Loop starts with first deal → current_deal = Deal #1
Get Timeline Events retrieves history for Deal #1 → saves to deal_timeline
Invoke LLM analyzes Deal #1 + timeline → saves insights to deal_insights
Update writes insights back to Deal #1 in HubSpot
End Loop → Jump back to step 2, current_deal = Deal #2
Repeat until all 50 deals are analyzed
Send Email (optional) notifies team

Example timeline: 50 deals × ~5 seconds per deal = ~4 minutes total

Example Output

What the AI Generates

For a deal named “Acme Corp - Enterprise License”:

{
  "health_score": 7,
  "risks": "Customer has not responded to follow-up in 5 days. Technical questions from demo suggest potential integration concerns. Close date is 30 days away but no next meeting scheduled.",
  "next_steps": "1. Send follow-up email addressing technical questions\n2. Offer integration consultation call with solutions engineer\n3. Share case study from similar customer in manufacturing industry\n4. Schedule technical deep-dive meeting\n5. Create custom proposal addressing integration concerns",
  "close_likelihood": "Medium-High (65%)"
}

What Appears in HubSpot

In the deal record, you’ll see:

AI Health Score: 7
AI Risks: “Customer has not responded to follow-up in 5 days…”
AI Next Steps: “1. Send follow-up email… 2. Offer integration consultation…”
AI Close Likelihood: “Medium-High (65%)”

Sales reps can see these insights directly on the deal record and take action.

Customization Ideas

Different Target Stages

Change which stage to analyze:

Search Filter Value: Change from “presentationscheduled” to:
- “qualifiedtobuy” - Recently qualified deals
- “decisionmakerboughtin” - Deals nearing close
- “contractsent” - Contracts waiting for signature

Multiple Stages

Run separate workflows for different stages, or add an If Condition inside the loop to handle different stages differently.

Different AI Focus

Customize the LLM prompt for different analysis types: For early-stage deals:

Focus on: qualification quality, budget alignment, decision maker access

For late-stage deals:

Focus on: contract negotiation status, closing risks, urgency signals

For stalled deals:

Focus on: reasons for stall, re-engagement strategies, win-back probability

Add Filters

Only analyze deals meeting certain criteria: After Get Timeline Events, add If Condition:

Condition: Check if timeline has any events in last 7 days
If no recent activity → Tag as “stalled”
If has activity → Run AI analysis

Segment by Owner

After Search, add another loop:

Group deals by hubspot_owner_id
Send each owner a summary of their deals

Troubleshooting

No Deals Found

Search returns empty array Causes:

No deals in that stage
Wrong stage name (check exact value in HubSpot)
Missing permissions

Fix:

Check HubSpot - do deals exist in that stage?
Get exact stage value: Go to HubSpot → Deal → Check “Deal Stage” property
Verify “Read Deals” permission

AI Insights Not Formatted Correctly

Deal properties contain raw JSON or malformed text Causes:

LLM didn’t follow JSON format instruction
Template variable rendering issue

Fix:

Make prompt more explicit: “Return ONLY valid JSON, nothing else”
Test with a single deal first
Try different LLM model (GPT-4 better at structured output than GPT-3.5)
Parse JSON in a Set Variable action before updating

Custom Properties Not Found

Error: “Property ‘ai_health_score’ does not exist” Causes:

Custom properties not created in HubSpot
Properties created but not for Deals object

Fix:

Go to HubSpot → Settings → Properties → Deals
Create custom properties:
- ai_health_score (Number, 0-10)
- ai_risks (Multi-line text)
- ai_next_steps (Multi-line text)
- ai_close_likelihood (Single-line text)
Save and try again

Timeline Too Long

LLM times out or returns incomplete response Causes:

Timeline has hundreds of events
Exceeding token limit

Fix:

Add result limit to Get Timeline Events action
Filter by date range (last 30 days)
Filter by event type (only important events)
Use LLM with larger context window

Loop Takes Too Long

Workflow times out Causes:

Too many deals (1000+)
LLM calls are slow

Fix:

Reduce search limit to 50-100 deals
Run multiple smaller workflows instead of one large one
Filter deals by date (only deals from last 30 days)

Tips & Best Practices

✅ Do:

Start with small search limit (10-20 deals) to test
Review AI-generated insights for a few deals before scaling
Adjust prompt based on what your team actually needs
Create custom properties in HubSpot before running workflow
Use scheduled trigger for daily automated analysis
Monitor execution logs to see how long each deal takes

❌ Don’t:

Analyze thousands of deals at once (splits into batches)
Forget to create custom properties in HubSpot first
Use vague prompts (be specific about what you want)
Skip testing with a few deals first
Analyze the same stage multiple times a day (redundant)

Cost optimization:

LLM calls cost money - monitor usage
Use cheaper models (GPT-3.5) for simple analysis
Limit timeline events to reduce tokens
Only analyze deals that changed recently (add date filter)

Actions used:

Related workflows:

HubSpot Contact Enrichment - Similar pattern for contacts
HubSpot Customer Onboarding - Multi-stage workflow example

Last Updated: 2025-10-01

Recipes

​Overview

​What You’ll Need

​HubSpot Setup

​Agent.AI Setup

​Step-by-Step Setup

​Step 1: Add a Trigger

​Step 2: Search for Target Deals

​Step 3: Start Loop

​Step 4: Get Timeline Events

​Step 5: AI Analysis

​Step 6: Update Deal with Insights

​Step 7: Close the Loop

​Step 8 (Optional): Send Summary

​How It Works

​Example Output

​What the AI Generates

​What Appears in HubSpot

​Customization Ideas

​Different Target Stages

​Multiple Stages

​Different AI Focus

​Add Filters

​Segment by Owner

​Troubleshooting

​No Deals Found

​AI Insights Not Formatted Correctly

​Custom Properties Not Found

​Timeline Too Long

​Loop Takes Too Long

​Tips & Best Practices

​Related Resources

Overview

What You’ll Need

HubSpot Setup

Agent.AI Setup

Step-by-Step Setup

Step 1: Add a Trigger

Step 2: Search for Target Deals

Step 3: Start Loop

Step 4: Get Timeline Events

Step 5: AI Analysis

Step 6: Update Deal with Insights

Step 7: Close the Loop

Step 8 (Optional): Send Summary

How It Works

Example Output

What the AI Generates

What Appears in HubSpot

Customization Ideas

Different Target Stages

Multiple Stages

Different AI Focus

Add Filters

Segment by Owner

Troubleshooting

No Deals Found

AI Insights Not Formatted Correctly

Custom Properties Not Found

Timeline Too Long

Loop Takes Too Long

Tips & Best Practices

Related Resources