HubSpot Hack: The "Double-Tap" AI Deal Autopsy

We’ve all been there. A deal moves to Closed Lost. The sales rep, eager to move on, selects a generic reason from the dropdown: "Product Fit" or "Price." But generic dropdowns don't tell you why you lost revenue. Was it a specific missing integration? A security compliance blocker? A competitor who undercut you at the last minute? That goldmine of feedback is usually buried in the chaos of the deal timeline—scattered across call transcripts, email threads, and meeting notes. For this hack, we’re chaining two specific HubSpot AI tools together to turn that unstructured noise into a structured strategy.

The Challenge

Directing an AI Agent to "read a timeline" without guidance can be tricky. Without a focused data source, AI can hallucinate or miss context. It needs a consolidated view of history to make an accurate judgment.

The Solution: Chaining AI Actions

We don't just ask the AI to guess; we build a workflow that prepares the data first. We call this the Summarize-Then-Analyze technique.

As seen in the workflow logic, this relies on the specific "Data Agent: Custom prompt" action. Here is the flow:

The Trigger: Deal Enrollment. The workflow listens for a specific negative outcome. In our example, we trigger only when a Deal in the "Sales Pipeline" moves to Closed Lost.

Step 1: The Aggregator (Summarize Record). First, the workflow triggers the native "Summarize Record" AI action. This acts as a net, sweeping through the deal’s emails, calls, and notes to generate a comprehensive synopsis of the entire sales conversation.

Step 2: The Analyst (Data Agent: Custom prompt). This is the brain of the operation. We use the Data Agent: Custom prompt action.

The Input: We feed the output from Step 1 (the Summary) directly into this agent.
The Prompt: Using the PARSE framework, we instruct the agent to review that summary, ignore administrative fluff, and identify the exact moment friction occurred.

Step 3: The Scribe (Edit Record). Finally, we don't just let the insight float away. We use the "Edit Record" action to copy the Data Agent's answer into a custom multi-line text property called "Inferred Lost Reason."

The Result

The Dropdown says: "Product Fit"
The AI Chain reveals: "The Specific Blocker: The deal was lost due to the prospect's compliance requirements for a self-hosted or private cloud solution, which our current offering does not support.
The Context: Tony Luciano, the main contact, confirmed that the procurement and security teams could not proceed with the deal because the absence of a self-hosted option and insufficient permission granularity were deal-breakers, particularly given their defense client requirements. As a result, they are unable to meet regulatory compliance in their operational timeline.
The Sentiment: Tony expressed a willingness to revisit the deal if an appropriate solution becomes available in the future.."

Why this matters

Zero Hallucinations: By summarizing first, you ground the AI in facts, ensuring the output is accurate.
Product Roadmap Fuel: Engineering gets specific data on why deals are stalling, not just vague categories.
Automated Integrity: You bridge the gap between quantitative reporting (What we lost) and qualitative reality (Why we lost it)—without the sales rep typing a single word.

A Note on Credits: This workflow consumes HubSpot AI credits for both the summary and the custom agent analysis. It is powerful, but should be scoped correctly (e.g., prioritized for high-value Enterprise deals) to maximize ROI.

Ready to chain your AI?

Building multi-step AI workflows requires architectural thinking to ensure data flows correctly from the Summary to the Agent, and finally to your Property.

By Seyi Allison