5 June, 2025 POR Luz Castillejo Flor

Advanced Research with the New ChatGPT Integration in HubSpot

In the competitive world of Customer Relationship Management (CRM), HubSpot has taken a decisive step by integrating ChatGPT through its new feature: the Deep Research Connector. This tool allows companies to perform advanced analysis on their own data—without the need for a dedicated data team or specialized analysts.

And the best part? It’s available for all paid plans of both HubSpot and ChatGPT. Let’s explore what this means and how it can transform the work of sales, marketing, support, and customer success teams.

What is the HubSpot Deep Research Connector?

It’s an integration that allows HubSpot users to connect directly from ChatGPT and run advanced natural language queries on their data, including:

  • Contacts

  • Companies

  • Deals

  • Tickets

  • Associations between them

Thanks to this connection, you can ask ChatGPT things like:

 “Give me an executive summary of my current sales pipeline.”
 “Which companies have the highest expansion potential?”
 “Which open deals are most likely to close this month?”

The tool does not modify data—it has read-only access. It also respects each user’s permissions within HubSpot, ensuring that only authorized data is visible.

Key Benefits for Companies

1. Insights Without Analysts
You can perform BI-level analysis without building a BI team. It’s like having a PhD-level data assistant available 24/7.

2. Speed and Efficiency
Forget slow reporting. You can get actionable insights in seconds.

3. Intelligence by Department
Each team—sales, marketing, support, customer success—can use custom prompts tailored to their specific goals and needs.

Real Use Cases

For Marketing

“Find the highest-converting cohorts and generate a tailored nurturing sequence.”
“What attributes do our fastest-converting leads share?”

→ This helps refine campaigns, nurture contacts, and automate workflows directly in HubSpot.

For Sales

“Segment my accounts by revenue, industry, and tech stack.”
“Compare win rate and sales cycle length this quarter vs. last.”

→ Ideal for prioritizing prospects and optimizing the slowest funnel stages.

For Customer Success


“Which accounts are inactive but show reactivation potential?”
 “Suggest plays to improve retention for these customers.”

→ Strengthens loyalty through more strategic actions.

For Support


“What ticket categories show seasonal peaks?”
“What are the 10 most common issues based on last quarter’s tickets?”

→ Enables better staffing forecasts and improves support experiences.

Technical Considerations

  • Requires a paid ChatGPT plan (Pro, Team, Enterprise, Plus, or Edu).

  • For EU customers, only Team, Enterprise, or Edu plans are allowed.

  • Available only on ChatGPT's web version.

  • Must be connected by a HubSpot Super Admin or someone with Marketplace access permissions.

  • Data usage is subject to HubSpot’s API limits and the privacy terms of both platforms.

What Does This Mean for Larger Enterprises?

It means greater autonomy for teams, faster response times, and a clear competitive edge through data-driven decision-making. No more waiting weeks for a report—any authorized user can now query and act in real-time.

Moreover, because the insights can plug directly into HubSpot workflows, audiences, and automation, analysis becomes action. It’s not just about understanding what’s happening—it’s about having the tools to respond.

Conclusion

The HubSpot–ChatGPT Deep Research Connector is more than a cool new feature. It represents a paradigm shift in how businesses interact with their data.

It’s a tool designed not just for enterprise giants, but for any team ready to take their CRM strategy to the next level—with smart, agile, frictionless intelligence.
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Working with large volumes of marketing data—whether it’s web traffic, keywords, users, or campaigns—can feel overwhelming. These data sets often aren’t organized or categorized in a useful way, and facing them can feel like trying to understand a conversation in an unfamiliar language.

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  5. This process repeats until the groups stabilize.

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  • Iterate thoughtfully: Test, review, and adjust based on how your data behaves.

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With K-Means in BigQuery, you can answer questions like:

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  • Which pages show similar performance trends?

  • Which campaigns are generating outlier results?

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Start simple: take your top-performing pages, group them by sessions and conversions, and see what patterns emerge. You might uncover insights that completely shift how you approach your digital strategy.

 
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