I know what you’re thinking—”Ryan, didn’t you just post a newsletter yesterday?” Yes. I did. But that was before I saw that HubSpot released a Deep Research connector for ChatGPT. This is such a big deal, that I am breaking my usual once-a-week schedule.
HubSpot is the first CRM to create a connector like this, which essentially lets you leverage your CRM data to create in-depth reports and analysis using ChatGPT’s Deep Research model. Teams that effectively leverage this tool will have a massive leg up on the competition—and will also have to navigate some interesting security questions.
I basically didn’t sleep last night because I was so fascinated with this tool, and I have come up with some innovative ways marketing, sales, and RevOps teams can put this connector to work. After that, I will dive into data privacy and try to answer the question of how concerned you should actually be.
6 Use Cases for HubSpot's ChatGPT Deep Research Connector
Go-to-market teams now have superpowers. This is especially true for small businesses, who likely don’t have data analysis resources in-house. ChatGPT is your new data analyst! Here’s 2 use cases each for Marketing, Sales, and RevOps.
Marketing Use Cases
1. Hyper-Personalized Campaign Segmentation & Content Strategy
Description: Identify what customer segments are the most likely to convert. Analyze complex behavioral patterns (What content did they consume? Which website pages did they visit? In what order?). Connect the dots between their actions, demographics, and conversion paths, and get super-tailored content and messaging strategies that speak directly to each unique group, boosting engagement and conversions.
HubSpot Data: Contacts, Companies, Deals (engagement activity, content views, lifecycle, firmographics).
Sample Prompt: Analyze our HubSpot contacts over the past 12 months. Identify the top 3 micro-segments of leads that converted to customers fastest, considering their lead source, specific content engagement (e.g., which blog topics, whitepapers, or webinars they consumed, and in what order), and company industry. For each segment, provide a detailed persona description, including their common pain points, interests, and preferred learning styles, and recommend a tailored content strategy, including specific themes, formats, and optimal distribution channels to accelerate future conversions.
2. Predictive Churn Identification & Proactive Retention Marketing
Description: Identify customers at high churn risk by analyzing HubSpot data, including support tickets (e.g., volume, sentiment), marketing engagement (e.g., email opens, website activity), account health, and historical deal data. The system flags at-risk accounts and suggests personalized re-engagement campaigns.
HubSpot Data: Contacts (engagement, CSAT, NPS), Companies (health score, deals, tickets, product usage), Tickets (volume, sentiment, resolution time), Deals (renewal dates, purchases, upsells).
Sample Prompt: From our existing customer base (lifecycle stage 'Customer'), identify the top 100 companies exhibiting early warning signs of churn over the last 6 months. Analyze their recent support ticket activity (e.g., increased volume, negative sentiment, specific categories, repeated issues), marketing engagement (e.g., decreased email opens, website visits, lack of interaction with new product announcements), and any associated account health scores. For each identified company, provide a summary of churn indicators and suggest a personalized retention strategy, including specific content, outreach cadences, and potential offers to re-engage and reinforce value, prioritizing based on potential revenue impact.
Sales Use Cases
3. High-Value Account Prioritization & Tailored Engagement Playbooks
Description: Identify high-potential enterprise accounts by analyzing firmographic data, tech stack insights, historical deal success, and engagement signals. Generate hyper-specific sales playbooks, identifying key decision-makers, likely business challenges, and successful messaging themes to optimize sales efforts.
HubSpot Data: Companies (firmographics, tech stack, contacts, custom strategic account properties), Deals (win/loss reasons, stage progression, products, sales activities, value, competitor data), Contacts (seniority, role, engagement, decision-making authority, activity history).
Sample Prompt: Analyze our HubSpot company data to identify the top 50 companies with the highest potential for enterprise expansion in the sector, based on their revenue, employee count, and identified technology stack. For the top 10 most promising companies, analyze past successful deals with similar profiles and suggest a tailored engagement playbook, including key decision-maker titles to target, their likely business challenges, and successful messaging themes that resonated with similar clients, along with recommended first outreach channels and content assets.
4. Competitive Landscape Analysis & Objection Handling Strategy
Description: Analyze closed-lost deals and sales activity notes to identify recurring competitive patterns, common objections, and competitor strengths/weaknesses. Synthesize this data into actionable objection handling strategies and concise competitive battlecards, empowering sales to differentiate offerings and articulate value in real-time.
HubSpot Data: Deals (closed lost reasons, competitor field, sales activities, notes, products, value, sales rep), Contacts (roles, industries, feedback in notes), Companies (competitors mentioned in notes/deals, industry).
Sample Prompt: Review all 'Closed Lost' deals in HubSpot from the last 12 months where a competitor was cited as a primary reason for loss (Closed Lost Reason). Identify the top 3 most frequently encountered competitors and the primary reasons for losing to each (Closed Lost Reason - Details). For each of these competitors, generate a concise competitive battlecard outlining their key strengths and weaknesses relative to our offerings, and provide 3-5 effective, data-backed objection handling responses for common objections raised by prospects when considering that competitor, including specific talking points and proof points from our CRM data.
RevOps Use Cases
5. High-Value Account Prioritization & Tailored Engagement Playbooks
Description: Provide RevOps leaders with a sophisticated diagnostic tool for real-time sales pipeline health. This involves multi-variable analysis of deals at risk of stalling or slipping (time in stage, last activity, historical conversion rates, sales rep activity). It also analyzes past forecast accuracy deviations, recommending interventions to improve pipeline velocity, deal progression, and forecast reliability.
HubSpot Data: Deals (stage, amount, close date, activities, rep, last activity, deal age, historical stage changes, custom health properties), Sales Activities (calls, emails, meetings, type, frequency), Contacts (engagement), Companies (firmographics, industry, historical deal success rates).
Sample Prompt: Perform a deep analysis of our current sales pipeline for Q3. Identify all deals that have been in their current stage for longer than the historical average for that stage, or where the last sales activity date is more than 14 days ago. For the top 20 deals identified as 'at risk' of stalling, provide a summary of the contributing factors (e.g., lack of engagement, stalled stage, high deal age, specific sales rep performance trends) and recommend specific actions for the sales rep or manager to re-energize the deal and improve the accuracy of our Q3 forecast, including potential coaching points or resource allocation adjustments.
6. Customer Journey Bottleneck Identification & Process Optimization
Description: Analyze the end-to-end customer journey in HubSpot to identify friction points, delays, or drop-offs between lifecycle stages or team handoffs. Propose specific process optimizations, automation improvements, or training needs to enhance efficiency, reduce customer friction, and improve overall customer experience and internal operational flow.
HubSpot Data: Contacts (lifecycle stage history, form submissions, lead source, engagement, lead score changes), Deals (deal stage history, time in stage, sales activities, win/loss reasons), Tickets (creation/resolution date, category, contacts/companies, time to first response, escalation), Sales Activities, Service Activities (meeting logs, task completion, internal notes).
Sample Prompt: Analyze the average time and key transitions for contacts moving from 'Lead' to 'Marketing Qualified Lead' to 'Sales Qualified Lead' to 'Customer', and then through their first 3 support tickets. Identify any stages or handoffs (e.g., Marketing to Sales, Sales to Onboarding, Onboarding to Support) that consistently show significant delays or drop-offs over the past 6 months, and correlate these with any negative customer feedback or increased ticket volume. Based on this analysis, recommend 3 specific process optimizations or automation improvements within HubSpot to reduce bottlenecks and improve customer journey flow and cross-functional efficiency, specifying which teams would be impacted.
Security & Data Privacy Concerns
Alright, let's talk about the elephant in the room: data privacy. When we connect our sensitive CRM data to a powerful AI like ChatGPT, it's natural to have questions.
Here's my cautiously optimistic outlook:
To start on a positive note, HubSpot explicitly states that customer data is NOT used for AI training within ChatGPT when using this connector. In fact, both HubSpot and OpenAI have put policies in place to safeguard this.
OpenAI's Stance: For paid plans (like ChatGPT Enterprise, Team, and Edu, which this connector requires), OpenAI does not use your business data (inputs or outputs) to train their models by default. Your data is encrypted both at rest and in transit.
HubSpot's Stance: While HubSpot can use your data to develop their own Breeze AI features (with an opt-out available), for this specific connector with ChatGPT, the commitment is no data usage for OpenAI's model training. They also ensure the connector only has access to the data the user who set it up is permitted to see within HubSpot, which is a critical layer of control.
That being said, we’ve seen instances before where so-called “protected” data was later revealed by an AI chatbot. So, what should we still be mindful of?
Data Usage vs. Model Training: Even though data isn't used for training, it is being processed by ChatGPT to answer our queries. This means data is temporarily in OpenAI's environment. While they have strong security protocols, it's good to understand the exact processing and retention specifics—which are usually covered in their enterprise agreements—before using sensitive customer data via the connector.
The Human Element: The biggest risk often comes from who is using the tool and how they are using it. Be mindful of prompt injection–trying to trick the AI into revealing sensitive information it shouldn't. While the connector is designed to respect HubSpot's access controls, a maliciously crafted prompt could attempt to bypass intent or extract information.
HubSpot's Internal Access: The connector relies on your existing HubSpot permissions. If someone has broad access in HubSpot, they will have broad access via the connector. So, robust internal access management remains paramount.
While no system is foolproof, the explicit commitment from both HubSpot and OpenAI not to use our CRM data for general model training should at least give you some air cover for sensitive data conversations.
Personally, I think the data privacy ship sailed long ago and, whether it is stored in HubSpot’s servers or OpenAIs, there is always a significant risk of a data leak. It’s just a part of operating in a technology-driven world. You should exercise sound judgement and take proactive steps to improve your data security, but if you completely avoid the use of AI for fear of what will happen to your data, you are going to fall behind your competition. AI can be a powerful tool, and with careful usage and good internal data governance, go-to-market teams can use it to gain a ton of value from it.