Podcast Summary: 9 ChatGPT Use Cases from the Top 1% of Marketers

Podcast: Marketing Against The Grain
Hosts: Kipp Bodnar (HubSpot CMO) & Kieran Flanagan (HubSpot SVP of Marketing)
Guest: Kyle Coyer (Creator, Growth Unhinged)
Date: October 28, 2025

Overview

This episode delves deep into how top marketers are leveraging ChatGPT for real business impact. Kipp and Kieran speak with Kyle Coyer, who shares exclusive survey data and case studies, moving beyond basic AI applications. They discuss high-performing, often unconventional ChatGPT use cases across product marketing, content, growth, and analytics—showcasing exactly how leading marketers are getting an edge.

Key Discussion Points and Insights

1. ChatGPT: The Most Impactful Tool for Marketers

• Kyle’s Data: Survey of 200 marketers—ChatGPT is named the single most impactful tool in today’s marketing stack ([02:24]).
  • “Number one is ChatGPT.” —Kyle Coyer [02:24]
• ChatGPT has overtaken even CRM tools like HubSpot in perceived impact—a seismic shift in only a few years ([02:25]).
• Marketers are hungry for practical and advanced ChatGPT use cases, beyond just content generation.

2. Persona Research & Predictive Buyer Intent

Beginner Use Case:

• Using ChatGPT to create highly realistic digital personas and predict customer behavior.
• Study Reference: LLMs now can predict purchase intent with 90% accuracy based on conversation cues ([03:41]).
  • Marketers can input sales call transcripts, chat logs, and win/loss data to help ChatGPT role-play as target customers.
  • “You can build an AI-powered version of your actual customer and have conversations... to ensure that person would go on then to actually buy my product.” —Kipp Bodnar [05:20]
• External data (e.g., reviews from G2 Crowd) can also serve as context for more authentic persona creation ([09:10]).

How-To Tips:

• Minimal context works, but more detail = better outputs.
• Upload recorded sales conversations, internal docs, or instruct ChatGPT to aggregate key persona descriptors from external reviews ([08:44]).

Pro Tip:

• Use “Deep Research Mode” (ask ChatGPT to recommend sources, cite research, and create research reports).
  • “It’s kind of like having an analyst from McKinsey or Bain or BCG in your back pocket.” —Kyle Coyer [10:40]

3. Product Positioning & Messaging Differentiation

Intermediate Use Case:

• Chief Marketing Officers and product marketers use ChatGPT for new product positioning—crafting narratives that stand out in noisy markets ([13:28]).
  • Feed in third-party analysis (e.g., Gartner reports), competitors’ messaging, but avoid legacy positioning docs to reduce bias ([13:38]).
  • Prompt Example: “Help me create a differentiated narrative for our product and category... clarify misunderstood category dynamics... build assets like a landing page, ebook, and video storyboard.”
• After persona research, upload findings to ChatGPT as context for ultra-targeted positioning ([15:00]).
• Identify “arbitrage opportunities”: have ChatGPT compare your research with competitor messaging to find prospect needs not being met ([15:08]).

Takeaway:

• Don’t skip differentiated positioning.
  • “If you skip the step of actually having differentiated positioning, you’re going to potentially have some AI slop on your hands.” —Kyle Coyer [16:13]

4. Automated Product Messaging Review Apps

Advanced Use Case:

• Build micro web apps through ChatGPT that review uploaded messaging collateral (like one-pagers or PDFs), then provide critiques and scores ([18:19]).
  • Detailed prompts can ask for evaluation by target audience, style, urgency, proof, and emotional impact ([20:22]).
  • “At some things [ChatGPT is] an intern, at others, a PhD... providing really thorough reviews and feedback, I think AI is actually incredible for.” —Kyle Coyer [19:55]
• This scales domain expertise—turn your proprietary frameworks into repeatable, AI-driven feedback tools.

5. Smart Content Marketing: AI as Research Partner (Not Writer)

Use Case:

• Avoid letting AI write generic content (now over 50% of web content is AI-generated; quality has dropped) ([22:41]).
  • “If you give human tools to do lazy stuff at scale, they’ll do lazy stuff at scale.” —Kipp Bodnar [23:06]
• Instead:
  • Upload primary source materials (e.g., call transcripts, SME interviews).
  • Prompt ChatGPT to extract key insights and produce detailed outlines ([25:42]).
  • “None of that [YouTube/podcast] content is in the models and you can just... grab the transcript... find commonalities, spicy takes, interesting ideas...” —Kieran Flanagan [26:28]
• Result: Human writer brings unique POV and voice; AI does the heavy lifting on research, outline, and repurposing ([27:39]).

6. Programmatic SEO & Landing Pages via Community Data (Advanced/Technical)

Use Case:

• Scrape and analyze community conversations relating to your product ([28:42]), e.g. via forums, Slack, review sites.
  • Use these to automatically generate ultra-specific landing pages or FAQ sections matching real user questions.
• AI Engine Optimization: Unlike Google SEO (few high-volume keywords), LLM-powered search relies on hundreds of conversational, long-tail queries ([29:18]).
  • Community-driven content = higher likelihood of being surfaced for those queries ([31:19]).
• Traditional keyword tools may not catch these hyper-niche opportunities—letting you outpace competitors with LLM-driven content ([32:20]).

7. Growth Marketing & Data Analysis

A. Attribution Analysis (Beginner)

• Correlate paid media spend to organic or direct signups by uploading daily metrics into ChatGPT ([33:08]).
  • Example: Uploads export from analytics stack; ChatGPT runs regression/correlation, interprets results within minutes.
  • “ChatGPT is a data analyst. Such a powerful use case.” —Kipp Bodnar [34:07]

B. Analyzing and Cleaning Qualitative Survey Results

• ChatGPT efficiently normalizes, combines, and stack ranks unstructured survey responses—tasks that were “painful” to do manually ([34:24]).
  • E.g., deduplicating brand names, consolidating data, and generating top-20 lists.
  • “The open-ended stuff in particular is just a goldmine for ChatGPT.” —Kyle Coyer [36:14]

C. Growth Experiment Prioritization

• Using a custom GPT to analyze product onboarding/event data, surface user drop-off points, and recommend experiment focus ([37:33]).
• Also: Input all ongoing marketing activities and business goals; ChatGPT objectively recommends which activity to prioritize for maximum impact ([38:23], [40:04]).

Notable Quotes & Memorable Moments

• On ChatGPT’s Rise:
  • “I can’t imagine two years ago thinking that ChatGPT would be seen as the most impactful marketing tool.” —Kyle Coyer [02:25]
• On Using AI Authentically:
  • “The newsletters that sound like ChatGPT have the most unsubscribes...” —Kyle Coyer [23:11]
  • “If I brainstorm with ChatGPT and I get certain answers back, I actually want to find a point of view that is different from what ChatGPT is going to tell me is the prevailing point of view.” —Kyle Coyer [24:18]
• On AI in Content:
  • “If you give human tools to do lazy stuff at scale, they'll do lazy stuff at scale.” —Kipp Bodnar [23:06]
• On Data Analysis:
  • “The qualitative stuff, you had to code pretty manually historically... and ChatGPT is exceptional at it.” —Kyle Coyer [36:14]
• Trusting AI:
  • “I feel like we used to caveat that we used ChatGPT for something... now we’re almost using it to, like, build more confidence.” —Kyle Coyer [40:04]
  • “It’s kind of wild, right? It’s still the bleeding edge use case, but... people just say, well, AI told me to do this, so I’ll just do it because I implicitly trust it.” —Kipp Bodnar [40:24]

Timestamps of Key Segments

• [02:19] – Survey: ChatGPT as top tool
• [03:41] – LLMs predict purchase intent (Persona research intro)
• [08:44] – Persona research: minimal vs. rich prompt context
• [10:40] – Deep Research Mode explained
• [13:28] – Product positioning with AI
• [19:22] – Automated product messaging grader apps
• [22:41] – Content marketing pitfalls of AI
• [25:42] – Best practice: content outlines via primary source uploads
• [28:42] – Programmatic SEO/landing pages from community content
• [33:08] – Attributing paid marketing spend impact
• [36:14] – Analyzing and coding qualitative survey data
• [37:33] – Growth experiment prioritization via funnel data
• [40:04] – The growing trust in AI for marketing decisions

Conclusion

The episode offers a rare look at how the world’s best marketers actually use ChatGPT: not as a generic writer or intern, but as a thought partner, analyst, product advisor, and content machine—always backed by real data and domain expertise. The big message: AI gives you leverage, but only when paired with your own insight, creativity, and business context.

Whether you’re an executive revamping your product messaging, a content director scaling your expertise, or a growth marketer seeking data breakthroughs, this episode’s playbook is rich with immediately actionable ChatGPT prompts and strategies.