What GTM teams are doing with ChatGPT

Real-life prompts for content, growth marketing, PMM, and more

👋 Hi, it’s Kyle Poyar and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups. Quick update: this newsletter just passed 75,000 subscribers! THANK YOU for reading.

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I expected GTM teams to embrace a bunch of specialized AI tools for different jobs. What I didn’t expect was this much consolidation around a single winner: ChatGPT.

Sure, there are other tools folks are excited about (see: Clay, Claude, Lovable), but they’re rarely described as critical. In the latest MKT1 survey of 200+ B2B marketers, ChatGPT ranked as the #1 tool people are obsessed with and the #2 most essential in their stack (just behind HubSpot).

That made me wonder: what exactly are GTM teams doing with ChatGPT that makes it so indispensable?

So I asked readers of Growth Unhinged to share their favorite use cases. The responses were way better than I expected. Y’all are doing everything from automating messaging audits to turning call recordings into ICP insights to spinning up localized content across multiple languages.

In this post, I’m sharing the 12 best examples along with step-by-step instructions for how to reproduce them yourself. They’re grouped by GTM function and by complexity (from quick-start hacks to full-on systems). Let’s get into it.

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ChatGPT for product marketing

Use case #1: Persona research

Complexity: Beginner

By: Francesca Krihely-Price, director of PLG and self-service at dbt Labs

I've spent most of my career catering to technical practitioners and I have a lot less experience building GTM motions for executives. I wanted to see if a conversation with ChatGPT could help close some of the gaps for me.

Did I use these outright in my final product? No. But I was able to get my work started and come to the problem a bit more informed.

Here are some of my prompts:

Imagine you are a data executive at a larger public company. Your team is using an older data stack with technologies like [Competitor 1], [Competitor 2] and [Competitor 3]. How are you thinking about modernizing your data stack? What are you using to learn about your migration process?

I think these suggestions are more in line with how an IC Data Engineer would keep up with news. Can you push a bit to see what other learning paths exist for these executives?

I would love to construct these insights into an adoption map that I can use at my Company. Can you build me an adoption map that goes from awareness to consideration to purchase and help me identify events, content and campaigns that can help move these executives between stages.

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Use case #2: New product positioning

Complexity: Intermediate

By: Jarod Greene, chief marketing officer at Vivun

My favorite use case was around structuring a positioning shift ahead of a major launch last Spring. Positioning is one of the hardest, but most critically important, jobs to be done, and ChatGPT provided the means to structure the cross-functional exercises and reviews to do this right.

I didn’t share any proprietary data with ChatGPT, but did give it context from authoritative sources (ex: Gartner) to teach it about the market’s point of view on AI, AI agents, and AI for sales. A lot of what we wanted needed to be ‘from scratch’ and we didn’t want any of the legacy positioning or messaging to affect the output. We of course refined and iteratively created the materials.

Here’s a condensed version of the prompt:

Help me create a differentiated narrative for our [product/category] that separates us from the noise in the market. I want to:

1. Clarify the misunderstood category dynamics
2. Position our approach as uniquely valuable
3. Build assets like a landing page, ebook, and video storyboard to tell the story

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Use case #3: Product messaging grader

Complexity: Advanced

By: Nathan Burke, chief marketing officer at 7AI

I regularly get asked for help analyzing messaging for cybersecurity startups. Eventually I realized I could use AI tools like custom GPTs, Claude, and Gemini to build a simple web app to automate this for me. You could use the same approach for things like checking every blog post has the right tone or making sure every blog post has links to other places on your site for SEO purposes.

There were three steps: (1) I created a messaging framework, (2) I used AI to create a simple web app where startups can upload their one-pagers, (3) the web app looks at the one-pager, compares it to the framework, and makes suggestions (including a re-write of the one-pager).

Here’s the prompt:

Act as a brand expert and create a simple web app that takes a PDF, then reviews the document critically to suggest improvements to give it the most impact.

Specifically, I want you to create a framework for analyzing an early stage startup’s one pager that includes criteria and scores for: 

1. Audience. This document will be mainly given to CISOs, so think about whether using the tone of “you are buried under alerts” makes sense, or if it should change to talk about “your team is buried…”

2. Style consistency. Many of these docs change tense, tone, and style throughout the document.

3. Urgency. Is this document a statement of fact or a call to action?

4. Clarity. Does it clearly articulate what the startup does, and why the audience should care?

5. Differentiation. How is this different from what’s currently in the market?

6. Proof. Does it use any supporting evidence to prove its claims?

7. Emotional ties. Does it evoke emotion?

Suggest any other evaluation criteria, a way to score/judge a startup’s one-pager, and then let’s use that framework to evaluate the one-pager along with specific recommendations. Remember, the audience is made of CISOs that have heard a million vendor claims, are inundated with vendor pitches, and need something crisp, succinct, and different to stand out and urge them to pay attention. This should be part of both the scoring rubric and how we evaluate this specific one-pager.

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ChatGPT for content marketing

Use case #4: Producing detailed outlines from primary source material

Complexity: Beginner

By: Gail Axelrod, senior content marketing director at Jellyfish

One of my go-to workflows for writing content starts with a primary source, usually a recorded call transcript, which I’ll drop into ChatGPT as the initial input. From there, I’ll ask ChatGPT to surface the main highlights of the conversation to help me build an outline. I think of these bullets as the headers for any resulting content piece. What used to take hours – combing through a 20 page transcript for instance – can now be done in seconds. It saves a ton of time and helps me focus on the most interesting aspects of any call.

For prompting, I’ll usually start with what I know to be the main takeaways of the conversation and ask ChatGPT to surface details around those points. It produces a very workable outline to write from and helps me get more done each week.

Is this groundbreaking? No. Is it efficient and useful? Definitely. The real key is starting with excellent source material. If your inputs are vague or generic, you’ll get the same in return. But if the transcript is rich, specific, and unique, ChatGPT does a great job surfacing what’s most important and providing a strong outline to work from and expand upon.

Here’s a sample prompt:

Go through this transcript and pull out the key highlights of the conversation, add sub sections for each of the main points. Be sure to include any relevant quotes from each speaker [list speaker names for ChatGPT to identify], add time stamps for clarity. Leave placeholders for an introduction, conclusion and call to action.

Another fun use case I’ve found for ChatGPT is to brainstorm headline options, article titles and email subject lines. Sometimes the results are hilariously bad, but I usually end up with a few workable options. ChatGPT is great at taking very specific directions like character counts or word limits and putting them into action. And as an internal team of one, ChatGPT is like my little assistant.

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Use case #5: Localizing content into multiple languages