Hey there 👋,
We have a guest post by Tanyo from CopyTG this week, and it’s all about the importance of collecting first-party data, enjoy…
Operators spend hours perfecting their AI prompts.
They test Claude against ChatGPT.
They build custom workflows.
They write detailed instructions about tone and structure.
Then they publish and get zero replies.
Because their newsletter sounds exactly like the other 1000 newsletters their subscribers get.
Your AI workflow isn't the problem.
You're just not feeding it anything unique.
Operators Don't Know Who They're Writing For
Someone subscribes to your list.
You get their email.
Maybe their name.
Maybe you know they came from Twitter or LinkedIn.
That's it.
So you write your welcome sequence based on assumptions.
You create content for a persona you invented.
You build offers for an audience you're guessing about.
And when you use AI to write your newsletter, you feed it general stuff about your market.
Not actual data about the people on your list.
The content sounds generic because it is generic.
You're writing for "SaaS founders" or "newsletter operators" instead of writing for the actual people who subscribed.
This used to be fine.
In 2025, it's a death sentence.
AI Made Everyone Sound The Same
Anyone can write a decent newsletter now.
ChatGPT works.
Claude works.
They're both trained on the entire internet.
They both understand structure and tone.
Give them a prompt about your market, and they'll give you something publishable.
This created a new problem.
Everyone's using the same models. Everyone's reading the same internet. Everyone's prompting with similar instructions.
So everyone's content converges.
General insights don't differentiate you anymore. They're commoditized.
What matters now is specificity.
Writing about the exact problem your subscriber has right now.
Not their industry.
Not their role.
Their specific situation.
AI can do this.
But you need to give it something to work with.
First-party data is that something.
Just Ask Them
Build a post-subscribe survey.
3-5 questions.
That's it.
Someone subscribes.
They confirm their email.
The survey pops up immediately.
You're asking three things.
First question - who are they and what do they do?
Not "what's your job title."
That's useless.
Ask what type of business they run.
What stage they're at.
What they're actually doing day-to-day.
Second question - what are they dealing with right now?
What's the biggest problem they have in your domain?
What are they stuck on? What's not working?
Third question - what do they want?
What does success look like for them?
What are they trying to achieve in the next 90 days?
Keep it short.
Make every question count.
You're not doing market research.
You're getting visibility into what your subscribers actually need.
What Changes When You Have The Data
You can actually segment your list.
Not by where they came from.
Not by whether they opened your last email.
By the problems they have.
By what they're trying to do.
By what stage they're at.
You write different welcome sequences for different people.
You send different content based on different needs.
You pitch different offers to different goals.
Your AI prompts get specific.
You're not telling Claude to "write a newsletter about email marketing."
You're saying "write for SaaS founders at $50K MRR who can't monetize their list and want to launch a paid community in Q1."
The output stops being generic. It speaks to real people.
And monetization gets easier.
You know exactly who's on your list. You know what they need. You know what they'll pay for.
Selling sponsorships? You tell brands the exact breakdown of who reads your newsletter. Not "founders." The specific problems. The specific budget levels. The specific buying intent.
Launching products?
You're building for known demand.
Not guessing.
You Don't Know Who's On Your List
I had a client building his newsletter for 10 months.
Growing it. Publishing weekly. Decent open rates.
He thought he didn’t have any leads on his list.
We built a post-subscribe survey.
Two weeks later, he found 10 people on his list who were perfect for his high-ticket offer.
They had the exact problems he solved.
They were at the exact stage where they needed help.
They had a budget.
He had no idea they existed.
Without the survey, they were just email addresses.
With it, they were qualified prospects.
How To Do This
If you're on beehiiv, this takes 5 minutes.
Go to your settings.
Add the survey.
Set it to trigger after someone subscribes.
3-5 questions. Multiple choice when you can.
Open the text when you need it.
Responses come in. You segment based on what people tell you.
Start manually if you need to. Automate once you see the patterns.
Then feed the data into your AI workflow.
Reference actual survey responses when you write. Include audience data when you prompt. Match offers to problems people actually told you they have.
Your workflow doesn't get harder. It gets better.
Everyone Has AI Now
Every newsletter operator can generate decent content.
ChatGPT is free. Claude is free. The tools are the same for everyone.
So content quality isn't the differentiator anymore.
Relevance is.
The newsletter that wins is the one that speaks most directly to what the reader is dealing with right now.
Not the best-written one. Not the one with the most tips. The most relevant one.
You can't be relevant without knowing what people are actually dealing with.
You can't know without asking.
First-party data isn't an optimization. It's the foundation.
Without it, you're writing for a generic audience with generic insights using the same AI as everyone else.
With it, you're writing for specific people with specific problems.
One approach builds a newsletter that people need.
The other builds noise.
Data Beats Assumptions
You're not competing on content quality.
AI solved that.
You're competing on how well you understand your audience.
The operators who ask questions about their subscribers will beat the operators who assume.
Every time.
For 7+ years, Tanyo has helped startups build, grow, and monetize email lists, working with 200+ clients to launch campaigns that generate real results. If you need to grow and monetize your list more effectively, book a free 45-minute strategy call with him here.
🧠 OPERATOR INTEL
LinkedIn outlines seven promotion tactics for newsletter subscriber growth
Andy Crestodina built 16,000 email subscribers in 15 years. His LinkedIn newsletter hit 265,000 in 5 years. LinkedIn automatically invites connections to subscribe when you publish, and subscriptions require one tap since the platform already has emails. Article Ads generated 2.3x higher click-through rates than Single Image Ads. The seven tactics: Article Ads with targeting, consistent publishing schedules, email signature integration, cross-platform linking, influencer partnerships, conversation-focused content, and explicit share requests with specific instructions instead of generic "please share" language.
So You Want To Paywall?
Traffic will decline with paywalls but subscriber revenue is predictable. Use dynamic/metered paywalls instead of hard paywalls. Leave high-reach platforms like Google Discover free for email signups, paywall for highly engaged users. Rankings drop over 3-4 months before settling. Subscription success requires differentiation: exclusive data, deep niche insights, or unique positioning. You're competing with Netflix and Spotify for disposable income. Important: LLMs and Google are ingesting content from JavaScript-based paywalls for training whether you like it or not.
Creator economy ad spend set to hit $43.9B in 2026
U.S. creator economy ad spend reaching $43.9 billion in 2026, up 18% from 2025. Paid amplification of creator partnerships up 48% to $13.2 billion. Direct creator partnerships up 21% to $11.6 billion. Platform priorities: TikTok (26%), Instagram (23%), YouTube (19%), Facebook (18%). Marketers shifting strategy: 92% plan to work with both macro (100K-500K followers) and micro (5K-100K followers) influencers instead of just mega creators. AI impact: 79% of marketers increased spend on generative AI creator content and plan to increase again in 2026.
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What topics would you like us to explore next?
Reply and let us know:
Content workflows (how to build copilot systems)
Technical deep dives (context engineering, tool design)
Research process (how we validate insights)
Business models (operator economics).
– Richard & Maciej