I’m Matteo Tittarelli, founder & solopreneur at Genesys Growth, a B2B SaaS product marketing and content consulting.

I’m a solo operator, which usually means my capacity is the ceiling. With AI, I try to stretch that ceiling to the max, allowing me to scale my output without scaling headcount.

In 2025, that leverage allowed me to handle up to 6 clients per month on my own, while keeping a (mostly) normal workweek without turning his calendar into a sleep-deprivation experiment.

Today, The Workflow scrutinizes one of my wildest and most successful AI experiments so far. Scaling competitor and category comparison pages (1640+, to be exact), killing manual work while keeping the quality bar high.

Impossible? Let’s see.

PS. Subscribe to The Workflow if you haven’t already.

✅ What you’ll learn

• How to scale 1600+ competitor pages without sacrificing quality

• How to scale SEO content without producing AI slop

• How to use Clay for programmatic SEO

🧨 What triggered The Workflow

Comparison pages became a recurring client request for Matteo. SaaS in crowded markets with thousands of products and features need content that explains why they’re different (and better). There are so many ways to approach this: comparing tools, feature categories. Then adjacent categories. Before long, you’re staring at hundreds of pages.

Doing this manually wasn’t an option if he wanted to keep his sanity. Pricing changes. Reviews age. Feature claims drift. At scale, errors spread fast.

So the GTM engineer re-engineered the process.

He built a workflow that pulls together live market data, reviews, and pricing, anchored by a structured positioning library. The result is a scalable comparison-page engine that stays fresh, doesn’t become a maintenance nightmare, and drove a 38% increase in organic impressions just two weeks after the first pages went live.

🛠️ How to build The Workflow

To build this workflow, start by defining what matters in your ICP’s buying decision. Only then use AI to handle the grunt work needed to scale. You don’t just want to create pages, you want to express how you see the market.

Here’s how the pieces connect, from the positioning that drives every comparison to a scalable hub of product marketing pages.

Steal Matteo’s prompts and save yourself the trial and error.

Step 1: Build a structured messaging and competitor library

Ahhhhh, the good old foundational work required to do anything interesting with AI.

Use Octave to centralize the building blocks of a company’s narrative. Core messaging, sales decks, customer intelligence, and selected competitor data live in one structured place.

Each product is broken down into the same core blocks: description, differentiated value, capabilities, problems solved, and customer proof. That structure also applies to personas, use cases, segments, reference customers, and competitors.

“You’d be surprised how many companies don’t have a real source of truth for their messaging.”

Matteo, we believe you.

Congrats! You just created a reusable positioning library that feeds comparison pages, sales enablement, and outbound personalization, without breaking consistency.

Step 2: Messaging fine-tuning

The last thing you want is to scale wrong or inconsistent messaging.

Use Octave’s natural language chat interface to review what was ingested, correct inaccuracies, resolve contradictions, and clean up inconsistencies across products, personas, and competitors in your library.

Step 3: Wireframing

While refining the messaging, it’s critical to think ahead about how that information will live on the pages. This step intentionally overlaps with messaging work to prevent bottlenecks later.

Matteo recommends collecting examples of ideal landing pages, then building section-by-section wireframes for each page type in Figma. Make sure to follow a clear hierarchy:

• a central competitor hub that acts like a homepage,

• reusable templates for product vs competitor, product vs category, and competitor vs competitor pages.

Each template should follow the same core structure, from navigation and hero to comparison tables and supporting sections.

Every wireframe is designed with future automation in mind. Each section corresponds to a specific data input that will later be generated and populated programmatically. Locking the structure and hierarchy at this stage allows the system to scale to hundreds of interlinked pages without collapsing later.

Step 4: Build the programmatic comparison powerhouse in Clay

With the wireframes locked, move into production. Clay is the workspace where every comparison page is assembled at scale, using the messaging and positioning already defined in Octave.

Instead of creating pages one by one, Matteo creates tables. Each table represents one type of page. One table powers product vs competitor pages. Another powers product vs category pages. A third handles competitor vs competitor pages for any specific category (and you can scale this for as many adjacent categories you have!). The layout of each table is almost fixed (only a few comparison fields can change, but it’s up to you). What changes from row to row is the page’s subject, such as a specific competitor, category, or pairing.

Think of each row as one future page, and each column as one piece of that page. That’s the foundation everything else builds on.

First come the input columns. These define what each row represents, such as a competitor, category, or URL. This is the minimum information needed to anchor a page.

Next are the research columns. Clay scrapes competitor websites, pulls reviews from sources like G2 and Capterra, and fetches logos via Brandfetch.

Then come the copywriting columns. This is where Octave comes back in. Use the Octave content agent inside Clay to pull messaging and positioning from the library created in step one. That context is combined with the research data to generate SEO metadata, headlines, comparison tables, pricing sections, differentiation paragraphs, and FAQs.

Because each column maps to a specific section of the page, Clay is able to fill predefined slots.

Tip: Work in small batches to protect quality and avoid burning credits (or connect your Anthropic API for saving Clay credits altogether!). Test prompts on single rows, refine them, and run the full table only then. Once it holds, the setup can be reused across categories with minimal changes.

Grab Matteo’s best prompts to populate your Clay tables

Step 5: Push pages live to your Content Management System

Time to bring these gems live! Matteo loves Webflow as a CMS because it offers precise field mapping and solid SEO controls.

Each output column in Clay maps to a specific Webflow CMS field. Headlines, subheadlines, logos, comparison tables, pricing sections, FAQs, and SEO metadata all have predefined destinations, which makes publishing smooth as butter.

Pages can be pushed either to staging or directly live, but one rule holds: never publish too many at once. Start with a small batch, review it carefully, fix copy or layout glitches, and only then scale up to publishing hundreds of pages.

The real win is maintenance. Because pages are generated from Clay tables, updates don’t require manual rework. Dynamic fields like pricing or reviews can be rerun and pushed to Webflow without turning updates into a nightmare.

🤖 Tools powering the Workflow

This isn’t an easy workflow, but the logic applies to any project that needs to scale. AI and automation only amplify the foundation you give them. Here, positioning comes first and speed follows. And yes, Matteo more or less invented a whole new use case for Clay 🤯

🎢 Highs, lows and Workflow warnings

✅ What shines

• Crazy results, fast
  It took about 1.5 months to launch the first 500 pages. But two weeks after launch, organic impressions were up ~40%, clicks rose ~150%, and CTR doubled (on an already three-digits baseline ;). Fresh pages also started surfacing regularly in AI overviews for queries like “[competitor] pricing”.

• Set up once, reuse forever
  The real strength of this workflow is its versatility. The same structure supports every type of comparison page, so expanding into new categories like “[your brand] vs [category]”, “[competitor] vs [category]”, or “[category] vs [category]”, or adding new players, becomes almost plug-and-play.

❌ What doesn’t shine

• Quality assurance is a pain
  Even when everything looks perfect in Clay, issues tend to pop up once content hits the CMS. Fields don’t always stitch together cleanly (especially with custom HTML code blocks), mappings break, and small glitches appear. Expect manual fixes and ongoing QA maintenance, especially at scale!

• Slow time to value
  The upfront work is heavy. Wireframing, design, prompt tuning, tone calibration, CMS field mapping, and custom HTML code and workflow setup all take time. Matteo spent roughly two months getting the first large batch live. After that, the ROI compounds.

⚠️ Workflow warning

This workflow only makes sense in hypercompetitive, mature markets with complex, feature-rich products. To make it work, you need to know how to build strong landing pages and understand both buyer intent and LLM scraping behaviour. AI and automation aren’t the only hard part here.

✨ The Goldflow

While everyone and their mother is obsessing over scaling faster, Matteo is obsessing over getting the fundamentals right.

“AI needs to be told what good looks like. That only works if you have context: your ICP, your market, your competitors.”

Yet another reminder that scale and tools aren’t strategy. They never were. They never will be.

Sara Stella & Diandra ✌🏼

🏗️ Send this to the friend who still builds comparison pages one by one.

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The age of AI is no longer emerging — it is dominantly. Upon. Us.

As of late 2025, a staggering 88% of organizations mentioned they use AI in at least one business function. In marketing and sales, this has led to a widespread tactical use of LLMs for everything from summarizing meetings with Granola to generating ad copy with Claude, to full-blown workflows of intent-based outbound across Clay and Smartlead, or AI content generation with n8n, AirOps, and Webflow.

But here’s the paradox nobody is talking about. While everyone is using AI — or trying to — it’s very few who are really succeeding with it.

We’re in a state of “prompt anarchy” — a tactical free-for-all grab of prompt templates, libraries, and masterclasses (even we have some!) free that’s seemingly creating value, but can also be so overwhelming that is creating some analysis paralysis.

This AI and prompting craze is the reason why we’re witnessing a massive “GenAI Divide”. On one hand, they say it’s spreading brain rot making content marketers and operators lazier. On the other hand, it’s skewing the real GTM alpha on the teams with the larger, most structured, data sets — and the ambition to mine them.

The proof is in the numbers. A recent MIT report found that a shocking 95% of generative AI pilots at enterprise companies are failing to make it to production. This represents a massive waste of resources and a catastrophic failure to capture AI value.

This is compounded by two other critical business risks:

• The infrastructure gap: While 77% of executives feel urgent pressure to adopt AI, only 25% believe their IT infrastructure is actually ready for enterprise-wide scaling. It’s like trying to run a Tesla on a lawnmower engine. This shows not just that we’re still early, but that to look at real innovation in AI usage, we’re better off turning to the smaller, more nimble players that are building these AI workflows software — that’s where the true AI alpha lies. More on this below.

• The prompt management gap: Alex Halliday, founder of AirOps, mentioned: “Your prompts are your IP — keep them optimized and manageable like an engineer would”. But instead, most GTM teams treat prompts like disposable scripts instead of strategic assets. This leads to inconsistent quality and zero reusability — a far cry from the industry benchmark of 40-60% prompt reuse that high-performing teams achieve.

So, in an environment where 95% of initiatives fail, how can GTM leaders cross the GenAI Divide and join the top 10% mining the real GTM Alpha? How do you transform scattered, failing pilots into a cohesive, strategic, and scalable growth engine that drives measurable business results?

There’s not a one-size-fits-all solution. BUT, we believe it starts with managing your prompts better — like they were actual IP to nurture, polish, and constantly refine.

And doing so in a system fashion — with what we call a Prompt-Led Engine (PLE).

This requires treating prompts with the same rigor as other critical GTM assets like ad campaigns or sales playbooks. It’s what we see top operators doing at the GTM Engineer School, where students from the likes of Ahrefs, Google, Slack, and more learn how to build automated systems across AI GTM like Clay, n8n, Cargo, Octave, and AirOps to automate 10-hour manual processes and reduce manual effort by 80%.

In this post, we’ll walk you through our systematic approach to designing, testing, managing, and deploying prompts as core components of your automated GTM strategy.

And we’ll give you examples of who’s doing it very well, so you know who to look up to for what good looks like!

Let’s dive in.

1. The 100x value multiplier of a Prompt-Led Engine

The journey from prompt anarchy to a Prompt-Led Engine isn’t just about getting organized — it’s about unlocking exponential value.

It’s a strategic shift from treating prompts as disposable commands to architecting them as durable, scalable assets.

As Brendan Short of The Signal notes, the future of GTM tech is about selling automated labor, not just software”. According to Brendan,

“The GTM company that will break out beyond a $1B valuation (and into $100B territory) will be the one that builds something that looks like labor in the form of swiping a credit card (instead of sourcing+ramping+managing people, equipped with software).”

However, as we’ve learned in our careers, you can’t “boil the ocean in one pot” — you have to think in phases, in maturity levels like Crawl, Walk, Run. This is how you build that future, one level of maturity at a time.

And here’s how you can apply this thinking to your prompt-led engine.

Level 1: The Individual Prompt

An operator uses a one-off prompt to trigger a task in a chat UI like ChatGPT or Claude. Nothing new here.

Example: A marketer uses a prompt to write a single blog post. The value is limited to that one asset. This is where 95% of AI pilots die, trapped in a cycle of one-off efforts.

You might use a specific prompt generator to structure your prompt but the prompt is not saved, shared, or optimized. It’s a dead end, offering a fleeting moment of productivity but no lasting value.

Level 2: Shared, Manual Prompt Library

Want to know all the details of the other levels,  plus real-life applications of this framework from real companies? Head over to read the full article.

Read the full article on GTM Strategist

Disclaimer: this is a sponsored product deep dive by Clarify, the autonomous CRM for founders. Their team paid me to write this long-form newsletter. As always though, I’ll remain as unbiased as possible in this deep dive.

Introduction

Most early-stage B2B founders — whether building alone or with a co-founder — find themselves covering the entire go-to-market operation. Even with a co-founder, the duo often ends up juggling everything: sales calls, writing follow-up emails, creating content, supporting customers, and steering the product roadmap. This used to mean choosing between scaling the business or keeping founders deeply involved in every critical touchpoint.

However, there's a challenge that's becoming impossible to ignore: hiring early reps doesn't solve the core problem. Or bringing on a VP of Marketing or Head of Sales often creates more overhead — both financial and operational — than actual leverage because you’re still writing the playbook. These hires are best suited for when you have a bit of a signal on what’s working in your early GTM motion, sales process, etc. so they can accelerate.

Meanwhile, founders who try to do everything manually hit an inevitable ceiling — their personal capacity becomes the bottleneck that limits company growth.

So how do you scale founder influence and expertise without diluting what makes founder-led GTM so effective in the first place — the context, authority, grit, and speed that only comes from the person who’s building the product?

The breakthrough lies in a fundamental reframe: successful founders in 2025 aren't just great at executing GTM tactics — they're exceptional at designing AI-powered systems that multiply their unique advantages.

Instead of hiring their way out of hands-on involvement, they're building lightweight, intelligent systems that handle the repetitive work while preserving the high-impact moments that only they can deliver.

In this post, I’ll argue what these systems look like and how you — early-stage founder of a VC-backed startup — can implement them.

1. The founder credibility that no hire can replicate

First things first, let’s discuss why founder-led GTM is still critical.

Early-stage founders convert prospects at rates that would make junior sales hire jealous, and there's a simple reason why: they possess an irreplaceable combination of market context, depth of product knowledge, authority, and decision-making speed.

When a founder says "we built this feature specifically because customers like you told us X," that carries weight no sales rep can match. When a prospect asks about roadmap priorities or technical capabilities, founders provide answers with confidence and nuance that builds trust instantly.

But here's the trap most founders fall into: they assume this advantage only exists in 1:1 interactions. They schedule endless demos, write personalized emails one by one, and turn every customer conversation into a marathon call. The founder's influence remains trapped in individual moments — and an impossible to manage calendar — instead of scaling across the entire GTM motion.

The insight that separates thriving founder-led companies from overwhelmed ones is understanding that founder’s calendar is a zero-sum game — both early-stage founders and Elon Musk have all 24 hours in a day.

Once you work back from this insight, you realize that founder credibility can be expanded and scaled — systematized. The goal isn't to remove the founder from the process — it's to design systems that amplify your voice, capabilities, and output across multiple channels and touchpoints, from initial outreach to customer success.

So how do you scale that influence without burning out?

2. The mindset shift: From top seller to GTM system designer

The most successful founders have made a crucial mental shift: they've stopped thinking of themselves as the person who does all the work and started thinking like the architect who designs compounding systems, workflows, and loops to scale their work.

Here are some founder-led GTM examples I follow that substantiate the above:

• Austin Hughes, co-founder and CEO of Unify, provides a blueprint for founder-led GTM driven by transparency, social content, and rigorous systems.

  • Hughes credits intentional team-building and daily public content creation as core drivers in scaling Unify to 56 employees and a major Series B in just 2.5 years.

  • “Pick a social channel and work at it every day. The teams that understand viral content today have an unfair distribution advantage. As a founder, its so important to figure out how to nail one. For us that's Linkedin.” he wrote in August 2025, while explaining that distribution is the new bottleneck, not product.

  • Using LinkedIn as a compounding distribution engine, Austin posts tactical insights and playbooks, making Unify’s GTM motion both transparent and repeatable for other founders.

  • This founder-as-systems-architect approach is evident in his willingness to document what worked (and what didn’t), inviting the ecosystem along for the journey and setting the tone for next-gen founder-led SaaS.

• Adam Robinson, CEO of RB2B and Retention.com, exemplifies founder-led GTM at scale by turning LinkedIn into a compounding engine for both revenue and growth and product feedback.

  • Within two years of consistent content, Robinson’s posts have driven 140k+ followers and made RB2B’s LinkedIn presence its core distribution channel. He’s frank about the power of founder narrative, writing, “Building a personal brand on LinkedIn is the fastest, most efficient, and highest leverage way to grow a startup that has ever existed.”

  • By sharing granular journey updates — product launches, lessons, frameworks, and failures — Robinson has built not just a company but a movement, driving $5M in ARR with a five-person team in just one year.

• Amos Bar-Joseph, CEO of Swan AI, demonstrates the new frontier of founder-led GTM by using content and AI to scale revenue per employee to unprecedented levels.

  • With only three founders and zero outbound or paid ads, Swan AI generates $1.20 in pipeline for every LinkedIn impression, reaching $30M+ ARR after just a few months on the market.

  • Bar-Joseph’s LinkedIn content acts as a top-of-funnel rallying cry: each manifesto-style post reframes how founders should think about intelligent-scale and operational simplicity. “We replace scaling challenges with intelligence, not bodies,” he explains, outlining how AI agents absorb system drudgery so humans can focus on high-impact decisions.

  • This systematic, high-leverage approach — articulated in dozens of detailed posts — positions Amos as both a teacher and chief architect, showing how founder-led GTM is no longer about hustle, but compounding trust and efficiency.

• Anton Osika, CEO and co-founder of Lovable, offers a deeply personal account of founder-led go-to-market in his July 2025 LinkedIn post announcing Lovable’s $200M round.

  • He writes: “I’ve never shared how it started: I called my friend at 6AM and told him I wanted to build the world’s first AI platform that anyone could use. I did the first 20 sales calls myself. I personally wrote, called, or DM’d our first 100 beta users to learn what was broken and what they loved. Being on the front lines gave me the conviction and clarity to move harder and faster — those early GTM loops became the playbook we scaled with the team.”

  • This firsthand narrative highlights how Anton himself executed the core founder-led GTM motions — personally driving early sales, running product feedback loops, and architecting the motion from direct customer interaction. He frames these activities as the foundation for Lovable’s explosive, system-driven growth.

• Amanda Zhu, founder of Recall, shows how founder-as-educator content can turn complex, technical products into movements customers rally behind.

  • Instead of relying solely on sales outreach, Amanda uses her LinkedIn presence as a live journal of building Recall, weaving product storytelling with candid lessons in founder resilience. Her posts range from detailed breakdowns of AI workflows to transparent reflections on the emotional toll of scaling a startup — building Recall’s narrative as much around authenticity as technology.

  • Where others might treat social updates as marketing collateral, Amanda’s voice serves as Recall’s differentiator. She translates raw conversations with researchers, operators, and early adopters into digestible thought leadership that doubles as customer enablement.

  • Amanda’s approach — turning what she’s learning in real time into frameworks, metaphors, and stories that resonate with both buyers and peers — illustrates the next stage of founder-led GTM. Her content isn’t detached branding; it’s an open-source field guide, where every post compounds Recall’s credibility, community, and demand.

• Gal Aga, CEO of Aligned, demonstrates how system-led content drives outsized pipeline for founder-led SaaS.

  • Gal built a repeatable, metrics-driven workflow that elevated LinkedIn into Aligned’s top channel for pipeline creation, with his leadership content frequently generating 1.5M+ impressions a week.

  • He doesn’t just recount personal wins; his LinkedIn feed is a steady stream of “hard lessons learned” and sales frameworks, systematizing experience into influential, teachable moments. In June 2025, his post summed up the ethos: “I led sales teams between $1M-$100M ARR. Built 4 orgs from scratch. Made every mistake in the book...” Gal’s approach — turning individual expertise into content and feedback loops that reliably bring in new customers — is the blueprint for founder-led GTM that scales beyond the founder’s calendar.

Each of these founders shows that the modern founder-led GTM playbook is about designing compounding, system-driven workflows — content, product, and outreach loops that operate at scale — rather than purely individual effort. Their systems create an outsized surface area of influence, trust, and revenue, all rooted in authentic founder context and voice.

• Instead of manually researching prospects, they build research workflows.

• Instead of writing individual follow-up emails, they create intelligent sequences.

• Instead of hoping valuable sales conversations turn into content, they build systems that automatically extract insights and generate marketing assets.

This system-designing approach tackles three core areas that typically consume founder bandwidth:

1. Outbound prospecting, outreach, and pipeline management.

2. Product feedback, support, and roadmapping.

3. Content creation that scales their reach, authority, and brand awareness.

Each system is designed with the same principle — handle the repetitive, time-intensive work automatically while preserving space for founder involvement in the high-leverage moments that drive real business outcomes.

The founders who master this approach often share a similar realization story: they spend one week building a system that saves them 10+ hours every subsequent week, and they immediately see the compound effect. What used to require constant founder attention now runs with minimal oversight, freeing them to focus on product development, strategic partnerships, and the customer conversations that actually move the business forward.

In the GTM Engineering School I run, VC-backed founders like Andrea from Aptus learned how to design these systems hands-on — from scaling prospecting and research to personalized outreach and automated content creation.

This is the mindset shift — with the right attitude, tooling, and a bit of work, founders can become the foundational GTM Engineer of the company, balancing deep business understanding, endless grit and commitment, with creativity and scalable processes.

3. The three-system framework with tools that compound

The most effective founder-led GTM systems operate across three interconnected areas, each designed to multiply founder impact rather than replace founder involvement.

Picture this scenario:

• A founder used to spend 40+ hours per week on GTM activities — manually researching prospects, writing personalized emails, transcribing sales calls, and trying to remember which customer said what about which feature.

• Six months later, that same founder spends 12 hours per week on GTM while generating 3x more qualified pipeline.

The difference? They built what we call the "founder multiplication engine" — three interconnected systems that transform individual effort into scalable competitive advantage.

The founders who crack this code start with a carefully chosen set of interconnected tools. But here's the twist — tools are powerful, but it's the system design between them that drives exponential results.

System 1: Outbound that runs without you

The architecture begins with an outbound motion that operates like an intelligent researcher.

• Smart founders use tools like Octave for defining, storing, and updating ICP, messaging, and positioning in real-time.

• Then, using these strong PMM foundations, they call Octave agents to enrich, qualify, and write sequences directly into Clay, which is used for TAM sourcing and orchestration — so every touch is hyper-tailored and sent only to ICP-fit opps.

• Then, messages are piped into Smartlead, Instantly, or other cross-channel tools across LinkedIn, email, phone calls.

• Last but not least, this compounding loop is closed with Clarify as the autonomous CRM that centralizes all interactions and learns from every call — automating follow-ups, meeting prep, and pipeline management.

This first system can scale the founder-led sales multiple folds by automating prospect research, outreach personalization, and follow-up — but here's the crucial element: AI manages the process until the founders step in for the highest-leverage conversations. The system learns from every interaction, constantly improving personalization and timing.

System 2: Customer conversations become product features

The second system turns every customer interaction into compound product prioritization and value.

Instead of letting valuable sales conversations disappear into memory, systematic founders use AI-native CRMs like Clarify to automatically extract patterns from every customer call, identifying common objections, successful positioning approaches, and emerging use cases.

These insights feed directly into product feedback, feature prioritization, and roadmapping. Tools like Linear can ingest structured feedback from Clarify to inform lean product and engineering organizations as to how to build the right features faster — thanks to AI code editors like Cursor, Copilot, or Windsurf.

Lean GTM-savvy founders that truly value speed and shipping will also use tools like Lovable to vibe code mini-products, websites, or feature upgrades with 10X time-to-market and a fraction of the costs.

This is another important mindset shift that is happening with AI — from documenting feedback to directly building and shipping based on feedback-based prompts.

System 3: Content that is grounded in your ICP and scales

The third system creates content that compounds over time rather than requiring constant creation.

The most efficient founders have discovered something counterintuitive: their best product marketing insights around positioning, messaging, GTM, and content already exist in internal documentation, sales call recordings, and customer conversations.

• They use conversation intelligence from CRMs like Clarify to extract patterns from sales calls, identify common objections and success stories, and automatically generate a backlog of product marketing and content themes.

• Insights from these calls can be fed back into Octave in plain English to update ICP models, product messaging, comparative analyses with competitors, customer proof points, and more. These insights will trickle down immediately into your sequence agents and re-align your outbound messaging accordingly.

• With this new system, a single customer conversation — recorded as a podcast episode — can also become the foundation for a case study, sales enablement materials, a LinkedIn video clips post series, a blog article, and even be featured in thought leadership internal research for sales enablement purposes.

This reveals another fundamental mindset shift — create once, use a thousand times — across multiple placements.

• Riverside and Opus.pro are great, cost-effective tools for podcast creation and clip distribution — including automatic cross-channel bulk scheduling on social, while tools like AirOps or Scripe are great to turn call transcripts into long- and short-form text-based content that sound just like the founder.

• These tools all enable human-in-the-loop reviews to maintain voice and accuracy while letting AI handle the heavy lifting of adaptation and distribution.

• And for the crazier lead magnets, mini-website, or full-fledged digital products — depending how ambitious you are — there is also Lovable.

• This content is crucial for founders to build organic audiences and momentum on must-have platforms like LinkedIn and Substack. The key is tracking which content drives qualified traffic and pipeline, not just impressions, creating a feedback loop that improves content quality over time.

Each tool has a specific role in these systems, but the strategic advantage comes from how they connect together — creating workflows where customer insights automatically improve outreach and product marketing messaging, where successful conversations become marketing assets, and founder authority scales across every touchpoint without diluting its authentic impact or adding overheads.

This is the mechanism that lets founders scale their authority without adding more overheads — one authentic customer conversation becomes the foundation for weeks of assets.

Want to build your founder-led GTM engine? I’ve helped 40+ SaaS founders build their system-led stack and motions. Book a call with me to collaborate.

Book a call with Matteo

4. The modern founder-led GTM stack is made of tools that connect and compound

But here's where the compounding effect really emerges: these systems don't operate in isolation — they form an intelligent ecosystem where each component makes every other component more effective.

But every system needs a central node and source of truth to coordinate the most important data — customer relationships. And in B2B SaaS, such a central node has always been and still is the CRM.

AI-native CRMs like Clarify serve founder-led GTMs as autonomous command centers, learning from every customer interaction and feeding insights back into each systems we outlined above — outbound, product, and content.

When your prospecting system learns from your sales conversations, when your content creation pulls from actual customer language, and when your follow-up sequences adapt based on conversation intelligence, you've moved beyond tool collection into true system design.

The power multiplier comes from this system integration — when your outreach tool learns from your sales conversations, when your content creation system pulls themes from actual customer language, and when your follow-up sequences adapt based on conversation analysis.

This is where conversation intelligence becomes the command center of the entire system.

Instead of manually updating spreadsheets or trying to remember important details from last week's sales calls, the system captures and processes every conversation, turning raw interactions into actionable intelligence.

5. Scaling founder-led GTM without losing authenticity

The biggest fear most founders express about systematizing GTM is losing what makes their personal involvement so valuable. They worry that automation will make their outreach feel robotic, that AI-generated content won't capture their voice, or that intelligent follow-up will miss the nuances that build real relationships.

The reality is that well-designed systems — with thought-through foundational context from the founder — can amplify authenticity rather than diminish it.

• When AI handles prospect research, founders can spend more time on personalized insights that actually matter.

• When conversation intelligence extracts patterns from customer calls, founders can speak more precisely in their customers' language.

• When content systems generate drafts from founders' actual words and experiences, the final output becomes more authentic, not less.

The key is designing systems that preserve founder involvement in the moments that matter most while eliminating the busywork that dilutes their impact.

Start with 80% founder involvement and 20% system support, then gradually shift to 20% founder involvement in high-leverage moments and 80% system handling everything else. This transition allows founders to maintain their authentic voice and relationship-building advantage while dramatically increasing their reach and efficiency.

The founders who nail this transition often describe a similar outcome: they're having more meaningful customer conversations, creating better content, and driving more qualified pipeline — all while working fewer hours on GTM activities. Their systems handle the repetitive work, but their personal touch shows up exactly where it creates the most value.

Closing thoughts

The most successful startups in 2025 won't be the ones with the biggest marketing budgets or the fastest hiring timelines. They'll be the companies where founders learned to design systems that multiply their unique advantages — their product context, market authority, and decision-making speed — without burning out in the process.

This systematic approach to founder-led GTM creates a compound advantage that traditional hiring can't match. Every customer conversation improves the system's intelligence, every piece of content strengthens the founder's market authority, and every successful outreach sequence becomes a template for scaling further.

The future belongs to founders who don't just sell — they design. They turn customer conversations into intelligence, insights into systems, and personal influence into scalable competitive advantages.

Disclaimer: this is a sponsored product deep dive by Taplio — their team paid me to write this long-form newsletter. However, I’ve used Taplio and it has helped me over and over streamlining my content research and production operations. Also, I’ll remain as unbiased as possible in this deep dive, sharing my product feedback to make Taplio an even better product for solopreneur marketers like me!

PS. Read until the end to get an exclusive promo I’ve worked out with the Taplio team just for you!

Introduction

LinkedIn has become a noisy, look-alike feed, and cracking the code on standout content is tougher than ever. When every post feels like a remix of the last, even figuring out what “good” looks like can feel like hunting for a hay in a haystack, especially if you’re just doom-scrolling.

And if you’re a one-person consultant like me who has to actually write LinkedIn content for a living, things can get even harder.

Here’s what that a day in the life of a solo LinkedIn content ghostwriter means in practice:

• Run account and content research across different industry verticals

• Stay on top of the viral formats, accounts, and trends for each vertical and ICP

• Ghostwrite in multiple founder voices without losing their nuances or writing styles

• Prove content’s impact on audience, leads, and pipeline — not just vanity metrics

• Do all of the above for your own consulting business

• And craft and share your own opinions as a marketing operator, showcasing case studies, testimonials, learnings, and wins to attract your next customers

Keeping that many plates spinning begs the question — how do you balance:

• Trendiness: Staying aligned and up to date with “the trend” and formats that go viral

• Differentiation: While carving a standout, “different”, and unique POV

• Volume: And shipping consistently to stay top of mind?

I’ve found Taplio to be of great help to meet the right balance — blending research, AI writing assistance, and light-touch project management in a single UI.

In this newsletter, I’ll walk you through my end-to-end LinkedIn workflow and show exactly how Taplio can help streamline it. I’ll also include some tips for the Taplio team on where I’d love to see the product go next. 🙂

The LinkedIn content lifecycle

In B2B SaaS, LinkedIn only compounds when every step of the content loop feeds the next — here’s the full circuit I run, end-to-end, for both my clients and myself:

1. Research: I start by abstracting specific topics mapped to ICP pain points and use cases, scanning viral formats for algorithm-friendly hooks, and pinpointing the tastemakers and trendsetters accounts who shape the vertical’s opinions and trends.

2. Strategy: blend those insights together to carve a unique point of view and a series of content pillars — and related sub-topics — aligned with your value prop and what your ICP cares about.

3. Writing: turn your strategy topics into actual posts packaged with scroll-stopping hooks, easy-to-skim structure and formats; that sound unmistakably “you.”

4. Collaboration & feedback: share your draft with collaborators, designers, or your clients for feedback until the message is crisp, the tone of voice ticks, the creative is matching, and you’re ready to ship.

5. Distribution & scheduling: slot each post into a visible calendar so you (and your clients) know what’s up next and how it ties into the rest of the calendar for the week(s).

6. Engagement & comments: jump into comment threads where your buyers, peers, and tastemakers already hang out, adding perspective that keeps your brand top-of-mind or reinforces your unique POV.

7. Tracking & learning: close the loop by measuring impressions, followers growth, engagement rate, but also newsletter sign-ups, which formats/topics convert best, and influence on pipeline. Then, feed those insights back into your next research sprint.

Nail this rhythm and every post becomes a data point that sharpens the one after it — compounding reach, credibility, and pipeline over time.

1) How Taplio streamlines research & strategy

Now that we’ve got the core components of the LinkedIn content lifecycle, here’s how Taplio can help you streamline each of them. First, let’s start with research and strategy.

Taplio helps you turn LinkedIn from a never-ending scroll into a structured research engine. Here’s the flow I run before writing a single line of content:

• Topic & format discovery: Navigate to the Inspiration tab to search for relevant keywords, star stand-out posts, and build a living swipe file. I prefer to limit results to the past 15 days to optimize for recency and ignore follower counts to discover posts from emerging accounts instead of the largest, most established ones.

  

• Create curated contact lists: Then, I use Collections to organize each post or account find into collections by content pillar (topic) or winning format, one click at a time. I use lists to keep track of ICP buyers and niche tastemakers to dedicated lists. You could also segment these lists by client brand so every feed instantly reflects the trends and nuances of every industry you’re working on.

  

• Chrome extension on the go: when on LinkedIn and you land on a cool profile, you can also use Taplio’s Chrome extension to surface the account’s top-performing posts, and star both the profile and its top content into your Taplio lists without leaving LinkedIn.

  

BONUS. Export for deeper analysis. You can also export your saved posts or contacts from your collections into your preferred LLM to reverse-engineer hooks, spot pattern breaks, or uncover correlation between format and engagement. Use the prompt below to run your research and strategy analysis.

```

<ROLE>

Act as a top-tier content intelligence analyst and copywriting strategist.

Your specialty is reverse-engineering social media content (specifically LinkedIn posts) to detect patterns in format, hooks, tone, and structure that correlate with high engagement. You excel at breaking down viral and underperforming posts into components, and translating raw post data into concrete content insights.

<TASK>

- Analyze the provided exported LinkedIn posts or profiles (in CSV format).

- Break down each post into the following elements:

* Hook type (question, contrarian, story teaser, stat lead, bold claim, etc.)

* Format (text-only, carousel, poll, document, video, image, etc.)

* Tone of voice (personal, educational, inspirational, humorous, provocative, etc.)

* Structure (how many lines before CTA or shift in post, story arcs, etc.)

* CTA type (comment bait, lead magnet, share prompt, none, etc.)

- Tag patterns or outliers where the post significantly overperforms or underperforms based on engagement (likes, comments, shares, impressions).

- Identify what correlates most with high or low engagement:

* Does post length correlate with performance?

* Which hook types repeatedly win?

* Which format outperforms plain text? Polls, carousels, images, or videos?

- Highlight pattern breaks—posts that diverged from the author's norm and either flopped or took off.

- Suggest 3 key content hypotheses the creator could test based on this analysis.

- Provide 5 hook formulas derived from the most successful posts, ready to plug into future content.

<GOAL>

The goal is to reverse-engineer a set of LinkedIn posts or profiles to uncover replicable patterns and strategic content decisions that drive above-average engagement.

<STYLE AND TONE>

- Use clean, analytical, professional language.

- Keep it insight-driven, using concrete examples and clear comparisons.

- Structure output for clarity: use headers, bullets, and charts/tables if needed.

- Be neutral in tone—objective, not flattering or overly critical.

<OUTPUT FORMAT>

- Summary insight section

- Breakdown table or bullet list per post

- Correlation matrix or bar chart (if engagement stats are numeric)

- Pattern break highlights

- Final content recommendations and hypotheses

<INPUTS>

- Exported LinkedIn post or profile data (CSV, Notion, Google Sheets, etc.)

- OPTIONAL: Add 1–3 posts that performed exceptionally well or poorly and you'd like dissected in more detail

- OPTIONAL: Add your current post structure or brand voice doc to help with calibration

```

Come up with your analysis prompt own using my very own Genesys prompter.

Create your own prompts

No matter how time-consuming this research process can be, rest assured it compounds — keeping track of the hot people, content, and formats your ICP are moved by forces to keep your juices flowing and keep you relevant in a fast-moving industry.

Product notes for the Taplio team: Taplio would be even better if you could bring richer account analytics into the core UI — think things like posting cadence, common posting days/times, format mix across images, text-only, or carousels, engagement benchmarks; and even advanced filters for segmentation (eg. by media type, follower brackets, language, and country). That data would close the loop between research and planning even more, all in one pane of glass.

2) How Taplio streamlines writing, collaboration, and distribution

What I like about Taplio is that, once research and strategy are squared away, you can switch straight to creation mode — turning a tangled content pipeline into a smooth production line. Here’s how I use Taplio’s main features to streamline my content creation workflow:

• AI tools settings: In Taplio’s AI tools, you can enter your brand kit in settings to personalize the content created to your (or your client’s) business. Why is this important? Because as I recently wrote here, one of the biggest rules of AI is Garbage in = garbage out — meaning that, without grounding the AI in proper context, it won't give you good output. That’s why it’s critical that you provide Taplio with this context. And if you don’t have anything at hand, you can get your own brand kit with my Genesys Brand Kit generator to get a headstart. Then, you can refine and paste the output in the AI tools setting sections.

• AI tools: now that the AI is grounded in some context about your writing style, you can use the AI tools to:

  • Come up with fresh post ideas

  • Or drop a blog paragraph, and Taplio will expand on it to create a 90% ready LinkedIn post. What I like about this feature is that you can also tweak the style and tone of each post, choosing from a few presets.

    

  • Propose scroll-stopping hooks

  • Repurpose long-form pieces into bite-sized LinkedIn posts

    

  • And even auto-build carousels that match your colours and fonts. Just pick your brand colors, customize the look and feel, and insert your copy — all without switching between Figma or Canva.

• Drafts kanban board: once you have your post drafts ready, you can drag each of them through a custom kanban board with columns that mirror your very own workflow. I normally keep the following states: Backlog, In progress, In design, In review, Ready to publish, Published. Again, no more rogue Google Docs or Notion tables — now you have everything streamlined, which is a real benefit if you’re doing this at scale.

• Queue: Once a draft is green-lit, I drop it into Taplio’s scheduler and pick the exact day and time you want it to go live. Your calendar shows everything at a glance, so you can spot gaps, avoid overlaps, and keep a steady cadence without manual reminders. You can even tap into Taplio’s own data to check the best performing times for your own geo, which can be pretty handy to run your tests.

  

  

• Collaboration: And if you’re working with clients or teammates, you can push any draft straight into their Taplio workspace for final sign-off. Likewise, clients can send posts back to you for tweaks and approval, keeping feedback loops tight and version control painless on both sides. Note: they do need to have a Taplio paid account to make this work.

Product notes for the Taplio team: if the AI tools tab was housed under a dedicated “writing” tab the UI could reflect an even more natural and integrated LinkedIn content lifecycle. Plus, if you could bundle the current AI tools with proven or trendy content templates (hook + body + CTA) with editable tone-of-voice guidelines, that would make the content creation process even more tailored — giving you the option to build your very own combination of differentiated content. Oh, and also being able to pull multiple clients’ posts into drafts or queues would make multi-brand orchestration feel as seamless as a single feed.

Want me to write a deep dive similar to this for your product? I love deep diving and using the latest SaaS and share my learnings and tips with my audience. Let’s chat!

3) How Taplio streamlines engagement

As discussed above, posting on LinkedIn is only half the battle — the real reach compounds when you comment, and Taplio makes this process effortless. In Taplio’s Engage tab, you can:

• My post comments: see every new comment to all of your posts in one feed, so you can fire back fast, and ensure no prospect slips through the cracks. Remember, replying to each comment on your own posts will double the number of comments and increase your reach, especially when tagging commenters.

• With people (your lists): filter the comment feed to just your ICPs and tastemakers’ lists, so you can add thoughtful takes on their posts and stay top-of-mind without doom-scrolling.

  

• Best for you: Taplio also surfaces net-new conversations based on the keywords and interests you set, letting you expand reach beyond your immediate connections.

• Auto-like and comment suggestions: you can also tap once to drop a response and a subtle like, signalling you’re engaged without breaking flow (and yes, ALWAYS layer your own voice on top — please never use a 100 % AI-canned reply).

Product note for the Taplio team: being able to tag people or add images in the comments directly from within Taplio would make this tab even more powerful. Also, you could customize different people’s lists to prioritize who to engage with depending on your latest focus, and hide comments you’ve already addressed to so the suggestion feed stays fresh and you keep more focused.

4) How Taplio helps tracking post analytics

Your content only improves at the speed you can learn from it, and Taplio puts those insights in plain sight:

• Unified performance dashboard: go beyond native LinkedIn views and reactions by pulling metrics like post’s likes, comments, shares, profile visits, and follower growth into a single view.

  

• Deeper winners list: pin top-performing posts or tag stand-out hooks, visuals, and formats so you can spot repeatable patterns without trawling spreadsheets, and feed directly to your repurposing machine and queue.

• Export for more analysis dives: as discussed before, make sure to export your post engagement data into your favourite LLM to mine correlations between hook style, post length, and conversion lag. Use the prompt above, refine it or create your own with my GPT prompter.

Product note for the Taplio team: adding a time-series overlays that help tie metric spikes directly to specific posts could give operators instant clarity on what moved the needle — and native AI-suggestions that understand mine your and others’ posts context could help extrapolate hypotheses as to why that post over- or under-performed.

Closing thoughts

LinkedIn rewards operators who do content with consistency, relevance, and a unique POV — but trying to hit all three as a team of one can feel like sprinting on a treadmill. Taplio help streamlining that sprint into a flywheel:

• Research the right people, topics, and formats without drowning in the feed.

• Create on-brand posts fast, then move them through a clean Kanban and scheduler.

• Engage in the comments that matter with your and others’ posts.

• Track the right KPIs so the next post is better than the last.

I wrote this deep dive with Taplio’s support, yet every suggestion and wishlist item comes from my own daily grind as a solo consultant and my experience using the product.

If you want to see how streamlined your LinkedIn can get, give Taplio a spin — and use code MATTEO1X1 at checkout to get 1 month of their Pro plan for just $1 dollar. Try the workflow, measure the lift, and drop me a note with your wins (or gaps) so we can keep this content loop compounding.

Content marketing is an indiscussible channel for early-stage B2B startups. It can drive results across the whole funnel — brand awareness, acquire leads, revenue, and even expansion — while making acquisition organic and scalable, which is what every VC seeks.

Content should be among your first channels as an early-stage SaaS. Once you’ve developed the foundation of your product marketing messaging, content is the first avenue to help you articulate that into a compelling narrative. Yes, outbound should also be among your first channels, as it’s mostly free, highly scalable, and provides faster feedback loops than content.

However, in B2B you’re selling to professionals; and no professional or company will buy from someone they don’t trust. Content is a unique avenue that enables you to show your audience you’re an expert in what you're selling. Differently from ads or outbound, content helps you project confidence in your prospects, helping them build a relationship of affinity so that when the time is right, they’ll trust you with their money, and choose you over competitors.

Where Every Startup Gets Content Marketing Wrong

To this day, the cheapest and most scalable content starts in written form, especially thanks to AI which gave us “infinite labor”. But where AI solves for execution, the strategy gap remains wider than ever. Most startups we advise struggle with content strategy. Common pitfalls include:

• Not investing in product marketing ahead of content,

• Inconsistent production and distribution,

• Over-reliance on AI-generated content that drives volume but lacks depth and taste.

But above all, one of the biggest mistakes we see is that we need to figure out where to start with content.

“We should talk about our vision”, “Let’s do a series about the key customer pain points”, or “We need to target these high-volume keywords”.

More often than not, these mistakes are rooted in the founders’ inner conviction and vision, because they hire junior marketers on their first rodeo; or because even when they hire senior experts, they discard their advice (yes, this happens all the time!).

How can your startup overcome these pitfalls and develop an effective content marketing strategy that delivers tangible results?

In this post, we’ll go beyond the cookie-cutter advice like “you must know your audience”. We’ll devise a strategy process intertwined with product marketing by defining your buyer’s stages of awareness, selecting your best starting point for content production, and then sharing how to build robust content programs using both human writers and AI.

Disclaimer: Before Doing Content, Please Invest in Product Marketing

Before writing content, you must invest in product marketing.

Product marketing is the strategic foundation that defines your TAM and target personas (aka. Ideal Customer Profile), analyzes their goals, the alternatives/competitors they’re using to achieve them, and the problems arising from them; helping you “message” and “position” your company as a compelling solution.

We see many founders and teams under-indexing in product marketing, tremendously diluting their content. Product marketing comes before content; if you skip it, you’ll just have weaker messaging, weaker content, and more confused prospects.

So if you haven’t invested in product marketing, you might not be ready for this guide. Instead, check out these resources:

• The Ultimate Framework to Build Your Ideal Customer Profile

• The Importance of Targeting in B2B Demand Gen

• The perfect SaaS homepage template

• The definitive SaaS homepage framework

• Follow Anthony Pierri or Robert Kaminski on LinkedIn

Need to get your messaging right before jumping into content?

Book a call with Matteo

Step 1. Map Your Stages of Awareness

Now that you’ve set your product marketing foundation and are ready to confidently articulate your value proposition in a language relatable to your ICP, you need to map your awareness stages.

The Stages of Awareness is a framework built by Anthony Pierri that helps you understand the level of awareness your customers have towards the outcome they’re trying to achieve with your product. FYI, this is not content, but product marketing.

For example, Anthony used the B2B gift-sending platform Sendoso to explain the framework. Let’s go through it in a bit more detail.

1. Outcome Awareness. At this stage, prospects are aware of different potential outcomes they want to achieve. Outcomes as intended here refers to the JTBD definition according to Bob Moesta — the process a consumer goes through when they aim to change their existing situation into a preferred one, but cannot do so because of constraints that stop them. In the case of Sendoso:

• Maybe they want to focus on making their sales team happier at work.

• Maybe they want to make their teams more productive overall.

• Maybe they want to close more deals.

This is typical of a prospect that is in the earliest stages of their purchasing decision and/or in new markets — they know what they want to achieve, but are not aware of how to do it or which solutions are out there. Instead, they are battling against “the old/current way” of doing things, because they’re not aware of a “new way” (well, that could be you!).

2. Use Case Awareness: once a prospect has picked their outcome, they start evaluating different things they could do to achieve that goal. These options reflect the next stage of awareness, where customers are shifting to explore solutions to achieve their outcomes, according to Anthony Ulwick’s definition of JTBD. In the example of Sendoso:

• Maybe they want to focus on training their team in new sales methodologies.

• Maybe they want to create new sales enablement material.

• Or maybe they want to start sending gifts to prospects during the sales process.

Let's assume they decide to go with sending gifts to prospects as the key use case.

3. Product Category Awareness: once they've chosen a use case, they need to figure out the best product category that will allow them to send gifts to customers. They start debating between:

• eGift card platforms

• Branded merch platforms

• and gift-sending platforms.

Each of these product categories will help them accomplish the use case but in very different ways, depending on their product capabilities, features, and related benefits. This awareness stage is typical of the middle stages of the purchasing funnel, but also of mature markets where customers are already aware of the existing product categories to solve specific issues. Ultimately, they decide to go with gift-sending platforms.

4. Product Awareness: now that they’ve committed to a product category, they evaluate different alternatives of gift-sending platforms to compare each feature and functionality to see which one best fits what they need to be able to do until they make a purchasing decision. They check out Reachdesk, Postal, and Sendoso. Depending on the growth model of your company, they will:

• PLG: sign up for trials of these products, and compare capabilities against them.

• Sales-led: book demos and go through the sales process to compare pitches and prices.

5. Purchase Decision: after completing their research of competitive alternatives, they commit to the purchase decision. Again, depending on the motion of your company, they will:

• PLG: upgrade to a paid plan.

• Sales-led: sign a contract.

Note: In the real world, some stages of awareness might be mixed or have different orders. The awareness stages, like every other framework, attempt to make complex real-life scenarios more linear and simple. This prescribes that each prospect is 100% clear about the outcomes they want to achieve, their use cases, the product categories to help them do so, and each provider. However, only some prospects have perfect clarity or perfect knowledge of the market; and on the other hand, only some companies know how to portray themselves against competitors. That shows how critical product marketing can be in helping prospects to make the right choice.

Want to build your first content program with a senior operator with 9+ years of startup hands-on experience? Book a discovery call with Matteo.

By the end of this section, you should have mapped out your awareness stages for every potential outcome your company can help with. Based on that, you can work back the related use cases product features, and related categories.

For example, in the case of Cello:

Step 2. Decide Which Awareness Stage to Start with

The stages of awareness are great for mapping out your key product marketing messages and contextualizing them to your customer journey. You can use these not just to reverse engineer a compelling content map, but also to understand where to start devising your content programs.

As mentioned above, most marketers and advisors will tell you: “Start creating BOFU (bottom of the funnel) content.” In our opinion, it’s not that easy, but it depends on the awareness stages of your prospect and the maturity of your market.

• Mature market: start from Product Awareness. If you’re in an established market with multiple competitors, you need to convince your market your product is the best in your category. Start a content program around your players in your category and purchase decisions. If your competitors have high traffic, you’ll also piggyback from prospects searching for their brand name. We’ve listed below a list of content pages you should start with. In the example of Cello, that’d mean creating comparison pages with their competitors or boasting about why Cello is a superior alternative for their target segments.

• Emerging market: start from Product Category Awareness. If you’re in an emerging market and your product category is rising as a better way to reach an outcome, you need to convince your market your product category is the go-to alternative. For example, in the case of Cello which is targeting founders looking to scale their organic acquisition, they might want to create a program around why a user referral program is the best way to do so compared to other alternatives like SEO or outbound.

• New market: start from Use Case Awareness. If you’re building an innovative product or starting a new market, there might be no direct competitor and you might be battling against “the old way of doing things”. For example, that could be doing things on spreadsheets, manually, etc. In this case, you should invest in creating awareness at the higher stages, around your customer outcomes and product use cases. In this way, you’ll be able to educate your prospects about the fact that there is a “new way of doing things”, and you’re building it for them. For example, Cello could create content that showcases why user referrals through an API solution are 10x better than doing it in-house.

By following this strategy and contextualizing your content to the market, you’ll reach your target market at its current stage of awareness, speaking to your prospects in their language, and saving countless resources on pushing messages that are either too early or too late for the market.

Let’s continue with the Cello example. Because they operate in a fairly competitive and mature market, I’d start creating content around the product awareness stage, covering competitive products within the user referral category to build a differentiating wedge with other players.

After completing one content program around this awareness stage (more on this below), I’d move up or down the awareness stages depending on the results or reactions around the content.

Step 3. Think in Content Programs, not Posts

Now that we’ve identified which stage of awareness we want to start from, we can move into content production strategy. Each stage of awareness has specific post types — think of them as “template angles” that generate awareness in that specific stage of your buyer’s journey, helping spark reactions, and conversations, and ultimately “graduate” your prospects to the next stage of awareness.

To make it easier for you, we’ve created a list of 25+ content types, including. examples.

Outcome awareness

1. Glossaries — What is {Term}?

   • Example: Ahrefs

   • Example: SEMRush

2. Formulas — How to calculate {Term}

   • Example: Paddle

3. Statistics — X New {Topic} Statistics for 2024

   • Example: Spendesk

   • Example: DevSquad

4. How many — How many {Statistic} {Topic}

   • Example: ExplodingTopics

5. Tips — X {Topic} Tips for 2024

   • Example: Moonsend

6. Ideas — X {Topic} Ideas

   • Example: BSS Commerce

7. Examples — X {Topic} Examples for 2024

   • Example: Hubspot

8. Difference — {Topic} vs {Topic}: What's the Difference?

   • Example: Ahrefs

9. Types — X Types of {Topic} You Should Use/Avoid

   • Example: Cognism

Use case awareness

1. How-to — How to {Use case} (with current solution)

   • Example: Databox

   • Example: Drivetrain

   • Example: Freshbooks

   • Example: Descript

   • Example: User Pilot

   • Example: Paddle

   • Example: Expandi

   • Example: Evaboot

   • Example: Accountable

   • Example: Raaft

2. Process — Step-by-Step Guide to {Use case}

   • Example: Embedery

   • Example: Hubspot

3. Ways — X Ways to {Use case}

   • Example: Orbit

   • Example: Atlassian

   • Example: Hubspot

4. Mistakes — X Top {Use case} Mistakes to Avoid

   • Example: Klenty

   • Example: Cognism

   • Example: Authoredup

5. Best Practices — X Best Practices to {Use case}

   • Example: PhantomBuster

   • Example: Loomly

   • Example: Churnkey

6. Templates — {Use case} Template: Free Download & Step-by-Step Guide

   • Example: Notion

   • Example: Miro

   • Example: Figma

   • Example: Softr

Product category awareness

1. Product list — X Best {Product Category} Tools:

   • Example: Asora

   • Example: Close

   • Example: Convertkit

   • Example: Kontentino

   • Example: Taplio

   • Example: Skrapp (Best tools of a of related product category)

   • Example: Clickup (Best tools of a of related product category)

2. Swipe files — X {Product Category} Resources

   • Example: Lemlist

   • Example: Really Good Emails

   • Example: Instapage

   • Example: Godly

3. Ultimate guide — {Product Category} 101: The Ultimate 2024 Guide

   • Example: Hubspot

Product awareness

1. Deep Dives — How {Product} Helps you with {Use case}

• Example: GrowthMentor

2. Comparison — {You} vs {Competitor}

• Example: Owner

• Example: June

• Example: Asana

• Example: Shopify

• Example: Polar Analytics

• Example: Active Campaign

• Example: Plausible Analytics

3. Comparison — {Competitor} vs {Competitor}

• Example: Outplay

4. Comparison — {Competitor} vs {Competitor} vs {You}

• Example: Houseware

5. Comparison — {Competitor} Alternative

• Example: Cello

• Example: Senja

6. Alternatives — X Best {Competitor} Alternatives for {Pain Point} in 2024

• Example: Monday.com

• Example: Hubspot

• Example: ClickUp

Purchase decision

1. Checklists — X Things to Consider Before Buying {Product Category}

• Example: LaunchChecklist

• Example: SendGrid

• Example: Hygraph

2. Buying Guides — X Best {Product Category} Software for {Role/Company Size}

• Example: Baremetrics

• Example: TLDV

3. Review — {Product) Review 2024: Is This {Product Category) Any Good?

• Example: Backlink

4. Case study — How {Customer} Achieved X with {Product}

• Example: Owner

• Example: Livestorm

• Example: Coana

• Example: Genesys Growth

PS. These post types work across formats:

• Blog posts,

• Landing pages,

• LinkedIn posts

• Short-form videos,

• Audio, etc.

You should pick your format based on your understanding of your customer. For example, if you think that your customer’s attention span is particularly low, invest in video or landing pages; if they’re a technical user needing lots of details, you might want to go for long-form.

Don’t forget to repurpose your content. You can easily create 50+ LinkedIn posts from a single topic.

Use our 12 LinkedIn post templates to create posts that drive leads

Note: if you’re asking “What about keywords and SEO?”. Our view is that content is the art, and SEO is the science — the former answers nuanced questions, influences decisions, and builds brand credibility; the latter appeals to search engines and social algorithms to rank or distribute your content in front of your audience. With the rise of AI and the fine-tuning of search algorithms, SEO has been shifting from a keyword-based to a topic-based approach. And because of this, it’s important to invest in building topical authority as opposed to just blandly “target keywords”. By creating content around customer outcomes following your buyer’s journey and their level of maturity, you’ll be able to build topical authority because the algorithm will create content series around a specific topic (eg. outcome, use case, product category, etc.), boosting your authority around that topic.

Step 4. Content Distribution & Repurposing

Back to our content strategy.

Successful content requires consistency. A single post on each stage or topic won’t get you far, as prospects need to repeat the same thing over and over and across multiple angles and touchpoints. When approaching your first content production, you need to think in terms of series or programs vs. single pieces. This is what builds your credibility and influences prospects’ decisions.

What does this mean in practice?

1. Pick one outcome vertical in your stage of awareness map; select the vertical that is the most painful, urgent, and widely recognized by your prospects or customers. In the example of Cello above, let’s say I select “I want to increase virality” because my customer interviews confirm this is the biggest outcome my ICP wants to achieve.

2. Devise a series of X content pieces within that outcome vertical at your selected awareness stage. In the case of Cello — because the starting point is around product awareness — you could start creating content 5-10 competitor-related landing pages and/or buying guides covering the competitors with your highest traffic, articulating how Cello is different and/or better for their specific ICP, based on the above examples.

3. Building a tight syndication and distribution muscle to share one core piece across multiple placements and/or turn it into multiple micro-pieces syndicated on social. For example, let’s say Cello publishes 12 competitors buying guides on their site; they could then share each of them on 12 newsletter editions, use them as content for 12 webinars, or repurpose them to create 12 short-form LinkedIn posts shared throughout the yearly content calendar.

The goal of this strategy is to focus resources on capturing demand around a specific stage of awareness — which has the highest impact depending on your audience's maturity — instead of bouncing across topics across different awareness stages, which would dilute your resources. Once you’ve honed it, move on to the next one.

By doing this, you will also build topical authority and brand credibility around your customer outcomes; and position your company and how it can help prospects achie

ve that outcome.

Once you have picked your first content program to capture your stage of awareness, you can plan production and distribution across a content calendar. Feel free to copy our template below (it contains all guidelines inside the template).

Copy our content calendar template

When approaching distribution on LinkedIn or X, feel free to consider the guidelines below to appeal to the algorithm.

Step 5. Know When to Use AI to Execute, Which One, and How

Leveraging AI tools can significantly enhance content execution. Startups need to know when and how to use AI and when not to. The risk is not to be penalized by Google like Casual did, but to avoid being perceived as a “fast-food brand” that lacks taste and depth, and is only focused on clickbait. This is critical especially if you’re starting to build credibility and topical authority.

Some rules of thumb we follow when using AI:

1. The closer to purchase you are in the awareness stage, the more the percentage of human writing is needed to capture technical nuances and details.

2. The newer your market is and the less existing content has been written about it, the more human writing you’ll need to bootstrap existing content.

How we use AI when approaching content:

1. Use perplexity.ai to conduct research: we use perplexity to research topics, case studies, benchmarks, and people on subject matter experts.

2. Use Humata.ai to scan your customer recordings at scale: we use it to upload PDFs of customer call transcripts from Gong, fireflies, or equivalent tools and identify key pain points and topics for blog programs. You can use it also to confirm your stages of awareness.

3. Use ChatGPT to create and iterate the outlines and briefs: I’ve created and trained a custom GPT with specific prompts that follow specific storytelling frameworks and arches to create content briefs and outlines for blog posts, podcasts, videos, etc.

4. Use Midjourney or ChatGPT to create high-quality original images for your articles: it’s important to use Midjourney (or DALL-E) with advanced prompts so that don’t get you images people will instantly know to be AI-generated.

5. Use ChatGPT to iterate on blog post drafts. You can use ChatGPT to automatically integrate feedback comments from Google Docs, or iterate on your draft by adjusting the tone, word count, or topic distribution. You can also ask ChatGPT to optimize your blog’s SEO or to create social posts from your core content piece. You can use the checklist below (courtesy of Jake Ward) so ChatGPT can optimize your piece for SEO.

6. Use Relume to create landing page wireframes. With Relume, you can create sitemaps, and landing page sections, with fairly high fidelity, and then hand them over to designers.

7. Use Swell.ai to turn your audio or video content into clips and social posts. Swell uses AI to automatically create transcripts and video clips from your podcast episodes or long-form videos, then helps you automate creating social captions for multiple social placements, and schedule them directly on your channels like Buffer.

8. Use byword.ai to create (scalable) content in the higher awareness stages like glossaries, checklists, or equivalents. As mentioned before, it’s better to use byword for less technical articles knowing the AI can safely scrape from a lot of internet content around that topic.

9. Use easygen.ai to create great LinkedIn posts better than ChatGPT — just add few bullets on the topic of your post and let the AI do its magic. Ensure to add your refinements and personal touches before posting, but this helped me save 80% of the time when creating and repurposing content for LinkedIn.

Last Disclaimer: Before You Start Publishing

Congrats, by now you should have; crafted your audience and product marketing messaging; identified the best starting point to implement your content strategy; devised your first content program; and even used cutting-edge tools to bring it to life.

Before you start publishing your content, please take this advice too.

1. Get a “How did you hear about us?” question on your lead form. If your content gets you leads and demos from content, you’ll want to know that right away. The easiest and arguably most accurate way of knowing it is to get your prospects to self-select “social” or “content” from this form (see examples). Because the last and ultimate KPI of your content is to generate demand for your products, in the form of leads/demos.

2. Drive people to subscribe to your newsletter. We’ve had clients who’ve been posting on LinkedIn for months and generated more than 1/3 of leads from content but ignored setting up a newsletter. Then, when they needed to prepare for a big launch, they didn’t have a single list to announce it to. Trust me, a newsletter is not something you want to treat as an afterthought because you’ll miss all the people who are not ready for a demo The second KPI of your content programs is to drive newsletter subscribers. I recommend using Loops or customer.io as your newsletter and lifecycle marketing SaaS.

3. Invest resources to post and engage on LinkedIn. You don’t just need to post, but also comment on your posts, and posts from other relevant influencers or prospects. No one is going to know about your content if you don’t share it where your audience is every day. The first thing to measure the success of your content is monitoring your LinkedIn followers and engagement rate.

Conclusion

In this guide we’ve seen how content marketing is an essential channel for early-stage B2B startups, driving results across the funnel from brand awareness to revenue. However, many startups often skip crucial product marketing steps, leading to diluted content and confused prospects.

To succeed, map your buyer’s stages of awareness, and start content production depending on your market maturity. Implement content programs rather than individual posts, focusing on consistency and topical authority.

Lastly, leverage AI tools effectively, ensuring human oversight for nuanced and high-quality content. With these strategies, your content can become a powerful engine for growth and deliver demos and pipeline.

Want to build your first content program with a senior operator with 10+ years of startup hands-on experience?

Book a call with Matteo

1. How to identify, outreach, and partner up with NFT projects

2. How to create mystery and hype together with your NFT partners

3. Tactics to grow adoption and revenue via NFT partnerships

In recent years, NFTs have emerged as a novel channel to bootstrap communities and drive coordinated user engagement. In this post, I want to share with you the playbook for how utility-based products, like DeFi or SaaS, can partner with NFT communities to fast-track product adoption, revenue growth, and brand differentiation through a mix of utility and social identity. This is the story of how we did it at Orion Money.

Earlier last year, I was working with Orion Money, back then a prominent DeFi project on Cosmos. Their product was simple — when you deposited crypto on Orion, you earned higher-than-your-bank yield.

Together, we came up with a strategy to collaborate with the top NFT projects in our ecosystem and engage their communities to drive millions worth of deposits in just a few days, gain ~25% market share, and stand out from competitors.

Our strategy started with simple hypotheses: 

• We knew that our DeFi product had good utility (eg. high yield). However, other players had similar propositions, which made it harder for users to distinguish alternatives. Also, the learning curve to get your wallet set up and reach the aha moment was steep, meaning activation cycles are long.

• NFT ownership was bringing a great sense of social identity, cohesion, and belonging to Web3 communities. By just owning a similar avatar on social, people were helping each other go through their learning curves faster, answering each other’s questions, re-sharing content, and driving adoption.

If we could replicate this social identity within our audience, we thought we could differentiate our brand from competitors. Despite being known in the ecosystem — we had an audience of ~200k followers —  we didn’t have an NFT collection of our own. But we quickly realized we could tap into existing NFT communities to achieve an even greater impact.

Based on this hypothesis, we developed a two-phase strategy: 1) Stealth mode, and 2) Pull the curtain.

Phase 1: Generate Hype in Stealth mode

The first phase was about partnering with NFT communities while building hype with the broader space.

We started identifying the top NFT projects in the ecosystem by asking directly to our audience which one we should partner up with. That made people feel part of the decision-making process, creating a sense of community while qualifying our partner outreach.

Once we identified these NFT projects, we swept the floor of their collections. In NFT lingo, floor-sweeping means buying en-masse a lot of NFTs of a specific collection. This had the following effects:

• It instantly grew the floor price of these NFT collections, increasing the worth for existing holders and quickly generating questions as to “what’s going on here?”.

• It attracted the attention of the NFT project founding teams, showing them that we were putting skin in the game and were committed to their success.

• It generated even more hype with non-holders, driving more attention and buying pressure toward those NFTs collections.

We then publicly announced we were including those NFTs as part of our official treasury — again showing the wider NFT community our commitment to our partners and the overall NFT movement.

The whole stealth mode was based on this meme idea: “Show me that you’re doing X without telling me you’re doing X” — in our case, ‘X’ meant launching a new product with NFT-enabled features. This meme is powerful because:

• It creates mystery and hype, as it leads people to ask questions like “Why are they doing this?” and “What is the point of all this?”.

• Hype leads to speculation and discourse — people in the audience try to build opinions about why the brand is making these announcements, which in turn drive social mentions and sharing that quickly compounds an already engaged community.

Phase 2: Pull the Curtain for The Big Reveal

Once we’ve generated enough attention and mystery with our audience, we moved into the “pull the curtain” phase. This was about coming out of stealth and announcing the big reveal: “Why did we sweep the floor of those NFT projects and make all that noise?”

First off — and in a pretty bold move — we formally declared war on our competitors who were offering similar utility-based value props. Together with our NFT partners, we made a coordinated announcement revealing we were partnering with the top NFT communities in the space. The announcement went out on every Twitter, Telegram, and Discord server of each of our NFT partners, and was broadcast to hundreds of thousands of people, compounding the wave of momentum that we were building. Plenty of memes were used in the announcement, creating lore and making the whole thing fun for everyone involved.

To substantiate our partnerships, we selected a few of our NFT partners to co-design ultra-limited 1/1 NFTs as part of their collections, adding the Orion Money brand as a unique trait of their NFTs. 

This was powerful because:

• It literally baked our brand identity into the aesthetic culture of the ecosystem.

• It forged the pact between our brand, representing a practical, financial utility, and our NFT partners, which were instead a symbol of social identity and community.

This had the compounded benefit of scaling our brand reach to multiple, engaged communities, while also differentiating from competitors that were marketing their utility as a standalone. By adding NFTs to the mix, we offered these audiences another reason why to choose us— the chance to be part of something bigger, even bigger than Orion Money itself.

But we didn’t stop there. Shortly after, we announced an NFT staking feature — owners of NFTs belonging to our partners could stake them on Orion Money to generate even higher yield and access premium features. At this point, we were building a feature where their social identity component — their NFT avatar — was itself contributing to increasing their utility.

As a final tactic, we announced an aggressive NFT giveaway campaign. Remember all the NFTs we bought back in phase 1 with the floor-sweeping? We’ve decided to give them all back to the community to encourage product adoption. If you made a crypto deposit to Orion Money the next day, you could win a random NFT of our selected partners every day until stocks last.

The outcomes

The aggressive growth tactics of phase two drove an incredible inflow of deposits — we managed to accumulate over $16M worth of staked crypto across 3,000 new users, gaining over 25% market share of that specific token deposit.

And that was only about 5 days after the public announcement!

Closing thoughts

In conclusion, NFTs can be a powerful marketing channel to create a sense of belonging and a brand moat.

If you have a utility-based product like DeFi or SaaS, partnering with various NFT projects can be even more powerful than building your own collection because:

• It lets you tap into multiple audiences and communities while growing your own, scaling your potential reach, and driving audience cross-pollination.

• You also piggyback off existing engaged NFT communities. These users are likely to be more technically savvy and to adopt your product, as NFT owners have higher-risk profiles and might be more committed to the Web3 space, even in the bear market.

By using a two-phase strategy that creates mystery and speculation, a stealth mode can be an effective product marketing strategy in creating hype and anticipation ahead of a product launch. This is something every product marketer — in Web3 but also in SaaS — could leverage to shape the narrative around utility-based products, creating lore and storytelling that drives engagement and brand-building.

We’ve also seen how it’s the combination of practical utility (eg. yield) and social identity (eg. NFT) drive both product adoption and brand moat. As more and more utility-based SaaS enter the Web3 space, I think more of them will use NFTs to bootstrap acquisition, shorten their activation cycles, and drive retention by leveraging NFT-enabled sense of belonging and product features.

If you are a growth or product marketer working in DeFi, NFTs, or SaaS, I hope this playbook will spark ideas on how to approach your community building and customer acquisition motions, especially as NFTs become part of growth channels beyond just  Web3.