💰 How to turn your Series B into $4.2M in pipeline

ISSUE #276

This week in SaaS, the traditional GTM playbook is getting rewritten in real-time. ChatGPT went from chatbot to pipeline source, email evolved from broadcast tool to prediction engine, and the rise of GTM engineers signals that growth itself is becoming programmable.

In this week's roundup, we cover:

• ChatGPT as a revenue channel - how one company turned AI search into 13% of their high-intent leads

• The GTM engineer emerges - why companies are hiring operators who code growth plays instead of just running them

• Funding announcements that actually drive pipeline - the $350K campaign that returned $4.2M

Let's dive in!

Ian at SaaS Weekly

THIS WEEK IN SAAS
Trends across the industry

🔍 GTM Strategy | The search playbook every marketing team should be testing right now
Docebo (a corporate LMS) generates ~13% of its high-intent leads from ChatGPT with just one person managing it. The playbook: target late-funnel money keywords, structure content for LLM consumption, and measure success through UTM attribution. Semrush backs this up with data showing this channel now drives traffic to 30K+ domains daily, with 70% of queries representing entirely new search intents [Growth Unhinged].

Why it matters: The teams winning at search aren't just optimizing for Google anymore. While 85% of Docebo's traditional search traffic comes from people who already know them, this new channel delivers net-new discovery. With 800M weekly active users asking questions, forward-thinking GTM teams are using tools like Xfunnel and Profound to track visibility and test what works.

🛠️ GTM Infrastructure | GTM engineers are the new growth secret weapon
Hundreds of companies are hiring for a role that didn't exist two years ago: the GTM engineer. Armed with tools like Clay, these operators sit between data and revenue - building AI-powered research workflows, orchestrating personalized campaigns at scale, and turning creative growth ideas into repeatable systems [Saphire Ventures].

Why it matters: The old GTM stack was built for following playbooks. The new one is built for inventing them. While most teams still treat each tool as its own silo, GTM engineers use Clay as a creative canvas - connecting data, automation, and execution into coordinated plays. If you're not building this capability, you risk falling behind the growth curve.

💰 Funding Strategy | Turn your raise into a $4.2M pipeline machine
One founder spent $350K on their Series B announcement and generated $4.2M in pipeline (a 12x return). The playbook? Treat the funding announcement like a full GTM campaign with video at the center, physical postcards that cut through digital noise, and customers as the heroes. But instead of pushing demos, they sent DoorDash gift cards with a simple "lunch is on us" - giving prospects a reason to engage that actually felt generous [Amanda Zhu].

Why it matters: Most companies waste their funding momentum on a single press release and LinkedIn post. This approach shows how to milk every ounce of attention from your raise - from behind-the-scenes content that humanizes your brand to tying the product and mission together. Pretty good.

BUILDING THE PLAYBOOK
Frameworks and resources to design your growth play

📧 Email Marketing | 5 min read | Tristen Taylor, HubSpot
How to make email one of your highest-converting channels

In theory: Email marketing hits the same wall at scale: you can segment audiences and craft messages, but you're still guessing who needs what right now. Marketing automation helped us schedule and trigger emails, but we're still broadcasting to groups rather than individuals.

Now machine learning is making the next leap accessible - from automation to prediction. With ML built into some email marketing systems, teams can predict which specific content, timing, and value driver will resonate with each contact based on their unique behavior. The result: campaigns that adapt to buyers instead of forcing buyers to adapt to campaigns.

In practice:

• Start with strategy, not software. Map your buyer journey, document conversion goals, and understand which behaviors indicate buying intent.

• Fix your data infrastructure first. Unify contacts across systems, standardize fields, and track meaningful events (demo requests, pricing views, feature usage).

• Test with discipline, not faith. Run 70/30 splits between ML-powered and traditional campaigns for 4-8 weeks. Measure revenue per email and pipeline contribution, not vanity metrics like open rates.

My take: The smart GTM teams figured out that email was never broken - we just couldn't personalize beyond basic segments. ML changes that by actually predicting what each person needs instead of guessing.

But here's the thing: you don't need fancy infrastructure to start. You can prompt ChatGPT with "this VP of Sales from a Series B fintech company just visited our pricing page twice - write them a personalized email and recommend which case study to include." It's a hack that lets you test ML-style personalization before committing to a platform overhaul.

TOP READS FROM LAST WEEK

1. A Founder's Guide to Go-to-Market Strategy [Tomasz Tunguz]

2. The Framework That Aligns Marketing with the Business [SparkToro]

3. State of Software 2025: Rethinking the Playbook [ICONIQ]