A conversation with Elena Verna, the Head of Growth at Lovable, produces more insights-per-minute than perhaps anyone else in tech. When she’s speaking on a podcast or at an event, I know it’s something I won’t want to miss.

Elena is joining Metronome on December 10th to unpack lessons from Lovable around how to price AI. She’ll cover what makes AI pricing so complex, why traditional SaaS pricing models fall short, and how Lovable is experimenting with new monetization strategies. Grab you spot today.

👋 Hi, it’s Kyle and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.

Many have predicted the demise of outbound. Well, it’s alive and kicking. GTM leaders say intent-based outbound is the #2 channel where they plan to invest the most going into next year based on data from the 2025 State of B2B GTM report.

I wanted to explore the modern outbound playbook for 2025. For help I turned to Fivos Aresti, co-founder of Workflows.io. He shared an extremely tactical guide including first-hand learnings and recipes you can steal.

We hit $1M ARR three months after launching Workflows.io, an AI-first growth studio for B2B tech companies. Now we’re on track to reach $2M ARR by the end of the year.

LinkedIn content has been our main acquisition channel. But here’s the problem with lead generation through content: you can’t choose who’s going to book a meeting with you.

Outbound, instead, lets you choose precisely who you can go after. With proper qualification and scoring, you can pick the accounts you’d love to work with and know you can provide the most value to.

Ten years ago, most outbound activities were completely manual. Today, everyone is talking about automated outbound with AI.

The truth? You need both.

This is our outbound playbook to add $1M in ARR and finish the year beyond $2m ARR. Spoiler: It’s NOT just automated outbound.

We see four pillars of successful outbound in 2025. There’s cold calling (yes, it still works!), email campaigns (automated sequences), LinkedIn campaigns (signal-based engagement), and manual prospecting for Tier 1 accounts.

When running outbound, we needed to choose where to turn the dial between signal-based plays versus cold outreach. And we needed to choose between where to invest in automation versus a manual, more personalized approach. Here’s where we landed.

Signal-based outbound is great, but realistically only a fraction of our target market was showing signals at any given point of time. We still needed to convert a cold audience in our ICP. We run cold outreach continuously, with micro and signal-based campaigns layered on top.

For our cold outreach program, the first step was to decide exactly who we were going to target. We defined our ICP and created a scoring model around it. We used this framework for ICP modeling and here’s what the end result looked like.

The second step was to map out our total addressable market (TAM), capturing every single SaaS company we could before filtering down to our target account list where we layer on more AI account research. By going broad before condensing down, we minimized the risk of missing accounts by relying on standard database filters.

We used five sources to create the initial list:

We pulled CSVs from all five providers, uploaded them into one Clay table, and normalized and deduped everything. Initially, we found 66,000 companies. But that was a very broad list of SaaS companies that COULD fit our ICP.

Next, we eliminated as many irrelevant companies as possible. Here’s the exact prompt we used to qualify if the company was actually a SaaS:

And these were all the data points we found to qualify companies further:

For Q4 we wanted to focus only on VC-funded companies using HubSpot, which narrowed it down to 5,700 accounts. Then we applied our scoring model to tier the accounts based on the criteria we’d set earlier.

We were left with a qualified and scored target account list. Here’s how we broke down tiers and allocated resources:

Next, we layered in account research for personalization and segmentation. For each account, we captured: (1) who’s their ICP, (2) what data they’d want to find on their ICP, (3) their LinkedIn posting activity, and (4) their sales department structure

The final step was writing the email and LinkedIn copy for our sequences. We tested two to three variants for each building block of the message including the first line, body, and call-to-action. (Here’s a cheat sheet around how to write a winning message.)

There’s been a lot of noise about signals lately and, realistically, a lot of potential signals. At Workflows.io were considered these to be the most important ones to automate:

We used these signals to create initial lists, and layered in AI qualification to avoid spamming accounts that might demonstrate multiple signals.

There are hundreds of signal-based plays we could’ve run; the best ones to start with depend on your GTM motion and level of sophistication. We prioritized five as our favorites: