The Results
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The Challenge
Helply had everything a pre-launch startup could want: a credible founding team, a proven adjacent product, and real insight into their target buyer. What they did not have was traction. Their one previous outbound attempt had involved scraping Zendesk users and reaching out cold. It produced almost nothing. The product was positioned generically as a chatbot, which in a crowded market was impossible to cut through with. They had two specific goals: get real market feedback on their messaging, and book early demos to build pipeline and validate the product with real buyers.
Our Strategy
We built five systems across outbound, LinkedIn, and full TAM coverage. The first priority was offer development — before any volume went out, we needed a message worth sending. We helped Helply move their positioning from 'we have a chatbot' to 'only pay for successfully resolved tickets' — a performance-based model that immediately differentiated them from every other AI support tool in the market.
We helped Helply move their positioning from 'we have a chatbot' to 'only pay for successfully resolved tickets' — a performance-based model that immediately differentiated them from every other AI support tool in the market.
SYSTEM A Message-Market Fit Testing Across the Full TAM |
• Enriched SaaS companies for their current CX tool provider |
• Positioned Helply as an add-on to existing tools rather than a replacement |
• Ran 320+ message experiments to identify what resonated with SaaS founders |
• Each experiment fed directly into the next — offers refined continuously over 12+ months |
SYSTEM A.1 Full TAM Map - 240,000 SaaS Founders |
• Acquired 240,000 SaaS founders from 10 data providers |
• Enriched each contact for tech stack and scored by fit |
• Tier 1: 1-to-1 manual outreach for highest-value targets |
• Tier 2: LinkedIn and email prospecting for mid-tier accounts |
• Tier 3: High-volume cold email outbound for broad TAM coverage |
SYSTEM B Inbound-Led Outbound via LinkedIn |
• Leveraged founder's 60,000 LinkedIn followers and 200,000+ engagements |
• Ran all post engagements through an ICP filter to identify warm prospects |
• Used LinkedIn to distribute value-add assets: community offers, free tools, frameworks |
• Converted organic engagement into outbound conversations at scale |
SYSTEM C Offer Development |
• Repositioned from a generic chatbot to a performance-based model |
• Core offer: only pay for successfully resolved tickets |
• Tested offer variations systematically across all three tiers |
• Identified the highest-converting angle for each segment within the TAM |
The Turning Point
Month 6 was when the systems converged. The message experiments had produced enough data to know exactly which offer, angle, and audience combination worked. From that point, scaling was a matter of volume — the playbook was already written by the data.
"Cameron helped us set up a system that allowed us to send a massive amount of emails at scale, while still split testing ideas and messages. It has generated literally thousands of replies for us and hundreds and hundreds of demos."
— Tom Morkes
The GTM Company Flywheel
For Helply, the flywheel started before they had a single paying customer. Their sister company GrooveHQ gave us real customer conversations and market intelligence. We fed that directly into 320+ message experiments, testing what would resonate with SaaS founders at scale. Each experiment told us something new. Each demo booked refined the offer further. By month 6, that compounding loop had taken them from zero traction to thousands of leads and hundreds of demos — and a product with proven message-market fit.
The Results
Over the course of the engagement, Helply went from a pre-revenue product with no proven message to a company with thousands of leads in pipeline, hundreds of booked demos, and a GTM motion built to scale across their entire addressable market.
Their outbound now runs as a full TAM prospecting system — hitting every prospect with the right message based on the right signal, at the right tier of personalisation.
More importantly, they have message-market fit. 320+ experiments told them exactly what their buyers respond to. That is the kind of asset that compounds for years.
"If you are looking to get your GTM going, I cannot recommend Cameron highly enough. Him and his team are really good at what they do."
— Alex T
Category
Tech
Year
2024
Platforms
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