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Helply — case study

Pre-Revenue to Thousands of Leads and Hundreds of Demos

A pre-launch AI support product with no GTM motion, no proven message, and no traction. We built five systems and ran 320+ experiments to find what worked.

1,030
Qualified leads
435
Booked meetings
200k
Monthly sending volume
320+
Message experiments
Industry
AI Customer Support SaaS
Location
United States
Duration
12+ months
Campaign type
Full TAM outbound · LinkedIn inbound-led outbound · offer development

Where Helply started

Helply is an AI-powered customer support product and sister company to GrooveHQ, a well-established help desk platform. The team came to us pre-revenue — product built, not yet in market. They had deep knowledge of the support space, but no proven GTM motion for Helply as a standalone product.

Before The GTM Company
–Pre-revenue with no outbound system in place
–One previous outbound attempt — scraped Zendesk users, produced almost nothing
–Positioned generically as a chatbot, impossible to cut through
–No message-market fit and no data on what would resonate
“
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.
— Helply Founder
Our approach — Offer before volume

The first priority before any volume went out was the offer. 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. Then we built five systems covering the entire addressable market at three tiers of personalisation.

System A — Message-market fit testing
Enriched SaaS companies for their CX tool, positioned Helply as an add-on, and ran 320+ experiments to find what resonated.
System A.1 — Full TAM map
240,000 SaaS founders from 10 data providers, enriched and scored by fit. Tier 1 got 1-to-1 outreach; Tier 3 got high-volume cold email.
System B — Inbound-led outbound
Leveraged the founder’s 60,000 LinkedIn followers, filtering engagements through an ICP to turn organic reach into conversations.
System C — Offer development
Repositioned to a performance-based model and tested offer variations systematically across all three tiers.
The results

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 — with true message-market fit from 320+ experiments.

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