A rapid-fire strategy, brand pivot, and landing page design for an AI-based churn prediction platform for gym owners.

Company: AI Startup Industry: SaaS Engagement Type: Brand strategy, Brand Story, UX, and Visual Identity Timeline: 3-week sprint Role: Brand Strategist, Copywriter, Creative Director, Product Designer

Designed and launched in under 2 weeks


Received their first pilot applications


Message-market resonance (based on CTA interaction and scroll depth)


Enabled testing without building a refined product


Defined a scalable pattern for future niche tests

After a conflict of interest derailed their initial direction, the founders needed to pivot quickly. They had no product built, no niche defined, and only weeks to identify and test a new market.

The goal was clear: Prove there’s demand before investing in development.

I recommended we test-market to gym and fitness business owners using a simple, compelling landing page — instead of building a product.

Daniel Priestley’s Oversubscribed approach to create scarcity

StoryBrand principles to clarify the problem and solution

A lean UX funnel to convert curiosity into qualified early interest

Validate the niche

Measure early traction

Gather feedback before building

A one-page early access site focused on speed, clarity, and trust. Key elements:

Headline & stats: “67% of gym churn is preventable”

Problem framing: “Your gym is silently bleeding customers”

Value drivers: Plug & play, personalized AI, CRM/CSV compatibility

Social proof: Placeholder testimonials, early adopter results

Action pathway: Simple 3-step “How It Works” walkthrough

Scarcity signals: “Limited pilot spots available” and staged roadmap

Mobile-first design with multiple CTAs

Every section was designed to guide users from awareness to interest with minimal friction.

Unvalidated idea with no niche

No landing page or funnel

No user signals

After:

A tightly focused vertical (fitness)

Live landing page with working signup funnel

Market-tested messaging based on real language

Early adopter insights and pilot applicants

Option to run a pilot using historical .CSV data