Designing the Future of Golf Coaching:
Leading SneakySwing's Launch and Growth
SneakySwing is an AI-native B2B2C golf coaching platform that bridges the gap between lessons. Through automated swing analysis, students get structured feedback between sessions, while coaches gain a tool to better manage their students and monetize the feedback they were already giving for free. I owned the product design end-to-end, from 0 to 1.
Project Overview
The TL;DR
Led the product transition from beta to its official public launch.
Only 26% of users completed their first AI analysis, making acquisition spend wasteful. I redesigned the onboarding funnel to drive a 14% activation improvement.
Co-Founder x2, Designer x1, Engineer x3, Researcher x2
520+
ORGANIC DOWNLOADS (FIRST MONTH)
28%
WEEKLY ACTIVE USERS
26% → 40%
ACTIVATION RATE (DOWNLOAD → FIRST AI SWING REPORT)
Shipped to App Store
User Pain Points
Who is SneakySwing for?
Golf Student
The “Blind” Practice Cycle
In-person golf lessons are expensive, so most beginners can't afford to take them frequently. This creates a gap during solo practice, where players often reinforce bad habits or suffer from a lack of validation.
Coach
Giving free feedback with no way to monetize it
Students already DM swing videos for free. SneakySwing changes that: AI pre-analyzes each swing so coaches can focus on higher-level feedback, add voice or drawing annotations on top, and get paid for every session — while managing students more effectively and building stronger relationships.
Phase 1 — Pre-Launch Sprint
From 0.5 to 1: Bridging the Gap to MVP
When I joined the team, the technical prototype was largely functional, but the user experience was fragmented. With the official launch just weeks away, I was tasked with transforming these disparate features into a cohesive, market-ready MVP. My primary goal was to map out a seamless end-to-end user journey — from initial download to premium conversion — ensuring the product could withstand the pressures of a public release.
Phase 2 — Business Logic
Defining Business Logic & The “Apple Pivot”
As a designer with a strategic marketing lens, I focused on building a sustainable subscription model.
The Challenge
Mid-sprint, we encountered a critical platform constraint: iOS guidelines prohibited our planned Stripe integration for digital services.
The Strategy
To avoid delaying the launch, I led the decision to pivot to a “100% Free Preview” model for the initial release. This allowed us to gather user data and build momentum while I concurrently designed the Apple In-App Purchase (IAP) infrastructure and Paywall for the subsequent update.
Phase 3 — Post-Launch
Plugging the Leaks: Driving Activation from 26% to 40%
Post-launch data revealed a critical bottleneck: only 26% of users successfully completed their first AI swing analysis. For a 0-to-1 product, this drop-off was fatal. Without a functional onboarding “bucket,” any marketing spend on user acquisition would be wasted.
The Action
I conducted a deep-dive funnel analysis and qualitative user interviews to identify friction points. My hypothesis: the onboarding was too long and lacked a “Quick Win.”
The Iteration
I redesigned the onboarding flow to prioritize immediate value and clearer instructions for the AI recording process.
The Result
User activation (first AI report completion) surged to 40%, creating a stable foundation for growth.
Phase 4 — Ongoing
The Next Frontier: Optimizing the Core Experience
While the launch was a success, the heart of the product — the AI Report — remains an area for ongoing innovation. I am currently tackling two primary design challenges.
1. Combating Information Overload
Our AI provides a wealth of data: body mechanics, phase annotations, and actionable plans. However, without clear visual hierarchy, users often feel overwhelmed. I am exploring a “Prioritized Insight” model that highlights the single most impactful “Cause & Fix” before diving into secondary metrics.
2. Managing the “Perceived Wait Time”
The high-precision AI analysis requires roughly 40 to 50 seconds to process. To reduce perceived latency, I am designing an interactive “Wait Experience” — integrating progress storytelling and educational tips — to transform a dead-end loading screen into a meaningful part of the user's learning journey.
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