Dillon Chi

Dillon Chi

UX Researcher and AI Generalist

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Case Study edtechsaas

Enrollment & Marketing Funnel Research

Research supporting virtual public-school enrollment and in-product apps across a B2B2C EdTech platform.

Cover image for Enrollment & Marketing Funnel Research
My role
Lead UX Researcher
Client
Stride K12
Year
April 2025 to present

Virtual K–12 at scale, students across 50 states.

Stride (formerly K12 Inc.) is the largest provider of online public school in the US.

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Teaser Line / Preview

Under NDA: Stride/K12 Portfolio Walkthrough

A behind-the-scenes look at how mixed-methods research, user testing, semi-automated user testing, and technical QA resolved multi-million dollar funnel leaks. Three deep-dives mapping the path from raw diagnostics to shipped production features.

  1. 01

    Lead Funnel Conversion & Experimentation

    Tracked and analyzed 12+ marketing funnels and hundreds of pages using Heap. By developing a bespoke set of metrics to prioritize rapid VWO experiments across layout, copy, and required-field indicators, we bypassed friction points to systematically improve user experience and discovery.

  2. 02

    Mixed-Methods Research & Interactive Prototyping

    Blending qualitative screeners and automated audits to scope a major redesign. We surveyed visitor intent across 100+ sites using Qualtrics, synthesized cross-network priority datasets in Tableau, and scraped mobile breakpoints with Playwright. To validate the solutions, we built interactive mobile prototypes in Replit because traditional prototyping would have been clunky and time-consuming. I also built bespoke tools to organize and map the Information Architecture (IA) of 70+ school sites in a single, centralized dashboard.

  3. 03

    End-of-Funnel Conversion & LCP Tuning

    Rescuing lost revenue in the K12 Enrollment Portal. By pairing a new approach of granular event micro-tagging in Heap (tracking actions rather than simple page-level analytics) with throttled mobile QA and Figma UX audits (validated against Salesforce drop-off data), we optimized document-generation LCP from 3–30s to under 2 seconds and unlocked a +9.9pp conversion increase.

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