GetWeed - Conversion Rate Optimization (CRO)

The objective for GetWeed was pure speed and conversion efficiency across their 4 locations. I managed a Conversion Rate Optimization (CRO) focused build, stripping away unnecessary checkout steps to create the fastest path to purchase.

Challenge

Optimizing High-Velocity Retail (GetWeed)

With a brand name like “GetWeed,” the mission was absolute technical simplicity. The previous architecture suffered from high-friction registration walls and fragmented search indexing, leading to elevated cart abandonment rates. The goal was to engineer a high-velocity user journey that removed every possible barrier between product discovery and final checkout.

Conversion Friction

Legacy multi-step checkout protocols were creating significant user drop-off during the final transaction phase.

Marketplace Data Isolation

The brand lacked automated API feeds to major aggregators (HiBuddy), resulting in inventory discrepancies and lost referral traffic.

Loyalty Stack Complexity

Integrating AlpineIQ and Birchmount into a seamless, single-sign-on experience required complex frontend logic and secure data synchronization.

Action & Results

How did we tackle the challenge?

During the kick-off call, I used Figma to walk the client through the streamlined checkout flow, demonstrating how we would cut clicks by 50%.

DevOps Orchestration: I led the multi-tenant deployment of Breadstack instances across 4 physical locations, ensuring 100% one-click payment compliance via CovaPay.
Critical Path Management: Orchestrated the “Go-Live” sequence for all 3rd-party integrations (Shipping, Loyalty, Payments), ensuring a zero-downtime transition on Day 1.
UX Validation: Used Figma prototypes to demonstrate a 50% reduction in “Click-to-Purchase” velocity, securing stakeholder buy-in for a “Search-First” navigation strategy.

Tools/Techniques Used

CRO Architecture

Architected a hyper-efficient, single-page checkout flow to eliminate technical drop-off points.

Elasticsearch Implementation

Managed the deployment of Elasticsearch for instant predictive indexing, reducing discovery time to under 2 seconds.

Aggregator Data Pipeline

Engineered automated inventory and pricing feeds to ensure multi-channel data integrity across the marketplace ecosystem.

Multi-Stack Integration

Orchestrated a complex API integration involving Cova Pay, AlpineIQ, and Birchmount into a unified frontend.

The Spotlight Feature: Instant Predictive Search

The Problem

Slow, non-predictive search was a major friction point. Users were abandoning sessions because they couldn't find specific products quickly on mobile devices.

The Tech

Implemented Elasticsearch with custom indexing logic. This allows the system to predict and surface products as the user types, pulling from a live inventory database with near-zero latency.

The Win

Reduced the path to purchase to under 5 seconds for repeat users, directly correlating to a significant spike in the session-to-purchase ratio.

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