30% reduction in customer complaints
The Challenge
A global premium golf e-commerce company faced two critical operational pain points. First, the CEO and Operations team were spending a significant amount of time manually analyzing carrier performance—exporting raw shipping data, running pivot tables, and chasing down insights on whether delivery partners were meeting their SLAs. This manual process consumed leadership bandwidth and delayed decision-making on which carriers to prioritize or phase out.
Second, the Customer Support team struggled to proactively manage shipping delays during periods of high volume, particularly around the holidays. Without a reliable way to flag at-risk shipments, they often found themselves reacting to customer complaints rather than getting ahead of the problem—damaging customer experience and increasing inbound support volume. The company needed a way to turn its fragmented shipping data into actionable intelligence—without replacing its core systems or adding unnecessary complexity to its workflows.
Our Approach
- •Built a Postgres database to track orders, shipment events, and inventory, with PII-stripped architecture
- •Developed a Looker Studio dashboard for real-time carrier performance analytics by carrier, geography, and shipping method
- •Designed an AI-powered delay risk model leveraging shipment status, location, and expected delivery window
- •Built a Google Sheets-based triage dashboard for Customer Support, powered by Postgres data and mapped to Shopify order data for customer notification.
The Solution
We built a privacy-conscious Postgres database that tracked order records, shipment events, and inventory—enabling the team to analyze carrier performance and shipping trends in real time, without exposing customer PII. A Looker Studio dashboard provided leadership with clear visibility into carrier SLAs by geography, shipping method, and partner—allowing them to adjust delivery expectations and make smarter decisions about their carrier mix.
For Customer Support, we developed an AI-powered delay risk model that used current shipment status, location, and delivery window data to flag orders likely to be delayed. This data fed into a triage dashboard built in Google Sheets, linked to Shopify order data for seamless notification management. The process kept a human in the loop—support staff could review flagged orders and, with a single action, trigger personalized, pre-drafted delay notifications staged for approval in their outbox. The result: a faster, more proactive support experience that improved customer trust and reduced inbound complaint volume.
Impact
Stack Highlights



