Case File: Automating Pricing Logic via Native Ruby

Category: Native Architecture

Reading Time: 3 Minutes

Audience: CTOs, Operations Directors ($10M+ GMV)

Scale exposes the fragility of manual workflows. For high-volume merchants, "App Bloat" manifests not just as latency, but as operational friction.

I recently consulted for a Global Crafting Supplier managing 3,500 SKUs. They presented with a severe operational bottleneck: highly complex B2B/B2C pricing tiers managed via manual CSV uploads.

The merchant was utilizing frontend "Discount Manager" apps to handle backend logic. This resulted in "price flickering" (client-side DOM manipulation) and a fragile checkout experience dependent on third-party servers.

They did not need a UI refresh. They needed an architectural migration from manual input to server-side automation.

Subject Profile: Operational Stagnation

The merchant serves two distinct segments: retail hobbyists (single unit) and wholesale partners (500+ units). Pricing rules relied on a matrix of quantity, customer tag, and material type.

The Pathology:

• Operational Drag: The Operations team allocated 15-20 hours per week to manually updating pricing CSVs.
• Human Error Variance: Manual entry resulted in 45-60 support tickets/month regarding incorrect pricing.
• Latency-Induced Abandonment: A third-party "Smart Filter" app added 1.5 seconds of latency to collection pages, resulting in a 62% bounce rate on discovery paths.

The business was functionally throttled. Scaling the catalog to 10,000 SKUs was impossible under the existing manual framework.

The Surgery: Server-Side Logic Implementation

The standard approach, installing a more expensive app, ignores the root cause. Logic belongs on the server, not the browser.

I utilized Shopify Scripts (Ruby) to migrate the pricing logic directly to Shopify's infrastructure. This ensures complex pricing rules are calculated instantaneously on the backend before the page renders.

Architectural Note: While this specific deployment utilized Shopify Scripts (Ruby), this API is now legacy technology. Today, I achieve these identical outcomes with superior performance using Shopify Functions (WASM/Rust). The core principle remains unchanged: Server-Side Logic > Client-Side JavaScript.

Step 1: The Automated Pricing Engine

I replaced the manual CSV workflow with a bespoke script.

• Logic: The script queries the cart object for customer.tags and line_item.quantity, then applies the appropriate arithmetic operation.
• Execution: Zero latency. The calculation happens during the checkout object initialization.
• Outcome: The "Monday Morning CSV" task was deprecated.

Step 2: Native Filtering Architecture

The client’s third-party filtering app was injecting 1.2MB of JavaScript to parse product tags.

The Fix: I rebuilt the filtering system using Shopify OS 2.0 Native Filtering.

I utilized native Liquid loops to query product data directly from the Shopify index.

• External Scripts: 0.
• Load Time Impact: Negligible.
• UX: Instant facet selection without page reloads.

Step 3: Modular JSON Architecture

The legacy theme required a "Page Builder" app to distinguish "Fabric" layouts from "Tool" layouts.

The Fix: I migrated the theme architecture to Online Store 2.0 (JSON Templates).

This allowed the creation of modular, data-driven templates managed natively in the Shopify Customizer. I decoupled the presentation layer from third-party app dependencies.

The Results: Operational & Financial Outcomes

By shifting logic to the server and removing manual friction, the merchant achieved immediate OpEx reduction.

Operational Directive

This project demonstrates that custom code is an asset; apps are a liability.

While the initial architectural build requires capital expenditure (CapEx), it permanently eliminated a recurring 15-hour/week operational expense (OpEx). The merchant can now scale to 10,000 SKUs with zero marginal labor cost.

You do not need an app to manage complexity. You need native logic.

Are you managing your store with spreadsheets?

If your Ops team is manually calculating what code should be automating, or if your discount logic is slowing down checkout, we need to speak.

I build Native Architectures that convert manual labor into automated execution.

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