How do you implement BigCommerce performance optimization?

Long Answer

A durable BigCommerce performance optimization plan begins with explicit goals: pass Core Web Vitals at p75 on mobile and remain stable during promotions. Treat the storefront like a pipeline with budgets for bytes, round-trips, and main-thread milliseconds, then enforce them in CI and dashboards.

Stencil theme performance
Keep templates lean. Collapse nested partials and avoid expensive helper loops in category/product listings. Inline a route-specific critical CSS slice (home, PLP, PDP, cart/checkout) and ship the remainder deferred; run purge tooling per route to strip unused rules. Replace legacy utility libraries with native browser APIs; mark nonessential scripts defer/async, gate heavy work behind requestIdleCallback, and split bundles per route so checkout doesn’t load PLP code. Prefer CSS transitions/animations for micro-interactions to cut JavaScript main-thread time and reduce INP.

Images & media
Images dominate storefront payloads. Generate responsive sets with srcset/sizes, supply AVIF/WebP with fallbacks, and cap intrinsic dimensions to prevent layout shifts. Use DPR-aware variants so high-density devices get crisp but not oversized assets. Inline tiny SVG icons, or use an SVG sprite; avoid blocking icon fonts (or set font-display: swap). Preconnect to image origins and rely on the platform CDN (or a dedicated image optimizer) for format negotiation and quality tuning. Add priority hints for hero media to improve LCP and lazy-load everything below the fold via IntersectionObserver.

Edge caching & HTML strategy
Exploit edge HTML caching for anonymous traffic on hot routes (PLP, PDP, marketing landers). Use short TTLs plus stale-while-revalidate so cache misses don’t thump origin during spikes. Key cache by locale, currency, and device class. Fragment cache repeated components (navigation, footer, recommendation carousels) and compose on the server for higher hit rates. Pre-warm top URLs before campaigns and after deploys. Measure edge hit rate and origin latency; treat regressions as release blockers.

BigCommerce APIs
Prefer Storefront GraphQL to fetch shaped data in one round-trip. When using REST, batch and paginate; parallelize with Promise.all and add jittered backoff on 429/5xx. Attach ETags/Last-Modified and do conditional requests to avoid refetching. Coalesce identical in-flight requests so the origin isn’t hit repeatedly for the same SKU list. Where allowed, denormalize frequently read fields (price, promo flags, inventory badges) into an edge key-value store to avoid origin trips on every PDP.

Backend for Frontend (BFF)
Introduce a lightweight BFF to hydrate Stencil pages with pre-composed payloads, enforce timeouts, and unify business logic. The BFF can collapse multiple API calls, normalize shapes, and attach cache headers coherently. Add circuit breakers and bulkheads: when the recommendation service fails, fall back to cached content; never block the main path. Emit standardized error objects so the storefront can gracefully degrade.

Third-party scripts
Audit tags quarterly. Load non-critical vendors via a consent/tag manager after first interaction; convert synchronous tags to async, sandbox heavy widgets in web workers where feasible, and sample analytics during campaigns. Defer chat tools until user intent is clear. Every third-party gets an SLA (size, latency) and a kill-switch.

Checkout
Keep checkout minimal. Lazy-load address autocomplete, tax/shipping quotes, and payment SDKs late in the funnel. Avoid long synchronous scripts; scope 3DS/PCI snippets tightly and load them just-in-time. Pre-fetch order-confirmation assets once the payment step begins to smooth TTI.

Operations & guardrails
Define budgets per page type (KB, requests, main-thread ms). Gate merges on p75 LCP/INP/CLS and API percentile latency. Run synthetic tests hourly per route/locale and attach RUM to real users. Before each promo, run load tests that include bot behavior (aggressive PLP pagination, search spam) to validate rate limits and cache coalescing. During live events, watch edge hit rate, origin CPU, and error budgets; ship mitigations behind feature flags. With these layers, Stencil theme performance stays crisp and BigCommerce APIs remain predictable even at 10× traffic.

‍

Table

‍

Common Mistakes

Shipping one giant bundle to every route and wondering why INP tanks. No route-level CSS purge, so “unused” styles bloat LCP. Loading carousels/heroes without lazy-loading or priority hints. Skipping edge HTML caching for anonymous traffic—every PLP/PDP hits origin during sales. Calling BigCommerce APIs serially, no ETags or batching, and no coalescing of identical in-flight requests. Trusting third-party tags to behave: synchronous chat/analytics blocking first paint. Over-scripted checkout with payment SDKs loaded on page load. No budgets or RUM dashboards—teams notice regressions only when users complain. Missing circuit breakers, so a single dependency stalls the entire storefront under load.

‍

Sample Answers (Junior / Mid / Senior)

Junior:
“I’ll inline critical CSS, split JS by route, and lazy-load images with AVIF/WebP. I’ll enable CDN caching and defer third-party scripts so Core Web Vitals improve.”

Mid:
“I implement route-based chunks and per-route CSS purge for Stencil. We cache HTML for anonymous users with short TTL + SWR keyed by locale/currency/device. For BigCommerce APIs, I prefer Storefront GraphQL, add ETags to REST, batch/paginate, and coalesce duplicates. RUM tracks p75 LCP/INP/CLS.”

Senior:
“We run a BFF to pre-compose data and hydrate templates, enforce timeouts, and add circuit breakers. Our BigCommerce performance optimization includes responsive media, edge caching with pre-warm, and tag governance with kill-switches. Budgets gate merges; load tests simulate bots; we watch edge hit rate and API p95 during promos.”

‍

Evaluation Criteria

Look for layered BigCommerce performance optimization tied to Core Web Vitals. Strong signals: critical CSS + route-based purge, route-level JS chunks, responsive AVIF/WebP with priority hints, and edge HTML caching with SWR for anonymous traffic. For BigCommerce APIs, expect Storefront GraphQL for shaped data, REST ETags, batching/pagination, and request coalescing. Operational excellence: budgets in CI, RUM + synthetic coverage, tag governance, pre-warm routines, and circuit breakers with graceful fallbacks. Weak answers stay at “minify and cache” without addressing cache keys, API shaping, or third-party control.

‍

Preparation Tips

Build a Stencil demo with per-route bundles and critical CSS inlined; purge unused CSS per page type. Add responsive images (srcset) with AVIF/WebP and priority hints; lazy-load below-fold media. Configure edge HTML caching (short TTL + SWR) keyed by locale/currency/device; pre-warm top PLP/PDP/lander URLs. Use Storefront GraphQL to shape data; add ETags/If-None-Match for REST, batch/paginate, and coalesce duplicates. Create a simple BFF that composes responses, enforces timeouts, and exposes a kill-switch for recommendations/chat. Wire budgets into CI and add RUM dashboards for LCP/INP/CLS by route. Run a sales-event load test that includes botty pagination and search spam.

‍

Real-world Context

A fashion retailer cut LCP ~35% by inlining critical CSS and splitting Stencil bundles by PLP/PDP; INP improved after deferring legacy scripts. A marketplace stabilized promos by enabling edge HTML caching with SWR keyed by locale/currency; origin CPU stayed flat at 10× traffic. An electronics brand moved product data to Storefront GraphQL, added ETags, batching, and request coalescing; API p95 dropped 40%. Checkout reliability rose after lazy-loading payment SDKs and deferring chat widgets. With budgets in CI and RUM guardrails, Stencil theme performance and BigCommerce APIs stayed fast—and ad spend could push harder without breaking UX.

‍

Key Takeaways

• Treat speed as a budget: bytes, RTTs, and main-thread ms.
  
• Inline critical CSS; split JS per route; lazy-load images with AVIF/WebP.
  
• Cache HTML at the edge (SWR), keyed by locale/currency/device; pre-warm hot paths.
  
• Prefer GraphQL; add ETags, batching, pagination, and coalescing to BigCommerce APIs.
  
• Enforce budgets in CI; monitor p75 Core Web Vitals; govern third-party tags.
  ‍

Practice Exercise

Scenario:
Your BigCommerce storefront suffers LCP > 4s on mobile and API spikes during sales. Marketing needs landing pages to stay hot while product listings and search surge.

Tasks:

1. Extract per-route critical CSS for home/PLP/PDP/checkout; inline it and purge the rest.
   
2. Split Stencil JS by route; mark non-critical bundles defer/async; gate heavy work with requestIdleCallback.
   
3. Convert hero and gallery images to AVIF/WebP, add responsive srcset/sizes, priority hints for LCP assets, and lazy-load below-fold media.
   
4. Enable edge HTML caching with short TTL + stale-while-revalidate; key by locale/currency/device; pre-warm top PLP/PDP and promo URLs.
   
5. Move product queries to Storefront GraphQL; add ETags to remaining REST calls; batch/paginate and coalesce identical in-flight requests.
   
6. Introduce a tiny BFF to compose data for PLP/PDP, enforce timeouts, apply circuit breakers, and provide cached fallbacks for recommendations.
   
7. Defer third-party tags via a consent manager; convert sync to async; sandbox chat/analytics where feasible.
   
8. Add budgets (KB, requests, main-thread ms) and RUM; gate merges on p75 LCP/INP/CLS; run a load test with botty pagination and search spam.
   
   

Deliverable:
A short deck + dashboards proving better LCP/INP, higher edge hit rate, reduced API TTFB, and stable checkout during a 10× traffic surge—using repeatable BigCommerce performance optimization patterns.