Improving Optimizely Load Speed

A technical remediation project that ensured client-side experimentation stayed reliable, even with legacy baggage and aggressive growth targets.

Starting Point

Optimizely Web (client-side) was executing after the paint, producing layout flashes and inconsistent variant delivery. Marketing teams were losing trust in results, and engineers were wary of adding more tests during peak traffic.

Diagnosis Highlights

• Multiple tag managers loading the Optimizely snippet sequentially
• Blocking third-party scripts (chat, analytics) running before the experiment bundle
• Legacy variations written in heavy jQuery selectors, causing race conditions
• Server-side cache headers invalidating frequently, forcing visitors to download the 200KB experiment bundle on each session

Remediation Plan

1. Snippet optimisation. Migrated to the asynchronous Optimizely async loader with a preconnect to CDN endpoints. The snippet moved higher in the head with a tiny bootstrap script that blocked paint for sub-300ms when experiments were active.
2. Critical experiment audit. Rewrote active variations using vanilla DOM APIs and mutation observers. We removed duplicated CSS overrides, consolidated event listeners, and introduced a standard helper library that shipped with every test.
3. Caching & deployment. Configured Cloudflare cache rules to honour Optimizely’s immutable file naming, added stale-while-revalidate, and reduced bundle fetches by 48%.
4. Monitoring. Implemented a lightweight synthetic test that recorded pre/post DOMContentLoaded timings, variation application timestamps, and flicker occurrences. Alerts fired in Slack when execution drifted beyond 400ms.

Results

• 42% faster Optimizely execution (from 620ms median to 360ms)
• 0.3s improvement in First Contentful Paint on experiment-heavy templates
• Zero flicker regressions tracked across eight subsequent experiments
• Experiment velocity restored: marketing reinstated the weekly launch cadence after confidence scores rebounded

The engagement delivered more than speed—it rebuilt trust in the experimentation stack. With the technical debt cleared, the team could focus on impactful hypotheses instead of firefighting execution issues.