Author Archives: Bernadette Galang

BigCommerce Catalyst Storefronts and When a Next.js Front End Beats a Traditional Theme

frontend coding

BigCommerce Catalyst is a Next.js-powered storefront approach designed to outperform traditional Stencil themes when merchants need better speed, flexibility, and scalability. This post explains when a Catalyst rebuild makes sense, what tradeoffs to expect, and how to protect SEO, integrations, and ROI during the transition.

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Ecommerce Accessibility Audit for EU and UK Retailers: Fix Revenue-Blocking UX Before Peak Sales Weeks

website backend ecommerce

An ecommerce accessibility audit helps EU and UK retailers identify and fix usability barriers that can block shoppers from browsing, checking out, and completing purchases. By combining manual testing, WCAG checks, and platform-specific remediation, retailers can improve compliance, conversions, and revenue before peak sales periods.

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BigCommerce Catalyst Implementation: When a Composable Front End Is Worth the Move

online store shopping

BigCommerce Catalyst offers merchants a modern, React-powered composable storefront that can improve speed, SEO, and conversion rates when a traditional theme becomes a bottleneck. This post explains when a Catalyst migration is worth the investment, along with the integration, performance, and SEO planning needed for a successful rollout.

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European Accessibility Act Ecommerce Audit: Fixing Online Retail Barriers for EU Buyers

European Online Shopping

A European Accessibility Act ecommerce audit helps retailers selling into the EU identify and fix accessibility barriers across the full shopping journey, from navigation and product discovery to checkout and third-party integrations. By prioritizing compliance, usability, and conversion impact, merchants can reduce legal risk and create a smoother experience for all buyers.

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LLMs.txt for Ecommerce Websites: Helping AI Assistants Understand Your Catalog Without Exposing Sensitive Pages

Codes on a screen

LLMs.txt helps ecommerce websites guide AI assistants to the most useful catalog, category, and policy pages while keeping sensitive areas like admin, customer, and pricing data protected. This article explains how to implement LLMs.txt alongside robots.txt, schema, feeds, and crawl controls to improve discovery without sacrificing privacy or performance.

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