NexCart: Next.js E-Commerce Rebuild with AI Recommendations Increased Conversions 2.7x
A complete Next.js 15 storefront rebuild with LLM-powered product recommendations, automated email sequences, and a 1.1-second load time lifted conversion rate from 1.2% to 3.2% and average order value by 45%.
Published September 5, 2025
The Challenge
NexCart had a WooCommerce store loading in 6.4 seconds on mobile with a 1.2% conversion rate and zero personalization. The catalog had 4,000+ SKUs but customers were abandoning after viewing 1.8 pages on average. The business was spending $8,000 per month on paid traffic that was not converting. There was no email automation, no abandoned cart recovery, and no mechanism to surface related or complementary products.
Our Solution
Sciensify rebuilt the storefront on Next.js 15 with the App Router, server-side rendering for catalog pages to ensure SEO indexability, and Stripe for payments. We implemented an LLM-powered recommendation engine that analyzed purchase history, browsing sessions, and product embedding similarity to surface contextually relevant products at four touchpoints: product page sidebar, cart upsell, post-purchase, and email sequences. Abandoned cart recovery emails with personalized product suggestions were automated via a Resend integration triggered by Supabase edge functions. We reduced image sizes by 73% using next/image with AVIF format, eliminated all render-blocking JavaScript, and achieved a 1.1-second Largest Contentful Paint on mobile. A custom analytics dashboard in the admin panel showed real-time conversion funnels by traffic source.
The Results
“We went from burning $8,000 per month on ads that did not convert to having a site that turns visitors into buyers almost automatically. The AI recommendations alone added $12,000 in monthly revenue.”
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