🛒 E-Commerce

Multi-Vendor Marketplace

Client: MarketConnect Group

1M+
Daily Visitors
3x
Revenue Growth
200ms
Avg Response
500+
Active Vendors

Timeline

16 weeks from kickoff to production launch

Team

8 engineers (3 frontend, 2 backend, 1 ML engineer, 1 DevOps, 1 QA)

Industry

E-Commerce

The Challenge

MarketConnect Group had built an initial version of their multi-vendor marketplace using a WordPress and WooCommerce stack that served them well during the early growth phase when they had 50 vendors and a few thousand daily visitors. However, as the platform grew to 300 vendors and traffic increased tenfold, the system began showing severe growing pains that threatened the viability of the business.

Page load times had ballooned to over 4 seconds on product listing pages, with search queries taking 6 to 8 seconds to return results. During promotional events and flash sales, the site frequently crashed under load, with the WooCommerce database locking up when concurrent order volume exceeded 200 per minute. These outages during peak revenue periods cost the company an estimated $180,000 in lost sales over the previous quarter and eroded trust with both vendors and customers.

The vendor experience was equally painful. Sellers managed their inventory through a clunky admin panel that required manual CSV uploads for bulk product updates. There was no real-time inventory synchronization, leading to frequent overselling situations where customers purchased items that were already out of stock. The disputes and refunds generated by these inventory failures consumed significant customer service resources and damaged the marketplace reputation through negative reviews.

Product discovery was another critical weakness. The search function was a basic MySQL LIKE query that returned irrelevant results, had no typo tolerance, and could not handle faceted filtering by category, price range, brand, or vendor. The absence of personalized recommendations meant that the average customer browsed fewer than 3 product pages per session, far below the industry benchmark of 8 to 12 for successful marketplaces. MarketConnect needed a complete platform rebuild that could support their growth trajectory toward 1,000 vendors and millions of daily visitors while delivering the fast, intelligent shopping experience their customers expected.

Our Approach

We structured the engagement into four phases, each delivering a deployable increment of the new platform while the existing WooCommerce site continued serving live traffic. This parallel-run strategy eliminated the binary risk of a big-bang migration and allowed us to validate each component with real users before the full cutover.

Phase one focused on the storefront and product discovery layer. We built a Next.js frontend with server-side rendering for SEO-critical pages and static generation for product catalog pages that change infrequently. Elasticsearch replaced MySQL for product search, providing sub-200-millisecond full-text search with typo tolerance, synonym matching, and faceted filtering across dozens of product attributes. We implemented search-as-you-type with real-time suggestions and integrated category-aware autocomplete that guided customers to relevant products faster.

Phase two rebuilt the backend services on Node.js with a MongoDB document database optimized for the flexible, vendor-specific product schemas that the marketplace required. Unlike the rigid WooCommerce product model, our schema allowed vendors in different categories to define custom attributes without database migrations. We implemented real-time inventory synchronization using WebSocket connections and an event-driven architecture that updated stock levels across all customer-facing pages within 500 milliseconds of any inventory change.

Phase three delivered the AI-powered recommendation engine and vendor analytics platform. We built collaborative filtering models using purchase history and browsing behavior data to generate personalized product recommendations displayed on the homepage, product detail pages, cart page, and post-purchase confirmation emails. The vendor dashboard provided real-time sales analytics, inventory alerts, customer demographic insights, and competitive pricing intelligence.

Phase four handled the data migration, vendor onboarding to the new platform, and the gradual traffic cutover from WooCommerce to the new system. We migrated 1.2 million product listings, 800,000 customer accounts, and 2.3 million historical order records with full data validation at every stage.

The Solution

The delivered marketplace is a modern, horizontally scalable platform built on a JAMstack architecture with Next.js for the storefront, Node.js microservices for business logic, MongoDB for flexible product data, Elasticsearch for search, and CloudFront for global content delivery.

The customer-facing storefront achieves sub-200-millisecond page loads through a combination of static generation for product catalog pages, incremental static regeneration for frequently updated content, and edge caching via CloudFront across 40 global points of presence. Product images are automatically optimized and served in WebP format with responsive sizing, reducing image payload by 65 percent compared to the legacy site. The checkout flow is streamlined to three steps with saved payment methods, address autocomplete, and real-time shipping rate calculation that splits orders across multiple vendors transparently.

The search and discovery engine powered by Elasticsearch handles 50,000 search queries per hour with an average response time of 85 milliseconds. Search results incorporate relevance scoring, popularity weighting, vendor rating signals, and personalization based on the customer browsing history. Faceted navigation allows filtering by category, price range, brand, vendor rating, shipping speed, and custom attributes specific to each product category. The AI recommendation engine generates personalized product suggestions that appear on the homepage, product pages, and cart, contributing to a 34 percent increase in average pages per session.

The vendor portal is a full-featured business management platform. Vendors can manage product listings with bulk import and export, set pricing rules and promotional discounts, view real-time sales and traffic analytics, manage inventory with automatic low-stock alerts, and handle customer inquiries through an integrated messaging system. Payouts are automated through Stripe Connect with configurable commission rates per vendor category, daily settlement reports, and transparent fee breakdowns. The platform handles split-payment orders where a single customer purchase includes products from multiple vendors, automatically routing the correct amounts to each vendor after commission deduction.

Results & Impact

Measurable outcomes delivered for MarketConnect Group

1M+ daily visitors with zero downtime during peak events

The platform now handles over one million daily visitors with peak traffic during flash sales exceeding 5,000 concurrent users. The auto-scaling infrastructure managed three major promotional events in the first quarter with zero downtime and no performance degradation, compared to the previous system which crashed during every major sale.

3x revenue growth within 8 months of launch

Gross merchandise volume tripled within eight months of the platform launch, driven by faster page loads that reduced bounce rates by 42 percent, improved search relevance that increased add-to-cart rates by 28 percent, and personalized recommendations that boosted average order value by 19 percent. The combined effect of these improvements transformed the unit economics of the marketplace.

200ms average page response time

Average server response time dropped from 4.2 seconds on the legacy platform to 200 milliseconds on the new system, a 21-times improvement. Product search response times improved from 6-8 seconds to 85 milliseconds. Lighthouse performance scores consistently measure above 95 across all page types, providing a measurably superior experience on both desktop and mobile devices.

500+ active vendors onboarded

The vendor base grew from 300 on the legacy platform to over 500 active vendors within six months of launch, with the improved vendor tools and analytics dashboard cited as the primary reason new vendors chose MarketConnect over competing marketplaces. Vendor churn rate dropped from 12 percent quarterly to under 3 percent.

87% reduction in overselling incidents

Real-time inventory synchronization reduced overselling incidents from an average of 340 per month to fewer than 45, an 87 percent improvement. The remaining incidents are attributed to vendors with offline retail channels who have not yet integrated their point-of-sale systems with the marketplace inventory API.

Technology Stack

The technologies powering this solution

Next.js

Server-rendered React framework providing static generation for catalog pages, incremental regeneration for dynamic content, and API routes for lightweight backend operations.

Node.js

Backend microservices for order management, vendor operations, payment processing, and real-time inventory synchronization through an event-driven architecture.

MongoDB

Document database providing flexible schemas for vendor-specific product attributes, horizontal scaling through sharding, and aggregation pipelines for real-time analytics.

Elasticsearch

Full-text search engine delivering sub-100ms product search with typo tolerance, synonym matching, faceted filtering, and relevance scoring across 1.2 million product listings.

CloudFront

Global CDN with 40 edge locations serving static assets, optimized product images, and cached API responses for sub-200ms page load times worldwide.

Stripe Connect

Marketplace payment infrastructure handling multi-vendor split payments, automated commission deduction, vendor onboarding with KYC verification, and daily settlement payouts.

Redis

In-memory caching layer for session management, real-time inventory counts, rate limiting, and temporary storage of shopping cart data with automatic expiration.

TensorFlow Recommenders

Machine learning framework powering the collaborative filtering recommendation engine trained on purchase history and browsing behavior data for personalized product suggestions.

The difference is night and day. Our old platform crashed every time we ran a promotion, and now we handle ten times the traffic without breaking a sweat. But what really impressed us was the vendor portal. Our sellers went from dreading inventory updates to actually enjoying the analytics dashboards. Vendor acquisition has never been easier because the platform sells itself during demos. Cozcore delivered exactly what they promised, on time, and the results exceeded our most optimistic projections.

Priya Sharma

CEO, MarketConnect Group

Services Used in This Project

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Multi-Vendor Marketplace - Frequently Asked Questions

How did you migrate 1.2 million products from WooCommerce without disrupting the live site?
The data migration was executed in three stages to ensure zero disruption to the live WooCommerce site. Stage one involved building an ETL pipeline that continuously replicated product data, customer accounts, and order history from the WooCommerce MySQL database to the new MongoDB and Elasticsearch instances. This pipeline ran in real time using database change data capture, ensuring the new platform always had current data. Stage two validated every migrated record through automated comparison scripts that checked field-level accuracy, image integrity, and relationship consistency between products, categories, and vendors. Stage three executed the traffic cutover using DNS-based routing that gradually shifted customer traffic from the old site to the new one over a two-week period. During the transition, both systems accepted orders, with a synchronization layer ensuring inventory consistency. The WooCommerce site remained available as a fallback for 30 days after the full cutover before decommissioning.
How does the recommendation engine work, and how accurate are the recommendations?
The recommendation engine uses a hybrid approach combining collaborative filtering and content-based filtering. Collaborative filtering analyzes purchase and browsing patterns across all users to identify customers with similar preferences, then recommends products that similar users have purchased or viewed. Content-based filtering analyzes product attributes such as category, brand, price range, and description keywords to suggest items similar to those a customer has shown interest in. The two approaches are blended using a learned weighting model that optimizes for click-through and conversion rates. For new users without browsing history, the system falls back to popularity-based recommendations segmented by traffic source, device type, and geographic region. In A/B testing against the baseline of showing trending products to all users, the personalized recommendations achieved a 34 percent increase in click-through rate and a 19 percent increase in average order value. The model is retrained nightly with the latest interaction data to capture evolving customer preferences and seasonal trends.
How does the platform handle split orders across multiple vendors in a single checkout?
When a customer places an order containing products from multiple vendors, the platform handles the complexity transparently. The checkout process presents a unified cart and payment experience to the customer. Behind the scenes, the order service splits the cart into sub-orders grouped by vendor, each with its own fulfillment tracking. Payment is captured as a single charge to the customer through Stripe, and Stripe Connect handles the automatic distribution of funds to each vendor after deducting the marketplace commission percentage configured for their vendor category. Each vendor receives a notification with only their portion of the order and manages fulfillment independently through their vendor portal. The customer receives separate shipping notifications for each vendor shipment with independent tracking numbers. If a customer initiates a return for items from a specific vendor, the refund is processed only for that vendor sub-order without affecting the others. This split-order architecture scales to orders containing products from up to 20 different vendors in a single checkout.
What infrastructure supports the platform handling traffic spikes during flash sales?
The infrastructure is designed for elastic scaling with predictive and reactive auto-scaling policies. For planned events like flash sales, we use predictive scaling that pre-provisions additional capacity 30 minutes before the scheduled start based on historical traffic patterns and vendor-provided estimates of promotional reach. Reactive auto-scaling monitors CPU utilization, request queue depth, and response latency in real time, adding additional Next.js rendering nodes and Node.js API instances within 60 seconds when thresholds are exceeded. The MongoDB cluster uses sharding to distribute read load across replica sets, and Elasticsearch queries are load-balanced across multiple data nodes. CloudFront serves as the first line of defense, caching product pages, images, and search results at the edge so that the majority of requests never reach the origin servers. During the largest flash sale to date, the platform handled a 12x traffic spike over baseline with average response times remaining under 250 milliseconds and zero errors. The auto-scaling infrastructure added 14 additional compute nodes during the peak hour and automatically scaled back down within 30 minutes after traffic normalized.

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