Client Success Story
Client Overview
The client is an established consumer electronics company specializing in premium and rugged smartphones and related accessories. They operate a direct-to-consumer web store as their primary sales channel and maintain active seller accounts across five major online marketplaces—Amazon (covering eight regional storefronts), eBay, Newegg, and OnBuy. Their cross-border selling footprint gives them reach across North America, Europe, and beyond.
Client Requirement
The client required a purpose-built automation solution to replace a set of time-intensive manual workflows. Their needs fell into three distinct areas: managing product data across their web store and all marketplace accounts; keeping inventory synchronized with supplier feeds in real time; and maintaining accurate and competitive pricing, including automated competitor price monitoring and supplier cost comparisons across channels.
Before engaging Team4eCom, the client had already evaluated off-the-shelf multichannel management platforms, including ChannelAdvisor and SellerCloud. Each failed to meet their needs for different reasons: high licensing costs, rigid configurations incompatible with their catalog structure, poor integration reliability, inconsistent vendor support, and a steep learning curve that added operational overhead rather than reducing it—which is why they needed a custom-developed platform built around their workflows.
Key automation requirements:
Key Challenges
Before the automation work could move forward, the team had to resolve a serious performance issue in the client’s web store. High website traffic was fully using server memory and causing database deadlocks, which slowed page loading, increased bounce rates & session timeouts, and affected sales performance.
A second challenge appeared after the first automation model was deployed. More than 100 recurring background processes were running on the main application server along with the live web store. This pushed memory utilization to 100% and added more pressure to the existing setup.
To scale the automation system without affecting the live storefront, these scheduled processes had to be moved away from the primary server. The change needed to be handled carefully so the website, marketplace listings, and ongoing operations remained stable.
Our Solution
Our team designed and delivered a centralized operations system in structured phases. The first working model was deployed within seven months of starting the project. Post-release efforts focused on customization, performance improvements, and ongoing maintenance.
Soon after deployment, the system supported nearly 90% task automation across the client’s channel management workflows, with a human-in-the-loop component retained for bulk uploads requiring manual review.
A multi-dashboard management console was built to consolidate all sales channels and supplier dashboards under one interface. Custom plugins and extensions were developed to support workflows that nopCommerce could not handle natively, and BI and analytics tools were integrated to provide unified cross-channel reporting.
Two dedicated data operations specialists were assigned to support the client’s team in managing product listing data at volume during and after the rollout phase.
To resolve the 100% memory utilization and database deadlock issues, more than 100 scheduled background processes were migrated from the primary application server to a dedicated daemon server. This reduced load on the main server, restored normal page load times, and provided the capacity needed for the automation system to scale without negatively impacting storefront performance.
Supplier inventory was connected to the client’s central database through direct API integrations. Amazon’s RESTful Selling Partner API, deployed on AWS with Amazon EC2 for hosting and scaling and AWS Lambda for event-based processing, was used to access and synchronize Amazon Marketplace data.
Parallel integrations were built using eBay, OnBuy, and the Newegg APIs to sync supplier inventory from those marketplaces into the centralized database.
A Python-based price monitoring algorithm was developed and deployed to support the main system. It extracted competitor pricing data from across the web, produced structured CSV file outputs, and added results into a pricing engine. The algorithm incorporated supplier costs, exchange rate fluctuations, inbound shipping charges, and fulfillment fees to calculate and publish optimized listing prices automatically — without any manual intervention.
Results Achieved
Increase in Sales
Reduction in Bounce Rate
Decrease in Operational Overheads
Tasks Automated Across All Channels
Get In Touch
Manual listing updates, inventory checks, and pricing changes can slow sales operations and increase errors. Team4eCom builds custom automation solutions that help sellers manage product data, inventory, pricing, and marketplace workflows from one centralized system.
Partner with us to reduce manual work and keep your operations easier to manage. Reach us at info@team4ecom.com to discuss project requirements.