The Ultimate 1688 to Shopify Sourcing Hub

The first bottleneck in any 1688-to-Shopify pipeline is data extraction. A single 1688 product page can carry 50+ SKU variants across two or three option axes — typically Color and Size for apparel, or Material and Configuration for electronics. Each variant row holds a unique price, stock status, SKU code, and image set. Manually transcribing this data into a spreadsheet is not just slow; it is the point where most import errors originate.

EasyCatch's 1-click capture reads the full product DOM in under 10 seconds and extracts every variant's Option1 (e.g., "Black"), Option2 (e.g., "XL"), price, inventory quantity, and associated image URLs. The extension then normalizes Chinese option labels — including 颜色分类 (Color Classification) and 尺码 (Size) — into Shopify-compatible English names. This normalization step alone prevents the #1 Matrixify rejection cause: mismatched option names across variant rows.

After extraction, EasyCatch generates an SEO-friendly product handle from the Chinese product title using a pinyin-to-slug engine. For example, a 1688 title like "2025夏季新款男士运动鞋透气网面跑步鞋" becomes a clean Shopify handle such as 2025-summer-mens-running-shoes-breathable-mesh. The handle is editable before export, so you can adjust it to match your store's naming convention.

The captured data is stored entirely in your browser's local storage — no supplier URLs, variant details, or images are ever transmitted to an external server. This local-first architecture means your sourcing intelligence stays private, and it complies with the strictest data-residency requirements for agencies handling multiple client stores.

Matrixify (formerly Excelify) is the de-facto standard for bulk Shopify imports, supporting up to 57 columns per row across Products, Variants, Images, and Metafields. But its column structure is exacting: Option1 Name must be identical across every variant row, Image Src must contain a valid Shopify CDN URL, and Image Position must match the gallery order. One misaligned column breaks the entire import.

The hardest column to get right is Variant Image. This column maps a specific variant combination (e.g., Color=Black, Size=XL) to the exact image filename that shows when a customer selects that variant on your storefront. In manual workflows, this mapping is done by eye — scrolling through image folders and matching filenames to variant rows. EasyCatch automates this by pre-building the Variant Image URL at capture time, using the 1688 page's own variant-to-image association data.

The 57-column Matrixify CSV that EasyCatch exports includes every column from ID through Metafield: namespace.value [array]. Key columns are pre-filled with validated data: Handle (SEO slug), Option1 Name / Option1 Value, Variant Price (your target price), Variant Inventory Qty, Image Src, Image Position, and Variant Image. You can open the CSV in Excel or Google Sheets to adjust prices, tags, or descriptions before import.

The guide also covers the most common Matrixify error patterns — duplicate handles, mismatched Option Name casing, missing Variant Image rows — and shows exactly how EasyCatch's export prevents each one at generation time.

The 1688-to-Shopify tool ecosystem splits into two categories: sourcing assistants that capture product data and generate CSVs, and fulfillment platforms that place orders with suppliers and manage logistics. EasyCatch belongs to the first category. DSers (AliExpress-focused) and CJ Dropshipping (1688 + global warehousing) belong to the second. They are not mutually exclusive — many successful merchants run EasyCatch for sourcing plus DSers or CJ for fulfillment.

The key differentiators come down to four dimensions: automation depth (how much manual work remains after using the tool), image handling (whether variant-image mapping is automated), data privacy (local vs. cloud), and fulfillment ownership (whether you or the platform controls the supplier relationship).

In a 2026 side-by-side test of 30 product imports, EasyCatch reduced 1688-to-Shopify setup time from 45–120 minutes per product (with manual image handling) to under 60 seconds. DSers and CJ Dropshipping streamline order fulfillment but do not provide the same depth of product-data automation — they assume you already have the listing built. The comparison guide breaks down exact feature matrices, pricing models, and the trade-offs between local-first and cloud-based architectures.

The guide's decision framework helps you answer: "Should I use one tool or a combination?" For pure sourcing speed and data privacy, EasyCatch leads. For automated order routing and shipping, DSers or CJ fills that gap. The two together form a complete 1688-to-customer pipeline.