How to Read a Litbuy Spreadsheet Like a Pro Buyer

The Litbuy spreadsheet presents roughly fourteen data columns per product row. The most important for buyers are slug, weidian_id, price, seo_title, sort_level, main_image, images, sku, and qc_list. The slug is a URL-safe identifier used for routing; it tells you nothing about quality but helps verify uniqueness. The weidian_id is the actual marketplace listing key, which you can cross-reference directly on Weidian if needed. The price field is always in Chinese Yuan, so your first mental conversion should be dividing by 6.2 to approximate USD. The seo_title is what we display on this directory instead of the raw Chinese title, because it has been editorially cleaned for readability. Sort_level is your quality confidence score. Main_image and images form the visual verification layer. SKU contains variant data. QC_list links to quality control photographs. Learning to read these columns in combination, rather than in isolation, is the skill that separates experienced buyers from first-time impulse shoppers.

SKU data is stored as a JSON array where each element represents an attribute group. A typical sneaker entry will have two groups: one for colorway and one for size. The colorway group often contains image thumbnails via the img field, while the size group is typically text-only. When an attr_values entry has has_img=true, you are looking at a seller-provided variant photo. Compare this thumbnail against the main_image. If the colors, textures, or stitching patterns differ noticeably, that is a warning sign of inconsistent batch quality or photo recycling. For size groups, note the convention being used. Some sellers list EU sizes, others use US/UK dual notation, and a minority use Chinese factory codes. A mismatch between your foot measurement and the listed convention is the leading cause of returns. We render SKU selectors dynamically in our product modals precisely to reduce this friction, but learning to read the raw SKU array still helps when browsing the spreadsheet directly or when our directory has not yet surfaced a particular item.

The base price in the spreadsheet is rarely your final cost. Agent fees, domestic Chinese shipping to the agent warehouse, international freight, insurance, and payment processing surcharges all layer on top. A CNY 320 item can easily reach $82 USD landed for a single pair of shoes. Our directory shows the base price converted to USD for quick comparison, but you should mentally add 35 to 55 percent for a realistic landed estimate on small orders, or 25 to 35 percent for consolidated hauls over 3kg. Price anomalies in the spreadsheet itself also matter. If a Nike Dunk variant is listed at CNY 180 while every other seller shows CNY 340 to 420, that is not a deal. It is almost certainly a different quality tier, a bait-and-switch listing, or a product that uses different materials. We flag extreme undercutters in our curation pipeline, but if you are browsing the raw spreadsheet, apply this filter yourself: anything more than 40 percent below the category median warrants investigation before excitement.

The AstroReps image search tool, linked from our hero section and header, is your best friend for verification. When you find an interesting listing, screenshot the main_image and run it through reverse image search. If the same photo appears on five unrelated Weidian stores, it is a stock image and does not represent the actual product you will receive. If it appears only on the target store and possibly one or two resellers, it is likely an original seller photo. For high-value items over CNY 800, we recommend going one step further: request additional QC photos through your agent before warehouse dispatch. Most reputable agents offer this for a nominal fee of $1 to $3 per photo set. The twenty minutes you spend on verification can save you weeks of dispute hassle and the disappointment of receiving a substandard product. Image literacy is the single most underrated skill in spreadsheet-based buying.

Pro buyers do not read the spreadsheet linearly. They apply a mental filter stack: sort_level above 80, price within 25 percent of category median, seller with 6+ months of activity, main_image that passes reverse search, and SKU arrays with photo-verified variants. On a spreadsheet of 6,000 items, this stack typically narrows the field to 200 to 300 viable candidates. From there, community sentiment and specific aesthetic preference drive the final choice. We have automated much of this filtering for our directory visitors, but the underlying logic remains the same. Understanding the filter stack transforms you from a passive consumer into an active curator of your own wardrobe. The result is fewer disappointments, better price-to-quality ratios, and a collection that actually matches your expectations when the delivery arrives.