电商商品连带分析数据

以天猫旗舰店为例，该平台数据交易量大，能更好代表整体电商销售情况，通过对商品进行连带分析，发现顾客在一次购物中购买不同商品之间的关系，可以揭示商品之间的购买模式，帮助商家更好地了解顾客的购买行为，从而优化库存、促销策略和顾客体验。该数据由天猫后台导出，由于后台导出日期最多为一周，所以需要人工去做数据合并，以月为时间单位计算有效数据。具体算法如下：统计商品访问人数，根据历史订单明细汇总支付件数，支付金额。选取关联商品，统计关联访问人数，关联加购人数和关联支付人数；关联访问率=关联访问人数/总访问人数×100%；关联加购率=关联加购人数/关联访问人数×100%；关联购买率=关联支付人数/关联访问人数×100%。识别出经常一起购买的商品组合，可以设计捆绑销售或交叉销售策略，提高销售额。

Taking Tmall flagship stores as an example, the platform has large transaction volumes and can better represent the overall e-commerce sales scenario. By performing product association analysis to identify the relationships between different products purchased by customers in a single shopping session, this dataset can reveal the purchase patterns among products, helping merchants better understand customer purchasing behaviors, and thus optimize inventory management, promotional strategies and customer experience. The dataset is exported from the Tmall backend. Since the backend only permits exporting data for a maximum of one week, manual data consolidation is required to calculate valid data on a monthly basis. The specific algorithms are as follows: first, count the number of product visitors, and aggregate the number of paid items and total payment amount based on historical order details. Next, select associated products, and count the number of associated visitors, associated cart additions, and associated paid customers. The associated access rate is calculated as (Number of associated visitors / Total number of visitors) × 100%; the associated cart addition rate is (Number of associated cart additions / Number of associated visitors) × 100%; and the associated purchase rate is (Number of associated paid customers / Number of associated visitors) × 100%. By identifying product combinations that are frequently purchased together, merchants can design bundling sales or cross-selling strategies to boost sales revenue.