Basket analysis is a data mining method that studies customer purchasing behavior by analyzing the product combinations in customers' shopping baskets. A classic example is the "beer and diapers" phenomenon discovered by Walmart in the 1990s in the United States. This phenomenon stemmed from the fact that fathers who needed to buy diapers on weekends often bought beer as well. The core purpose of basket analysis is to optimize product configuration, understand customer needs, and analyze sales trends. Its analysis relies on three key indicators: support (the probability that a product combination will be purchased simultaneously), confidence (the conditional probability that product B will be purchased after purchasing product A), and boost (the boosting effect of purchasing product A on the probability of purchasing product B).
Basket testing primarily targets shopping baskets used in retail and supermarket settings, testing multiple indicators such as hygiene, materials, and load-bearing capacity to ensure they meet industry standards and consumer safety requirements. Testing items mainly include material composition analysis, microbial testing, load-bearing capacity testing, abrasion resistance testing, impact resistance, plasticizer and heavy metal migration, color fastness, and handle strength. The testing scope covers various types of shopping baskets, including plastic, metal, foldable, biodegradable materials, and smart sensor baskets. The relevant testing methods reference a series of domestic and international standards such as GB/T, ISO, ASTM, and EN, including GB/T 2918-1998, ISO 22196, and ASTM D638.