How Amazon clothing sellers use product portraits to improve operational efficiency
The Amazon platform is different from other domestic e-commerce platforms. The A9 algorithm makes sellers pay more attention to the product itself rather than user portraits. Amazon clothing sellers can use digital methods to conduct product research and create product portraits to provide a basis for product selection and operations. This article will introduce in detail the important data tags and their significance of Amazon store product portraits.
Inventory sell-out rate
The inventory sell-out rate refers to the ratio of sales to inventory within a certain period of time from the time the product is purchased and put into storage. For the clothing category, the inventory sell-out rate is within the normal range of 65% to 85%. The specific reference values are as follows:
- New product 30-day inventory sell-out rate: 15%~30%
- New product 60-day inventory sell-out rate: 45%~55%
- 90-day inventory sell-out rate of new products: 60%~75%
The inventory sell-out rate represents whether the product is selling well, and sellers need to adjust the time period based on the actual financial situation.
Inventory to sales ratio
Inventory-to-sales ratio refers to the ratio of average inventory quantity to sales volume within a certain period of time. The calculation formula is as follows:
[ text{Inventory to sales ratio} = frac{(text{Beginning inventory} + text{Ending inventory}) / 2}{text{Sales quantity}} times 100% ]
The inventory-to-sales ratio is mainly used to evaluate the overall status of the product’s immediate inventory. The reference value is 3.0~4.0. By calculating the inventory-to-sales ratio, sellers can control product inventory in a timely manner to avoid out-of-stock or slow sales.
Sales contribution rate
Sales contribution rate refers to the ratio of the sales of a single product to the overall sales of the store. The calculation formula is as follows:
[ text{Sales contribution rate} = frac{text{Single product sales}}{text{Store sales}} times 100% ]
Sales contribution rate can be used to compare the sales proportion of multiple products in a store to avoid the sales concentration problem caused by the “Rule of 28”. Sellers also need to continue to pay attention to new styles in advance for next season to make long-term sales more stable.
Clothing attribute tag
By organizing the attribute tags of clothing category products, sellers can build product portraits containing 7 types of data tags:
- Wearing scenes: Casual, Club & Night Out, Cocktail, Formal, Work, Wedding
- Overall version: Slim, Loose, Oversized
- Neck styles: Round Neck, V Neck, High Neck, Off Shoulder, Others
- Sleeve length: Long Sleeve, Half Sleeve, Short Sleeve, Sleeveless, Others
- Skirt length: Mini, Midi, Maxi
- Printing styles: Plain, Floral, Stripe, Dots, Plaid, Others
- Special designs: Wrap, Empire Waist, Crisscross, Ruffle, Others
By cross-analyzing these attribute tags, sellers can find out more style features.
Data analysis example
Take the two keywords Summer and Casual as an example. Sellers can add data tags in batches to obtain the overall characteristics of Summer Dress and Casual Dress. By cross-comparing the neckline and sleeve length, sellers can find:
- The number of round neck and short-sleeved styles under Summer Dress is about 20% higher than that under Casual Dress
- The number of V-neck short-sleeved styles under Casual Dress is about 25% higher than that under Summer Dress
In addition, the quantity of sleeveless and off-shoulder styles in Casual Dress is significantly higher than that in Summer Dress. Sellers need to pay special attention to this type of style.
By comparing the highest rankings and average rankings under different keywords, sellers can find the most beneficial direction for promoting their products.
Product portraits can not only make extensive comparisons between store products and platform products, but can also be used together with user portraits during the operation process to build a more complete store data system.