Sales performance is a data that can very intuitively show the performance of a store over a period of time.
Data 1 is the store overview data, which shows the store’s operating status. You can see the store’s traffic (number of product views), click-through conversion rate (“Buy” button click rate), checkout conversion rate, and transaction amount and other core data within a specific date range (in weeks).
Data 2 displays the data of a single product within the date range. This page of data is an important basis for us to eliminate and optimize single products.
Data 3 shows the sales amount and trend of each country, but it does not show the data for one week, but the monthly data from three months ago.
Data 4 is the same as Data 2, showing the product’s operating performance data. The difference is that Data 4 can trace back the product data of previous weeks, that is, this week you can still view the data performance of a single product in the previous 19 weeks.
Therefore, through data 4, the data of single products can be arranged in weekly units to view the impact of product conversion on traffic, and at the same time, the life cycle of single products can be judged as a basis for stocking and developing new products.
In sales performance, the most important data are the “buy” button click rate (referred to as “conversion rate”) and the checkout conversion rate, so for the “buy” button click rate, we need to do more data processing and analysis. In addition to arranging and comparing the conversion rates of single products, we can also sort out the data of the entire store.
It can be seen that the conversion rate of the store will directly affect the overall traffic of the store after 2 to 3 weeks. In addition, it can be seen that when the store conversion rate is above 0.1%, the store traffic will continue to rise, so when operating a store, at least keep the weekly conversion rate of your store above 0.1%.
If you want to optimize this data, you should not only look at the data of the entire store, but also analyze the main single product data, and promptly remove the single products that have no conversion or low conversion rate in the store for a long time (generally 2 to 3 months after listing).
By analyzing the data of a single product, the selling price and the quantity of inventory in stock can be gradually adjusted. For the click-through conversion rate and shopping cart conversion rate standards of a single product, the conversion rate standards of the store can be referred to. However, each single product has its own life cycle, and it is necessary to combine other sales software to view the sales of the single product in the market.
In the store and product data, there is a performance of the “No. 1 store (or product)”.
Merchants can refer to this data to understand the situation of their own stores.
The click-through rate of the “Buy” button here is the number of clicks on the “Buy” button divided by the number of product views, and the checkout conversion rate is the number of orders divided by the number of shopping cart views. The checkout conversion rate of a single product can be used to determine whether the pricing of the single product is reasonable and whether the logistics timeliness of the store is competitive. The standard for the checkout conversion rate can usually be benchmarked against the store (or product) ranked first.