Before considering the conversion rate, first set several necessary parameters.
Order S;
Total traffic N;
a product traffic n;
Average conversion rate P;
< p>a product conversion rate P;
Total number of store products x;
According to the above parameters, the conversion rate calculation formula can be in two forms. Temporarily set all traffic-related data to fixed value.
Many operators like to focus on data fluctuations, but this formula itself is meaningless for optimizing conversion rates. The conversion rate is for a certain product. Unless it is a brand optimization for a store, the overall data cannot reflect the excellence of a single conversion rate, so it needs to be used as a reference when optimizing the conversion rate.
Before using it for optimization reference, you need to do an important task – distinguishing the major categories of store products. The classification of product categories refers to classifying all products in the store according to the sales ratio. Generally, the classification method of 25%+50%+25% is used.
Based on the above ratio, all products in the store are first divided into three major categories. The first category is popular models (generally products with an average daily sales volume of more than 40 pieces); the second category is rising models ( Generally, they are products with an average daily sales volume of more than 5 pieces and a small quantity of 40 pieces); the third category is low single items (generally, products with an average daily sales volume of 15 pieces).
Finally, based on the above product classification, the conversion rate calculation method is changed into the following formula: order amount = conversion rate of popular models × traffic of popular models + conversion rate of rising models, conversion rate of rising models + conversion rate of small models × small models Traffic
If traffic changes are not taken into account, then which type of products should be focused on optimizing the conversion rate of the three types of products?
——Focus on optimizing rising products (that is, accounting for 50% of sales) middle style).
The reasons are as follows:
(1) Popular products have steadily generated a large number of orders every day. Once the conversion rate optimization is performed to change the current product description, introduction and even pictures, the consequences will be It may be that the order volume increases, or that the conversion rate does not increase but decreases. Therefore, for popular products, it is recommended to maintain the status quo.
(2) For low-perform products, due to the large number of these products and the extremely small order quantity, the conversion rate is likely to fluctuate significantly between 10% and 100%, so rashly optimizing the conversion rate Not only is the workload huge. And the effect will not be significant.
(3) The rising items should account for more than 40% of a store’s sales. A large number of rising styles can even account for 60% of store sales. Some of these styles will often become popular in the store in the future, and stable styles in the rising period will also have a large amount of traffic every day, so once the conversion rate increases , performance will also rise significantly. Even if optimization errors occur and the conversion rate drops, it will not cause irreparable losses like popular products.