Finally achieve the purpose of data guiding operations. So how to disassemble the core data indicators? The key method is to sort out the entire path of the user. Determine the data executive email list indicator on each path step, and this indicator is the secondary or tertiary data indicator we want. For example, the user path of this public account selling course articles is: Then it is obvious that the secondary indicators are the number of readings of the executive email list article and the number of visitors to the product, and the third-level indicators are the total exposure of the official account article.
The number of clicks on the article, the number of clicks on the products in the article and the number of clicks on the purchase button. At this point, we can establish the executive email list correlation model between the core indicators and the three-level indicators. Assuming that the core indicators are the number of orders placed X, the exposure amount is A, the article opening rate is B, the product click conversion executive email list rate is C, and the order conversion rate is D, then we have obtained the formula model for the number of orders. Step 3: Attribution Attribution.
Sort out all the factors that affect the three-level indicators. After knowing which data is related to the number of orders, our focus is to improve the data in each executive email list link. The influencing factors of these data are obvious, and this step is also the most important step in the overall data operation. First of all, A is the exposure of the article. This data is basically fixed and depends on the overall resources of your WeChat ecosystem. For executive email list example, how many fans do you have on your official account, and do you have some private WeChat personal accounts and WeChat groups. This time, we have only 6 WeChat accounts.