In our daily work. We always hear people talking about data indicators related to traffic such as “Dau”. “Conversion rate”. “Retention rate” and so on. Of course. We can judge the current opportunities and risks of the entire product through these macro data. But if we want to gain insight into deeper user usage issues. Then we need to switch back to the interaction between users and the product from the perspective of data . For example. Also look at the “Clicks on the home page” data: From the perspective of macro data . The question to think about is “How is the ctr of each module?” “How is the distribution capability of the home page?” From the perspective of interaction behavior . The thinking should be “Which part of the user? What behavior did it produce?”
Compare the user’s click action with the execution result data
“What attracts more users’ attention?” “For Bolivia WhatsApp Number List what purpose did the user click here?” 2. Infer user psychology through behavioral data In the event of the user using the app. From the perspective of the order of occurrence. It can be abstracted into: The user opens the app with a certain purpose (view/purchase. Etc.); Some actions were performed (browse/swipe/click. Etc.); Leave after reaching or failing to reach the goal. For example: “Our toilet is clogged. We have to find someone who can pass the toilet and hurry up.” according to the expectations in the product design. The user’s operation should be done in one go: The user opened the 58 daojia app with the purpose of “Finding a master to unclog the toilet”;
Looking at data from a behavioral perspective
Find the “Toilet clearance buy now” service. Select the Bolivia WhatsApp Number List item/confirm the price/fill in the address/fill in the door-to-door time. Etc.; Leave after placing the order and paying. After the user opens the app. The traces left by a series of operations to meet the purpose are called “User behavior data”. Assuming the development of the whole event is very smooth. Then the data such as page exposure pv&uv. Click pv&uv of each module. Browsing time and other data should correspond to each other. But when we sort out the real online behavior data. We found the user’s “Extraordinary” behavior . When the service module is selected and there are only 2 options.