3. Data analysis
The purpose of data analysis is to dig out the business email list information behind some seemingly disorganized data and extract the internal laws of the target object. For enterprises, the essence of data analysis is to create business value and drive business growth.
Four methods are mainly introduced here: AARRR model, funnel analysis, retention analysis, and channel analysis.
The AARRR model is the pirate model and a classic model for user analysis. It reflects growth throughout the various stages of the user life cycle, namely Acquisition, Activation, Retention, Revenue, and Referral.
Operators promote through various channels, acquire target users by various means, evaluate the effects of various marketing channels, and constantly adjust operational strategies to continuously reduce customer acquisition costs. For example, Baidu's SEO, app's aso, etc.
Key indicators: exposure, clicks, downloads, installations, activations, installation rate, activation rate, registration conversion rate, retention rate, payment rate, etc.
Activation refers to increasing the active level of users, mainly through novice rewards, promotions, content, product guidance, etc. to make users the most valuable active users. It is necessary to grasp the behavior data of users and monitor the health of the product.
Key indicators: proportion of new and old users, DAU/WAU/MAU, average daily login times, average daily usage time, etc.
Usually, the cost of maintaining an old user is much lower than the cost of acquiring a new user, so not only should you attract new users, but you should also pay attention to user stickiness, where and why users churn. And take corresponding measures to encourage these users to continue to use the application before they churn.
Key indicators: new user retention rate, old user retention rate, active user retention rate, daily, weekly and monthly retention rate, churn rate, etc.