In the era of big data, a good data analysis tool is essential for better product operation:
Mob statistical analysis can help refine operations.
– Custom multi-dimensional cross event analysis – say goodbye to pulling data from engineers
– Semantic analysis of public opinion inside and outside the game — real-time insight into players’ mood changes
— Prediction with machine learning — predicting key events like churn, recharge, etc
Practical tools necessary for fine operation in the era of big data
Advantages of Mob statistical analysis
1. Independent deployment to ensure data privacy and security
2. Accurate user behavior analysis; Android, IOS, WAP, Web, server all buried point; Multi-dimensional cross analysis;
3. Make more in-depth analysis models based on vertical industries, such as RPG mobile games
Basic data analysis bids farewell to seek technical pull data from now on
Abstract game behavior data into a general data model to help operators efficiently complete basic data statistical analysis.
– Analyze basic indicators such as additions, activity, retention and revenue
– Create detailed target analytics users with custom groups, customize advanced analytics models such as level residency, ingot consumption distribution, etc
– Locate the cause of the problem and sort out the solution
During the National Day period, the users who bought VIP members by participating in A activities spent the yuan bao in what places?
① Select target analysis users according to conditions
Practical tools necessary for fine operation in the era of big data
② Create a custom analysis model
Practical tools necessary for fine operation in the era of big data
Through the customized model, target users can be screened according to the conditions and the consumption behavior track in the time period can be analyzed. Can evaluate the effect of operation activities, continue to optimize the follow-up activities, improve the activity conversion rate. Through the analysis of user behavior data, users’ behavior habits can be understood in more detail, the core needs of users can be mined, the data can drive the product iteration direction, and the iteration effect can be timely fed, and the experience can be constantly optimized.