In an era when user stock is king, as an operation official, only by truly understanding the user's KPIs can we avoid being eliminated. At this time, data analysis has become our Sunflower Book. If you practice well, you can plan "star" content with both export monuments and traffic. But there are thousands of treasure books, which ones are what I really need? How should we use the data correctly?
With so much data, what should I do?
User data is massive, and it is unrealistic to analyze them all, so it needs to be classified from different dimensions of the data. In my opinion, it can be divided into two categories: basic data and personalized data.
Basic data is data that every APP needs to clearly understand when operating, such as the male-female ratio of users, age components, user activity, etc. This data is the basis for the operation work. If you don’t understand this data yet, please stop the work at hand and do a new employee training again.
Personalized data is targeted data, and is labeled and extracted and filtered according to different user scenarios or operation needs. Take the daily activities and operations of APP users as an example:
During early planning, the user's group portrait can guide the planning direction of the activity, and the user's needs determine the goal of the activity; by understanding the user's interests, determine the content and display method of the activity; by understanding the consistency of user behavior, determine the time node for the activity promotion.
During operation, through detailed event statistics and custom burial points, users' behavior in the event will be further analyzed, the data conversion situation of each link of the event will be understood, and activities will be optimized and activities will be adjusted based on data feedback.
At the end of the event, you can analyze the user's new, active, retention, and even uninstallation to evaluate the effect of the entire event, providing valuable data comparison reference for the next event.
Therefore, as refined operations become more and more important, the statistics, analysis and application of personalized data are the core capabilities of data operations and will also become the key to operational success.
Operations are long-lasting, how can we grasp the fickle hearts of users?
Users are fickle, we don’t know what they want, how can we expect to be with users forever. Data reflects the results of a single dimension. How to combine these data into a real portrait of the user, analyze it in a comprehensive manner, and truly understand the user and understand the user, it will test the operational students' ability to apply data.
– User data requires a multi-dimensional combination (the picture is from the Internet)-
First, the data that constitutes the user's portrait can be divided into attribute data, behavior data and scene data.
Attribute data reflects the objective attributes of the user, that is, data that will not change for a long time, such as gender, age, consumption level, etc.
Behavioral data reflects the user's recent behavior, such as the application that the user likes recently and the scenes he has been to recently.
Scene data reflects the scenes the user is in real time. By using LBS geofencing technology, the user's current scenario is determined based on the user's geographical location.
These three big data can be used organically to form hundreds of user tags, truly concretely making users' faces unique and convenient for operators to do refined user operations. Here I recommend the "Personal Image" user analysis tool I often use. Personal images can help me analyze users' online and offline behavior data, and form a very complete and accurate user portrait through dozens of attribute labels and hundreds of hobby labels of the "Electronic Photo" platform.
- "Personal Image" user tag system –
These rich user tags can help me find the target user group more accurately. For example, when promoting movies, accurate data operations are very helpful for distribution strategies. Users who like to watch "Kang Rinpozi" will have certain common characteristics, such as heavy users of movie APPs who like to write movie reviews or prefer to use literary and youth APPs. Then we can use data analysis to mine these young users and interact with them, and drive a larger audience market through them.
The concept we want to focus on here is the user's recent behavior data. It can reflect the user's growth cycle, the transfer of user's interest points, etc., and is particularly important for content operation. For example, travel APPs can use users’ recent behavioral data to understand the travel scenarios they have been to avoid repeated recommendations; understand users’ recent behavioral preferences, and recommend suitable travel content from the perspective of users’ interests.
There is no harm without comparison, let the data tell the truth?
Mining data connotation is a technical job. For operations, the most basic data analysis is data comparison, and only with comparison can there be true phenomena. For operators, there are two types of data that need to be carefully analyzed: one is the APP own data, that is, the data generated by users when using the APP, such as browsing data on pages within the APP, consumption data, etc.; the other is the APP external data, such as industry public data, research data, etc.
In the analysis of APP's own data, we can make "fancy" comparisons by adding time points, ring nodes, and comparing data.
Taking marketing activities as an example, not only should we look at the final sales data, but we also need to bury points in the entire marketing link to count the conversion situation of each link. For example, the opening of the marketing activity page, click on the product introduction page, click on the adding shopping cart situation, etc. There will be conversions and losses in every link of the entire marketing activity, but in which link the user loses the most is the key to the operators really need to ask about it.
– Pay attention to event process and conversion at various buying points –
It is difficult for many companies to do external data to independently perform comparisons. They often lack large-scale data coverage and industry trend comparisons. At this time, it is necessary to use the help of third-party data service providers.
It is understood that some third-party big data service providers, now at the top of the industry, can help enterprises conduct more comprehensive data analysis through the massive data accumulated over the years and their strong data analysis capabilities. Two days ago, I planted another product to promote the application data statistics analysis. What attracts me most is that it can provide unique data analysis services such as industry comparison and uninstallation analysis, which is very valuable for optimizing operations.
Industry comparison index can help operators understand the overall development of the market, the industry competitiveness of the APP, and the development stage of their own APP, and it can guide operators' decisions.
The application scenarios for uninstalling user analysis are more targeted: 1. Comparison of customer acquisition and churn data to assist in determining the product's life cycle; 2. Analyze the uninstall rate of users from various sources and optimize the advertising delivery strategy; 3. Combined with custom buried points to deeply explore the uninstallation characteristics and analyze the reasons for uninstallation; 4. During the event, analyze the uninstallation data in association and evaluate the degree of negative impact of the activity on users.
– “Number” uninstall user flow display –
Fully interpreting data and mining the value behind the data can provide more objective feedback for operational work and effectively avoid artificial cognitive bias.
To sum up, under the trend of refined operations, we increasingly need to "recognize" the users' original appearance, and the rational and effective use of data has become a skill that must be obtained and upgraded. Only by using the right method can we have a deeper understanding of users and provide new ideas for operational work.
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