Two days ago, I saw an interesting big data report — “The time spent by” otaku “learning apps has nearly doubled, are they really not motivated? . This data report actually gave me an Epiphany.
It was originally thought that “otakus” were heavy users of games and video apps, but it was unexpected that they would spend a lot of time on learning and fitness apps. In particular, learning apps not only lead the increase in the use time, but also rank top three in the total use time. The results were so different from what you’d expect from a report like this that I started to wonder if the user base I was operating with had changed.
In the era when user inventory is the king, as an operation dog, only by really understanding users to keep pace with The Times and complete KPI, can we not be led by the dog. At this time data analysis has become our sunflower treasure book, good practice will be able to plan the export monument and flow double harvest “star” content. But which ones do I really need? How do you use the data correctly?
What do you want with all that data?
There is a huge amount of user data, and it is impractical to analyze all of them. Therefore, it needs to be classified from different dimensions of data, which in my opinion can be divided into basic data and personalized data.
Basic data is the data that every APP operation needs to know clearly, such as the ratio of male and female users, age composition, user activity, etc. If you don’t already know these numbers, stop what you’re doing and start all over again.
Personalized data is targeted data, which is extracted and screened based on different user scenarios or operation needs. Take the daily operation of APP users as an example:
In the preliminary planning, the user’s group portrait can guide the planning direction of the activity, and the user’s demand determines the goal of the activity. Through understanding the user’s interest, to determine the content of the activity and the way of presentation; By understanding the consistency of user behavior, we can determine the timing of the campaign promotion.
In operation, through detailed event statistics and customized burying points, users’ behaviors in activities are further analyzed to understand the data transformation in all aspects of the whole activity, and then activity optimization and activity input adjustment are carried out according to the feedback of data.
At the end of the activity, the effect of the whole activity can be evaluated by analyzing the new, active, retained and even unloaded users, providing valuable data for the next activity.
Therefore, as refined operations become more and more important, statistics, analysis and application of personalized data are the core capabilities of data operations, and will also become the key to the success of operations.
Operation is a long love, how to seize the fickle heart of users?
Users are fickle and we don’t know what they want, so how can we expect to stay with them forever? Data reflects the results of a single dimension. How to combine these data into real portraits of users, analyze them in a integrated way, and really understand users and read users will test the ability of operation students to apply data.
– User data needs multi-dimensional combination (picture from network) –
First of all, the data that constitute the user portrait can be divided into attribute data, behavior data and scene data.
Attribute data reflect the objective attributes of users, that is, data that will not change in a long period of time, such as gender, age, consumption level, etc.
Behavioral data reflect users’ recent behaviors, such as the applications they like and the scenes they have been to.
Scenario data reflects the real-time scenario of the user. Through the use of LBS geographical fence technology, combined with the user’s geographical location to determine the user’s current scene.
The combination of these three data can form hundreds of user labels, which can really visualize the thousands of users and facilitate operators to do fine user operations. Here I recommend the user analysis tool “like”. The image can help me analyze the online and offline behavior data of users, and form a very complete and accurate user portrait through dozens of attribute tags and hundreds of interest and hobby tags of the platform.
– the user tag system of “The image” –
These rich user tags can help me find the target user group more accurately. For example, when it comes to film marketing, accurate data operations can be very helpful to distribution strategy. Users who like Watching Kailash have some common characteristics, such as heavy users of movie apps like to write movie reviews or prefer to use wenqing apps. Then we can explore these wenqing users through data analysis and interact with them to drive a larger audience market.
The concept we want to highlight here is the user’s recent behavior data. It can reflect the growth cycle of users, the transfer of users’ interest points, etc., which is particularly important for content operation. For example, tourism apps can learn about the recent travel scenes of users through their recent behavioral data to avoid repeated recommendation. Understand users’ recent behavior preferences and recommend suitable travel content from the perspective of users’ interest.
No comparison, no harm. Let the data speak the truth?
Data connotation mining is a technical work. For operations, the most basic data analysis is data comparison, shang hai. For operators, there are two kinds of data that need to be carefully analyzed: one is the APP’s own data, that is, the data generated by users when using the APP, such as browsing data and consumption data on the page inside the APP; The other is external data of APP, such as industry open data and research data.
In the analysis of APP’s own data, we can make “fancy” comparison by adding time points, link points, comparison data and other methods.
Take marketing activities as an example, not only to see the final sales data, but also need to be buried in the whole link of marketing, statistics of the transformation of each link. For example, the marketing campaign page is opened, click the product introduction page, click add shopping cart, etc. There will be transformation and loss in each link of the whole marketing activity, but in which link users lose the most is the key that operators really need to ask.
– Pay attention to the process and transformation of events in each link
Comparison and analysis of external data is difficult for many enterprises to do independently, because they often lack mass data coverage and comparison of industry trends. At this time, it is necessary to rely on the help of third-party data service providers.
It is understood that some third-party big data service providers at the head of the industry can help enterprises to conduct more comprehensive data analysis through the accumulation of massive data and powerful data analysis capabilities for many years. Two days ago, I also planted a push of application data statistical analysis of the product “number”. What attracts me most is that It can provide industry comparison, unloading analysis and other unique data analysis services, which are very valuable for optimizing operation.
The industry comparison index can help operators understand the overall development of the market, the industry competitiveness of APP, and the development stage of their own APP, which can guide operators in making decisions.
The application scenarios of unloading user analysis are more targeted: 1. It can compare customer acquisition and loss data to help determine the life cycle of products; 2. 2. Analyzed the unloading rate of users from various sources and optimized advertising strategies; 3. Analyze the unloading reasons based on the user characteristics of user-defined buried point deep mining; 4. During the activity, associated analysis was performed on unloading data to evaluate the negative impact of the activity on users.
– Indicates the number of uninstalled users
Fully interpreting data and mining the value behind data can provide more objective feedback for operation and effectively avoid artificial cognitive bias.
To sum up, in the trend of refined operations, we increasingly need to “see” users as they are, and the rational and effective use of data has become a skill that must be acquired and upgraded. Only with the right approach can we learn more about our users and provide new insights into our operations.