Shulou(Shulou.com)06/03 Report--

preface

With the rapid development of mobile Internet market, the era of rough operation of "××× enclosure" has become a thing of the past. The change of the general environment also leads to the data statistical analysis of mobile terminal playing a more and more important role in product R & D, decision-making, operation and other aspects."Fine operation" has become a hot word for a time. From large factory to entrepreneurial team, whether it is self-built data statistical system or with the help of a third party, the market demand for simple, easy to use, stable and reliable data statistical scheme has never declined.

challenges

Product operators urgently need more detailed, multidimensional mobile data, and expect data to be presented in an intuitive and clear way. If you build your own application data statistics system, you will need the cooperation and assistance of many parties: developers need to make certain efforts in data acquisition, especially for statistical needs without buried points; data personnel need to undertake the arduous task of massive data analysis, some small teams lack data-related posts, and can only hand over this work to server-side students to complete, but the latter relatively lack experience and ability in big data analysis, and it is difficult to ensure the quality of analysis.

Therefore, I personally think that when the team's resources are limited, we can consider seeking professional third-party solutions, which can not only enable R & D students to not have to rack their brains for constantly changing data statistics requirements, but also enable product operation colleagues to extract silk from more professional data results.

data statistical analysis

In the past, mobile data mainly came from two mainstream system applications: iOS applications and Android applications; recently, the top ten manufacturers are vigorously promoting fast applications based on the Android platform, and fast applications are also rapidly developing, which is expected to become the third pole of the application market. Therefore, the data statistics work at this stage should cover three kinds of application statistics objects, namely: iOS application, Android application and fast application.

Currently, the mainstream mobile statistics SDK on the market only supports these three kinds of application statistics. Although different platforms have different ways of accessing the SDK, the objects of data analysis are the same. This article takes the access and use of the iOS SDK as an example to share the best practices of mobile data statistical analysis and some of my own thoughts.

The statistical analysis of mobile data is mainly divided into two parts, namely, data induction and visual display.

data statistics

The number of iOS SDK integration tutorials can be viewed: iOS SDK integration documentation, this article will not go into the specific integration process.

Mobile data can be divided into two parts:

One part is the basic data of the application, such as: new users of the application, active users, launch times, active duration, etc. Base data is often the most intuitive indication of the overall activity quality of an app, so accuracy is critical. This part of data can be automatically recorded and reported by SDK after integrating and starting SDK.

#import 'GTCountSDK.h'

#define kGcAppId @"xxxxxxx"

@implementation AppDelegate

(BOOL)application:(UIApplication )application didFinishLaunchingWithOptions:(NSDictionary )launchOptions {

//Start the SDK to automatically collect basic application data

[GTCountSDK startSDKWithAppId:kGcAppId withChannelId:@"appstore"];

return YES;

}x

The other part is refined page data, event data, and counting statistical events.

In the SDK, the no-buried-point scheme enables accurate statistics of pages. For applications that integrate the number SDK, the number will count the number of launches and active duration of related pages, effectively solving the pain points of traditional manual buried points and realizing the automation of the process.

Event statistics and counting statistics can calculate the occurrence time and number of user-defined buried points, such as advertising clicks, the number of text messages, etc., with high autonomy:

(1) Count the number of times a specified behavior is triggered.

(2) Duration statistics: statistics of the time consumed by the specified behavior, in seconds; eventBegin and eventEnd interfaces need to be used in pairs to take effect.

By calling the SDK API interface, developers can easily perform statistical work, such as burying points at the start and end of a certain piece of music with ID music001:

-(void) musicStart{

//For proper statistics, make sure that the parameter self.eventProperty memory address of the start and end interfaces is consistent.

self.eventProperty = @{@"key":@"value1"};

[GTCountSDK trackCustomKeyValueEventBegin:@"music001" withArgs:self.eventProperty];

}

(void) musicStop{

[GTCountSDK trackCustomKeyValueEventEnd:@"music001" withArgs:self.eventProperty];

}

Or count the number of clicks on a purchase button with an ID of goods001:

(IBAction) buyButtonClick:(id)sender {

[GTCountSDK trackCountEvent:@"goods001" withArgs:@{@"cKey1":@"cValue1"}];

}

After having the corresponding data, in order to cope with various problems caused by different network environments, it is also very important to improve the data cache and reporting mechanism, so we need to set a data reporting policy that conforms to the current network environment and optimizes the user experience. The SDK uses a variety of reporting strategies to suit various network environments:

The SDK data reporting policies include the following 5 types (default is GESHU_STRATEGY_PERIOD, cycle is 60 minutes):

Number Policy Name Policy Description 1GESHU_STRATEGY_REAL_TIME is sent in real time, and every message generated by the app will be sent to the server. 2GESHU_STRATEGY_WIFI_ONLY is only sent in wifi state, and cached locally in non-wifi case. 3GESHU_STRATEGY_BATCH is sent in batches. By default, it is sent once when the number of messages reaches 32. 4GESHU_STRATEGY_LAUNCH_ONLY is sent only at startup, all data generated this time is sent at next startup. 5GESHU_STRATEGY_PERIOD is sent at intervals and sent to the server once at intervals.

Considering that the cost of reporting data in the WIFI network environment is relatively small, the real-time reporting policy is used by default in the WIFI environment, that is, the intelligent reporting mode. To close this policy, you can call the following interface to close it:

/

Intelligent reporting

After the device is turned on, the WIFI will be reported in real time.

Otherwise, report according to the global policy

default open

*/

@property (nonatomic, assign)BOOL smartReporting;

**

It is recommended that you use the default combination mode of intelligent reporting + periodic reporting, that is, use the real-time reporting strategy in the WIFI environment and the periodic reporting strategy in the non-WIFI environment. This combination mode can report the consolidated data as much as possible in real time without consuming user traffic, so that the background can see the latest analysis results at the first time. Of course, users can selectively optimize the combination of data reporting strategies according to the characteristics of their own products to meet the actual data reporting needs.

data analysis and display

After obtaining the data, the next step is the most troublesome big data analysis part. However, by using the number SDK and the years of big data research and development experience accumulated behind it, the product operation students can now have a panoramic view of all the data analysis results of the application by opening the background of the number (students who want to Early Access can log in to the background to view the demo DEMO):

The number statistics part includes active statistics, component statistics, page statistics, channel statistics and event statistics. Such multi-dimensional refined data analysis and display effectively helps product operation save time and fully understand the actual operation of products.

summary

The mobile R & D practice part of this article uses the data analysis of iOS applications to illustrate, and other platforms can also refer to analogies. In general, products and operations can use the Quantity SDK to automate application base data and page statistics, and then use more autonomous custom timing and counting event burials based on the actual needs of the project. The selection of data reporting strategy is mainly determined according to specific scenarios. We suggest adopting the default combination mode of intelligent reporting + periodic reporting.

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