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Data analysis and mining

Baidu MTC is the industry's leading mobile application testing Service platform, providing solutions to the cost, technology and efficiency issues faced by developers in mobile application testing. At the same time share the industry's leading Baidu technology, the author from Baidu employees and industry leaders and so on.

1. Overview

1.1 User Research Overview

The key to the success of mobile apps is marketing and product design, and the core of data analysis and mining solutions is customer orientation in the marketing process and improved user experience during product design. Providing targeted users with the products and services they need is the secret to success in any mobile app. And how to find the target customers, how to understand the user's product requirements, you need to rely on the power of data analysis and mining. Whether it's customer orientation or user experience, Jiede or user research, the success of mobile app products is no different from any other type of product.
User research can be carried out from qualitative analysis and quantitative analysis of two different dimensions: qualitative analysis is the method of discovering new things from small-scale data samples, which is mainly applied to user experience survey; Quantitative analysis is a method of testing and proving certain things with large data volumes, which is mainly applied to the analysis of user behavior data.

1.2 Data analysis and mining process specification

The construction of data analysis and mining system is different from the traditional business operation system, and has its own characteristics and rules. Data analysis and mining is an important part of database Knowledge Discovery (Kdd:knowledge-discovery in Databases), and KDD is the non-trivial process of identifying effective, novel, potentially useful, and ultimately understandable patterns from a data set.
The cross-industry data Mining standard process (Crisp-dm:cross-industry standards process for data mining) is the leading position in the KDD process model, with a throughput of nearly 60% A data analysis and mining process model, drafted jointly by EU agencies. The CRISP-DM consists of 6 different links, as shown in:
1. Business Understanding (Understanding):
The initial phase focuses on understanding the project objectives and understanding the requirements from a business perspective, while translating this knowledge into the definition of data mining issues and the initial plan for accomplishing the goals.
2. Data understanding:
The data understanding phase begins with the initial data collection, through the processing of some activities, to familiarize yourself with the data, to identify the quality of the data, to discover the internal properties of the data for the first time, or to probe the assumptions that generate the implied information from the subset of interest.
3. Data preparation (Preparation):
The data preparation phase includes all activities that construct the final dataset in data that has never been processed. This data will be the input value of the model tool. This phase of the task can be executed multiple times, without any prescribed order. Tasks include the selection of tables, records, and attributes, and the conversion and cleansing of data for model tools.
4. Data modeling (Modeling):
At this stage, different model techniques can be selected and applied, and the model parameters are adjusted to the optimal values. Generally, some techniques can solve the same kind of data mining problem. Some techniques have special requirements for data formation, so it is often necessary to jump back to the data preparation phase.
5. Model Evaluation (EVALUATION):
At this stage, you have developed a high-quality display model from the perspective of data analysis. Before you begin the final deployment of the model, it is important to thoroughly evaluate the model, examine the steps to construct the model, and ensure that the model can accomplish the business goals. The key objective of this phase is to determine whether there are important business issues that are not adequately considered. At the end of this phase, a decision on the use of data mining results must be achieved.
6. Model Release (Deployment):
Typically, the creation of a model is not the end of the project. The role of the model is to find knowledge from the data, and the acquired knowledge needs to be re-organized and presented in a way that is easy for users to use. Depending on the requirements, this phase can generate a simple report, or a more complex, repeatable data mining process. In many cases, this phase is performed by the customer, not the data analyst, to deploy the work.

2, User behavior data analysis

2.1 Goals

User behavior data refers to the interaction between user and mobile app, which is the quantitative analysis part of user's research dimension, and obtains information of user's usage information and user equipment and network environment of mobile app product by analyzing user's login and operation log.

2.2 Methods

User behavior Data acquisition is usually done in the form of data embedding, through the recording of user detailed operation log, to understand the user and product detailed interaction behavior, as well as the user access to mobile app device, network environment and other information. The traditional method of data burying requires the enterprise to develop its own information collection program and log processing program, realize the cost and development workload specific, if the compatibility platform difference at the same time, the cost will be greater, so it is not suitable for the new mobile app. The analysis of user behavior data can be carried out by the mature data statistical analysis platform.

2.3 Tools

Baidu Mobile Statistics platform is a Baidu company launched a professional mobile app statistics and analysis tools, support iOS and Android platform. Developers can easily embed the statistical SDK to achieve comprehensive monitoring of mobile applications, real-time mastery of product performance, accurate insight into user behavior.
The Baidu Mobile statistics platform provides powerful application statistical analysis capabilities for mobile apps, including:
1. Traffic Source: Channel traffic comparison, segmentation channel analysis, accurate monitoring of different promotional bit data, real-time access to channel contributions;
2. Audience Insight: Based on Baidu's massive data accumulation, multi-dimensional analysis and presentation of user portrait information;
3. Terminal Analysis: Equipment distribution at a glance (device model, brand, operating system, resolution, networking, operators, etc.);
Baidu Mobile Statistics function interface as shown:

2.4 Output

User behavior Data analysis results are user role portrait, build user's tag model, user tag data acquisition is mainly dependent on data mining algorithm, the structure of the label system for different industries, different businesses, different users, each has a different, need more professional industry user portrait model, do not do too much discussion. The user portrait output results are shown in the following example:

3. User Experience Data analysis

3.1 Goals

To be successful, a mobile app must provide a good user experience in addition to satisfying the user's functional needs. The user experience refers to how the product is connected to the outside world and works, i.e. how people "touch" and "use" the product. The user experience forms the user's overall impression of the enterprise or product, defines the difference between the enterprise or the product and the competitor, and determines whether or not the user will again patronize. A high-quality user experience is an important asset for your business or product, enabling you to increase your return on investment (ROI) and increase your conversion rate (conversion).

3.2 Methods

The premise of improving the user experience is to obtain the user experience data, the user experience data can use the traditional direct contact users to understand the user, but also through the Internet Mode remote offsite online research to understand the user, the two complement each other and complement each other. Direct contact user mode through user interviews and on-site investigation, communication is full, the effect is significant, but the target research object selection, communication costs and sample size are limited by time, capital investment. Internet remote on-line research model to achieve offline problems online, through online question and answer, can save costs, expand sample size, is a direct contact with the user model A useful supplement. The main characteristics of the two are as follows:

3.3 Tools

Baidu Common test Platform is the development of Baidu's crowdsourcing model in software and product testing on the extension and typical application, it will be related to enterprise product testing work to the community to complete the network, is a task crowdsourcing platform, that is, to serve Baidu's own products, but also face the public to provide services. The purpose of the platform is to use the public testing capabilities and testing resources, in a short period of time to complete a large workload of the product experience, and to ensure quality, the first time the experience results feedback to the platform, and then by the platform managers to collect information, to developers, so that from the user's point of view, improve product quality, Enhance the user experience.
Baidu testing platform mainly provides the following kinds of test tasks:

1. Quick Judgment task: it is generally a simple single choice, the user can quickly complete the judgment.
2. Questionnaire Survey Task: Users only need to complete the online survey to get the corresponding gift certificate reward;
3. Product Pick-up task: Experience a new product, submit a bug or suggest improvements to the product.
4. Special tasks: Businesses can set special tasks for specific purposes, such as the ongoing creative solicitation of Suntech's educational institutions.
5. Field research tasks: Research object recruitment projects, through the launch of field research tasks, recruit eligible research objects, participate in user site communication.

Baidu Testing Platform first page operation interface as shown:

For more dry foods, please pay attention to "Baidu MTC Academy" Http://mtc.baidu.com/academy/article

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