Characteristics, Classification and Development Trend of 1. Internet Advertising

Internet advertising refers to a form of advertising that delivers commercial information to the target audience through Internet media and platforms. Internet advertising has the following characteristics:

It covers a wide range of areas, time and space, reaching billions of Internet users around the world;

Interactive, can achieve two-way communication and feedback between advertisers and audiences, improve advertising effectiveness and user satisfaction;

High measurement, can be through a variety of data indicators, such as click-through rate, conversion rate, yield, etc., the advertising effect of accurate evaluation and optimization;

High customizability, according to the characteristics, behavior, interests and needs of the audience, personalized advertising and content customization, improve the pertinence and attractiveness of advertising;

It is innovative and can use the multimedia, multi-dimensional and multi-scene characteristics of the Internet to create a variety of novel, interesting and valuable advertising forms and content.

According to the media and form of Internet advertising, Internet advertising can be divided into the following categories:

Search engine advertising refers to the search engine platform, according to the user's search terms or web content, display and related advertising, such as Baidu, Google, Bing, etc;

Social media advertising refers to the display of advertisements related to the user's social network, social behavior, social content, etc. through social media platforms, such as WeChat, Weibo, Facebook, Twitter, etc;

Video website advertisements refer to the display of advertisements related to the user's viewing history, viewing preferences, video content, etc. through the video website platform, such as Youku, iQiyi, YouTube, Netflix, etc;

Portal advertising refers to the display of advertisements related to the user's visit frequency, visit duration, website content, etc. through the portal platform, such as Sina, Tencent, NetEase, MSN, etc;

Mobile application advertising refers to the display of related advertisements through the mobile application platform according to the user's usage scenario, usage habits, application functions, etc., such as Alipay, Taobao, Douyin, Instagram, etc;

Email advertising refers to the display of advertisements related to the user's email address, email content, email subject, etc. through the email platform, such as QQ mailbox, Gmail, Outlook, etc;

Pop-up advertisements refer to advertisements displayed in pop-up windows or pages through browsers or other software, such as 360 Security Guard, Thunderbolt, Sogou Input Method, etc.

The Internet advertising industry is a rapidly developing, highly competitive and changing field, and its development trend is mainly reflected in the following aspects:

Data-driven refers to the use of big data, artificial intelligence, cloud computing and other technologies to conduct data-based management and decision-making on the placement, analysis, optimization, and evaluation of Internet advertising to improve the efficiency and effectiveness of Internet advertising;

Content marketing refers to a marketing method to attract, influence and retain the target audience by creating, disseminating and sharing valuable content, so as to realize the promotion and sales of brands, products or services, and improve the quality and value of Internet advertising;

Native advertising refers to an advertising form that is integrated with the content, form and style of the media or platform, and does not appear abrupt or interfering, so as to improve the adaptability and affinity of Internet advertising;

Programmatic advertising refers to an advertising model that realizes the delivery, transaction, and settlement of Internet advertising through automated procedures to improve the efficiency and effectiveness of Internet advertising;

Cross-screen advertising refers to an advertising mode that realizes the delivery and display of Internet advertising through a variety of terminal devices, such as computers, mobile phones, tablets, televisions, etc., to improve the coverage and reach of Internet advertising.

The purpose, object, content and method of market research 2. the Internet advertising industry.

Market research is an activity that helps market decision-making through systematic, purposeful and planned collection, recording, analysis and interpretation of market-related information. The purpose of market research in the Internet advertising industry is mainly the following:

Understand the size, structure, characteristics, trends and changes of the market in order to formulate reasonable market strategies and plans;

Understand the strengths, weaknesses, strategies and behaviors of competitors in order to develop effective competitive strategies and responses;

Understand the needs, preferences, behavior, feedback and satisfaction of the target audience to develop appropriate advertising content and format;

Understand the factors, evaluation indicators, optimization methods and improvement programs of advertising effectiveness to improve advertising efficiency and effectiveness;

Understand the opportunities, threats, challenges and risks of the market to grasp the direction and potential of the market.

The Internet advertising industry's market research objects are mainly the following:

The market environment refers to the various external factors that affect the Internet advertising industry, such as politics, law, economy, society, culture, technology, nature, etc;

Market subjects refer to various internal factors involved in the Internet advertising industry, such as advertisers, advertising media, advertising agencies, advertising service providers, etc;

Marketing activities refer to the various operational processes and results of the Internet advertising industry, such as advertising, advertising transactions, advertising analysis, advertising evaluation, etc.

The contents of the market survey of the Internet advertising industry mainly include the following:

Market demand refers to the demand, demand structure, demand characteristics and demand changes of market subjects for Internet advertising;

Market supply refers to the supply volume, supply structure, supply characteristics and supply changes of Internet advertisements by market subjects;

Market competition refers to the competitiveness, competitive strategy, competitive behavior and competitive result among market subjects;

Market effect refers to the effect of Internet advertising on market subjects, such as brand awareness, product sales, customer loyalty, market share, etc;

Market problems refer to the problems faced by the Internet advertising industry, such as advertising fraud, advertising harassment, advertising supervision, advertising ethics, etc.

The methods of market research in the Internet advertising industry are mainly as follows:

Literature analysis refers to a method of obtaining market information by collecting, sorting and analyzing literature related to Internet advertising, such as books, periodicals, reports, papers, etc;

Observation method refers to a method of obtaining market information by directly or indirectly observing the placement, display, click and conversion of Internet advertisements;

The survey method is a method of obtaining market information by designing, issuing, recycling, analyzing questionnaires or conducting interviews, seminars, group discussions, etc;

The experimental method refers to a method of observing, measuring and comparing the effects of advertisements by setting, controlling and changing the conditions, contents and forms of Internet advertisements;

Case analysis refers to a method of obtaining market information by selecting, describing and analyzing typical or successful cases of Internet advertising.

Market Research Techniques and Tools for 3. Internet Advertising Industry

Market research technologies and tools in the Internet advertising industry refer to the various technologies and tools used to collect, analyze, present and apply market information, including data collection, data analysis, data visualization and data application, as well as the advantages, limitations and development direction of these technologies and tools.

3.1 data collection

Data collection refers to the process of obtaining data related to Internet advertising through various channels and methods, such as user data, advertising data, media data, competition data, etc. Data collection techniques and tools are mainly the following:

Web crawler refers to a technology that automatically grabs data from the Internet by writing programs, such as Scrapy, BeautifulSoup, Selenium, etc;

Network analysis refers to a technology that obtains data by analyzing Internet protocols, traffic, packets, etc., such as Wireshark, Fiddler, Charles, etc;

Network survey refers to a technology to obtain data by publishing questionnaires on the Internet or conducting online interviews, such as questionnaire star, Tencent questionnaire, SurveyMonkey, etc;

Network monitoring refers to a technology that obtains data by installing monitoring codes on the Internet or using monitoring software, such as GoogleAnalytics, Baidu Statistics, Youmeng, etc;

A network interface refers to a technology that obtains data by calling a data interface or API provided on the Internet, such as Baidu Index, Weibo API, FacebookAPI, etc.

The advantages of data collection techniques and tools are as follows:

Large amount of data, can obtain a large amount of Internet advertising data, improve the representativeness and credibility of the data;

Real-time data can obtain real-time updated Internet advertising data to improve the timeliness and sensitivity of the data;

Data diversity, can obtain a variety of types and formats of Internet advertising data, improve the richness and dimension of data;

The data is convenient, and the Internet advertising data can be obtained through simple operation and setting, so as to improve the efficiency and cost of data acquisition.

The limitations of data collection techniques and tools are the following:

Data quality, there may be inaccurate, incomplete, inconsistent, unclean data, etc., affecting the quality and availability of data;

Data security, there may be data leakage, tampering, theft, abuse and other issues, affecting the security and legitimacy of the data;

Data privacy, there may be data infringement, exposure, use, harm and other issues, affecting the privacy and ethics of data;

Data analysis, there may be too much data, too complex, too difficult and other issues, affecting the analysis and understanding of the data.

The development direction of data collection technology and tools mainly includes the following:

Data intelligence refers to the use of artificial intelligence, machine learning, deep learning and other technologies to improve the intelligence and automation of data collection, such as intelligent crawlers, intelligent analysis, intelligent monitoring, etc;

Data fusion refers to the use of data mining, data warehouse, data integration and other technologies to improve the degree of integration and integration of data collection, such as data fusion, data cleaning, data standardization, etc;

Data sharing refers to the use of data openness, data exchange, data collaboration and other technologies to improve the degree of sharing and collaboration of data collection, such as data platforms, data markets, data alliances, etc;

Data protection refers to the use of data encryption, data authorization, data audit and other technologies to improve the protection and standardization of data collection, such as data security, data privacy, data ethics, etc.

3.2 data analysis

Data analysis refers to the process of processing, processing, analyzing and interpreting the collected data by using statistics, mathematics, computer and other knowledge and methods to discover the laws, characteristics, relationships and values of the data. There are several main techniques and tools for data analysis:

Statistical analysis refers to a technique of describing, inferring, testing and modeling data by using descriptive statistics, inferential statistics, multivariate statistics, etc., such as SPSS, SAS, R, etc;

Data mining, refers to the use of classification, clustering, association, prediction and other methods, data mining, discovery, extraction and utilization of a technology, such as WEKA, KNIME, RapidMiner, etc;

Machine learning refers to a technology that learns, identifies, optimizes, and applies data through the use of supervised learning, unsupervised learning, and reinforcement learning, such as TensorFlow, PyTorch, and Scikitlearn;

Deep learning refers to a technology that learns, understands, expresses, and generates data in depth by using neural networks, convolutional networks, and circular networks, such as Keras, Caffe, Theano, etc;

Natural language processing refers to a technology that processes, understands, generates and interacts with data by using word segmentation, part-of-speech tagging, named entity recognition, sentiment analysis, text generation, etc., such as NLTK, Gensim, BERT, etc.

The advantages of data analysis techniques and tools are as follows:

Data insight, you can find valuable information from the data, such as laws, characteristics, relationships, values, etc., to improve data insight and creativity;

Data support, which can be used to support decision-making, strategy, action, evaluation, etc., to improve the support and persuasiveness of data;

Data optimization, data can be used to optimize advertising content, form, delivery, effect, etc., to improve the optimization and improvement of data;

Data innovation, through data to innovate advertising form, content, mode, value, etc., to improve the innovation and leadership of data.

The limitations of data analysis techniques and tools are the following:

The data is complex, and there may be problems such as high-dimensional, nonlinear, unstructured, and unbalanced data, which increase the complexity and difficulty of the data;

Data uncertainty, there may be data noise, anomalies, missing, changes and other issues, affecting the certainty and stability of the data;

Incomplete data, there may be insufficient data, incomplete, not new and other issues, affecting the completeness and validity of the data;

The data is not interpretable, and there may be problems such as black box, chaos and fuzziness of the data, which affect the interpretability and comprehensibility of the data.

The development direction of data analysis technology and tools mainly has the following:

Data fusion refers to the use of data mining, data warehouse, data integration and other technologies to improve the degree of integration and integration of data analysis, such as data fusion, data cleaning, data standardization, etc;

Data visualization refers to the use of graphics, charts, animation, interaction and other technologies to improve the visualization and presentation of data analysis, such as Tableau, PowerBI, ECharts, etc;

Data intelligence refers to the use of artificial intelligence, machine learning, deep learning and other technologies to improve the intelligence and automation of data analysis, such as intelligent analysis, intelligent prediction, intelligent optimization, etc;

Data application refers to the use of the results and value of data analysis to improve the application and transformation of data analysis, such as data-driven, data marketing, data innovation and so on.

3.3 Data Visualization

Data visualization refers to the process of presenting data to users in an intuitive, beautiful and effective way through the use of graphics, charts, animation, interaction and other technologies to enhance data understanding, communication and decision-making. There are several main techniques and tools for data visualization:

Graphic design refers to a technology that designs the visual effect of data by using elements such as color, shape, font, layout, etc., such as Photoshop, Illustrator, Sketch, etc;

Chart making refers to a technique for making graphical representations of data by using columns, line charts, pie charts, scatter charts and other types, such as Excel, Numbers, GoogleSheets, etc;

Animation production refers to a technology that produces dynamic display of data through the use of dynamic, sound, video and other elements, such as Flash, AfterEffects, Premiere, etc;

Interactive design refers to a technology that designs the interactive function of data through the use of buttons, menus, sliders and other components, such as Axure, Balsamiq, Sketch, etc;

A visualization platform is a tool that enables one-stop visualization of data by providing functions such as data import, analysis, visualization, and sharing, such as Tableau, PowerBI, and ECharts.

The advantages of data visualization techniques and tools are as follows:

The data is intuitive, and the data can be displayed in the form of graphs, charts, animations, etc. to improve the intuitiveness and perception of the data;

The data is beautiful, and the data can be decorated with elements such as color, shape and font to improve the aesthetics and attractiveness of the data;

The data is valid, and the data can be presented in an appropriate, clear and prominent way to improve the validity and importance of the data;

Data interaction can enhance the data with dynamic, sound, video and other elements to improve the interactivity and interest of the data.

The limitations of data visualization techniques and tools are the following:

Data distortion, there may be data deformation, distortion, misleading and other issues, affecting the authenticity and accuracy of the data;

Data overload, there may be too much data, too mixed, too dense and other issues, affecting the clarity and legibility of data;

Single data, there may be a lack of data, monotonous, boring and other issues, affecting the richness and diversity of data;

Data isolation may cause data isolation, isolation, and non-sharing, which affects data connectivity and collaboration.

The development direction of data visualization technology and tools mainly includes the following:

Data stories refer to the use of data visualization technologies and tools to present data in the form of stories to improve the storytelling and emotionality of data, such as data news, data cartoons, and data videos;

Data exploration refers to the use of data visualization technologies and tools to present data in an exploratory manner to improve the exploratory and discoverability of data, such as data dashboards, data maps, and data sandboxes;

Data communication refers to the use of data visualization technologies and tools to present data in a communicative manner to improve data communication and sharing, such as data reports, data presentations, and data communities;

Data intelligence refers to the use of data visualization technologies and tools to present data in an intelligent way to improve the intelligence and automation of data, such as data recommend, data prediction, and data optimization.

3.4 data application

Data application refers to the process of realizing the application and transformation of data by using the results and value of data analysis to improve the utilization, creation and value of data. The main technologies and tools for data applications are as follows:

Data-driven refers to a technology that drives decision-making, strategy, action, evaluation, etc. by using the results and value of data analysis, such as data-driven decision-making, data-driven marketing, data-driven innovation, etc;

Data marketing refers to a technology that optimizes and improves the content, form, delivery and effect of advertising through the use of the results and value of data analysis, such as the planning of data marketing, the execution of data marketing, and the evaluation of data marketing;

Data innovation refers to the innovation and leading technology of advertising form, content, mode and value through the use of the results and value of data analysis, such as the design of data innovation, the development of data innovation, the promotion of data innovation, etc;

Data services refer to the provision of data services, the use of data services, and the improvement of data services through the use of data services.

The advantages of data application technologies and tools are as follows:

Data utilization can transform the results and value of data analysis into practical applications and benefits, and improve the utilization rate and rate of return of data;

Data creation, the results and value of data analysis can be used to create new products and services, and improve the creativity and competitiveness of data;

Data value, the results and value of data analysis can increase the value of advertising brands, products or services, and improve the value and influence of data;

The data society can analyze the results and value of data, promote the progress and development of society, and improve the sociality and responsibility of data.

The limitations of the technologies and tools used in data applications are as follows:

Data dependence, which may have problems such as unreliable, unstable, and non-updated data, affecting the dependence and sustainability of data applications;

Data difficulty, there may be data complexity, difficult to understand, difficult to operate and other issues, affecting the difficulty and threshold of data application;

Data risk, there may be data leakage, abuse, misuse and other issues, affecting the risks and consequences of data application;

Data ethics may have problems such as unfair, unreasonable, and immoral data, which affect the ethics and legitimacy of data applications.

The development direction of data application technology and tools mainly includes the following:

Data collaboration, refers to the use of data application technology and tools, to achieve data collaboration and sharing of a way, such as data collaboration platform, data collaboration model, data collaboration specifications;

Data education refers to a way to realize data education and training by using data application technology and tools, such as data education curriculum, data education method, data education evaluation, etc;

Data entrepreneurship refers to a way to realize data entrepreneurship and innovation by using data application technologies and tools, such as data entrepreneurship projects, data entrepreneurship teams, data entrepreneurship support, etc;

Data public welfare refers to a way to realize the public welfare and contribution of data by using the technology and tools of data application, such as the goal of data public welfare, the activities of data public welfare, and the effect of data public welfare.

4. Conclusion

The Internet advertising industry is a fast-growing, highly competitive and changing field, and the demand and challenges for market research are also increasing. Starting with the characteristics, classification and development trend of Internet advertising, this paper analyzes the purpose, object, content and method of market research in Internet advertising industry, and focuses on the market research techniques and tools of Internet advertising industry, including data collection, data analysis, data visualization and data application, as well as the advantages, limitations and development direction of these techniques and tools. This paper also shows the practical application and value of market research technology and tools in the Internet advertising industry, combined with the specific case of market research services provided by Shangpu Consulting Company for Internet advertisers.

The purpose of this paper is to provide a reference and guidance for the market research of the Internet advertising industry, hoping to help the practitioners and researchers of Internet advertising, make better use of market research techniques and tools, improve the efficiency and effectiveness of Internet advertising, and realize the development and innovation of Internet advertising.