In the realm of big data utilization, we often romanticize its profound impact, envisioning scenarios like precision-targeted advertising, streamlined social security management, and the intelligent evolution of the pharmaceutical sector. While these aspirations are undeniably shaped by the influence of data analysis, it prompts us to pause and ponder a crucial question: how do we concretely bring big data to fruition and analyze it effectively?

This contemplation is paramount in the realm of data analysis reporting, where the practical application of big data takes center stage. Delving into the intricacies of generating insights and facilitating informed decision-making for enterprises, such as achieving precision in advertising placement, is at the heart of this discourse. As we explore examples of data analysis reports and interactive report data analysis dashboards, we embark on a journey to unravel the nuanced art of transforming raw data into meaningful narratives that empower decision-makers.

Note: All the data analysis reports in this article are created using the FineReport reporting tool. Leveraging the advanced enterprise-level web reporting tool capabilities of FineReport, we empower businesses to achieve genuine data transformation.

1. What is the Data Analysis Report

According to McKinsey, big data encompasses a collection of datasets exhibiting characteristics that far exceed the capacities of conventional database reporting tools in terms of acquisition, storage, management, and analysis. These datasets are distinguished by their extensive volume, swift data flow, various data types, and low-value density—crucial factors for generating comprehensive data analysis reports.

Based on my understanding of this definition, I summarize big data analysis as the process of acquiring data, breaking down silos, integrating information, identifying patterns, and promptly deriving actionable insights for decision-making. These insights are then communicated through data analysis reporting, exemplified by detailed data analysis reports and interactive data analysis dashboards.

1.1 Data Acquisition

I categorize data sources into three types:

(1) First-party data: User factual data, such as financial products purchased at a certain institution, time of purchase, issuing branch, name, phone number, or operational data, such as user behavioral data on a financial app.

(2) Second-party data: Often referred to as advertising delivery data, including metrics like ad impressions, page clicks, and ad sources. Some companies consider this as third-party data when integrated with audience data, leading to the creation of their own DMP. These companies are generally termed third-party entities.

(3) Third-party data: Industry data, also known as public data, such as association data or internet behavioral data. For instance, data on user behavior on a website from a certain internet company or the installation and active user list collected from reporting apps embedding SDKs, along with offline data.

1.2 Integration

Integration involves collecting and consolidating first, second, and third-party data using key points. For example, we can integrate first and third-party data using phone numbers or utilize cookies or IMEI numbers to integrate second and third-party data. However, due to regulatory controls on sensitive data like phone numbers and technical challenges in cross-platform integration of Internet and mobile reporting data, our current matching rates are relatively low, reaching around 20% in ideal scenarios, excluding telecom data.

1.3 Pattern Recognition

The goal is data cleansing, transforming unstructured data into structured data for statistical purposes, data exploration, pattern recognition, and the formulation of viewpoints for data analysis reports. This will be elaborated on in the third part of this article.

1.4 Immediate Decision-Making

Systematizing or productizing the viewpoints from data analysis reports is crucial for immediate decision-making. Currently, most companies still rely on manual decision-making.

1.5 Why Big Data Analysis Report?

While big data analysis seems to follow these steps, starting from data sources, it inherently reflects the disorderly nature of big data. Therefore, the need for big data analysis arises from the challenge of deciphering patterns from such chaotic data and ensuring alignment between analysis content and objectives.

Today, big data analysis often utilizes data analysis reports to reflect on enterprise operations. These reports distill viewpoints for guiding operations based on statistics, hotspots, and audience analysis. The central question now is how to use data analysis to guide decision-making effectively.

2. Approach to the Data Analysis Report

In my comprehension of data analysis, I categorize data reports into three main types: market analysis, operational analysis, and user behavior analysis.

Starting with a mobile-centric viewpoint, the structure of data analysis reports unfolds across three broad categories: market analysis, operational analysis, and user behavior analysis. These classifications serve as the foundation for the subsequent exploration of data analysis reports, reporting practices, and the integration of insights into data analysis dashboards. To illustrate, let’s delve into specific examples within each category.

2.1 Market Analysis

Conducting market analysis, a combination of qualitative and quantitative assessments is a common practice among consulting firms specializing in data analysis reporting. These firms often employ interviews and surveys to compile comprehensive reports, such as data analysis reports, shedding light on market share and consumer perspectives.

In the context of mobile internet data, the data source typically involves public or third-party data, forming a critical foundation for subsequent data analysis reports. Embedding SDKs into developer applications allows the collection of installation and usage data. This process results in valuable insights, generating rankings for installed apps and usage metrics, all of which contribute to a robust data analysis reporting framework.

Market analysis serves the pivotal role of summarizing a company’s marketing endeavors. For example, a financial company focused on customer acquisition conducts marketing activities, and subsequent months’ installation numbers for their app become crucial data points for data analysis reports. Competitor performance is also scrutinized to observe ranking fluctuations linked to marketing efforts. However, due to the nature of market data, extensive searches on official websites or internet advertising are often required to speculate on competitors’ rankings and their correlation with marketing activities. These valuable insights gleaned from market analysis become instrumental in constructing meaningful data analysis reports and developing insightful data analysis dashboards.

2.2 Operational Analysis

The methodology proposed by operational analysis is 2A3R. In my work, I found this methodology applicable to website analysis as well. In essence, 2A3R can be summarized as follows:

Acquisition → Activation →Retention → Revenue → Referral

It’s essential to note that operational analysis serves as a baseline for companies. It enables product managers, operations personnel, and marketing teams to make informed decisions based on their company’s data. Operational data serves as a reference or warning, and a detailed analysis is necessary for more specific insights. For example, questions about app redesign, how to implement changes, or which channels to collaborate with require a more nuanced analysis tailored to the company’s unique circumstances. This kind of detailed analysis forms the foundation for robust data analysis reports and informs the development of insightful data analysis dashboards.

2.2.1 Awareness

Analyzing advertising data serves the purpose of assessing the inflow generated by an app or website channels. Simultaneously, it aids advertisers in designing monitoring tables to measure the effectiveness of their advertising campaigns quantitatively.

However, advertising data is typically held by advertising monitoring companies or publicly available tools such as GA. To utilize this data effectively, we rely on advertising companies to design marketing processes, like campaign pages, and incorporate monitoring codes. This may involve adding codes in media outlets or app stores to facilitate ad performance tracking. However, accessing such data can be challenging, usually provided by app stores or media outlets. Furthermore, monitoring companies often offer only aggregated statistical values, withholding detailed data from advertisers. I will delve into a detailed analysis of this aspect in subsequent discussions; stay tuned for updates on my operational insights.

To bring the focus back, our objective in analyzing awareness data is essentially to evaluate the effectiveness of our substantial marketing expenditures. Metrics like ad impressions and clicks serve as key indicators of a company’s advertising department performance. Without effective ad campaigns, customer acquisition is hindered. Assessing the value of money spent and understanding how many customers it can attract sets the stage for the subsequent step – acquisition. This insightful analysis forms the basis for comprehensive data analysis reports and the development of data analysis dashboards.

2.2.2 Acquisition

Customer acquisition marks the initial phase of expanding advertising efforts, where users click on ads and proceed to download the app upon reaching the app store or landing page. The data collected after visiting the webpage or logging into the app is not typically provided by advertising companies or app stores, creating a dual purpose for customer acquisition.

• Purpose 1: Evaluate the accuracy of the initial data provided, specifically to identify potential channel fraud.
• Purpose 2: Assess the quality of the acquisition channel.
• Purpose 3: Determine the effectiveness of marketing campaigns.

In the case of media referral channels, we measure customer conversion rates, specifically the transition from clicks to user activation and subsequently from activation to registration. Insights from this analysis can guide focused collaborations with specific app stores. Such meticulous assessment contributes to the formulation of data analysis reports, exemplified by the detailed data analysis report example in the figure, and informs the development of strategic data analysis dashboards.

2.2.3 Activities

Post-customer acquisition, our attention turns to evaluating the performance of our newly acquired and active users, marking the third phase: Activities. This phase essentially provides data support for product managers when redesigning the app or webpage.

Active user analysis involves the following three steps:

First: Define key pages for analysis based on page views and unique visitor numbers. For example, if the homepage of a certain app has the highest page views and unique visitors, we prioritize analyzing the homepage.

Second: Create a click heatmap for the identified pages to aid product managers in subsequent page redesigns. For instance, by identifying and eliminating less-clicked buttons in the next redesign and reordering highly-clicked elements.

Third: Generate a click heatmap for the identified pages to support product managers in subsequent page redesigns. For example, we can delete less-clicked buttons in the next redesign and reorder highly-clicked elements. These meticulous analyses contribute to the creation of insightful data analysis reports, exemplified by a detailed data analysis report example, and guide the development of strategic data analysis dashboards.

2.2.4 Retention, Revenue, and Referral

These aspects are not extensively utilized in corporate practices, and a brief overview follows.

① Retention

After accumulating a certain number of users, we examine user stickiness through retention analysis. This is commonly employed when evaluating the effectiveness of activities to determine if users continue using our app after the event. However, given the nature of financial apps, which do not see daily usage like gaming applications, retention is not heavily emphasized in practical applications.

② Revenue

How much revenue do these retained customers contribute to the company? We assess revenue streams. Generally, companies do not include cash flow data in statistical platforms, but we need to present data on user-contributed transaction amounts for segmentation purposes.

③ Referral

Finally, we aim to encourage these customers to spread the word. The core is word-of-mouth marketing, where users spontaneously share links with other users, encouraging them to download the app or participate in activities. The next stage of referral then transitions back to marketing. However, referral faces numerous constraints, such as the lack of a reward mechanism resulting in minimal sharing. Additionally, measuring referral impact proves challenging, especially with a large user base on the internet, leading to resource code overlay and system burdens. Consequently, companies typically avoid designing such activities for marketing personnel reference. These considerations and analyses contribute to the formulation of comprehensive data analysis reports and the development of data analysis dashboards, enhancing decision-making processes.

2.3 User Analysis

If we delve into the core of big data analysis, it lies in user analysis. As mentioned earlier, the procedural steps for user analysis are as follows:

Within the feasible scope of data collection, the key is to integrate data, identify customer profiles, and enable precise marketing.

Firstly, we establish a list of filtering criteria. By applying conditions such as app usage, location, and user tags, we integrate data to characterize customers and formulate targeted marketing strategies. For instance, we may wish to filter financial customers (using app usage as a condition) present in five-star hotels (based on location) and belonging to the mother and baby demographic (as indicated by user tags). It’s essential to note that the more conditions applied, the clearer the user profile, but the user group becomes smaller.

Secondly, based on the filtered user group, we conduct online/offline statistical analysis or multi-dimensional modeling. For example, if we find that the selected user group is predominantly male, with a high prevalence of Apple device ownership and frequent use of mobile tools, we can tailor marketing strategies to this target audience. This might involve enhancing collaborations related to mobile tools or coordinating promotions with Apple to attract or activate customers.

Thirdly, we integrate the above data analyses to form a comprehensive user profile. These insights contribute to the creation of insightful data analysis reports, as demonstrated in a data analysis report example, and guide the development of strategic data analysis dashboards.

3. A Conclusion of Data Analysis Report

Drawing upon my extensive experience in data analysis, this article aims to present an integrated data analysis framework, offering a succinct overview of crucial elements that facilitate effective data analysis implementation. This framework requires significant data-cleansing efforts and a profound understanding of the industry. While providing a general perspective on data analysis, the article also acknowledges the importance of extracting finer details, especially in sections like user profiling, for a more in-depth analysis. The comprehensive insights gained from this framework play a pivotal role in crafting insightful data analysis reports, as illustrated in a data analysis report example. Additionally, these insights serve as guidance for the strategic development of data analysis dashboards.

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