In the digital age, business data analytics has become one of the key factors for business success. With the continuous advancement of technology and the growth of data, enterprises are faced with a flood of information and data. However, if this data is not fully utilized, it is just an accumulation of numbers and does not bring real business value. Therefore, the importance of business data analysis is highlighted. By digging deeper and analyzing data, businesses can uncover hidden patterns, trends, and insights to support decision-making. Accurate data analytics can help businesses understand market demand, optimize products and services, improve operational efficiency, and even identify new business opportunities. This article will delve into the entire process of business data analysis to help you better grasp how to effectively use data analytics to drive enterprise development and innovation.
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The first thing to be clear about is that to do business data analysis, you must rely on data to speakReplace vague, emotional, and colloquial questions with clear data, statistical caliber, and judgment criteria, which is the starting point for all subsequent analysis.
If you want to know what the needs of your boss and business department are, you can use the following methods:
Determine the purpose of data analysis: First, you need to clarify what the business wants to achieve through data analysis. For example, they want to understand the buying preferences of their customers, or they want to understand trends in sales, etc.
Identify data sources: Determine the data sources required for your analysis, including your format, storage location, and so on.
Determine the analysis method: Determine the data analysis method to be adopted according to the requirements of the business unit. For example, if they want to understand trends in sales, they can use methods such as time series analysis.
Determine how the results are presented: Determine how the results are presented according to the requirements of the business department. For example, if they need a report, then they need to determine the format and content of the report, etc.
Regular feedback results: Regularly feedback the results of data analysis to business departments and communicate with them so that the analysis plan can be adjusted in a timely manner to meet their needs.
Let's take user churn, the most common problem encountered by the operations department, as an example, if you want to understand the specific needs of the business department, you need to sort out at least the following three points:
Since the capabilities of people in the business can be uneven, we can use a data analysis framework to help them solve the problems they encounter when doing business data analysis:
Descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis, as shown in the figure below
1.Descriptive analytics
Descriptive analytics is the foundation of data analysis, which allows for better understanding and interpretation of data by making accurate descriptions of facts that have already occurred with data. This approach to analysis can help us understand patterns, trends, and changes in the data for a better understanding of the data.
In descriptive analysis, we typically use graphs and statistical methods to present data. For example, we can use bar charts, line charts, scatter plots, etc., to show the relationship between different variables, or use frequency distribution tables, statistical summaries, etc., to summarize the basic characteristics of the data.
Through descriptive analysis, we can derive the basic characteristics and patterns of the data and obtain valuable information from it. For example, we can use descriptive analysis to discover how well a product is selling in a market, determine the most popular product types and the most suitable sales strategies; Or find out the main problems and areas of people's lives that need improvement from social survey data.
2.Diagnostic analysis
Diagnostic analysis is based on descriptive analysis, by digging deep into the causes and mechanisms behind the data to find the root cause of the problem. Diagnostic analysis is more targeted and in-depth than descriptive analysis, allowing us to further understand why a particular problem occurs.
In diagnostic analysis, we usually use various analysis methods, such as regression analysis, factor analysis, cluster analysis, decision tree, etc., to determine the relationship and influence of different variables, as well as the possible causes and mechanisms of problems. For example, we can perform a regression analysis on sales data to find out the reason for the decline in product sales, which may be due to a decrease in market demand or a competitor launching a more attractive product, and propose a corresponding improvement strategy.
Through diagnostic analysis, we can dig deep into the causes and mechanisms behind the data, find the root cause of the problem, and propose corresponding solutions based on the analysis results.
3.Sexual analysis
*Sexual analysis is based on descriptive and diagnostic analysis, and models are built to make ** what may happen in the future. It helps us understand specific trends and patterns and make decisions and plans accordingly based on those trends and patterns.
In sex analysis, we typically use a variety of statistical methods and machine learning algorithms to build models. For example, we can use time series analysis to determine trends and seasonality in sales volumes, or algorithms such as regression analysis and decision trees to market demand and consumer behavior. Through these models, we can plan and strategy accordingly for what is likely to happen in the future and based on the results.
*Sex analysis has a wide range of applications in the business field. For example, enterprises can plan product development and sales strategies through market demand and consumer behavior, so as to gain a better competitive advantage; In the financial sector, banks can develop corresponding risk management strategies through customer default rate and credit risk.
4.Prescribing analysis
Prescribing analysis is based on the best analysis, in order to deal with possible future situations, to develop corresponding action plans and strategies. It helps us to prepare well in advance and respond effectively to potential problems and risks.
When conducting a prescriptive analysis, we need to develop an action plan based on the results and goal setting. These action plans can include the following:
Adjust business strategy: Evaluate whether the current business strategy adapts to future trends based on the results. If market demand will decline, you can consider adjusting your product portfolio or opening up new market areas. If the competition will intensify, you can consider improving product quality or adopting other competitive strategies.
Risk management: For potential risks, formulate corresponding risk management plans. For example, if bottlenecks may occur in the chain, you can set up a backup vendor or purchase raw materials in advance. If churn rates are likely to increase, you can develop a customer retention plan and improve the customer experience.
Resource planning: According to the results, reasonable planning and allocation of resources. For example, if the demand for a product will increase significantly, you can increase the investment of related resources to ensure that production and demand can be met. If the demand for a certain market will decline, resources can be deployed to other potential market areas.
Develop an emergency plan: Formulate a corresponding emergency plan for possible accidents or emergencies. These solutions can be used to respond to a variety of unforeseen circumstances, such as natural disasters, economic fluctuations, and technical failures, to help businesses stay operational and stable during a crisis.
When the business department has formulated an initial improvement plan, such as optimizing the terms of service, providing more valuable products, etc., the business data analysis can conduct further in-depth analysis.
By analysing user profiles, we can assess the range of users that the program may affect; By analyzing the investment behavior of users, we can assess the attractiveness of alternative products and make selections; Through logistic regression** user responses, we can develop retention models, anticipate performance, and optimize programs. In short, as the business scenario becomes concrete, the analysis can also be deepened.
Business analytics is a cyclical and iterative process. Through quantitative analysis, we can continuously narrow down the scope of the question, focus on the content of the discussion, dissect the essence of the problem, and finally arrive at the answer. This process can take a long time, or it may take several small analysis processes to be stitched together. This is the real process of data guiding the business, because the business itself needs to evolve, think, try and review. Business data analytics itself is a process of focusing, experimenting, and evolving.
In today's competitive business environment, business data analytics has become an indispensable tool for business success. By leveraging data analytics, companies can better understand their business and market trends, optimize decision-making, and achieve more efficient operations and more targeted marketing. Data-driven decision-making can help companies reduce risk, increase competitiveness, and provide strategic guidance for future growth. With the rapid development of artificial intelligence and machine learning, the future of business data analytics is even broader, which will further drive enterprise innovation and growth.
Data analysis