What is data analysis?

Data analysis includes: classification analysis, matrix analysis, funnel analysis, correlation analysis, logic tree analysis, trend analysis, behavior trajectory analysis, etc. I will use the work of HR as an example to illustrate how to perform the above analysis to gain insights.

01) Classification analysis

For example, it can be divided into different departments, different job levels, and different age groups to analyze the brain drain rate. For example, if you find that the turnover rate of a certain department is particularly high, you can analyze it.

02) Matrix analysis

For example, if the company has an assessment of values ??and abilities, then the assessment results can be made into a matrix chart. Employees with strong abilities and value matching, employees with strong ability and value mismatch The proportion of employees, employees with weak ability and value matching, and employees with weak ability and value mismatch account for each, thereby discovering the company's talent health.

03) Funnel analysis

For example, record recruitment data, submit resume, pass preliminary screening, pass first interview, pass second interview, pass final interview, accept offer, successfully join the job, pass trial In this period, this is a complete recruitment funnel. From the data, you can see which link can be optimized.

04) Related analysis

For example, if the talent turnover rate of each branch of the company is quite different, then the employee turnover rate of each branch can be compared with some characteristics of the branch (geographic location, salary level, welfare level, employee age, management age, etc.) factors to find the key factors that can best retain employees.

05) Logic tree analysis

For example, if it is found that employee satisfaction has decreased recently, then we will dismantle it. The satisfaction is related to salary, benefits, career development, and working atmosphere. Then the salary is divided into basic salary and bonus. This is dismantled layer by layer to find out the changing factors in the various influencing factors of satisfaction, so as to gain insights.

06) Trend analysis

For example, the change trend of brain turnover rate in the past 12 months.

07) Behavior trajectory analysis

For example, tracking the behavior trajectory of a salesperson, from joining the job, to starting to produce performance, to rapid performance growth, to the exhaustion period, to gradually stabilizing.

By providing one-stop big data analysis solutions for enterprise business scenarios, it can bring value contributions to enterprises from four perspectives: increasing revenue, reducing costs, improving efficiency, and controlling costs.

1. Increase revenue

The most intuitive application is to use data analysis to achieve digital precision marketing. Through in-depth analysis of user purchasing behavior, consumption habits, etc., we can create user portraits, transform the data analysis results into actionable customer management strategies, and reach more customers in the best way to achieve sales revenue growth.

The figure below shows the calculation and analysis of promotion revenue and expenditure, which provides a basis for decision-making on advertising placement.

The following figure shows channel sales analysis, providing data support for channel support.

2. Cost reduction

For example, financial and human resources management can be achieved through data analysis, thereby controlling various costs and expenses and reducing costs.

The following figure shows the production cost analysis to understand the cost composition.

The following figure shows a comparative analysis of the estimated expenses during the period to control the expenses.

3. Improve efficiency

Every enterprise will issue relevant reports. Using data analysis tools, business personnel who do not understand technology can also implement agile self-service analysis through simple drag and drop, without the need for Business personnel submit requirements and IT personnel prepare reports, which greatly improves the timeliness of reports and improves the efficiency of report usage.

Through data analysis tools, it can be displayed on the PC side, and mobile billboards are also supported, allowing you to gain insight into operations anytime and anywhere and improve decision-making efficiency.

4. Control risks

Is the budget overrun? Is the debt past due? Is it out of stock or out of stock? What is the customer's return rate? Is the equipment operating normally? Which product needs to be accelerated to achieve a balance between production and sales? ...In fact, almost every enterprise will encounter various risk problems. Through data analysis, it can help enterprises conduct real-time monitoring, proactively warn against deviations from the budget and values ??that deviate from the normal range, and reduce enterprise risks.

The figure below shows the tax burden rate indicator. When the comprehensive tax burden rate is too high, prompts and early warnings can be implemented.

The picture below is an early warning of important indicators, focusing on monitoring the gross profit margin of the project.