Data analytics are: categorical analysis, matrix analysis, funnel analysis, correlation analysis, logic tree analysis, trend analysis, behavioral trajectory analysis, and so on. I'll use HR's work as an example of how these analyses are done in order to draw insights.
01) Classification analysis
For example, it is divided into different departments, different job levels and different age groups to analyze the turnover rate. For example, if you find that a certain department has a particularly high turnover rate, then you can go ahead and analyze it.
02) Matrix analysis
For example, if the company has a values and competence assessment, then you can make a matrix diagram of the results of the assessment, what percentage of employees with strong competence and values matching, employees with strong competence and values mismatching, employees with weak competence and values matching, and employees with weak competence and values mismatching, so as to find out the health of the company's talent.
03) Funnel analysis
For example, record recruitment data, submit resume, through the initial screening, through one side, through the second face, through the final interview, take Offer, successful onboarding, through the trial period, which is a complete recruitment funnel, from the data, you can see which link can still be optimized.
04) Correlation analysis
For example, if the turnover rate of each branch of the company varies greatly, then you can correlate the turnover rate of each branch with some of the characteristics of the branch (geographic location, salary level, benefit level, age of the employees, age of the management staff, etc.), to find the key factors that are most capable of retaining employees.
05) Logic Tree Analysis
For example, if you have recently found that employee satisfaction has declined, then disassemble it, and the satisfaction is related to compensation, benefits, career development, and work atmosphere, and then the compensation is divided into the basic salary and bonuses, and then disassemble it in this way, to find out the changes in the satisfaction of the various factors that affect the factors inside the satisfaction, and thus to draw insights.
06) Trend analysis
For example, the trend of talent turnover over the past 12 months.
07) Behavioral Trajectory Analysis
For example, tracking a salesperson's behavioral trajectory from onboarding, to starting to generate results, to rapid growth in performance, to a period of fatigue, to gradual stabilization.
By providing a one-stop big data analytics solution for enterprise business scenarios, we are able to bring value contributions to enterprises from four perspectives: increasing revenue, lowering costs, improving efficiency, and controlling costs.
1. Increase revenue
The most intuitive application is the use of data analytics to achieve digital precision marketing. Through in-depth analysis of user purchasing behavior, consumption habits, etc., the user profile is carved, and the results of data analysis are transformed into actionable and executable customer management strategies to reach more customers in the best way to achieve growth in sales revenue.
The following figure shows the analysis of promotion revenue and expenditure measurement, which provides a decision-making basis for advertising.
The following chart shows channel sales analysis, which provides data support for channel support.
2, cost reduction
For example, through data analysis to achieve the management of financial and human resources, so as to control the costs, expenses, and realize the role of cost reduction.
The following figure shows the analysis of production costs to understand the cost components.
The following chart shows a comparative analysis of the period costs, to control the cost situation.
3, improve efficiency
Every enterprise will issue relevant reports, the use of data analysis tools, do not know the technical business personnel can also be achieved through a simple drag and drop agile self-service analysis, without the need for business personnel to raise demand, IT staff to do the report, and greatly improve the timeliness of the report to improve the efficiency of the use of the report.
Through the data analysis tool, it can be displayed on the PC terminal, and also supports the mobile Kanban, which can provide a perspective on the operation anytime and anywhere, and improve the efficiency of decision-making.
4. Risk Control
Is the budget overspent? Is the debt overdue? Is the stock out of stock, out of stock? How about the customer's payback rate? Is the equipment running normally? Which products need to accelerate production to achieve production and sales balance? ... In fact, almost every business encounters a variety of risk issues. Through data analysis, it can help enterprises carry out real-time monitoring, and provide proactive warnings for the parts that deviate from the budget and the values that deviate from the normal range, so as to reduce the risks of enterprises.
The following figure shows the tax burden rate indicator, when the comprehensive tax burden rate is too high, it can realize the prompt and warning.
The following figure shows the important indicators of early warning, focusing on monitoring the gross margin of the project.