What are the methods of data analysis

The methods of data analysis are: comparative analysis, group analysis, predictive analysis, funnel analysis, AB test analysis, quadrant analysis, formula disassembly, feasible domain analysis, two-eight analysis, hypothetical analysis.

1.? Comparative analysis: Comparative analysis refers to the comparison of indicators to reflect the quantitative changes in things, belonging to the methods commonly used in statistical analysis. Common comparisons are horizontal and vertical comparisons.

Horizontal comparison refers to the comparison of different things at a fixed time, for example, the comparison of the price of goods purchased by different levels of users at the same time, and the comparison of the sales volume and profit margin of different goods at the same time.

Vertical comparison refers to the change of the same thing in the dimension of time, for example, ring, year-on-year and fixed-base ratio, that is, the comparison of the sales of the current month with the sales of the previous month, the comparison of the sales of January of the current year with the sales of January of the previous year, the comparison of the sales of each month of the current year respectively with the average sales of the previous year, and so on.

The use of comparative analysis can be the size of the data, the level of high and low, fast and slow to make effective judgment and evaluation.

2.? Grouping analysis method: Grouping analysis method refers to the nature of the data, characteristics, according to certain indicators, the overall data is divided into different parts, analyze its internal structure and interrelationships, so as to understand the development of things. According to the nature of the indicators, the grouping analysis method is divided into attribute indicator grouping and quantitative indicator grouping. The so-called attribute indicators represent the nature of things, characteristics, etc., such as name, gender, literacy, etc., these indicators can not be calculated; while the data indicators represent data that can be calculated, such as the age of people, wages and income. Grouping analysis is generally used in conjunction with comparative analysis.

3. Predictive analytics: Predictive analytics is mainly based on the current data, the future trend of data changes in judgment and prediction. Predictive analysis is generally divided into two kinds: one is based on the time series of prediction, for example, based on past sales performance, predicting the sales of the next three months; the other is a regression type of prediction, that is, based on the causal relationship between the indicators of the interaction between the prediction, for example, based on the user's web browsing behavior, predicting the user's possible purchase of goods.

4. Funnel analysis: Funnel analysis, also known as process analysis, its main purpose is to focus on the conversion rate of an event in an important link, the application of which is more common in the Internet industry. For example, for the process of credit card application, the user from browsing the card information, to fill out the credit card information, submit the application, the bank audit and approval of the card, and finally the user to activate and use the credit card, in the middle of a lot of important links, the amount of users in each link is getting less and less, thus forming a funnel. The use of funnel analysis enables the business side to focus on the conversion rate of each link, monitor and manage it, and when the conversion rate of a certain link is abnormal, it can be targeted to optimize the process and take appropriate measures to improve the business indicators.

5. AB test analysis: AB?test analysis is actually a comparative analysis method, but it focuses on comparing the samples of A and B, which are similar in structure, and analyzes their differences based on the sample index values. For example, for the same function of an App, different style styles and page layouts are designed, and the two styles of pages are randomly assigned to users, and finally the advantages and disadvantages of different styles are evaluated based on the user's browsing conversion rate of the page to understand the user's preferences, so as to further optimize the product.

In addition to this, in order to do a good job of data analysis, readers also need to master certain mathematical foundations, such as the concept of basic statistics (mean, variance, multitude, median, etc.), measures of dispersion and variability (extreme variance, quartile, interquartile range, percentile, etc.), data distribution (geometric distribution, binomial distribution, etc.), as well as the basis of probability theory, statistical sampling, confidence intervals and hypothesis testing, etc., to make the results of data analysis more professional through the application of relevant indicators and concepts.

6. Quadrant analysis: the X-axis from left to right is the high and low click-through rate, the Y-axis from bottom to top is the high and low conversion rate, the formation of four quadrants, which is what we are talking about quadrant analysis.

Find the corresponding data labeling points for the click-through rate and conversion rate of each marketing campaign, and then categorize the effect of this marketing campaign into each quadrant, and the 4 quadrants represent different effect evaluation.

7. Formula dismantling method: the so-called formula dismantling method is for a certain indicator, the formula performance of the indicator's impact factor, such as daily sales of the impact factor is the sales of various commodities, to find the impact factor, you need to dismantle the impact factor of the impact factor.

8. Feasible domain analysis: Feasible domain analysis is actually a kind of self-established data analysis model, according to the specific data constantly revised to adjust the scope of the feasible domain, the business indicators for effective evaluation.

9. The two-eight analysis: the law of eight and the long-tail theory is relative, the law of two-eight tells us that you have to pay attention to the head of the user, that is, can produce 80% of the revenue of the 20% of the users or goods, and the long-tail theory tells us that we have to pay attention to the long-tail effect, that is, the remaining 20% of the revenue.

10. Hypothesis analysis: a simple understanding, the hypothesis method is in the known results of the data, in the multiple variables affecting the results of the hypothesis of a quantitative, reverse derivation of the process of data analysis methods.

Data analysis methods are? data statistics? among? application? very? widely used. The methodology is a very important one, and it has been used for many years. The specifics of the method are as follows Methods? There are many different methods. The specifics of how to use them will vary from person to person.