What are the data analysis methods
Data analysis methods include: comparative analysis, grouping analysis, structural analysis, retention analysis, cross-analysis, funnel analysis, matrix analysis, quadrant analysis, trend analysis, and metrics analysis.
1, comparative analysis
That is, comparative analysis, data comparison to analyze the differences between the data, including static comparison and dynamic comparison. Static comparison, also known as horizontal comparison, in the same time under the comparison of different indicators; dynamic comparison, also known as vertical comparison, is in the same overall conditions of different periods of comparison of the value of indicators. The purpose is to reveal the development and regularity of the things represented by the data.
2, grouping analysis method
combined with the comparison method, the overall different nature of the object is separated and compared in order to understand the internal data relationship.
3, structural analysis
Also known as specific gravity analysis, analyze the proportion of the overall components of the overall proportion of the overall as well as the composition of the changes in order to grasp the characteristics of things and trends.
4, retention analysis
For example, we can observe the situation of user retention in different time periods, by comparing the changes in user retention in various channels, activities and key behaviors, to find out the influencing factors to improve user retention, for example, to observe whether the retention rate of the users who have received a coupon is higher than that of those who haven’t received a coupon.
5. Cross-analysis method
The three-dimensional analysis method is often used to analyze the correlation between variables. The data of different dimensions will be cross-presented, the method of multi-angle combined analysis.
Comparative analysis of both horizontal and vertical comparison. If you want to compare both horizontally and vertically, there is a cross-analysis method. Cross-analysis is the cross-presentation of data from multiple dimensions for combined analysis from multiple perspectives.
When analyzing app data, it is usually viewed in ios and android. The main function of cross-analysis is to break down the data from multiple dimensions and discover the most relevant dimensions to explore the reasons for the data changes.
What are some common data analysis methods?
What are some common data analysis methods?
1. Trend Analysis
When there is a large amount of data, we would like to find the data information from the data faster and easier, then we need to use the graphing function. The so-called graphing function is to use EXCEl or other drawing tools to draw graphs.
Trend analysis is often used to track core metrics such as click-through rates, GMV, and active users over time. Often, only a simple data trend graph is produced, but not analyzed. It must look like the above. The data has those trend changes, be it cyclical, whether there are inflection points as well as analyzing the reasons behind it, internal or external. The best outputs for trend analysis are ratios, with chain, year-over-year and fixed base ratios. For example, how much GDP increased in April 2017 compared to March, which is the chain ratio, which reflects the recent change in trend but has seasonal implications. To eliminate the effect of seasonality, year-on-year data is introduced, e.g., how much GDP increased in April 2017 compared to April 2016, which is year-on-year. To better understand the Fixed Base Ratio, which fixes some reference point, e.g., using January 2017 data as the reference point, the Fixed Base Ratio is a comparison between May 2017 data and January 2017 for that data.
2. Comparative Analysis
Horizontal Comparison Ratio: Horizontal Comparison Ratio is a comparison with itself. The most common data metrics are the need to compare to target values to see if we have met our goals; and to see how we have grown from month to month compared to the previous month.
Longitudinal Comparison: simply put, it is comparison with others. We must compare ourselves to our competitors to understand our share and position in the market.
Many of you may say comparative analysis sounds simple. Let me give you an example. There is a login page for an e-commerce company. Yesterday’s PV was 5000. how would you feel about this kind of data? You won’t feel anything. If the average PV of this sign-in page is 10,000, it means there was a major problem yesterday. If the average PV of the check-in page is 2,000, there was a jump yesterday. Data can only be meaningful by comparison.
3. Quadrant Analysis
Based on different data, each comparison is divided into 4 quadrants. If IQ and EQ are divided, they can be divided into two dimensions and four quadrants, each with its own quadrant. Generally speaking, IQ ensures one’s lower limit and EQ raises one’s upper limit.
Say an example of the quadrant analysis method, used in practice: usually, the registered users of p2p products are dominated by third-party channels. If you can divide the four quadrants according to the quality and quantity of traffic sources, and then choose a fixed point in time to compare the effect of the cost of traffic for each channel, the quality can be used as a criterion for the dimension of the total amount retained. For high quality and quantity of channels, continue to add the introduction of high quality and low quantity of channels, low quality and low quantity of passes, low quality and high quantity of attempted strategies and requirements, for example quadrant analysis allows us to compare and analyze the time to obtain very intuitive and fast results.
Comparative analysis includes both horizontal and vertical comparisons. If you want to compare horizontally and vertically at the same time, you can use the cross-tabulation method. The cross-analysis method is to cross-display data from multiple dimensions and perform a combined analysis from multiple perspectives.
When analyzing app data, it is usually divided into iOS and Android.
The main function of cross-analysis is to break down the data from multiple dimensions and find the most relevant dimensions to explore why the data changed.