10 important types of data analysis methods


Before we dive into the seven essential types, it’s important that we run through a quick review process. Starting with the category of descriptive information from the analysis of the text, the complexity, and effort of analyzing the data increases, but so does the added value for the business.

a) Descriptive analysis – Why

The descriptive process is the starting point of all investigative research, does it seek to answer the question of what happened? It does this by measuring, managing, and interpreting data from various sources to transform it into useful information for your organization.

Descriptive analysis is important because it allows us to present our ideas in a meaningful way. Although it is worth mentioning that this analysis alone will not predict what will happen in the future or give you answers to questions such as the cause of what happened, it will leave your data organized and ready to go.

b) Analytical analysis – How to find out the relationship between the data.

As the name suggests, the main purpose of a research study is research. Before that, there is still a concept of the relationship between data and change. Once the data is studied, research analysis allows you to find connections and create ideas and solutions for certain problems. A common application area is data mining. c) Research analysis – Why it happened.

Diagnostic data analysis empowers researchers and managers by helping them gain a solid understanding of the cause of an incident. If you know why something happened and how it happened, you’ll be able to figure out exactly how to solve that problem or challenge.

Designed to provide accurate and feasible answers to specific questions, it is one of the most important research methods in the world, among its other organizational functions such as business research, for example.

c) Forecast analysis – What will happen?

Forecasting allows you to look into the future to answer the question: what will happen? To do this, it uses the results of the descriptive analysis, research, and analysis mentioned earlier, in addition to machine learning (ML) and artificial intelligence (AI). Therefore, you can find out future trends, possible problems or malfunctions, connections, and losses in your data.

Through predictive analytics, you can deploy and develop strategies that will not only improve your business processes but also help you gain a significant advantage over the competition. If you understand the causes of behavior, patterns, or events from the data, you will be able to create informed predictions about how things might turn out in that area of ​​the business.

e) Prescription assessment – How to do it.

Another type of research method is the most effective in the research. Prescriptive data systems are closely related to predictive analytics because they revolve around using processes or techniques to create responsive and profitable business plans.

Delving into subscription analytics, you will be actively involved in the data consumption process by taking structured datasets and using them as powerful solutions to emerging problems. many aspects, including marketing. , sales, customers. experience, HR, execution, finance, logistics analysis, and others.
Without further ado, here are 10 important data analysis methods among a few cases in the corporate world:

1-Cluster analysis

The act of grouping data in such a way that some things are more similar (in one way) to each other than others – hence the word “clustering”. Since there is no objective change during the collection, this method is often used to find hidden patterns in the data.

This method is also used to provide more context for a process or data structure. In a perfect world, marketers would be able to analyze each customer individually and provide them with personalized service, but let’s face it, with large customers, it’s not possible to do this. time.

That’s where the summary comes in. By grouping customers based on demographics, marketing, financial value, or anything else that may be beneficial to your business, you’ll be able to instantly optimize your efforts and give people Your shopping experience the best. depending on their needs.

2. Group analysis

This type of data analysis method uses historical data to analyze and compare aspects of the user’s determining behavior, which will be compared to others with similar behavior. Using this method, it is possible to obtain a lot of information about the needs of consumers or a strong understanding of a broad target group.

Group analysis can be very useful for conducting market research as it will allow you to understand the impact of your advertising on different groups of customers. For example, imagine you are sending an email campaign that encourages customers to visit your site.

To do this, you create two different types of ads, the CTA and the content of the ad. Later, you can use member analytics to track campaign performance over time and understand what content drives your customers to sign up, bounce, or engage. A useful tool to start doing group analysis is Google Analytics.

You can learn more about the benefits and limitations of using membership in GA in this helpful guide. In the image below, you will see an example of how you can visualize a group of members in this application. Domains (device access) are divided into day-to-day (device usage) and analyzed week by week to generate performance insights.

3. Review the return

Regression uses historical data to understand how the value of the dependent variable is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding the relationship between each variable and how they have changed in the past, you can anticipate the outcome and make better decisions in the future.

4. Neural network

Neural networks form the basis of intelligent machine learning algorithms. It is a type of research that tries, with minimal intervention, to understand how the human brain can extract information and predict patterns. Neural networks learn from each transaction of data, which means they evolve and improve over time.

5. Check things out

The causal analysis also called “reduced parts”, is a type of data analysis used to explain the relationship between observed variables and their association with a potentially smaller number of unobserved variables. the distance which is called something. The goal here is to identify independent variables, a good method for specifying some parameters.

6. Data mining

A data analysis process that is an umbrella term for engineering metrics and insights for value, direction, and cycle. Using an analytical approach, data mining seeks to identify relationships, patterns, and processes in order to create a higher level of understanding. When considering how to analyze data, adopting a data mining perspective is critical to success – as such, it’s an area worth exploring.

7. Text analysis

Text analysis, also known in the industry as text input, works by taking large amounts of text data and organizing them in a way that makes it easier to manage. By working on this cleanup process thoroughly, you will be able to extract data that is important to your organization and use it to create actionable insights that will move you forward.

8. Time series analysis

As the name suggests, time series analysis is used to analyze the pattern of data collected over a specific period of time. Although researchers use this method to analyze data points over a specific period of time rather than looking at them over a short period of time, the time-series method is not only used for collecting data over time. go. Instead, it allows researchers to understand whether changes have occurred over the course of the study, how the changes depended, and how they reached the final outcome.

9. Decision tree

Decision tree analysis is intended as a tool to assist in making intelligent and strategic decisions. By showing the vision, the results, and the costs that can occur in the plant species, researchers and consumers can analyze all the factors involved and choose the best method.

Decision trees are useful for analyzing quantitative data and they help improve the decision-making process by helping you find opportunities for improvement, reduce costs, and improve efficiency and productivity.
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10. Joint review

Last but not least, we have a conjoint analysis. This method is often used in research to understand how individuals value different features of different products or services and are one of the most effective methods for identifying customer preferences.

When it comes to buying, some customers may be more price-oriented, some may be plan-oriented, and some may be sustainable orientation, whatever your customers’ interests, you can find them in the joint analysis. So, companies can define pricing strategies, packaging options, subscription plans, etc.

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