Data mining is a technique for discovering patterns and rules by discovering rules that are useful for the management of data. In this article, we will explain the basics of data mining, analysis methods, and their points in an easy-to-understand manner. If you want to know the definition of data mining, or if you are in charge of the marketing department, please refer to it.
Table of contents
- Basic knowledge of data mining
- Industries where data mining is used
- Data mining execution process
- Data mining analysis method
- Points for utilizing data mining
- If you want to make effective use of data, leave it to TRYETING’s AI tool “UMWELT”!
- summary
Basic knowledge of data mining
Before explaining the basic knowledge of data mining, examples of its use, and specific methods, we will first explain the meaning, role, function, and type of data mining.
Meaning of data mining
Data mining mining has the meaning of “discovering” and refers to mining useful information from a huge amount of data using AI, pattern recognition, statistics, and so on. Data mining that uses the structure of a website or data on the Web is called “Web mining”, and the analysis method that extracts new information from unstructured text data is called “text mining”.
Data mining is a technology that supports management strategies and customer relationship management that aim to increase sales and profitability by improving customer satisfaction and customer loyalty.
Functions of data mining
Discovering hypotheses from vast amounts of data There are three functions that data mining can perform: prediction, classification, and relevance.
1. Forecast In
forecast, the probability of occurrence of an event such as purchase rate, order rate, churn rate, etc. is calculated from the collected data, and the cause of occurrence is clarified.
2. Classification In
classification, the collected purchase data, product data, and customer data are sorted according to their characteristics. For example, you will be able to classify good customers from purchasing data and classify a huge amount of information on sales destinations by sales target.
3. Relevance
Search and extract events that occur simultaneously or frequently from a large amount of accumulated data.
This relevance applies to the demonstration in “Paper diapers and beer” published in the US newspaper “Wall Street Journal” on December 23, 1992. As a result of collecting and analyzing data on the sales volume of goods and the purchase information of customers of Wal-Mart, a major supermarket in the United States, we have obtained amazing information about customers who purchase paper diapers.
Usually, people who buy disposable diapers were expected to buy baby products together. However, as a result of data mining, it was found that beer is a product that is often purchased with disposable diapers.
I found out that there are many cases where a father who came to buy diapers when his mother asked him to buy the beer he drinks with him. Without data mining analysis, it would have been difficult to notice the relationship between the two products.
After that, in the supermarket, the corners for disposable diapers and beer were placed so that they could be seen from each other, which led to an increase in sales volume.
Types of data mining
There are two types of data mining: “knowledge discovery” and “hypothesis testing”. Knowledge discovery is a method often used in machine learning and deep learning, and is a method for automatically discovering new patterns, rules, and knowledge from accumulated data.
Hypothesis verification is a method of collecting and analyzing data necessary for solving a problem to be verified based on a certain hypothesis or purpose. In addition to machine learning and deep learning, traditional statistical methods may be used. Knowledge discovery can be called “search-type data mining”, and hypothesis testing can be called “goal-oriented data mining”.
What is the difference between data mining and statistical analysis?
Data mining explores the correlation and regularity of data. Hypotheses are made from what is searched and excavated, but hypotheses are not made during mining. Statistical analysis, on the other hand, involves analysis to test the hypothesis.
Industries where data mining is used
So far we have seen an overview of data mining. Next, I would like to introduce six industries that utilize data mining, including the manufacturing industry.
Manufacturing industry
In the manufacturing industry, it is used for predicting the frequency of equipment maintenance at the manufacturing site, finding defects, designing, manufacturing, quality assurance, and scheduling. By utilizing predictive analysis, it becomes possible to prevent the occurrence of failures and defective products, and it is possible to improve productivity and reduce costs.
communication
By analyzing vast amounts of customer data, telecommunications companies can predict customer behavior and provide targeted and accurate campaigns. It is expected that data mining will be used to detect communication and line failures necessary to provide high-quality services and to estimate the causes.
Retail business
In the retail industry, you can adjust the purchase amount of products by analyzing the sales performance in combination with the weather, day of the week, time zone, and so on. It can also be used to carry out campaigns such as providing coupons to specific customers according to their attributes, hobbies, tastes, and history.
insurance
Insurers can leverage analytics to solve complex challenges such as insurance fraud, risk management, and customer retention. For example, analyzing the factors associated with life insurance churn can lead to lower churn rates. With automobile insurance, you can analyze the annual mileage and region to realize effective insurance premium setting.
Banking / financial industry
In bank management and the financial industry, it can be used for providing products at the right time by grasping needs on a customer basis, whether or not to lend, and loan credit screening estimated from the attributes of customers. It also helps you understand market risk and manage legal compliance obligations.
education
One of the uses of data mining in the field of education is the analysis of learner’s grade data. As a result, it is possible to grasp the areas of strength and weakness, and to realize appropriate guidance and education for each person. In addition, it is possible to predict the learner’s grades and learning outcomes.
Data mining execution process
Data science, which is often heard together with data mining, refers to cross-cutting from data collection to problem solving by making full use of statistics, information science, algorithms, and so on. Data mining is included in the field of data science, and is a term that refers to the process from data collection to problem solving, from data selection to analysis and model construction. Specifically, we will carry out the following steps.
Data selection
In order to perform effective data mining, we first understand the purpose, collect and investigate the data that matches the purpose, and prepare high-quality data.
Data cleansing
Next, we will perform “data cleansing” work to process and organize the noise in the collected data. Specifically, it searches for duplicates, errors, and notational fluctuations, and deletes, corrects, and normalizes them.
Analysis of exploratory data
Use a specific algorithm to create the appropriate type of model. The analysis is performed using logistic regression analysis, cluster analysis, market basket analysis, and machine learning to discover patterns. Each analysis method will be described later.
evaluation
After the analysis, verification and evaluation will be performed. If the model is not what you expected, change various parameters and recreate the model to get the optimum value. It is also essential to evaluate whether the model has achieved the business goals and whether all business problems have been incorporated. To finish the evaluation, decide how to utilize the data mining results.
Data mining analysis method
Next, I will explain the four analysis methods of data mining used for the analysis of exploratory data.
Logistic regression analysis
Logistic regression analysis is a method of predicting the probability that a binary result (yes / no, yes / no, and only two values) will occur from several factors. It is a method often used in marketing, and is used not only for the response rate to DM, but also for predicting the occurrence of sediment disasters using meteorological observation data and predicting the disease incidence rate from patient test values.
Cluster analysis
Cluster analysis is a method of grouping data based on similarities. It is mainly used for data analysis of on-site and market research in marketing, and efficiency of DM distribution based on customer information.
Market basket analysis
Market basket analysis is a method of analyzing combinations of products that are easy for visitors to purchase at the same time. Like the above-mentioned “diapers and beer”, market basket analysis leads to the discovery of unexpected rules and helps to create effective sales floors.
Machine learning
Machine learning, a type of artificial intelligence, is also an analytical method for data mining. Among them, Python, a programming language, has abundant convenient libraries useful for data analysis, and is an effective method for finding rules and relationships from data.
Points for utilizing data mining
From here, I will introduce two points of utilizing data mining.
Keep data quality
If the quality of the data is poor, you may get unreasonable results, so you need to be careful about the quality of the data, such as using data that has few missing or outliers and is unique.
Introduce data mining tools
It is expected that the burden on the site will increase for the analysis of huge amounts of data. In particular, we recommend installing a data mining tool in cases where your company does not have the expertise to analyze data, or when you cannot make effective use of data or do not know how to use it. Introducing a data mining tool has the following advantages.
- The time and effort required for data collection and analysis can be reduced.
- Analysis can be performed even in the absence of specialized staff
- Efficiently discover buried business issues
If you want to make effective use of data, leave it to TRYETING’s AI tool “UMWELT”!
To make effective use of huge amounts of data, we recommend TRYETING’s no-code AI cloud “UMWELT”. Since “UMWELT” can build various algorithms without code, it is possible to analyze and utilize data without hiring specialists in data science and AI. The period until the introduction of the conventional AI system is 1/4 of the industry average, and the introduction cost is 1/10 of the industry average, so it can be operated at a low cost.
summary
Data mining, which discovers valuable information from data, is an indispensable analytical method for marketing. It also supports strategies for building good long-term relationships with customers. Please use our UMWELT to efficiently find out the problems buried in the huge amount of data.
