Data Mining Examples: Most Common Applications of Data Mining 2023

This Tutorial Covers Most Popular Data Mining Examples in Real Life. Learn About Data Mining Application In Finance, Marketing, Healthcare, and CRM:

In this Free Data Mining Training Series, we had a look at the Data Mining Process in our previous tutorial. Data Mining, which is also known as Knowledge Discovery in Databases (KDD), is a process of discovering patterns in a large set of data and data warehouses.

Various techniques such as regression analysis, association, and clustering, classification, and outlier analysis are applied to data to identify useful outcomes. These techniques use software and backend algorithms that analyze the data and show patterns.

Some of the well-known data mining methods are decision tree analysis, Bayes theorem analysis, Frequent item-set mining, etc. The software market has many open-source as well as paid tools for data mining such as Weka, Rapid Miner, and Orange data mining tools.

The data mining process starts with giving a certain input of data to the data mining tools that use statistics and algorithms to show the reports and patterns. The results can be visualized using these tools that can be understood and further applied to conduct business modification and improvements.

Data mining is widely used by organizations in building a marketing strategy, by hospitals for diagnostic tools, by eCommerce for cross-selling products through websites and many other ways.

Some of the data mining examples are given below for your reference.

Examples Of Data Mining In Real Life

The importance of data mining and analysis is growing day by day in our real life. Today most organizations use data mining for analysis of Big Data.

Let us see how these technologies benefit us.

#1) Mobile Service Providers

Mobile service providers use data mining to design their marketing campaigns and to retain customers from moving to other vendors.

From a large amount of data such as billing information, email, text messages, web data transmissions, and customer service, the data mining tools can predict “churn” that tells the customers who are looking to change the vendors.

With these results, a probability score is given. The mobile service providers are then able to provide incentives, offers to customers who are at higher risk of churning. This kind of mining is often used by major service providers such as broadband, phone, gas providers, etc.

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#2) Retail Sector

Data Mining helps the supermarket and retail sector owners to know the choices of the customers. Looking at the purchase history of the customers, the data mining tools show the buying preferences of the customers.

With the help of these results, the supermarkets design the placements of products on shelves and bring out offers on items such as coupons on matching products, and special discounts on some products.

These campaigns are based on RFM grouping. RFM stands for recency, frequency, and monetary grouping. The promotions and marketing campaigns are customized for these segments. The customer who spends a lot but very less frequently will be treated differently from the customer who buys every 2-3 days but of less amount.

Data Mining can be used for product recommendation and cross-referencing of items.

Data Mining In Retail Sector From Different Data Sources.

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#3) Artificial Intelligence

A system is made artificially intelligent by feeding it with relevant patterns. These patterns come from data mining outputs. The outputs of the artificially intelligent systems are also analyzed for their relevance using the data mining techniques.

The recommender systems use data mining techniques to make personalized recommendations when the customer is interacting with the machines. The artificial intelligence is used on mined data such as giving product recommendations based on the past purchasing history of the customer in Amazon.

#4) Ecommerce

Many E-commerce sites use data mining to offer cross-selling and upselling of their products. The shopping sites such as Amazon, Flipkart show “People also viewed”, “Frequently bought together” to the customers who are interacting with the site.

These recommendations are provided using data mining over the purchasing history of the customers of the website.

#5) Science And Engineering

With the advent of data mining, scientific applications are now moving from statistical techniques to using “collect and store data” techniques, and then perform mining on new data, output new results and experiment with the process. A large amount of data is collected from scientific domains such as astronomy, geology, satellite sensors, global positioning system, etc.

Data mining in computer science helps to monitor system status, improve its performance, find out software bugs, discover plagiarism and find out faults. Data mining also helps in analyzing the user feedback regarding products, articles to deduce opinions and sentiments of the views.

#6) Crime Prevention

Data Mining detects outliers across a vast amount of data. The criminal data includes all details of the crime that has happened. Data Mining will study the patterns and trends and predict future events with better accuracy.

The agencies can find out which area is more prone to crime, how much police personnel should be deployed, which age group should be targeted, vehicle numbers to be scrutinized, etc.

#7) Research

Researchers use Data Mining tools to explore the associations between the parameters under research such as environmental conditions like air pollution and the spread of diseases like asthma among people in targeted regions.

#8) Farming

Farmers use Data Mining to find out the yield of vegetables with the amount of water required by the plants.

#9) Automation

By using data mining, the computer systems learn to recognize patterns among the parameters which are under comparison. The system will store the patterns that will be useful in the future to achieve business goals. This learning is automation as it helps in meeting the targets through machine learning.

#10) Dynamic Pricing

Data mining helps the service providers such as cab services to dynamically charge the customers based on the demand and supply. It is one of the key factors for the success of companies.

#11) Transportation

Data Mining helps in scheduling the moving of vehicles from warehouses to outlets and analyze the product loading patterns.

#12) Insurance

Data mining methods help in forecasting the customers who buy the policies, analyze the medical claims that are used together, find out fraudulent behaviors and risky customers.

Data Mining Examples In Finance

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The finance sector includes banks, insurance companies, and investment companies. These institutions collect a huge amount of data. The data is often complete, reliable and of high quality and demands a systematic data analysis.

To store financial data, data warehouses that store data in the form of data cubes are constructed. To analyze this data, advanced data cube concepts are used. Data mining methods such as clustering and outlier analysis, characterization are used in financial data analysis and mining.

Some cases in finance where data mining is used are given below.

#1) Loan Payment Prediction

Data mining methods like attribute selection and attribute ranking will analyze the customer payment history and select important factors such as payment to income ratio, credit history, the term of the loan, etc. The results will help the banks decide its loan granting policy, and also grant loans to the customers as per factor analysis.

#2) Targeted Marketing

Clustering and classification data mining methods will help in finding the factors that influence the customer’s decisions towards banking. Similar behavioral customers’ identification will facilitate targeted marketing.

#3) Detect Financial Crimes

Banking data come from many different sources, various cities, and different bank locations. Multiple data analysis tools are deployed to study and to detect unusual trends like big value transactions. Data visualization tools, outlier analysis tools, clustering tools, etc are used to identify the relationships and patterns of action.

The figure below is a study from Infosys showing the customer’s willingness to banking online system in different countries. Infosys used Big Data Analytics for this study.

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Applications Of Data Mining In Marketing

Data mining boosts the company’s marketing strategy and promotes business. It is one of the key factors for the success of companies. A huge amount of data is collected on sales, customer shopping, consumption, etc. This data is increasing day by day due to e-commerce.

Data mining helps to identify customer buying behavior, improve customer service, focus on customer retention, enhance sales, and reduce the cost of businesses.

Some examples of data mining in marketing are:

#1) Forecasting Market

To predict the market, the marketing professionals will use Data Mining techniques like regression to study customer behavior, changes, and habits, customer response and other factors like marketing budget, other incurring costs, etc. In the future, it will be easier for professionals to predict the customers in case of any factor changes.

#2) Anomaly Detection

Data mining techniques are deployed to detect any abnormalities in data that may cause any kind of flaw in the system. The system will scan thousands of complex entries to perform this operation.

#3) System Security

Data Mining tools detect intrusions that may harm the database offering greater security to the entire system. These intrusions may be in the form of duplicate entries, viruses in the form of data by hackers, etc.

Examples Of Data Mining Applications In Healthcare

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In healthcare, data mining is becoming increasingly popular and essential.

Data generated by healthcare is complex and voluminous. To avoid medical fraud and abuse, data mining tools are used to detect fraudulent items and thereby prevent loss.

Some data mining examples of the healthcare industry are given below for your reference.

#1) Healthcare Management

The data mining method is used to identify chronic diseases, track high-risk regions prone to the spread of disease, design programs to reduce the spread of disease. Healthcare professionals will analyze the diseases, regions of patients with maximum admissions to the hospital.

With this data, they will design the campaigns for the region to make people aware of the disease and see how to avoid it. This will reduce the number of patients admitted to hospitals.

#2) Effective Treatments

Using data mining, the treatments can be improved. By continuous comparison of symptoms, causes, and medicines, data analysis can be performed to make effective treatments. Data mining is also used for the treatment of specific diseases, and the association of side-effects of treatments.

#3) Fraudulent And Abusive Data

Data mining applications are used to find abnormal patterns such as laboratory, physician’s results, inappropriate prescriptions, and fraudulent medical claims.

Data Mining And Recommender Systems

Recommender systems give customers with product recommendations that may be of interest to the users.

The recommended items are either similar to the items queried by the user in the past or by looking at the other customer preferences which have similar taste as the user. This approach is called a content-based approach and a collaborative approach appropriately.

Many techniques like information retrieval, statistics, machine learning, etc are used in recommender systems.

Recommender systems search for keywords, user profiles, user transactions, common features among items to estimate an item for the user. These systems also find the other users who have a similar history of buying and predict items that those users could buy.

There are many challenges in this approach. The recommendation system needs to search through millions of data in real-time.

There are two types of errors made by Recommender Systems:

False negatives and False positives.

False negatives are products that were not recommended by the system but the customer would want them. False-positive are products that were recommended by the system but not wanted by the customer. Another challenge is the recommendation for the users who are new without any purchasing history.

An intelligent query answering technique is used to analyze the query and provide generalized, associated information relevant to the query. For Example: Showing the review of restaurants instead of just the address and phone number of the restaurant searched for.

Data Mining For CRM (Customer Relationship Management)

Customer Relationship Management can be reinforced with data mining. Good customer Relations can be built by attracting more suitable customers, better cross-selling and up-selling, better retention.

Data Mining can enhance CRM by:

1. Data mining can help businesses create targeted programs for higher response and better ROI.
2. Businesses can offer more products and services as desired by the customers through up-selling and cross-selling thereby increasing customer satisfaction.
3. With data mining, a business can detect which customers are looking for other options. Using that information companies can build ideas to retain the customer from leaving.

Data Mining helps CRM in:

1. Database Marketing: Marketing software enables companies to send messages and emails to customers. This tool along with data mining can do targeted marketing. With data mining, automation, and scheduling of jobs can be performed. It helps in better decision making. It will also help in technical decisions as to what kind of customers are interested in a new product, which market area is good for product launching.
2. Customer Acquisition Campaign: With data mining, the market professional will be able to identify potential customers who are unaware of the products or new buyers. They will be able to design the offers and initiatives for such customers.
3. Campaign Optimization: Companies use data mining for the effectiveness of the campaign. It can model customer responses to marketing offers.

Data Mining Using Decision Tree Example

Decision tree algorithms are called CART( Classification and Regression Trees). It is a supervised learning method. A tree structure is built on the features chosen, conditions for splitting and when to stop. Decision trees are used to predict the value of class variables based on learning from the previous training data.

The internal node represents an attribute and the leaf node represents a class label.

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Following steps are used to build a Decision Tree Structure:

1. Place the best attribute at the top of the tree (root).
2. Subsets are created in such a way that each subset represents data with the same value for an attribute.
3. Repeat the same steps to find the leaf nodes of all branches.

To predict a class label, the record’s attribute is compared with the root of the tree. On comparing, the next branch is chosen. The internal nodes are also compared in the same way until the leaf node reached predicts the class variable.

Some algorithms used for Decision Tree Induction include Hunt’s Algorithm, CART, ID3, C4.5, SLIQ, and SPRINT.

Most Popular Example Of Data Mining: Marketing And Sales

Marketing and Sales are the domains in which companies have large volumes of data.

#1) Banks are the first users of data mining technology as it helps them with credit assessment. Data mining analyzes what services offered by banks are used by customers, what type of customers use ATM cards and what do they generally buy using their cards (for cross-selling).

Banks use data mining to analyze the transactions which the customer do before they decide to change the bank to reduce customer attrition. Also, some outliers in transactions are analyzed for fraud detection.

#2) Cellular Phone Companies use data mining techniques to avoid churning. Churning is a measure showing the number of customers leaving the services. It detects patterns that show how customers can benefit from the services to retain customers.

#3) Market Basket Analysis is the technique to find the groups of items that are bought together in stores. Analysis of the transactions show the patterns such as which things are bought together often like bread and butter, or which items have higher sales volume on certain days such as beer on Fridays.

This information helps in planning the store layouts, offering a special discount to the items that are less in demand, creating offers such as “buy 2 get 1 free” or “get 50% on second purchase” etc.

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Big Companies Using Data Mining

Some online companies using data mining techniques are given below:

• AMAZON: Amazon uses Text Mining to find the lowest price of the product.
• MC Donald’s: McDonald’s uses big data mining to enhance its customer experience. It studies the ordering pattern of customers, waiting times, size of orders, etc.
• NETFLIX: Netflix finds out how to make a movie or a series popular among the customers using its data mining insights.

Conclusion

Data mining is used in diverse applications such as banking, marketing, healthcare, telecom industries, and many other areas.

Data mining techniques help companies to gain knowledgeable information, increase their profitability by making adjustments in processes and operations. It is a fast process which helps business in decision making through analysis of hidden patterns and trends.

Check out our upcoming tutorial to know more about Decision Tree Data Mining Algorithm!!

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