Data mining is a useful and versatile tool for today’s competitive businesses. Here are some data mining examples, showing a broad range of applications.
Data mining helps banks work with credit ratings and anti-fraud systems, analyzing customer financial data, purchasing transactions, and card transactions.
Data mining also helps banks better understand their customers’ online habits and preferences, which helps when designing a new marketing campaign.
Data mining helps doctors create more accurate diagnoses by bringing together every patient’s medical history, physical examination results, medications, and treatment patterns.
Mining also helps fight fraud and waste and bring about a more cost-effective health resource management strategy.
There is a new emerging field, called Educational Data Mining, concerns with developing methods that discover knowledge from data originating from educational Environments.
The goals of EDM are identified as predicting students’ future learning behaviour, studying the effects of educational support, and advancing scientific knowledge about learning.
Data mining can be used by an institution to take accurate decisions and also to predict the results of the student. With the results the institution can focus on what to teach and how to teach. Learning pattern of the students can be captured and used to develop techniques to teach them.
If there was ever an application that benefitted from data mining, it’s marketing! After all, marketing’s heart and soul is all about targeting customers effectively for maximum results. Of course, the best way to target your audience is to know as much about them as possible.
Data mining helps bring together data on age, gender, tastes, income level, location, and spending habits to create more effective personalized loyalty campaigns.
Data marketing can even predict which customers will more likely unsubscribe to a mailing list or other related service. Armed with that information, companies can take steps to retain those customers before they get the chance to leave!
The world of retail and marketing go hand-in-hand, but the former still warrants its separate listing. Retail stores and supermarkets can use purchasing patterns to narrow down product associations and determine which items should be stocked in the store and where they should go. Data mining also pinpoints which campaigns get the most response.
Billions of dollars have been lost to the action of frauds. Traditional methods of fraud detection are time consuming and complex. Data mining aids in providing meaningful patterns and turning data into information. Any information that is valid and useful is knowledge. A perfect fraud detection system should protect information of all the users. A supervised method includes collection of sample records. These records are classified fraudulent or non-fraudulent. A model is built using this data and the algorithm is made to identify whether the record is fraudulent or not.
7. Research Analysis
Data mining is instrumental in data cleaning, data pre-processing, and database integration, which makes it ideal for researchers. Data mining can help identify the correlation between activities or co-occurring sequences that can bring about change in the research. Data mining, in conjunction with data visualization and visual data mining, can offer clarity in data and research.
The historic or batch form of data will help identify the mode of transport a particular customer generally opts for going to a particular place, say his home town, thereby providing him alluring offers and heavy discounts on new products and launched services. This will thus be included in targeted and organic advertisements where the prospective leader of the customer generates the right to converted the lead. It is also helpful in determining the distribution of the schedules among various warehouses and outlets for analyzing load based patterns.
9. Manufacturing Engineering
The data can be assessed by ensuring that the manufacturing enterprise possesses the right set of knowledge as its asset lies in identifying the right set of product portfolios, product architecture, and the customer needs and requirements. Moreover, efficient data mining capabilities can ensure that product development is completed in the relevant time frame and does not exceed the budget allotted initially.
10. Market basket analysis
This is a modelling technique that uses hypothesis as a basis. The hypothesis says that if you purchase certain products, then it is highly likely that you will also purchase products that don’t belong to that group that you usually purchase from. Retailers can use this technique to understand the buying habits of their customers. Retailers can use this information to make changes in the layout of their store and to make shopping a lot easier and less time consuming for customers.