Data Mining and Business Intelligence: 5 real-life applications

 

A financial transaction is the process of providing funds for an organization's activities, purchases, or investments. Businesses, consumers, and investors obtain capital from financial institutions like banks to achieve their goals. A financing system is crucial in any economic system, because it allows companies to purchase items they would otherwise not be able to afford.

In  Data Mining Services, raw data is cleaned, patterns are discovered, models are created, and those models are tested. This process involves statistics, machine learning, and database management. Data mining usually involves several data processes and can easily be confused with analytics, data governance, and other data processes.

Today, data mining results are cleaned, processed, and interpreted by specialists who have coding skills and expertise. For data mining techniques to be completed correctly, specialists need to possess statistical knowledge and programming skills.

An anomaly, pattern, or correlation in a large data set is used as a prediction test. Through the use of a number of techniques, you can increase profits, improve customer relationships, cut costs, and reduce risks.

In light of the increasing importance of data analytics, more and more companies are using Business Intelligence and Data Mining. In this article, we will look at 5 real-world applications of these technologies and describe how to use them to improve your business.

What is the purpose of data mining?

If a company is to make use of its information, it needs to be able to identify patterns and relationships within it.  Data Mining Services enables that. Better business decisions can be made as a result of those insights. Additionally, data mining can reduce risk, allowing you to spot fraud, errors, and inconsistencies that can harm a company's reputation and cause it to lose profits. Data mining is used in a variety of contexts by diverse industries, but the goal remains the same: to better understand customers and businesses.

Services Provider

Service providers in the mobile phone and utility industries are the first to use data mining and business intelligence.  Data Mining Services and Business Intelligence are used by mobile phone and utility companies to forecast churn - the event when a customer leaves one provider for another. By analysing billing data, customer service interactions, traffic to the website, and other factors to assess each customer's risk of churn, they target offers and incentives specifically to customers who are more likely to churn.

Retail

The retail sector is a great example of Data Mining and Business Intelligence. Customers are segmented into ‘Recency, Frequency, and Monetary’ (RFM) groups and targeted by retailers with marketing and promotions. If a customer spends little but frequently, and the last time they did it was a few months ago, they will be treated differently than a customer who only spends big once every few years. Those who are loyal to the company may be offered upsells and cross-sells, while those who are dissatisfied may be offered a win-back deal.

E-commerce

E-commerce sites have become famous for being a prime example of Data Mining and Analytics. Through their websites, e-commerce companies often offer cross-sells and upsells based on Data Mining and Business Intelligence. Amazon, of course, is one of the most famous of these companies and uses sophisticated mining techniques to determine that the product the consumer viewed also liked this one.

Supermarkets

 Data Mining Services and Business Intelligence in supermarkets is another excellent example. A supermarket loyalty card program is known for gathering data about customers primarily, if not solely, for the purpose of data mining. This has happened with the American retailer Target, for instance. A company developed rules to determine if its shoppers would be pregnant using Data Mining. Customers who bought diapers (nappies), cotton wool, and so on were spotted by looking at their shopping basket contents and targeted promotional campaigns for them. 

Crime agencies

It is evident from our final example that Data Mining and Business Intelligence are not only applicable to corporate use. As well as using analytics and data mining for corporate purposes, crime prevention agencies use it to spot trends across a wide range of data - from where and when to deploy police manpower, who to search at border crossings (based on the vehicle type, number of passengers, border crossing history), and which intelligence is important in counter-terrorism operations.

Trends in recent times

The practice of extending small loans to poor entrepreneurs in developing countries has been growing steadily in the late 20th and early 21st centuries. The loan is often referred to as microcredit, and the practice is known as microlending. The goal of microlending is to increase the standard of living for individuals by helping them to generate income (for instance, through farming, weaving, or making crafts). A traditional bank loan is not available to most individuals who borrow money because they lack collateral and have no credit history.