Data Mining is used to find in the raw data new, previously unknown knowledge, which can then be applied in different spheres of human activity. It allows you to discover hidden patterns to subsequently make the right business decisions.
Application of Data Mining Technology
You can apply technology anywhere you have data. It is used most widely in retail, banking, telecommunications and insurance.
Data Mining and Retail: analyzing similarities, time, creating predictive models
Retail organizations collect data on each purchase and solve several challenges:
1. Analyzing the similarity of goods
Data Mining systems help to find the purchases that the buyer usually makes together. For example, most consumers can also buy a shoe cream in addition to a footwear. This allows you to immediately offer additional products, improve advertising, make the best presentation of goods, etc.
2. Time analysis
If a consumer buys a torch, it would be useful for the company to find out, when the person will get the batteries. This allows you to properly create inventory.
3. Predictive models
Trade enterprises can recognize the needs of different categories of customers and their behavior in order to create accurate advertising campaigns.
Data Mining and Banks: fraud prevention and customer segmentation
Banks solve following issues using Data Mining:
1. Fraud prevention
The bank can analyze past cases of fraud, find patterns and is more likely to avoid such cases in the future.
2. Customer segmentation
The bank can make its marketing strategy more focused and effective if subdivides customers.
Data Mining in telecommunications: analyzing call records and finding loyal customers
Thanks to the technology, companies can:
1. Analyzing call records
It helps to identify categories of customers with similar patterns of using services and develop the most attractive solutions for them.
2. Finding the most loyal customers
To allocate funds to where the impact is the most palpable.
Data Mining and insurance companies: risk analysis and fraud prevention
Data Mining features:
1. Risk analysis
Companies can identify how to reduce their losses. For example, an insurance company can find out that the amounts paid to people who are married are higher than the amounts claimed by single people. In this case, the company can change the marketing strategy and start giving discounts to married people.
2. Fraud prevention
The insurance company can analyze past cases of fraud, find patterns in applications for payment of insurance compensation and is more likely to avoid such cases in the future.
Examples of Data Mining in telecommunications, banking and retail
Beeline is an example of the successful Data Mining application. The company has already implemented several projects: segmentation of the subscriber base; identification and protection of subscribers from fraud; identification of groups of subscribers who use communication services on different types of devices, etc. The projected revenue from the use of the technology will be about 20% of the company's revenue by 2018.
Sberbank introduced a system for analyzing customer photos to identify them and prevent possible fraud in 2014. As a result, the losses from document fraud decreased 10 times.
The company is doing business in retail trade. It uses Data Mining to analyze buyers to make the optimal range of products and prices in each of the outlets. Macy's is growing rapidly, sales are increasing by 50% per year, and, according to company representatives, at least a tenth of revenue, is the result of using Data Mining.