Data mining examples
Data mining examples show the process of discovering patterns and identifying rules in large data sets. Machine learning, artificial intelligence, statistics, and database systems are methods frequently used.
How data mining is applied across industries is vastly different. That’s because the goal of data mining is understand customer behavior, predict future behaviors and identify steps that accelerate the desired outcome. And customers behave differently depending on the industry.
To show how, here are 10 data mining examples for 10 different industries.
Banking data mining
In banking, the main objective is to use data mining is to extract valuable information from distinct customer data. That’s because the key strategy for a bank is to reduce costs and increase bank revenues. The customer, and their accounting and personal information, is the backbone for data mining examples of every bank. They collect data such as their purchase history, geo-location preferences and other behaviors. Bank that effectively use this data mining analysis to create right product for the right customer.
Many e-commerce companies use data mining and business intelligence to offer cross-sells and up-sells through their websites. One of the most famous of these is, of course, Amazon, who use sophisticated mining techniques to drive their, ‘People who viewed that product, also liked this’ functionality. By thoroughly studying and analyzing past data and behaviors, Amazon categorizes products depending on the probability of your purchasing the product.
Educational data mining
Educational Data Mining (EDM) is increasing rapidly as more and more education systems are going online. It has opened new areas like new computer supported interactive learning methods, tools-intelligent tutoring system and simulation games. This has created opportunities to collect and analyze student data , to discover patterns and trends in those data and to make new discoveries and test hypothesis about how students learn through online classes. The data collected from online learning systems can be aggregated over large numbers of students and can contain many variables that data mining algorithms can explore for model building.
Lab tests are often essential to enable a health care provider to decide how to treat a patient. Applying data mining helps doctors discover things they might otherwise miss within laboratory results. In one study, researchers looked at more than 600 urine samples and used data mining to classify patients by life expectancy based on characteristics of their urine. Taking this approach reveals instances where patients are sicker than they seem, allowing doctors to take prompt action.
Marketing data mining
Data mining in marketing is used to explore increasingly large databases and to improve market segmentation. By analyzing the relationships between parameters such as customer age, gender, tastes and lifestyles, it is possible to predict behavior in order to direct personalized loyalty campaigns. Data mining also predicts which users are likely to unsubscribe from a service, what interests them based on their searches or what a mailing list should include to achieve a higher response rate.
Mass merchandisers are good data mining examples in action. Loyalty card programs are usually driven mostly, if not solely, by the desire to gather comprehensive data about customers. One notable recent example of this is Target. As part of its data dining program, the company developed rules to predict if their shoppers were likely to be pregnant. By looking at the contents of their customers’ shopping baskets, they were able to spot customers who they predict were likely to have a baby on the way. They then send them targeted promotions for diaper, wipes and other baby products. Their predictions were so accurate that Target made the news by sending promotional coupons to families who did not yet realize (or who had not yet announced) they were pregnant!
Mobile phones and utilities data mining
Mobile phone and utility companies are data mining examples that predict ‘churn’, the terms used for when a customer leaves their company to get their phone/gas/broadband from another provider. They collate billing information, customer services interactions, website visits and other metrics to give each customer a probability score, then target offers and incentives to customers whom they perceive to be at a higher risk of churning.
An analysis reveals a substantial portion of Medicaid patients are going to the ER more than 10 times per year. 2 or 3 trips to the ER is just a bad year, but more than 10 visits means that something has gone wrong. This prompts Medicaid employees to call these patients, take steps to increase their level of personal care at home and institute 24/7 nurse hotline to allow Medicaid patients to call in for medical help rather than going to the hospital. This lowers the costs of Medicaid ER visits by more than 20%.
Retail data mining
Retailers segment customers into ‘Recency, Frequency, Monetary’ (RFM) groups and target marketing and promotions to those different groups. A customer who spends little but often is handled differently to a customer who spent big but only once and some time ago. The former may receive a loyalty, up-sell and cross-sell offers, whereas the latter may be offered a win-back deal, for example.
A study published in the Journal of Advertising uses social media mining techniques to gauge users’ perception of a variety of common brand names. The study specifically looked at Twitter, examining tweets of brands in the following industries: fast-food restaurants, department stores, telecommunication carriers, consumer electronics products, and footwear companies. The researchers use the Twitter handles of each company (“@CompanyName”) as keywords to pull about ten million tweets for each of the twenty companies studied for results that are incredibly specific. For example, the study found 15.7% of tweets about fast-food restaurants are about promotions chains are offering. And, among cable companies, 66.7% of tweets about Comcast contain a negative sentiment.
Are these relevant data mining examples to you? Could you use guidance on data mining for your company