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10 data mining examples for 10 different industries 0

Posted on August 17, 2019 by Rob Petersen
data mining examples

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.

E-commerce

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.

Healthcare

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

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.

Nursing

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.

Social media

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

6 essential steps to the data mining process 0

Posted on October 01, 2018 by Rob Petersen

Data Mining Process Illustration

Data mining process is the discovery through large data sets of patterns, relationships and insights that guide enterprises measuring and managing where they are and predicting where they will be in the future.

Large amount of data and databases can come from various data sources and may be stored in different data warehousess. And, data mining techniques such as machine learning, artificial intelligence (AI)  and predictive modeling can be involved.

The data mining process requires commitment. But experts agree, across all industries, the data mining process is the same. And should follow a prescribed path.

Here are the 6 essential steps of the data mining process.

1. Business understanding

In the business understanding phase:

  • First, it is required to understand business objectives clearly and find out what are the business’s needs.
  • Next, assess the current situation by finding the resources, assumptions, constraints and other important factors which should be considered.
  • Then, from the business objectives and current situations, create data mining goals to achieve the business objectives within the current situation.
  • Finally, a good data mining plan has to be established to achieve both business and data mining goals. The plan should be as detailed as possible.

2. Data understanding

  • The data understanding phase starts with initial data collection, which is collected from available data sources,  to help get familiar with the data. Some important activities must be performed including data load and data integration in order to make the data collection successfully.
  • Next, the “gross” or “surface” properties of acquired data need to be examined carefully and reported.
  • Then, the data needs to be explored by tackling the data mining questions, which can be addressed using querying, reporting, and visualization.
  • Finally, the data quality must be examined by answering some important questions such as “Is the acquired data complete?”, “Is there any missing values in the acquired data?”

3. Data preparation

The data preparation typically consumes about 90% of the time of the project. The outcome of the data preparation phase is the final data set. Once available data sources are identified, they need to be selected, cleaned, constructed and formatted into the desired form. The data exploration task at a greater depth may be carried during this phase to notice the patterns based on business understanding.

4. Modeling

  • First, modeling techniques have to be selected to be used for the prepared data set.
  • Next, the test scenario must be generated to validate the quality and validity of the model.
  • Then, one or more models are created on the prepared data set.
  • Finally, models need to be assessed carefully involving stakeholders to make sure that created models are met business initiatives.

5. Evaluation

In the evaluation phase, the model results must be evaluated in the context of business objectives in the first phase. In this phase, new business requirements may be raised due to the new patterns that have been discovered in the model results or from other factors. Gaining business understanding is an iterative process in data mining. The go or no-go decision must be made in this step to move to the deployment phase.

6. Deployment

The knowledge or information, which is gained through data mining process, needs to be presented in such a way that stakeholders can use it when they want it. Based on the business requirements, the deployment phase could be as simple as creating a report or as complex as a repeatable data mining process across the organization. In the deployment phase, the plans for deployment, maintenance, and monitoring have to be created for implementation and also future supports. From the project point of view, the final report of the project needs to summary the project experiences and review the project to see what need to improved created learned lessons.

These 6 steps describe the Cross-industry standard process for data mining, known as CRISP-DM. It is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model.

Do these 6 steps help you understand the data mining process? What is your organization’s readiness for date mining?

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