Data mining is not just the art of extracting new information; In fact, it is about identifying new patterns from data that you’ve already collected.
There’s generally a misconception about it, so it necessitates for the right exposure.
To further enhance on the practicality of data mining methods and techniques, we have a list of realistic data mining examples that can help you promote your company or business in the long haul. Let’s get moving!
10 Best examples of data mining methods and techniques
Pay attention to data mining statistical methods better through these knowledge-based examples in terms of marketing.
- Cluster analysis to identify target groups
Cluster analysis targets a user within a database relying on the standard cross-sectional study including gender, age, geographical location and educational level etc. This method is mainly used in marketing to identify all diversified groups from the database and then it targets the right audience (doctors, business professionals, and mothers) to send the promotion of your product or service.
Cluster analysis is a useful technique of data mining to determine similarity in various groups for product benefits. It is done from these:
- Demographics
- Psychographics
- Geographical factors
- Behavioral segmentation
- Regression analysis to bring on marketing forecasts
This method gives marketing professionals a closed preview of the future by making them study changes, customer habits, and satisfaction levels and other factors linked to the marketing campaign budget. With the prediction reached, you can maintain your relationship between advertising and sales and so on.
There are three purposes of regression analysis in data mining, especially for business owners.
- To Describe
- To Predict
- To Control
For example, you invested these values for your advertising campaign.
- $1000 in November
- $2000 in December
Your sales grew and decreased in this period are:
- $500 in November
- $5000 in December
The above dependent variables will give you an idea of what you can change about your business for a more statistical future.
- Classification analysis to spot on spams and more
This data mining method analyzes data objects that induce the general behavior of the data. It also helps your recognizing new patterns inside your database and create a solution to improve your business performance while deleting useless information.
Classification analysis is an example of the complicated data mining method from which you can evaluate your potential customers, their financial backgrounds individually and classifying their histories. You can read more about this on our complete Data Mining Guide.
After carrying out classification, customers fall into three following levels on which you can quickly draw marketing conclusion of your business:
- High credit risk
- Medium credit risk
- Low credits risk
- Anomaly detection to identify abnormalities
A small mistake in data entry can put your product/service to a question. It could be unintentional, but the consequences of any mistakes will be immense.
Anomaly detection is a special data mining technique done by software to identify small to big errors made by your company’s employees, customers, suppliers or even if you’ve made it. The data mining software is designed to perform complex operations by detecting a hidden behavior in terms of known and unknown attacks in your database.
The model of anomaly detection using data mining techniques
There are five stages to the methodology of anomaly detection:
- Monitored Environment
- Parameterization
- Training
- Model
- Detection
- Intrusion detection for system security
To avoid viruses and hacking attacks in your database, Intrusion detection can help you solve all this. The Intrusion Detection System (IDS) decontaminates the database and guarantees more security in your network. The intruder detects anomalies and identify the relevant, hidden and duplicated data for the user effectiveness.
There are some algorithms used by intruders in data mining that starts from collecting data, send for pre-processing to remove the noise as well as removing irrelevant and missing attributes. The pre-processed data is then analyzed further, and if the outcome is normal, then it doesn’t need to ask for amendments, and if it’s not then, then it’s sent for report generation to create alerts.
- Rule learning to recognize links between data
Rule learning method of data mining is the perfect example for marketers which is used for sales activities of the product when you’re running a clothing e-commerce for instance. You need to create and distinguish interesting relationships between your data in your large database that you’ve barely imagined.
Adding a fact to your knowledge, that almost 90% of the online buyers are the same then by another. Association rule data mining technique analyzes if-then patterns in your large volume of data. This technique is carried out by identifying patterns frequently in your data and then it uses the principles of support and confidence to determine the most important relationships.
- Decision trees analysis to improve on project risk management
This method of data mining improves your decision-making skills for your project to perform well. Project decisions vary with occurrence due to prospective threats and opportunities. The decision trees get bigger and confusing with the in-return and costs and benefits you’re getting on your product/service. Therefore, it is very crucial for every businessman to calculate project risk management in terms of monetary value by using the tool “Decision Trees.” In other words, you can say it’s a qualitative risk analysis of simple to stressful situations.
Steps involved In decision trees analysis
Using data mining techniques in decision trees analysis to evaluate project risk, here are some main steps involved in it:
- Create documentation of a decision
- Calculate the probability of the risk about that decision
- Calculate the monetary value of the impact of the risk
- Compute the monetary value of each decision path
- Neural network application for automated learning
Neural network application used in data mining is comprised of clustering and decision trees. It is one of the latest technique that is used in marketing operations.
For example, your computer may identify similar relationships while detecting patterns containing elements that relate to each other. This application helps you decide your goal of the future and tells you how to pursue them. It’s the most value-added method for numerous industries and businesses to change the landscape of their business. It offers to gain vision of your target audience in a way that you know more about your customers like never before. It results in recognizing the assembly of your product and service and makes you target, potential customers, more effectively.
- Induction rule to prefigure data-based analysis
Induction rule is a data mining method which works with predictive analyses for your data and generates only “correct” rules, measured by the algorithm. These rules then further utilized to classify new cases with thousands and thousands of record of order numbers. This methodology saves time and produces a set of observations that something your competitors often forget to do.
- Data warehousing
The last effective data mining method is data warehousing that you must have heard before. Data warehousing regarding big data processing refers to simplifying your database which has large sets of information and then gaining interesting insights from it. These insights are based on your customers to make future predictive analysis by providing you with detailed reports and much more.
Data warehousing tool plays a vital part in the evolution of businesses and in marketing campaigns for a businessperson to study more in detail about their product.
