10 Essential Data Mining Techniques Every Company Needs

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10 Essential Data Mining Techniques Every Company Needs Data Mining
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Introduction

Having a lot of data can be a blessing and a curse. There’s more to play with, but there’s also much more to sort through. With the rise of big data, it’s not just critical to collect as much data as possible; it’s also vital to learn how to make sense of all this information. In the pool of data when you look for a specific piece of information, it’s easy to get distracted by data irrelevant to your goal. 

Luckily, there are plenty of methods you can use to mine the endless depths of the data and uncover valuable insights about the customers and product or service preferences that can help to improve your organization’s processes and grow the business. 

This list of ten essential data mining techniques will help you get started in no time!

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The Basics of Data Mining

Data mining is a process of extracting patterns and insights from data. It is a way to bring out useful information from data that might not be immediately obvious. Mining can be done manually, but it is often automated with the help of specialized software tools. The software tools can automate some of the more complicated tasks, but they also require time to set up and use properly.

Data mining helps to find patterns in data, which can then be used to make predictions about future events or trends. It can be used for various purposes and in various industries, such as business intelligence, customer relationship management, and marketing.

Data Mining Techniques

Data mining techniques are tailored to a specific business problem and offer unique insights. Mining techniques are used by organizations in different ways. For example, some organizations use it to find patterns in customer behavior, while others use it to find patterns in their business practices. Many data mining techniques have been developed over time, and no one technique works best for all circumstances. The following is a list of ten data mining techniques, listed in alphabetical order.

  • Association rule mining 

Association rule, also called frequent-itemset mining, is one of the most popular data mining techniques. This is a technique that is used to identify relationships between different variables in large databases. Association rule states that If an event A happens, then another event B has a tendency to happen. The idea behind it is quite simple. An analyst examines large databases and identifies relationships between different variables.  

One of the classic examples of association rule mining is beer and diapers. If a customer buys diapers (event A), there is a strong chance he will buy beer (event B). In other words, there is an association between buying diapers and buying beer.

  • Classification 

Classification is a data mining technique used to identify patterns and relationships in a given set of data. This technique involves collecting crucial and relevant data information. It is used to classify data into different categories. Classification is similar to clustering as it also segregates data records into distinguishable segments known as classes. It can then be used to assign class labels to new observations and generate predictions. 

For example, Classification predicts if an email is spam or not spam and labels it accordingly. 

  • Clustering 

Clustering is a data mining technique where it groups data into logical categories. Clustering can help you organize and uncover key insights within your data. Graphics are used in clustering techniques to indicate if the data distribution is in relation to certain parameters. Customer profiling can be done using this data.

For example, if you’re trying to find groups of customers who behave similarly, clustering can help identify these groups so that you know how to market them more effectively.

  • Decision Tree  

A decision tree is one of many different data mining techniques used to find patterns in large data sets. Decision trees represent a data set as nodes, with each node representing a category. A decision tree is made up of branches that split into more branches and so on until there is only one branch left. Each branch represents a question that can be answered by analyzing information about the data.

For example, In the data set, each branch has one question and If you know the answer to that question, you follow that branch; if not, you go down another branch until you reach a leaf node.

  • Neural Networks

A neural network is a powerful algorithm that turns raw data into useful information. These are frequently used in data mining applications and can be trained using backpropagation, which adjusts weights between nodes to minimize an error function. 

In layman’s terms, neural networks allow computers to learn by adjusting their internal parameters based on external inputs. This allows the recognition of patterns and trends in large datasets.

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  • Outlier detection 

One of the most common ways to discover a data anomaly is by using outlier detection. This works by identifying potential anomalies based on past data and then checking if those same anomalies occur in current data. If they do, there’s likely something going on in the business that needs attention. Outlier detection can help identify the problems before they get too big to handle. 

For example, In the case of an e-commerce site, the average transaction amount for a business is $100 and analysts notice that one of the products has been purchased at an unusually high rate over a while. It can be an outcome of a data anomaly.  

  • Prediction 

Predictive modeling is a technique for learning from data in order to make predictions. It extrapolates patterns from present or past data into the future. Predictions can be in the form of an estimate, forecast, or any other calculated result. Predictive analysis techniques allow organizations to estimate future values based on historical data, they are used extensively in forecasting and stock market trading.  

  • Regression analysis

Regression analysis is a mining technique that can be used to figure out how one or more variables affect another.   

For example, If a company wants to predict whether a customer will buy a product, then the company should know what factors affect the customer’s decision-making process.

  • Sequential patterns 

The sequential pattern mining technique helps in discovering a set of events that occur in a specific sequence. This approach is good when it comes to situations where there are clear patterns that can be easily spotted. 

One important point to note here is that sequential patterns can only uncover past and present relationships. They won’t be helpful in analyzing future trends and events.

For example, a sequential pattern analysis might reveal that customers who buy red cars also tend to buy sports cars. But It wouldn’t tell much about how a customer would react if they were presented with an offer on a blue car instead of a red one.

  • Tracking patterns  

Tracking patterns is the most basic data mining technique. It helps in recognizing and tracking trends or patterns in data in order to draw informed conclusions regarding business. Sometimes just by looking at trends in your data, you can draw conclusions that would have otherwise taken weeks to discover. 

A good example is tracking patterns on price or if you want to note when a customer is most likely to place an order.

The good news is that having a huge amount of data should no longer be a concern for businesses. With data mining techniques, they can find patterns and correlations using algorithms and discover relationships within the data collected, and can make predictions about future events or trends which will be beneficial for the business in the long run.

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