Using data mining techniques could boost your company’s revenue by 300%. In fact, research has found that 60% of professionals believe that data has generated revenue within their businesses. And 83% say existing products and services have been made more profitable.
If you’re not yet using data mining techniques to gain a competitive edge, you’re missing out. While data mining may sound complicated, the below breakdown will help you understand the basics.
Are you interested in using data mining for your business? Click here for 8 essential data mining techniques you need to use to boost your business.
Here are the 8 techniques you should be using:
Also known as Relation, Association is one of the most well-known and straightforward data mining techniques. This is when a simple correlation is made between two or more things to identify patterns.
One of the most famous examples of Association? Walmart. The company found that before a hurricane, sales of Strawberry Pop-Tarts increased seven times. Now, Strawberry Pop-Tarts are always available at the checkouts before a hurricane is due to hit.
Association can work for almost any type of business. For example, if you were tracking your customers’ buying habits, you may notice that they tend to buy life insurance and property insurance together. You could then suggest that they purchase life insurance the next time they move house and sign up for property insurance.
This is when data sets are identified as being similar to each other. An example would be how a library uses book management. When you walk into a library, there is a wide range of books on many different topics. These books need to be kept in a way that someone interested in a topic can easily grab a few books on that topic without spending hours finding them.
A library can use clustering to keep similar books on one shelf (or in one cluster) and label it with a particular name.
In business, clustering can help you create buyer personas. And this allows you to completely change your marketing strategy to better connect with your customers.
Classification allows you to build up an idea of an object, item, or type of customer. You simply describe multiple attributes which identify each class.
For example, if you sold cars, you could easily classify them into different types (convertible, 4×4, sedan) by identifying their different attributes (car shape, number of seats etc).
The same goes for customers. You can classify them by demographics such as social group, age, location, etc. This is particularly helpful when building buyer personas or creating content targeted towards a particular group.
Prediction is often used in combination with some of the other data mining techniques. It can help you do everything from predicting the failure of machinery or components to predicting company profits or identifying fraud.
Using Prediction for data mining involves analyzing relation, pattern matching, classification, and trends. By analyzing past instances and events, it’s easier to make a prediction about an event.
An example would be a credit card authorization. This would combine analyzing past transactions with historical patterns to decide whether a transaction could be fraudulent.
If you booked a flight to Mexico, and then followed that flight with transactions within Mexico on the same card, it would be likely that your transactions would be valid.
5. Outlier or Anomaly Detection
This involves the search for items within a data set that don’t match expected behavior or a projected pattern. Outliers are also called anomalies, surprises, contaminants, or exceptions. And they often provide businesses with actionable and critical information.
Outliers deviate significantly from the average within a combination of data or dataset. Anomaly and outlier detection is often used to detect risk or fraud within critical systems.
Regression analysis is when you attempt to define the dependency between two variables. You assume that one variable will respond to a one-way effect from another variable.
For example, you can use regressional analysis when analyzing your customers. You can determine different levels and types of customer satisfaction and how those levels affect loyalty. You can also see how service levels are impacted by variables such as the weather.
7. Sequential Patterns
This is one of the data mining techniques that are best used over the long term. Sequential patterns are a great way to identify regular occurrences or trends of similar events.
For example, Target may notice that customers are buying a particular collection of products at certain times of the year. They may be shopping for Halloween decorations while buying candy, or kids’ shoes and back-to-school stationary. They could then suggest items for online customers before they check out, based on their past purchasing history and frequency.
8. Decision Trees
Decision trees are one of the most commonly used data mining techniques. This is because it’s also one of the easiest models to understand.
Picture a tree. At its root is a simple condition or question with multiple answers. Each answer would then lead to a set of conditions or questions. A decision tree looks almost like a quiz you’d find in a magazine, with each answer leading to a new question.
These questions allow you to determine the data and make a final decision.
Decision trees are often used with predictive systems. Predictions are based on past historical experiences. They’re also used with classification systems so that type information can be attributed.
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Which Data Mining Techniques Will You Use?
It’s rare that you would only use one of the above techniques. Instead, you can combine two or more together to meet your needs.
Data mining can help move the needle if you’re aiming to become more competitive this year. You can use key insights into your business, leads, and customers to drive growth, innovation, and customer service.
Have you tried any of these techniques? Leave a comment below, or get in touch to learn how we can help with data services for your next project.