Far too many companies that I consult with sit on loads of good customer data…and do nothing with it. It’s truly amazing because in that data is a gold mine of insight.

An insight that can:

• Increase customer loyalty
• Unlock hidden profitability
• Reduce client churn

Are you sitting on loads of data that you aren’t using? Would you like to learn how you can use it? Here are the Data mining is necessary for business intelligence and helps generate valuable insights by identifying patterns in the data. In this article, we’ll walk you through the benefits of data mining, the different techniques involved.

What is Data Mining?

Data mining is the technique of discovering correlations, patterns, or trends by analyzing large amounts of data stored in repositories such as databases and storage devices. It’s a crucial part of advanced technologies such as machine learning, natural language processing (NLP), and artificial intelligence.

Data mining has to be done meticulously to get the best results. The broad steps discussed below can help you smoothly sail through the data mining process.

Data mining steps:

• Define your hypothesis or assumption.
• Identify all data sources relevant to the hypothesis.
• Discern data points from the data sources that need to be tested to validate or reject your hypothesis.
• Use data mining techniques such as correlation analysis to test statistical models that best connect data points.
• Interpret and report results and use gathered insights to frame your business decisions/actions.

Interesting Read: How DataMining Can Help You Get a Competitive Edge

Key Data Mining Concepts

Achieving the best results from data mining requires an array of tools and techniques. Some of the most commonly-used functions include:

Data cleansing and preparation

A step in which data is transformed into a form suitable for further analysis and processing, such as identifying and removing errors and missing data.

Artificial intelligence (AI)

These systems perform analytical activities associated with human intelligence such as planning, learning, reasoning, and problem-solving.

Association rule learning

These tools, also known as market basket analysis, search for relationships among variables in a dataset, such as determining which products are typically purchased together.

Clustering

A process of partitioning a dataset into a set of meaningful sub-classes, called clusters, to help users understand the natural grouping or structure in the data.

Classification

This technique assigns items in a dataset to target categories or classes with the goal of accurately predicting the target class for each case in the data.

Data analytics

The process of evaluating digital information into useful business intelligence.

Data warehousing

A large collection of business data used to help an organization make decisions. It is the foundational component of most large-scale data mining efforts.

Machine learning

A computer programming technique that uses statistical probabilities to give computers the ability to “learn” without being explicitly programmed.

Regression 

A technique used to predict a range of numeric values, such as sales, temperatures, or stock prices, based on a particular data set.

Interesting Read: Top Data Analytics and Data Mining Companies

Data mining techniques

Statistical methods and pattern recognition technologies commonly use the following data mining techniques:

Pattern detection

Simple pattern tracking involves recognizing a deviation in your data at certain time intervals (e.g., website traffic peaking early in the evening or late at night). This can be represented using simple line graphs or bar charts.

Classification and clustering analysis

This technique helps discover groups and clusters within your datasets. For example, based on the average value of all purchases customers make with per month, you can group them as “low margin” or “high margin” customers, and then devise different marketing strategies for the different clusters.

Association

This technique helps you track patterns that show the dependency (e.g., customers tend to buy headphones or phone cases when they purchase mobile phones).

Regression analysis

This technique helps identify variables and their effect on the metric you’re looking at (e.g., ice cream sales having a direct correlation with the temperature).

Prediction

This technique involves using data mining to build forecasting models that predict how independent variables will change in the future. For example, eCommerce firms can use sales and customer data to build models that predict which products are likely to be returned after a seasonal sale.

Outlier detection

Data Crawling helps identify data values that fall outside a defined normal range. Removing such outliers is important for accurate data analysis results.

How data mining can help in Your Business?

There are many benefits of data mining, including some specific ones that add value to your business:

1. Optimize marketing campaigns

Data mining helps businesses understand which marketing campaigns will likely generate the most engagement, classify customers, display personalized advertisements, and optimize marketing spend.

2. Detect possible fraud

Data mining helps businesses detect fraudulent activity and anticipate potential fraud. For example, analysis of point of sale (POS) data can help retailers detect fraudulent transactions. Banks and insurance agencies use data mining techniques to identify customers likely to default on premium payments or make fraudulent claims.

3. Make better business decisions

Rather than solely relying on your intuition or experience, insights generated from your own business data can help you make better decisions. For example, intuition may tell you that your product is not selling because of its high price point while data analysis reveals that it’s actually because of fewer distribution channels. Such insights allow your business to identify and dress the underlying issue.

4. Insight into employees and HR policies

Data mining not only helps improve external market performance but can also be used to understand employee behaviour, predict attrition, and evaluate HR policies.

Conclusion:

The more data you collect from customer the more value you can deliver to them. And the more value you can deliver to them the more revenue you can generate.