Data Mining Explained And Illustrated: 8 Business Use Cases

Nas Mouti, PhD
By Nas Mouti, PhD
LAST UPDATED 7 months, 3 weeks ago

Intro

Data mining is the collection of techniques used to make sense of data in general by gleaning patterns, relationships, and associations between various quantities. With the recent boom in AI, data mining has become a must for any company with data. Data mining can be an abstract concept for the non-expert, no matter how many technical or theoretical definitions they are given. I intend to overcome this obstacle in this article by listing eight clear business cases where data mining is used to give a good sense of the power of mining to the non-expert.
Data mining and AI are massively improving how business is conducted
Data mining and AI are massively improving how business is conducted

1 – Financial fraud detection

Data mining algorithms such as neural networks can be trained to recognize what constitutes user normal activity, and flag outliers as suspicious. This is commonly referred to as anomaly detection. If you've ever been on a trip and got a message saying your credit card has been suspended until you call your bank, your activity has been flagged by an algorithm for abnormal behavior (in this case, transactions far away from your residence).

2 - Consumer retention

Several algorithms are used in marketing to ensure customers are satisfied and keep coming back. Machine learning offers novel ways to calculate customer churn, lifetime value, as well as market segmentation. Algorithms can spot when a customer is at a high risk of stopping their interaction with the business and suggest incentives for them to come back.

3 – Ecommerce upselling and cross-selling

By now, everyone is familiar with recommendations engines such as Amazon's "frequently bought together" feature, or Netflix's and Spotify's recommended media. These use data mining algorithms to analyze past consumer behavior and make suggestions based on their findings. These algorithms can easily have other applications, such as a financial advisor's stock recommendations, a doctor's treatment suggestions or a resort's suggestions for people with a certain profile.

4 – Credit approval

Banks use data mining to calculate the chance that an applicant will default on their loan repayments. Based on that risk, they can approve or reject the application, as well as determine an interest rate on repayment.

5 – Grocery stores

The loyalty card's sole benefit to grocery stores is to track customer shopping habits and optimize their layout and offers according to them. A famous case is that of Target, who used customer shopping patterns to determine if they were expecting a baby, and was able to detect pregnancies and send diaper and baby supply coupons, sometimes before the people knew it themselves.

6 – Forecasting patient numbers in hospitals

One of the hardest metrics for a hospital to predict is emergency visits. They rely on many factors, including weather conditions and holidays. Data mining models can be trained to produce the most accurate predictions according to historical data. Limitations include unique events with few precedents, such as natural disasters and pandemic outbreaks. Both types of those events are very individual in their characteristics and rare in occurrence. Models cannot learn patterns from absent data.

7- Chatbots and virtual assistants

Data mining for text, also known as Natural Language Processing (NLP) has been advancing by leaps and bounds in the past few years, it already allows algorithms to extract powerful features from text such as sentiment. It also allows for the generation of original text. NLP advances have made chatbots and virtual assistants smarter than ever before, and it's not hard to imagine a future where virtual assistants are ubiquitous.

8 – Insurance quoting

When you apply online for insurance, the information you enter is processed by machine learning algorithms who have been trained by mining data from previous users, to determine your risk of filing a claim as well as the amount of the claim. That information is then used to determine your policy cost and an offer is made to you.

Conclusion

Data mining is already ubiquitous in our everyday life and has become synonymous in many applications with artificial intelligence or machine learning (though technically it is not the same despite a large overlap). We will only see more of it in the future, and it will soon become a necessary tool for every business to be competitive. Does your business use data mining? If not, it may be missing out on some great competitive edge.